Test-out Exam: INFS 501

Tuesday, January 17, 2012
2:00 PM
Eng 4201
Registration is required.

Abstract

Please email your intent to csadmin@cs.gmu.edu. Include your name, G number and the exams you wish to register for. A photo ID must be presented on the day of the exam. Each exam will be one hour in length.

It is important to note that you will be permitted to take each exam one time only. Failure to pass the exam will mean that you MUST take the foundation classes before enrolling in any core curriculum course.

Test-out Exam: INFS 515

Tuesday, January 17, 2012
3:30 PM
Eng 4201
Registration is required.

Abstract

Please email your intent to csadmin@cs.gmu.edu. Include your name, G number and the exams you wish to register for. A photo ID must be presented on the day of the exam. Each exam will be one hour in length.

It is important to note that you will be permitted to take each exam one time only. Failure to pass the exam will mean that you MUST take the foundation classes before enrolling in any core curriculum course.

Test-out Exam: INFS 519

Wednesday, January 18, 2012
2:00 PM
Eng 4201
Registration is required

Abstract

Please email your intent to csadmin@cs.gmu.edu. Include your name, G number and the exams you wish to register for. A photo ID must be presented on the day of the exam. Each exam will be one hour in length.

It is important to note that you will be permitted to take each exam one time only. Failure to pass the exam will mean that you MUST take the foundation classes before enrolling in any core curriculum course.

Test-out Exam: SWE 510

Wednesday, January 18, 2012
3:30 PM
Eng 4201
Registration is required

Abstract

Please email your intent to csadmin@cs.gmu.edu. Include your name, G number and the exams you wish to register for. A photo ID must be presented on the day of the exam. Each exam will be one hour in length.

It is important to note that you will be permitted to take each exam one time only. Failure to pass the exam will mean that you MUST take the foundation classes before enrolling in any core curriculum course.

GTA: Orientation

Thursday, January 19, 2012
11:00AM - 12:00PM
Eng 4201
This event is mandatory for all CS department graduate teaching assistants

SWE Seminar: Atomic Section Analysis Tool (AtSAT)

Friday, January 20, 2012
12:00 pm
Eng 4201
Lima Beauvais, Sr. SWE Pal-Tech Inc.

Abstract

Testing the presentation layer of web applications requires novel methodologies. In general analyzing, modeling, and testing web applications and their three main layers creates challenges. However the testing techniques used for traditional software can be applied to the data computation and data representation layers. This talk discusses the Atomic Section Analysis Tool (AtSAT), which helps to mechanize the process of testing the presentation layer of web applications. AtSAT is based on the proposed framework of Offutt and Wu (2009) and automates seven of the nine steps; reducing the time to apply the methodology and minimizing human errors.

Speaker's Bio

Lima Beauvais earned an MS degree in Instructional Technology at Bloomsburg University, PA in June 2001. He is currently a candidate for the Engineer Degree at GMU, Fairfax, with a concentration on software testing. He worked as a Senior Multimedia Developer at PerformTech, Inc. from June 2001 to November 2007, developing computer-based and web-based courseware. He has been working as a Senior Software Engineer at Pal-Tech, Inc. since November 2007, developing web applications and training packages. He taught seminar classes on multimedia development at the Art Institute of Washington in Arlington, VA and Sanford Brown college in McLean, VA. He is a member of the Corporate Advisory Council (CAC) of the Institute of Interactive Technology at Bloomsburg University.

SWE Seminar: A Tour of the Piazza Discussion Forums

Tuesday, January 24, 2012
12:00 pm
Research I, Rm. 163
Piazza Team

Abstract

Members of the Piazza team are visiting GMU on Tuesday, January 24 for a lunchtime seminar, with lunch provided. They will spend some time demonstrating the site, sharing best practices, and answering any questions.

Piazza is a free online gathering place where students can ask, answer, and explore 24/7, under the guidance of their instructors. Students as well as instructors can answer questions, fueling a healthy, collaborative discussion. Instructors can go into deeper detail on complex topics, and spot areas where students are struggling.

In SWE 432, we found that Piazza streamlined the teaching experience. All those hours spent responding to individual emails can now be put to better use. You will never have to answer the same question twice. Better yet, a student might answer it for you. On top of that, you always have complete editorial control over your class.

Most bulletin boards are organized top-down with the instructor creating and controlling all topics and threads. Piazza allows bottom-up organization by students, leading to a richer, more interactive, more collaborative, and more free learning experience. This leads to more participation from students and more learning by students.

You can read more about Piazza in this article from the New York Times: http://www.nytimes.com/2011/07/04/technology/04piazza.htm. Or you can see demos and sign up at http://www.piazza.com.

CS Dept Colloquium: Searching in Sequences of Documents and in Biological Sequences

Thursday, February 23, 2012
11:00 AM
Eng 4201
Dimitris Gunopulos

Abstract

We consider the problem of searching in two domains where the ordering is important, namely biological sequence data, and data from live, time-stamped data collections (such as blogs). As the number and size of such data collections increase, the problem of efficiently indexing and searching such data becomes more important. We present novel approaches for subsequence matching and for keyword search and event identification in document sequences.

Speaker's Bio

Dimitrios Gunopulos got his PhD from Princeton University in 1995. He has held positions as a Postoctoral Fellow at the Max-Planck-Institut for Informatics, Research Associate at the IBM Almaden Research Center, Visiting Researcher at the University of Helsinki, Assistant, Associate, and Full Professor at the Department of Computer Science and Engineering in the University of California Riverside, and Associate Professor in the Department of Informatics and Telecommunications, University of Athens. His research is in the areas of Data Mining, Knowledge Discovery in Databases, Databases, Sensor Networks, Peer-to-Peer systems, and Algorithms. He has co-authored over a hundred journal and conference papers that have been widely cited and a book. He has supervised 10 Ph.D. theses and 19 MS. His research has been supported by NSF (including an NSF CAREER award), the DoD, the Institute of Museum and Library Services, the Tobacco Related Disease Research Program, the European Commission, AT&T and Nokia. He has served as a General co-Chair in IEEE ICDM 2010, as a PC co-Chair in ECML/PKDD 2011, IEEE ICDM 2008, ACM SIGKDD 2006, SSDBM 2003, and DMKD 2000, and as an associate Editor at KAIS, at IEEE TKDE, at IEEE TPDS, and at ACM TKDD.

Host: Carlotta Domeniconi (carlotta@cs.gmu.edu)

Seminar: Probabilistic Hashing Methods for Fitting Massive Logistic Regressions and SVM with Billions of Variables

Friday, February 24, 2012
11:00AM - 12:00PM
Johnson Center, 3rd Fl, Room B
Ping Li, Department of Statistical Science, Cornell University

Abstract

In modern applications, many statistics tasks such as classification using logistic regression or SVM often encounter extremely high-dimensional massive datasets. In the context of search, certain industry applications have used datasets in 264 dimensions, which are larger than the square of billion. This talk will introduce a recent probabilistic hashing technique called b-bit minwise hashing (Research Highlights in Comm. of ACM 2011), which has been used for efficiently computing set similarities in massive data. Most recently (NIPS 2011), we realized that b-bit minwise hashing can be seamlessly integrated with statistical learning algorithms such as logistic regression or SVM to solve extremely large-scale prediction problems. Interestingly, for binary data, b-bit miwise hashing is substantially much more accurate than other popular methods such as random projections. Experimental results on 200GB data (in billion dimensions) will also be presented.

SWE Seminar: Taming Uncertainty in Self-Adaptive Software

Tuesday, February 28, 2012
12:00 pm
ENGR 4201
Naeem Esfahani, Ph.D. Candidate Computer Science

Abstract

Self-adaptation endows a software system with the ability to satisfy certain objectives by automatically modifying its behavior. While many promising approaches for the construction of self-adaptive software systems have been developed, the majority of them ignore the uncertainty underlying the adaptation decisions. This has been one of the key obstacles to wide-spread adoption of self-adaption techniques in risk-averse real-world settings. In this talk, I describe an approach, called POssIbilistic SElfaDaptation (POISED), for tackling the challenge posed by uncertainty in making adaptation decisions. POISED builds on possibility theory to assess both the positive and negative consequences of uncertainty. It makes adaptation decisions that result in the best range of potential behavior.

Speaker's Bio

Naeem Esfahani is a Ph.D. candidate in Computer Science Department, Volgenau School of Engineering. He got his B.Sc. degrees on Electrical and Computer Engineering from University of Tehran in 2005. He also received a M.Sc. degree in Computer Engineering from Sharif University of Technology in 2008. His current research mainly focuses on Software Architecture, Self-Adaptive Software Systems, and Software Quality of Service Analysis & Improvement.

CS Colloquium: Geometric Modeling for Humans

Friday, March 02, 2012
10:00am-11:00am
ENGR 4201
Yotam Gingold

Abstract

Digital content creation is fundamental to many areas of computer graphics, from image processing to 3D geometry processing and animation. For example, the creation and editing of 3D models impacts everything from the design of objects in the real world to visualization and digital entertainment. And yet, the tools used to create and edit 3D geometry are cumbersome, accessible only to a small group of experts.

In this talk, I will present my vision of accessible digital content creation for everyone, from novices to highly trained experts (and even computers). I will demonstrate tools that allow novices to participate in 3D modeling by leveraging skills they naturally possess. I will also discuss new ways to leverage the expertise of experts. We will be helped along the way by fast and stable optimization techniques.

I will conclude my talk by presenting a new way to approach problems in computer graphics. I will show how Human Computation enables us to make seemingly impossible algorithms a reality.

Speaker's Bio

Yotam Gingold is a post-doctoral researcher in the computer science departments of Columbia University and Rutgers University. His research interests include interactive geometric modeling, human computation, topology for computation, and game design. Yotam earned his Ph.D. in Computer Science from New York University in 2009 under the supervision of Denis Zorin.

SANG Seminar: Multipath Routing in Wireless Mesh Networks

Monday, March 05, 2012
12:30-1:30 PM
ENGR 4201
Li Xiao, Associate Professor, Michigan State University

Abstract

The wireless mesh network offers a breakthrough approach that delivers wireless services for a large variety of applications. Equipped with recent significant advances in wireless radios, multi-radio/multi-channel mesh routers provide both great promise and substantial research challenges to significantly enhance network performance while dramatically reducing cost. In this talk, I will present our research efforts on multi-path routing in wireless mesh networks. Multi-source video on-demand streaming has been applied in wired networks with great success. However, it remains a challenging task in wireless networks due to wireless interference. I will introduce our multi-path routing and rate allocation study to support multi-source video on-demand applications in wireless mesh networks.

Speaker's Bio

Li Xiao is an associate professor of computer science and engineering at Michigan State University. Her research interests are in the areas of distributed and networking systems, overlay networks and applications, wireless networks and mobile computing. Her research has been supported by the National Science Foundation, Microsoft Research, the Internet2 program, and the Michigan Space Grant Consortium. She is serving on the Editorial Boards of IEEE Transactions on Parallel and Distributed Systems and Peer-to-Peer Networking and Applications Journal. She has served as workshop program chair, conference vice chair and track chair, and on various technical program committees for conferences in the areas of distributed systems and computer networks. She received her PhD degree in computer science from the College of William and Mary.

Distributed Simulation Seminar: Parallel Computing & Massive Simulations at ISISLab

Tuesday, March 06, 2012
2:00 PM
ENGR 4201
Vittorio Scarano, Dipartimento di Informatica, Università di Salerno

Abstract

In this talk, we will report on the research that is conducted in our lab on how parallel computing can be used for massive simulations. In particular, our focus is on different sw/hw architectures (GPUs, MPI, heterogeneous clusters) and how the challenge of efficient simulations is differently tackled under different constraints.

ISISLab: http://www.isislab.it

GRAND Seminar: Quantitative Myocardial Perfusion MRI: From Sector-Based to Pixel-Based Analysis

Tuesday, March 06, 2012
12:00 PM
ENGR 4201
Li-Yueh Hsu, National Heart, Lung & Blood Institute, National Institutes of Health

Abstract

Dynamic contrast-enhanced myocardial perfusion MRI has become an important tool for the diagnosis of coronary artery disease. Quantitative assessment of cardiac perfusion images using estimates of myocardial blood flow and myocardial perfusion reserve has shown promising results in several pre-clinical and clinical studies. This presentation will provide an overview of current sector-based analysis for quantifying first-pass contrast-enhanced cardiac MR images. A new approach for pixel-based myocardial blood flow quantification will also be presented. The results of high-resolution perfusion pixel maps from both animal and human studies will be presented and discussed. Technical challenges in fully automated pixel-wise quantification of cardiac perfusion MR images will then be addressed.

Speaker's Bio

Li-Yueh Hsu received his D.Sc. degree in Biomedical Engineering from the George Washington University. He is currently a staff scientist at the National Heart, Lung, and Blood Institute at the National Institutes of Health. His broad research interests are biomedical imaging and image analysis, computational modeling in biology and physiology, computer-aided detection, and diagnosis systems. His recent research has focused on the cardiovascular magnetic resonance imaging and quantitative analysis of myocardial perfusion and tissue characterization.

SANG Seminar: An Analysis of the Business Behind Monetizing Spam

Friday, March 09, 2012
12:00AM - 1:00PM
ENGR 4201
Damon McCoy, Assistant Professor, Computer Science

Abstract

Spam advertising is a business that continues to exist despite attempts to intervene at many of the levels visible in the actual spam messages (i.e. spam filtering, URL blacklisting, domain and hosting takedowns). It continues to exist, in spite of these adversarial pressures, because it fuels a profitable enterprise. In this talk, I will present our efforts to perform a holistic empirical analysis that quantifies the full set of resources employed to monetize spam advertising counterfeit goods by collecting extensive measurements of three months of email sp chases from spam-advertised sites, along with an in-depth analysis of leaked data that provides a rare view into the inner-working's of major affiliate programs that monetize spam. Through this analysis we provide valuable insight into the cost structure of these affiliate programs and strong evidence of payment bottlenecks in their value chain.

Speaker's Bio

Damon McCoy is an assistant professor in the computer science department at George Mason University. He obtained his Ph.D. from the University of Colorado, Boulder in 2009. In 2010 and 2011, he was a Computer Innovation Fellow at the University of California, San Diego. His research includes work on the economics of e-crime, wireless privacy, anonymous communication systems, and cyber-physical security. More generally, he is interested in exploring and improving the security and privacy of large-scale systems.

CS Seminar: Bayesian Inference

Tuesday, March 20, 2012
PART 1: 8:00-11:00 AM. PART 2: 12:30-3:30 PM
SUB II (The Hub) Rooms 3, 4, and 5
John Myles White

Abstract

NOTE: This seminar is in two parts. The first part runs from 8 to 11 AM. The second part runs from 12:30 to 3:30 PM.

Section 1: An Introduction to Bayesian Inference - Introduce the Bayesian paradigm of inference as probabilistic calculation - Provide a loose treatment of the Cox axioms - Discuss useful statistical theory: - Likelihood functions - Maximum likelihood estimation - Fisher information - Bias, variance, consistency and the Central Limit Theorem for estimators - Review standard probability distributions - Go through the classical coin-filpping example in detail with a beta prior - Describe results of Bayesian inference as comparable to MLE with regularization added in

Section 2: BUGS as a Tool for Automating Bayesian Inference - Describe how to specify models using BUGS language - Go through many example models - Normal with unknown mean, known variance - Normal with unknown mean, unknown variance - Linear regression: unknown coefficients and variance, Normal priors - Linear regression with Laplace priors - Logistic regression - Hierarchical models - LDA - SNA models

Section 3 (Optional): Implementing Samplers and MCMC by Hand - Introduce the Ising model as a canonical distribution for sampling - Review sampling techniques: - Rejection sampling - Slice sampling - Metropolis-Hasting sampling - Gibbs sampling

Speaker's Bio

John Myles White is a Ph.D. student in the Princeton Psychology Department, where he studies how humans make decisions both theoretically and experimentally. Along with the political scientist Drew Conway, he is the author of a book recently published by O’Reilly Media entitled “Machine Learning for Hackers”, which is meant to introduce experienced programmers to the machine learning toolkit. John is now working with the statistician Mark Hansen on a book for laypeople about exploratory data analysis. He is also the lead maintainer for several popular R packages, including ProjectTemplate and log4r.

CS Colloquium: Procedural Content Generation for Game Design

Tuesday, March 20, 2012
11:00am-12::00pm
ENGR 4201
Gillian Smith

Abstract

The future of digital games lies in the development of new technologies that support new game genres and player experiences. One such technology is procedural content generation -- the use of a computer to create game content that would normally be made by a human designer -- which offers a number of opportunities for game design: it can be employed as an on-demand game designer, capable of assisting human designers with creating content or crafting a unique experience for each player. However, such abilities can only be achieved through imbuing the generator with an understanding of game design principles and creating a means for a human designer to communicate about these principles with the generator. This talk describes an approach to procedural content generation that incorporates an understanding of game pacing, and discusses the implications of this design decision for the game design process through examining two different projects: a tool that uses procedural content generation to support players in designing their own game levels, and a game that has players explore an infinitely generated world that morphs according to their choices.

Speaker's Bio

Gillian Smith is a PhD candidate in the Center for Games and Playable Media (Augmented Design Lab and Expressive Intelligence Studio) at the University of California, Santa Cruz. Her research interests sit at the intersection of artificial intelligence, human-computer interaction, game studies, and design studies. Her current focus is on procedural content generation and how it changes the game design process, in terms of both creating tools for novice designers and enabling entirely new kinds of games. Her latest project, Endless Web, is a game that uses procedural content generation to create an infinite world for the player to explore that adapts to the choices they've made. She is also interested in studying gender issues in games, and methods for increasing girls' and women's participation in computer science and game design.

SANG Seminar: A Server's Perspective of Internet Streaming Delivery to Mobile Devices

Friday, March 23, 2012
2:00 PM
ENGR 4201
Yao Liu

Abstract

Internet streaming services to mobile devices are getting more and more popular with the pervasive adoption of all kinds of mobile devices in practice. To understand and provide better streaming services to mobile devices, a number of studies have been conducted to investigate streaming services to mobile devices. However, these studies have mainly focused on the client side resource consumption and streaming quality. So far, little is known about the server side, which is the key for providing successful mobile streaming services.

In this talk, we present our investigation of the Internet mobile streaming service at the server side. For this purpose, we have collected a one-month server log (with 212 TB delivered video traffic) from a top Internet mobile streaming service provider serving worldwide mobile users. I will present our findings through the measurement and analysis.

Speaker's Bio

Yao Liu is a Ph.D. student of Computer Science Department at George Mason University.

CS Colloquium: Physical Motion Control and Analysis in Games, Visual Effects and Training.

Monday, March 26, 2012
11:00am-12:00
ENGR 4201
Brian Allen

Abstract

The synthesis of realistic motion is a key component of visual effects and computer games. Correspondingly, the dual problem of recognizing a particular grace in motion, has the potential to improve the training of movement skills and animation. As humans become proficient with a manual skill, their motion becomes more fluid, more efficient and more compliant. Physical simulation, now cheap and ubiquitous, is a promising means for creating and understanding motion. In contrast to key-frame animation or motion capture, characters driven by physical laws can move in new, dynamic and unforeseen ways in response to their environment and user interaction. However, a key challenge with using physically simulated characters is developing controllers capable of reproducing the fluidity and compliance of well-practiced motion.

In this talk, I will present new approaches for both the control and the analysis of fluid and compliant physical motion. For control, I will introduce a novel solution to a classic, low-level control equation. This solution provides an analytic method for determining the character's compliance, that is, how the simulated character will respond to unexpected collisions. I will also introduce a biologically inspired method for generating high-level controllers capable of complex and dynamic whole-body behaviors.

Additionally, I will show that such control techniques can also serve in the analysis of human motion, for example, in estimating motor-skill level based only on observed motion, or in predicting future movements. I will illustrate both synthesis and analysis of motion with examples from a range of applications in computer games, visual effects, robotics, virtual reality and medical training.

Speaker's Bio

Brian F. Allen is a Senior Research Fellow at the Institute for Media Innovation at the Nanyang Technological University in Singapore. His research focuses on natural motion and physical simulation with applications to games, visual effects and medicine. He has ten years of software development experience, including working with Industrial Light and Magic R&D, University of Southern California's Institute for Creative Technologies, and co-founding and serving as CTO of Silicon Age, a San Francisco-based software consultancy. He received his B.S. in Computer Science from Iowa State University and his PhD from the University of California, Los Angeles with the advisement of Petros Faloutsos.

GRAND Seminar: White-box Data Mining Algorithms

Monday, March 26, 2012
3:00pm
ENGR 3507
Boris Delibasic

Abstract

Choosing the right algorithm for data at hand was always a major problem in data mining. We propose a new architecture for decision-support systems for data mining, with the ability of generic algorithm design to help users choose the right algorithm. Opposite to the prevalent black-box approach of using algorithms in data mining were users have the ability to define inputs, setup parameters and read outputs, we propose using reusable component (RC) based algorithms. The RC-based algorithms are assembled from reusable components, which are standalone algorithm units which were originally found in black-box algorithms and their partial improvements. RC based algorithms have been proven to better adapt to data than black-box algorithms that, due to “hard” bindings of algorithm parts, are disabled to achieve best results on some datasets. On the other hand, the RC-based approach of algorithm design produces a galore of algorithms making it thus harder to search through the algorithm space. We show how this problem can be solved using meta-heuristics for searching through the algorithm space. We also propose further research directions that will enable to connect the proposed approach with meta-learning. We believe that users will be better supported in the future for choosing an adequate algorithm for the problem at hand, because the decision support system will be enabled to perform an intelligent search through the algorithm space that is based on dataset properties, algorithm performance results, empirical rules gained from meta-learning and theoretical support.

Speaker's Bio

Boris Delibasic is an associate professor at the University of Belgrade in Serbia. His main research interests are data mining, decision support systems, business intelligence, and decision theory. Dr. Delibasic is also an adjunct lecturer at the University of Jena in Germany. He has already published several research articles in top-ranked international journals. A project he is currently engaged with is dealing with design of white-box algorithms for data mining (www.whibo.fon.bg.ac.rs). In 2011, Prof. Delibasic received a prestigious Fulbright fellowship to work as a visiting scholar at Zoran Obradovic’s Center for data analytics and biomedical informatics at Temple University in Philadelphia, PA. His current research objectives are to design spatio-temporal algorithms for analysis of ski injuries and to discover ski injury patterns that could be used for injury prevention. Algorithms developed for ski injury analysis, are planned in a later stage to be extended, to analyze large scale data on road traffic accidents. Dr. Delibasic is also ski patroller on Serbian mountains during the winter season.

CS Colloquium: 3D Virtual Characters: Skinning, Clothing, and Weird Math

Thursday, March 29, 2012
11:00am-12:00pm
ENGR 4201
Ladislav Kavan

Abstract

This talk presents an overview of my research on real-time 3D graphics, focusing on technology related to virtual characters. First, I will talk about skinning, i.e., the problem of how to translate skeletal animation to full body deformations. I will explain the advantages of using dual quaternions as opposed to the more traditional matrix representation. Next, I will follow with my contribution to real-time cloth animation, discussing how to create upsampling operators that add fine-scale details to coarse mesh simulations. The techniques required to design efficient and robust upsampling operators include harmonic regularization (an extension of the classical Tikhonov approach) and "tracking," i.e., constraining fine-scale physics to follow a given coarse-scale animation. I will conclude with some ideas for future work, for example, how to make digital content creation more intuitive.

Speaker's Bio

Ladislav Kavan is a Senior Researcher at ETH Zurich, working in Interactive Geometry Lab with Prof. Olga Sorkine. Prior to joining ETH, he was a Research Scientist for Disney Interactive Studios, where he worked on next generation technology for computer games with Peter-Pike Sloan. Ladislav's recent work has focused on combining data-driven techniques with physically-based simulation, subspace methods, and real-time character animation. Some of these results have been used in production in the game and film industries. Ladislav received his M.S. in computer science from Charles University and Ph.D. from Czech Technical University in Prague.

GRAND Seminar: Pedestrians to Cities with Agent-based modeling and GIS

Tuesday, April 03, 2012
12:00pm
ENGR 4201
Andrew Crooks

Abstract

This talk will explore how agent-based models can be explicitly linked to "real world" locations with spatial information and be used to explore a wide range of social phenomena. From that of the small scale movement of pedestrians over seconds; to that of urban growth over decades. All the applications will focus on individuals or groups of individuals and how such interactions lead to more aggregate patterns emerging. Moreover, the talk will demonstrate how new technologies and sources of information (e.g. volunteered geographic information and twitter) can be used to inform the model building process.

Speaker's Bio

Andrew Crooks is an assistant professor in the Department of Computational Social Science and a researcher in the Center for Social Complexity at George Mason University. He holds a PhD from University College London.

His research relates to exploring, understanding and the communication of urban built and socio-economic environments using geographic information systems (GIS), spatial analysis, geovisualisation, social network analysis and agent-based modeling methodologies. Further information about these interests is available on his blog http://gisagents.blogspot.com/ or personal website http://www.css.gmu.edu/andrew/

SWE Seminar: Adding Criteria-Based Tests to Test Driven Development

Friday, April 06, 2012
1:30pm
ENGR 4801
Bill Shelton

Abstract

Test driven development (TDD) is the practice of writing unit tests before writing the source. TDD practitioners typically start with example-based unit tests to verify an understanding of the software’s intended functionality and to drive software design decisions. Hence, the typical role of test cases in TDD leans more towards specifying and documenting expected behavior, and less towards detecting faults. Conversely, traditional criteria-based test coverage ignores functionality in favor of tests that thoroughly exercise the software. This paper examines whether it is possible to combine both approaches. Specifically, can additional criteria-based tests improve the quality of TDD test suites without disrupting the TDD development process?

This paper presents the results of an observational study that generated additional criteria-based tests as part of a TDD exercise. The criterion was mutation analysis and the additional tests were designed to kill mutants not killed by the TDD tests. The additional unit tests found several software faults and other deficiencies in the software. Subsequent interviews with the programmers indicated that they welcomed the additional tests, and that the additional tests did not inhibit their productivity.

Speaker's Bio

William "Bill" Shelton is a PhD student in the Computer Science Department at the Volgenau School of Information Technology and Engineering at GMU, currently focusing on applying software testing research to real world situations. He received his bachelor’s degree in Music from Berklee College of Music, Boston, MA., and his M.S. degree in Software Engineering from George Mason University. He is currently employed as a Sr. Software Engineer at the new Consumer Financial Protection Bureau where his focus is, among many other software engineering tasks, test automation and continuous delivery.

SWE Seminar: Better Algorithms to Minimize the Cost of Test Paths

Friday, April 06, 2012
1:30pm
ENGR 4801
Nan Li

Abstract

Model-based testing creates tests from abstract models of the software. These models are often described as graphs, and test requirements are defined as subpaths in the graphs. As a step toward creating concrete tests, complete (test) paths that include the subpaths through the graph are generated. Each test path is then transformed into a test. If we can generate fewer and shorter test paths, the cost of testing can be reduced. The minimum cost test paths problem is finding the test paths that satisfy all test requirements with the minimum cost.

This paper presents new algorithms to solve the problem, and then presents data from an empirical comparison. The algorithms adapt approximation algorithms for the shortest superstring problem. The comparison is with an existing tool that uses a brute force approach to extend each subpath to a complete path. One new algorithm is based on the greedy set-covering algorithm and the other is based on finding a matching over a prefix graph. The comparison was performed on open software and showed that both new solutions generate fewer test paths than the brute force approach. The prefix-graph based solution takes much less time than the other two solutions when the number of test requirements is large.

The paper has been accepted for ICST 2012 in Montreal, Canada. This seminar is a practice for Nan's conference presentation.

Speaker's Bio

Nan Li is a PhD student in Computer Science Department, Volgenau School of Information Technology and Engineering at GMU. He received his bachelor’s degree in Software Engineering from Beihang University in China in 2006 and his M.S. degree in Computer Science from Fairleigh Dickinson University in 2008. His current research mainly focuses on mutation testing, model-based testing and test automation.

Oral Defense of Doctoral Dissertation: Defeating Insider Attacks via Autonomic Self-Protective Networks

Friday, April 06, 2012
10:00am-12:00pm
ENGR 1602
Faisal M. Sibai

Abstract

There has been a constant growing security concern with insider attacks on network accessible computer systems. Users with power credentials can do almost anything they want with the systems they own with very little control or oversight. Most breaches occurring nowadays by power users are considered legitimate access and not necessarily intrusions. Developing a solution for such problems is challenging because power users need flexible requirements to administer or maintain their systems. The increased usage of virtual environments, virtual systems, teleworking, and remote usage has made network access the preferred method for system administration.

This dissertation describes the design and implementation of a network Autonomic Violation Prevention System (AVPS) framework that is intended to defeat the insider threat in organizations. The AVPS sits between privileged users and applications. It monitors traffic that traverses the network and takes actions as needed. A proof of concept prototype for the system was developed in a virtualized environment. FTP and Telnet were part of the application testbed. Rules that pertain to privileged user administration were applied. Actions that were tested successfully included traffic monitoring, replacement, blocking, and dropping.

This work also examined the scalability of the AVPS design. An experimental testbed was built to obtain performance measures of the AVPS overhead, throughput, and response time. FTP, Database and Web servers were used in the application testbed. A variety of tests were performed including automated simultaneous transactions and manual simultaneous transactions. An M/M/N//M analytic queuing model was used to assess how well the AVPS system would perform for a finite population where the numbers of applications, users and AVPS engines vary under different load levels. The results showed that the AVPS exhibits a very low overhead and is therefore scalable.

The AVPS architecture design was further enhanced to automate how signatures are created. Autonomic self-protection capabilities were added into the framework by implementing high level rules that set the goal for how violations are detected and signatures are created. Supervised self-learning capabilities were added via the use of Support Vector Machines (SVM) in order to classify the raw data and make final decisions on what is considered a violation and what is considered normal insider behavior.

Speaker's Bio

Bachelor of Computer and Information Systems Science, King Saud University, 2000 Master of Science, George Mason University, 2008

SANG Seminar: S-STORE: Socially-Aware Distributed Data Storage

Friday, April 06, 2012
2:00-3:00pm
ENGR 4201
Duc Tran

Abstract

Online social networking has become ubiquitous. For a social storage system to keep pace with increasing amounts of user data and activities, an intuitive solution is to deploy more servers. A challenge then is how to partition the data across the servers so that server efficiency and load balancing can both be achieved. Another challenge is how to provide data redundancy in the forms of replication or erasure coding in order to improve the system's data availability. These challenges are especially amplified for social data storage because we should take into account the data's social relationships which imply how often certain data are accessed together in a transaction. This is not to mention the dynamics of social data which changes frequently over time. State-of-the-art storage techniques are not socially-aware, not seriously taking into account these issues. This talk presents S-STORE, a novel socially-aware storage framework which consists of three key techniques: S-PUT for data partition, S-CLONE for data replication, and S-CODE for erasure data coding. In addition to theoretical concepts, preliminary evaluation results are also discussed.

Speaker's Bio

Duc A. Tran is an Assistant Professor of Computer Science at the University of Massachusetts at Boston, leading the Network Information Systems Laboratory (NISLab). He received a PhD degree from the University of Central Florida (Orlando, Florida). Dr. Tran's research interests are in the areas of networking and distributed systems. The results of his work have led to research awards from the NSF, Best Paper Award at ICCCN 2008, and Best Paper Recognition at DaWak 1999. Dr. Tran has served as a Review Panelist for the NSF, Editor for the Journal on Parallel, Emergent, and Distributed Systems (2010-date) and ISRN Communications Journal (2010-date), Guest-Editor for the Journal on Pervasive Computing and Communications (2009), TPC Co-Chair for CCNet (2010, 2011), GridPeer (2009, 2010, 2011), and IRSN 2009, and TPC Vice-Chair for AINA 2007. Dr. Tran is a Senior Member of the ACM and a Professional Member of the IEEE.

SWE Seminar: Software Testing Research at ICMC

Wednesday, April 11, 2012
11:00am
ENGR 4801
Marcio Delamaro

Abstract

In this talk prof Delamaro will present the highlights of the research he and his group are developing at the Instituto de Ciências Matemáticas e de Computação of the University of São Paulo, in Brazil. The themes addressed include techniques, criteria and tools for software testing in different domains such as embedded systems and virtual reality environments.

Speaker's Bio

Prof Marcio Delamaro has a Bachelor and a Masters degree in Computer Science and a Doctorate degree in Computational Physics. From 1997 he has been working as teacher and researcher in universities in Brazil. In 2000 visited the Politecnico di Milano in Italy for a one year pot-doc stage. Currently he is Associate Professor at Universidade de São Paulo, the larger and most prestigious university in Latin America. His area of interest is software testing.

GRAND Seminar: Clustering Algorithms for Streaming and Online Settings

Friday, April 13, 2012
12:00pm
ENGR 4201
Claire Monteleoni

Abstract

Clustering techniques are widely used to summarize large quantities of data (e.g. aggregating similar news stories), however their outputs can be hard to evaluate. While a domain expert could judge the quality of a clustering, having a human in the loop is often impractical. Probabilistic assumptions have been used to analyze clustering algorithms, for example i.i.d. data, or even data generated by a well-separated mixture of Gaussians. Without any distributional assumptions, one can analyze clustering algorithms by formulating some objective function, and proving that a clustering algorithm either optimizes or approximates it. The k-means clustering objective, for Euclidean data, is simple, intuitive, and widely-cited, however it is NP-hard to optimize, and few algorithms approximate it, even in the batch setting (the algorithm known as "k-means" does not have an approximation guarantee). Dasgupta (2008) posed open problems for approximating it on data streams.

In this talk, I will discuss my ongoing work on designing clustering algorithms for streaming and online settings. First I will present a one-pass, streaming clustering algorithm which approximates the k-means objective on finite data streams. This involves analyzing a variant of the k-means++ algorithm, and extending a divide-and-conquer streaming clustering algorithm from the k-medoid objective. Then I will turn to endless data streams, and introduce a family of algorithms for online clustering with experts. We extend algorithms for online learning with experts, to the unsupervised setting, using intermediate k-means costs, instead of prediction errors, to re-weight experts. When the experts are instantiated as k-means approximate (batch) clustering algorithms run on a sliding window of the data stream, we provide novel online approximation bounds that combine regret bounds extended from supervised online learning, with k-means approximation guarantees. Notably, the resulting bounds are with respect to the optimal k-means cost on the entire data stream seen so far, even though the algorithm is online. I will also present encouraging experimental results.

This talk is based on joint work with Nir Ailon, Ragesh Jaiswal, and Anna Choromanska.

Speaker's Bio

Claire Monteleoni is an assistant professor of Computer Science at George Washington University. Previously, she was research faculty at the Center for Computational Learning Systems, and adjunct faculty in the Department of Computer Science, at Columbia University. She did a postdoc in Computer Science and Engineering at the University of California, San Diego, and completed her PhD and Masters in Computer Science, at MIT. Her research focus is on machine learning algorithms and theory for problems including learning from data streams, learning from raw (unlabeled) data, learning from private data, and Climate Informatics: accelerating discovery in Climate Science with machine learning. Her papers have received several awards, and she currently serves on the Senior Program Committee of the International Conference on Machine Learning, and the Editorial Board of the Machine Learning Journal.

CS PhD Club: Meeting

Friday, April 13, 2012
3:00pm-4:00pm
ENGR 4201
Jana Kosecka

SWE Seminar: Toward Harnessing High-Level Language Virtual Machines for Further Speeding Up Weak Mutation Testing

Friday, April 13, 2012
11:00am
ENGR 4201
Vinicius Durelli

Abstract

High-level language virtual machines (HLL VMs) are now widely used to implement high-level programming languages. To a certain extent, their widespread adoption is due to the software engineering benefits provided by these managed execution environments, for example, garbage collection (GC) and cross-platform portability. Although HLL VMs are widely used, most research has concentrated on high-end optimizations such as dynamic compilation and advanced GC techniques. Few efforts have focused on introducing features that automate or facilitate certain software engineering activities, including software testing. This research suggests that HLL VMs provide a reasonable basis for building an integrated software testing environment. As a proof-of-concept, we have augmented a Java virtual machine (JVM) to support weak mutation analysis. Our mutation-aware HLL VM capitalizes on the relationship between a program execution and the underlying managed execution environment, thereby speeding up the execution of the program under test and its associated mutants. To provide some evidence of the performance of our implementation, we conducted an experiment to compare the efficiency of our VM-based implementation with a strong mutation testing tool (muJava). Experimental results show that the VM-based implementation achieves speedups of as much as 89 percent in some cases.

Speaker's Bio

Vinicius Durelli is a Ph.D. candidate in Computer Science at University of São Paulo, Brazil. He received his M.S.on Computer Science from Federal University of São Paulo in 2008. His research interests focus on Software Testing, High-level Language Virtual Machines, and Refactoring. Currently, he has been trying to retrofit software testing features into high-level language virtual machines.

Oral Defense of Doctoral Dissertation: Performance Management for Energy Harvesting Wireless Sensor Networks

Wednesday, April 18, 2012
10:30-12:30
ENGR 4201
Bo Zhang

Abstract

A Wireless Sensor Network (WSN) consists of spatially distributed sensor nodes which monitors environmental conditions such as temperature, humidity, sound or pressure, etc. Recently there is increasing need to design Wireless Sensor Network systems that support applications with intensive monitoring and control activities. This application class often has significant data collection and processing requirements, requiring increased levels of energy consumption as compared to other WSN applications. Further, many deeply embedded WSN systems with these data collection and processing requirements are expected to operate without manual battery recharging for several decades, and therefore require energy harvesting techniques. For this class of systems, there are currently few effective approaches that balance careful energy management with high performance communication and computation requirements. My dissertation addresses the above problem. Specifically, I propose a set of algorithms and control methods for energy management in performance-sensitive WSN systems, and harvesting-aware rate allocation for application utility maximization. First I formally define the problem of energy harvesting-aware energy management as two optimization problems, one for individual sensor nodes and another for multi-hop sensor networks. I propose energy management algorithm to solve both problems optimally and efficiently. These solutions combine two energy saving techniques, Dynamic Voltage Scaling (DVS), and Dynamic Modulation Scaling (DMS), alongside with energy harvesting techniques. I then address a harvesting aware rate allocation problem with the objective of utility maximization. The problem is solved with an optimal centralized algorithm and a distributed algorithm. I conducted extensive simulation-based experiments to evaluate the effectiveness of my proposed algorithms. Specifically I developed simulation software using TOSSIM, the standard WSN simulator, and EPANET, a public domain, water distribution system modeling program. This software simulates in high fidelity the computation and communication activities of WSN nodes, and considers a variety of network setups, energy harvesting profiles (solar and water), and application scenarios, etc. My algorithms are implemented within this simulation environment and compared against a series of rival algorithms under various experimental setups. Extensive simulation results demonstrate the significant advantage of my algorithms over the rival algorithms.

Computer Science Colloquium: Searching in the "Real World"

Wednesday, April 18, 2012
1:30-2:30pm
ENGR 4201
Ophir Frieder

Abstract

For many, "searching" is considered a mostly solved problem. In fact, for text processing, this belief is factually based. The problem is that most "real world" search applications involve "complex documents", and such applications are far from solved. Complex documents, or less formally, "real world documents", comprise of a mixture of images, text, signatures, tables, etc, and are often available only in scanned hardcopy formats. Search systems for such document collections are currently unavailable.

We describe our efforts at building a complex document information processing prototype. This prototype integrates "point solution" (mature) technologies, such as document readability enhancement, OCR capability, signature matching and handwritten word spotting techniques, search and mining approaches, among others, to yield a system capable of searching "real world documents". The described prototype demonstrates the adage that "the whole is greater than the sum of its parts". Our complex document benchmark development efforts are likewise presented.

Having described the global approach, we describe some point solutions which we developed over the years. These include an image enhancer, an Arabic stemmer, and a natural language source integration fabric called the Intranet Mediator.

Speaker's Bio

Ophir Frieder is the Robert L. McDevitt, K.S.G., K.C.H.S. and Catherine H. McDevitt L.C.H.S. Chair in Computer Science and Information Processing and is Chair of the Department of Computer Science at Georgetown University. He is a Fellow of the AAAS, ACM, and IEEE.

SWE Seminar: Using Mutants to Detect and Locate Bugs

Monday, April 23, 2012
12:00pm
ENGR 3507
Mike Papadakis

Abstract

One of the most important challenges in software development is the detection and correction of software faults. Software testing and debugging techniques form the current practice for identifying, locating and fixing software defects. Both testing and debugging activities are among the most tedious tasks of software development which are usually performed by hand. Therefore, substantial benefits can be gained by the full or partial automation of these activities. Towards this direction, this talk will present emergent results of the use of mutation analysis in automating both testing and debugging. More precisely, automated techniques with respect to a) test case generation, b) test evaluation and c) fault localization will be presented in this talk.

Speaker's Bio

Mike Papadakis is a research associate at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) of Luxembourg University. He received his B.Sc., M.Sc. and Ph.D. degrees in Computer Science from the Athens University of Economics and Business (Greece). His research interests include software quality assurance and in particular, software testing, software debugging and mutation testing.

GRAND Seminar: Serious Games

Tuesday, April 24, 2012
12:00pm
ENGR 4201
Len Annetta

Abstract

This research presentation will illustrate how Dr. Annetta began studying Serious Games and the funded research he has been awarded throughout his career. His work in scientific visualization, with a specific focus on spatial visualization and mental rotation, will be highlighted from new data on high school students in the National Science Foundation funded GRADUATE project. Finally, this talk will communicate Dr. Annetta’s vision for how Serious Games can bridge a variety of disciplines.

Speaker's Bio

An associate professor of Science Education at George Mason University, Dr. Annetta’s research has focused on distance learning and the effect of instructional technology on science learning of teachers and students in underserved populations. Understanding the popularity of online, multiuser video game play, Dr. Annetta has been awarded over $5 million in grants to support his work on distance learning and the use of Serious Educational Games as a vehicle for learning STEM content and STEM career awareness. In 2008, he was honored with three awards for his extension work teaching K-12 teachers and students’ video game design and creation. These awards were progressive from the College of Education Outstanding Extension Service Award, to the induction into the NC State University Academy of Outstanding Faculty Engaged in Extension to the Distinguished Alumni Engaged in Extension and Outreach award. Moreover, Dr. Annetta has twice been awarded the National Technology Leadership Initiative Fellowship in Science Education and Technology from the Association of Science Teacher Education and the Society for Information Technology and Teacher Education.

Oral Defense of Doctoral Dissertation: A Self-managed Healthcare Emergency Department System

Friday, April 27, 2012
10:00am -12:00pm
ENGR 4801
Serene Al-Momen

Abstract

The delivery of cost-effective and quality Emergency Department (ED) services remains an important and ongoing challenge for the healthcare industry. ED overcrowding has become a common problem in hospitals around the world, threatening the safety of patients who rely on timely emergency treatment. Despite numerous advances in medical procedures and technologies, EDs continue to experience overcrowding problems. The combination of increased demand and diminished resources makes optimizing emergency departments a difficult problem for healthcare decision makers. We address this problem by applying an autonomic computing framework to self-managed emergency departments in order to maintain optimal Quality of Service (QoS) during its operation. This work has potential implications in guiding a hospital's effort to optimize their emergency department system while at the same time meeting cost constraints.

Improving the operational efficiency of an ED is a complex task due to the very large number of ED configurations that involve human and physical resources and due to the unpredictable nature of the ED's workload. Thus, managing the performance of EDs becomes difficult and expensive when carried out by human beings alone. The approach presented in this dissertation for a self-managed ED (SMED) consists in building into the ED the mechanisms required to self-adjust the ED's configuration parameters so that its QoS is constantly met. The design of an autonomic controller for the SMED and the evaluation of its effectiveness in optimizing QoS subject to cost constraints is described in this dissertation. The controller uses a combination of combinatorial search techniques with simulation models. Utility functions are used to represent stakeholder policies, which normally consist of multiple QoS metrics with competing priorities. Experimental results illustrate the operation of the controller and how it reacts to variations of patient interarrival times under various cost constraints.

The evaluation process consisted in analyzing the results obtained with the SMED on a hypothetical ED as well as on a real ED model. In both cases, it is shown that the SMED was able to find the near optimal configuration that optimizes QoS goals within the cost constraint. The results also show how the SMED can be useful in adjusting staffing policies and determining resource factors that play a major role in impacting ED QoS metrics.

Computer Science Colloquium: New Knowledge Discovery Techniques to Support Intelligence Analysts

Monday, April 30, 2012
11:00am-12:00pm
Research Hall, Room 163
Naren Ramakrishnan

Abstract

Intelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data, and rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. This talk will focus on “storytelling”, the investigative process by which analysts aim to “connect the dots” between seemingly disconnected information. We will introduce how the twin notions of redescriptions and biclusters form compositional building blocks of stories, and how efficient and effective algorithms for storytelling can be designed. In addition, we describe approaches to inject user feedback into the story construction process and the results of user studies demonstrating how participants are adept at using these notions to solve intelligence analysis tasks. Experimental results on large textual and multi-relational corpora will be described. This talk will conclude with a preview into an ongoing project in the area of automatic alert generation from public source data.

Speaker's Bio

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering and the Associate Head for Graduate Studies in the Department of Computer Science at Virginia Tech. At Virginia Tech, he directs the Discovery Analytics Center, a university-wide effort that brings together researchers from computer science, statistics, mathematics, and electrical and computer engineering to tackle knowledge discovery problems in important areas of national interest, including intelligence analysis, sustainability, and health informatics. Ramakrishnan serves on the editorial boards of IEEE Computer, Data Mining and Knowledge Discovery, and many other journals. He is an ACM Distinguished Scientist and was named to two "40 under 40" lists: Computerworld's innovative IT people to watch (2007) and Purdue University's list of distinguished alumni (2010).

Oral Defense of Doctoral Dissertation: Regression Learning in Decision Guidance Systems: Models, Languages and Algorithms

Monday, April 30, 2012
3:00-5:00
ENGR 2901
Juan (Judy) Luo

Abstract

The state-of-art research in the decision guidance applications is trying to build complex systems with predicting capability. This dissertation focuses on a framework, models, languages, and algorithms to integrate the machine learning functionality (regression learning) into DGMS applications as their first class citizen.

A framework CoReJava (Constraint Optimization Regression in Java), which extends the Java programming language with regression learning – the ability of parameter estimation for a function, is proposed and developed. CoReJava is unique in that functional forms for regression analysis are expressed as first class citizens, i.e., as Java programs, in which some parameters are not given in advance, but will be learned from learning data sets provided as input. The if-then-else decision structures of Java language are naturally adopted to represent piecewise functional forms of regression. Thus, minimization of the sum of squared errors involves an optimization problem with a search space that is exponential to the size of learning set. A combinatorial restructuring algorithm is proposed to guarantee learning optimality and furthermore reduce the search space to be polynomial in the size of learning set, but exponential to the number of piece-wise bounds. A Heaviside restructuring algorithm, which expresses the piecewise linear regression function using a unified functional format, instead of multiple pieces, is proposed to decrease the searching complexity further to be polynomial in both the size of learning set and the number of piece-wise bounds, while the learning outcome will be an approximation of the optimality.

A multi-step Expectation Maximization based (EM-based) algorithm (EMMPSR) is proposed to solve piecewise surface regression problem. The multiple steps involved are local regression on each data point of the training data set and a small set of its closest neighbors, clustering on the feature vector space formed from the local regression, regression learning for each individual surface, and classification to determine the boundaries for each individual surface. An EM-based iteration process is introduced in the regression learning phase to improve the learning outcome. The reassignment of a cluster identifier for every data point in the training set is determined by predictive performance of each submodel. Clustering quality validity indices are applied to the scenario in which the number of piecewise surfaces is not given in advance.

The Relational Database Management System (RDBMS) is extended with the piecewise regression learning capability as well. The functional forms are represented as database tables. The EMMPSR algorithm is implemented as stored procedures. A case study is undertaken to describe the decision optimization process based on the learning outcome of the multi-step Expectation Maximization based (EM-based) algorithm.

Evaluation of the resulting research is established by experiments and empirical analysis in comparison with those of related regression learning packages.

GRAND Seminar: Improving Drug Development by Connecting Medicinal Chemistry with Drug Repositioning and Modern Machine Learning Methods

Tuesday, May 01, 2012
12:00pm
ENGR 4201
Iwona Weidlich

Abstract

Developing drug candidates from scratch has turned into a billion-dollar expense that is not delivering enough profitable products to market. Novel approaches which merge chemistry with biology and informatics contribute to the development of selective lifesaving drugs needed by patients. We implement machine learning classifiers for HTS Data Analysis, Screening and drug repurposing with high probability of selecting drug candidates eligible for Phase II of clinical study free from ADME/Tox-related problems. We used small molecule bioactivity data for HCV RNA Polymerase to train and test QSAR models and apply these robust models for compound ranking and hit identification in drug repositioning techniques. Random Forest and kNN algorithms were used with Morgan fingerprints of 679 small molecules with curated IC50 values. After filtering various drug-like databases (DrugBank, MDL, NIAID-NIH, ComGenex) compounds were selected and tested against HCV. We discuss the challenges in drug repositioning faced in academia, government and pharmaceutical industry.

Speaker's Bio

Dr. Weidlich received her Ph.D. in Pharmaceutical Sciences from the University of Medical Sciences, Poznan, Poland in 2005. Her Ph.D. research focused on developing anti-cancer agents that are designed to be activated only inside a cancerous cell but have benign form in the systemic circulation. Her research interests also include using very large collections of chemical databases to filter and extract relevant subsets of molecules for closer analysis, and performing physicochemical and ADME/Tox property predictions. Specifically, she is interested in designing and evaluation of novel anti-cancer agents. She joined the Computer-Aided Drug Design (CADD) group at the Chemical Biology Laboratory, National Cancer Institute in Frederick, NIH as a postdoctoral fellow in October 2005. Dr. Weidlich has been conducting in silico screening for the inhibitors of cancer DNA, specifically tyrosyl-DNA phosphodiesterase (Tdp1) and Shc Src homology 2 (SH2) domain. She designed new, more powerful Tdp1 inhibitors and Shc SH2 domain-binding inhibitors: tetramer peptide-peptoid hybrids exhibiting up to 40-fold increase in affinity.

She accepted a faculty position at the University of Maryland Baltimore County (UMBC) in October 2010, remaining affiliated with NCI/NIH as a Guest Researcher. At UMBC she employs virtual screening to identify novel allosteric inhibitors of the Hepatitis C Virus NS5B Polymerase and also seeks to understand the mechanism by which small molecules inhibit NS5B.

Her current research is also related to combining robust QSAR modeling with drug repositioning and systems biology. This project of hers focuses on resolving problems arising from the management and analysis of huge amount of biological data as well as detailed study of multi-domain proteins and their function. Dr. Weidlich proposes techniques which could result in successful drug selection, and re-investigation of existing drugs for new therapeutic indications.

GRAND Seminar: Robotic Planning with Limited Sensing

Friday, May 04, 2012
11:00am
ENGR 4201
Jason O'Kane

Abstract

The usefulness of a mobile robot is limited by its ability to sense and interact with its environment. However, because information from sensors is limited and sometimes unreliable, robots are often confronted with substantial and difficult-to-resolve uncertainty about the state of the world. This talk will present two lines of research that make progress toward autonomy in spite of such uncertainty. First, I will describe new methods for localization and navigation that allow mobile robots with limited sensing capabilities and noisy actuators to move through their environments in provably reliable ways. Second, I will discuss target tracking applications in which a robot or team of robots seeks to locate and follow moving targets, under several different sensing and motion constraints. The overall theme is that many important tasks in robotics require surprisingly little sensing.

Speaker's Bio

Jason O'Kane is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina. He earned Ph.D. (2007) and M.S. (2005) degrees from the University of Illinois and the B.S. (2001) degree from Taylor University, all in Computer Science. He received an NSF CAREER award in 2010, and is a member of the DARPA Computer Science Study Panel. His research spans algorithmic robotics, planning under uncertainty, and computational geometry.

CS Seminar: A Design Theory for IT Supporting Online Communities

Monday, May 14, 2012
12:00-1:00pm
ENGR 4201
Paolo Spagnoletti

Abstract

Community-centered development methods provide practical guidelines for the design of software environments that effectively support the social interaction of community members. They focus on usability and sociability as general requirements to be addressed through a combination of IT modules and managerial policies. In this talk new design principles and evaluation methods for exploiting the potential of sustainable communities will be introduced. This issue is addressed by presenting a design theory for IT supporting Online Communities (ITsOC) based on transaction costs theory and on complex systems theory. The ITsOC theory is then instantiated in the context of a European project for the development of an intelligent multimedia platform providing innovative social e-services for elderly persons and their social entourage.

Speaker's Bio

Paolo Spagnoletti is assistant professor of Information Systems at LUISS Guido Carli University in Rome (Italy) where he is director of the Master in e-business (www.luiss.it/meb). Since 2011 he coordinates the Research Center on Information Systems (CeRSI) of LUISS. He holds a Ph.D. degree in Information Systems from LUISS University (2007), a Master degree in Business Engineering from Tor Vergata University (2003) and a M.Sc. degree in Electronics Engineering from La Sapienza University (2001). His research interests are related to the social studies of Information Technologies and the design of innovative solutions for transforming organizations: IT supporting online communities, e-health, IT governance, IS security management.

SWE Seminar: A Taxonomy and Survey of Self-Protecting Software Systems

Friday, May 25, 2012
12:00pm
ENGR 4201
Eric Yuan

Abstract

Self-protecting software systems are a class of autonomic systems capable of detecting and mitigating security threats at runtime. They are growing in importance, as the stovepipe static methods of securing software systems have shown inadequate for the challenges posed by modern software systems. While existing research has made significant progress towards autonomic and adaptive security, gaps and challenges remain. In this paper, we report on an extensive study and analysis of the literature in this area. The crux of our contribution is a comprehensive taxonomy to classify and characterize research efforts in this arena. We also describe our experiences with applying the taxonomy to numerous existing approaches. This has shed light on several challenging issues and resulted in interesting observations that could guide the future research.

Speaker's Bio

Eric Yuan is a Ph.D. IT student in the Volgenau School of Information Technology and Engineering at GMU. He received his bachelor’s degrees in Computer Science and Management Information Systems from Tsinghua University in China in 1993 and his M.S. degree in Systems Engineering from University of Virginia in 1996. He has over 15 years of professional experience in IT and management consulting in both commercial and public sectors. His current research interests include service oriented architectures, distributed computing, software engineering, and information security.

SWE Seminar: A Whitebox Approach for Automated Security Testing of Android Applications on the Cloud

Friday, May 25, 2012
12:00pm
ENGR 4201
Riyadh Mahmood

Abstract

By changing the way software is delivered to end users, markets for mobile apps create a false sense of security: apps are downloaded from a market that can potentially be regulated. In practice, this is far from truth and instead, there has been evidence that security is not one of the primary design tenets for the mobile app stores. Recent studies have indicated mobile markets are harboring apps that are either malicious or vulnerable leading to compromises of millions of devices. The key technical obstacle for the organizations overseeing these markets is the lack of practical and automated mechanisms to assess the security of mobile apps, given that thousands of apps are added and updated on a daily basis. In this seminar, we provide an overview of a multi-faceted project targeted at automatically testing the security and robustness of Android apps in a scalable manner. We describe an Android-specific program analysis technique capable of generating a large number of test cases for fuzzing an app, as well as a test bed that given the generated test cases, executes them in parallel on numerous emulated Androids running on the cloud.

Speaker's Bio

Riyadh Mahmood is a PhD student in the Computer Science department at George Mason University. Riyadh has been working in the software engineering / IT consulting field for over a decade. He holds a Bachelor’s degree in Computer Engineering and a Master’s degree in Information Technology from Virginia Tech. Riyadh is currently working on his dissertation proposal, focusing on security testing of Android applications on the cloud.

Oral Defense of Doctoral Dissertation: A Method for Using Cognitive Psychophysiological Event Related Potentials as a Biometric Modality to Differentiate between Information System Users

Monday, July 16, 2012
10:30am–12:30pm
ENGR 4201
Remo P. Perini

Abstract

“Biometrics” is the science and technology of authenticating human beings using biological data. Current biometric modalities, fingerprint, retinal scan, face recognition, etc., have limitations and operational restrictions. The results of this research demonstrate the success of a new biometric modality that makes use of psychophysiologal event related potentials (ERPs).

Psychophysiology is the study of physiological, cognitive and behavioral processes in the body. ERPs, sometimes called “Brainwaves” in the vernacular, are cognitive responses to stimuli. The brain involuntarily generates low-frequency signals that can be measured or monitored via electroencephalogram (EEG). ERPs are triggered by visual, auditory or tactile stimuli and through signal analysis, can be extracted from the normal EEG noise threshold providing measurable data in the microvolt (mV) range.

The methodology developed exploits a behavioral and physiological characteristic of humans that is an obligatory response to a visual stimulus. This unconscious response results in an electrical potential difference in the brain, triggered by the cognitive function of associating a stimulus with memories of the same or similar category stimulus. This type of response is commonly referred to as a Visually Evoked Potential (VEP).

The VEP used in this research is the P3 ERP. The P3 was evaluated to assess its usefulness in confirming the identity of an information system user, as an alternative to using a password or as an additional biometric factor. The methodology compares responses to target and non-target visual stimuli. Infrequent target images and random more prevalent non-target images were rapidly presented to subjects. For each subject, the P3 ERPs elicited by the target stimuli could be easily discriminated from the peak voltages in the same detection window following the non-target stimuli. For normative tests, the statistical difference between the target and non-target responses provided a biometric confidence level (CL) averaging 99.3 percent. In 41 of 53 normative tests, the CL exceeded 99.9 percent and was never less than 95 percent. These results demonstrate that the P3 ERP can be used as a biometric modality to differentiate between information system users.

The methodology described in this work is simple, does not require user training, does not require storage of biometric data, and can be adapted to any information system as an alternative to passwords or as an additional identity confirmation factor.

PhD Defense: Joint Reliability and Energy Management for Real-Time Embedded Systems

Wednesday, July 18, 2012
11:30am-1:30pm
ENGR 4201
Baoxian Zhao

Abstract

The Dynamic Voltage Scaling (DVS) technique is the basis of numerous state-of-the-art energy management schemes proposed for real-time embedded systems. However, recent research has illustrated the alarmingly negative impact of DVS on system reliability, in terms of increased vulnerability to transient faults, leading to soft errors. The main theme of this dissertation is to investigate several open problems and trade-off opportunities in energy, reliability, and timeliness dimensions.

This dissertation, first, investigates the problem of maximizing

the overall reliability of real-time embedded applications while meeting the deadline and a given energy budget constraint. Optimal static solutions and effective dynamic (online) solutions are developed.

Second, the dissertation proposes a new approach, called the shared recovery (SHR) technique, to minimize the system-level energy consumption while still mitigating the reliability loss induced by DVS. The main idea of the SHR technique is to avoid the off-line allocation of separate recovery tasks to the scaled tasks by assigning a global/shared recovery block that can be used by any task at run-time. Specifically, an array of reliability-aware energy management algorithms are presented for both independent and dependent tasks.

Third, the dissertation presents the foundations of a general reliability-oriented energy management framework, where the objective is to achieve any reliability level with minimum energy consumption and timeliness guarantees. For periodic real-time tasks, the framework is extended to address multiple reliability objectives that can be set by the designer and vary from task to task.

Finally, this dissertation considers the problem of minimizing the expected energy consumption for a real-time embedded application. This part of the research integrates optimally DVS and Dynamic Power Management (DPM) techniques that can put some system components to sleep states when they are not in use for the case where the workload is known only probabilistically.

Volgenau School of Engineering: Volgenau New Graduate Student Orientation

Wednesday, August 22, 2012
6:00pm-8:00pm
Enterprise Hall, Room 80

Event Info

For the sixth year, Volgenau School and Engineering is welcoming its newly admitted graduate students to a special orientation event.

Although orientation is not mandatory, it is highly recommended that both domestic and international students plan to attend. Essential information regarding university services for graduate students and program information from academic departments will be provided. Also, this is your opportunity to meet your peers, the administrative and academic staff members who will assist you during the pursuit of your graduate course work and degree.

Please RSVP online for orientation by visiting: http://volgenau.gmu.edu/graduateresearch/responseform/

Department of Computer Science: GTA Orientation and Reception

Thursday, August 23, 2012
12:00-2:00pm
ENGR 4201
Dr. Pearl Wang

Abstract

This orientation is mandatory for all CS department graduate teaching assistants.

Department of Computer Science: PhD Student Orientation and Reception

Thursday, August 23, 2012
11:00am-1:00pm
ENGR 4201
Dr. Ami Motro

Department of Computer Science: GTA Teaching Workshop

Thursday, August 30, 2012
12:00-3:00pm
ENGR 4201
Dr. Pearl Wang

Abstract

This workshop is mandatory for all CS department graduate teaching assistants.

New Student Fall Welcome 2012: BS ACS & BS CS

Thursday, September 13, 2012
11:30am-1:00pm
Research Hall 163
A Welcome event for first-year and transfer undergraduate students new to the Applied Computer Science and Computer Science programs.
All Students enrolled in CS101 are Welcome!

GRAND Seminar: Study of Transport and Dispersion of Pollutants Using Computational Fluid Dynamics

Monday, September 17, 2012
12:00pm
ENGR 4201
Fernando Camelli

Abstract

The need for efficient tools to study the transport and dispersion of chemical, biological, or nuclear (C/B/N) agents has been the center of attention for more than a decade. The increasing computational power combined with the improvement of algorithms has made CFD models attractive tools to study atmospheric releases at urban scales. However, these tools have not reached the desired rapidness yet. If the time frame provided by the National Research Council (NRC) is considered (immediate first response, 0 to 2 hours; early response, 2 to 12 hours; and sustained response support, greater than 12 hours), CFD tools can be expected to perform in the two upper brackets of this classification: early and sustained response. There have been attempts to make CFD usable in the immediate response time, but these approaches usually rely on the pre-calculations of the situations with interpolated and/or the simplified geometrical details of the modeled area. One common simplification is to consider the terrain as a flat surface ignoring all elevation differences on the landscape.

Speaker's Bio

Fernando E. Camelli is Assistant Professor in the School of Physics, Astronomy and Computational Sciences at George Mason University. He received his Ph. D. diploma in Computational Sciences and Informatics from George Mason University in 2002. His undergraduate studies were in the University of Buenos Aires Argentina. His research focuses in transport and dispersion of pollutants in urban settings, and Computational Fluid Dynamics (CFD). He developed algorithms to integrate data in GIS format into CFD models. He has researched the flow around the superstructure of ships for the Navy to help in the design of the HVAC systems of ships, and he studied the flow patterns in the landing decks of ships. He develops algorithms to preprocess the geometry extraction of buildings and complex terrain with almost no user interaction. He is part of the research team on the Center for Blast Mitigation at George Mason University.

SWE Seminar: Using Traditional Fault Prediction Metrics as Indicators of Software Vulnerabilities

Tuesday, September 18, 2012
11:00am
ENGR 4201
Yonghee Shin

Abstract

According to the Digital Forensics Association, the cost of security breaches that occurred between 2005 to 2010 reached $139 billion. The National Institute of Standards & Technology reports that the annual cost of inadequate infrastructure for software testing is estimated between $22 and $60 billion. Considering limited resources in organizations, it is essential to prioritize software testing effort to the most problematic areas of code. If problematic code has measurable attributes that can be distinguished from non-problematic code, then those attributes can be used to prioritize software testing effort. This talk discusses results from empirical studies in which a set of software metrics were used as indicators of software vulnerabilities. It also highlights the challenges and opportunities of using software metrics to find vulnerable code locations.

Speaker's Bio

Yonghee Shin received the BS degree in computer science from Sookmyung Women’s University in Korea and the MS degree in computer science from Texas A&M University. She received the PhD degree in computer science from North Carolina State University. She worked as a postdoctoral researcher at DePaul University and recently joined GMU as a postdoctoral researcher. Her research interests are in software testing and software metrics focusing on software security, and requirements traceability. Before she return to academia for her MS degree, she worked for Daewoo telecommunications and Samsung SDS in Korea for eight years.

CS Colloquium: Notes From the Field: Understanding and Intervening on Cybercrime From a Social and Economic Viewpoint

Wednesday, September 19, 2012
11:00am
ENGR 4201
Damon McCoy

Abstract

Modern day cybercrime is largely profit-fueled and much of modern day computer security is focused on developing new defenses that close security gaps, which allow criminals to exploit vulnerable systems. However, this focus on understanding the technical methods used by cyber criminals has not been matched by a complimentary effort to understand the underlying economic factors that drive much of this large scale cybercrime. In this talk, I will demonstrate that an understanding of the underlying value chain of a cyberciminal business can play a key role in allowing defenders to make better informed decisions about potential places to successfully intervene. As a case study, I'll show the decomposition of the value chain for spam advertised counterfeit goods and how an empirical analysis of this business can be used to launch an informed intervention directed at a vulnerable point along their value chain.

Speaker's Bio

Damon McCoy is an Assistant Professor in the CS department at GMU. Previously he was a Computer Innovation Fellow at the University of California, San Diego. He obtained his Ph.D. from the University of Colorado, Boulder. His research includes work on wireless privacy, anonymous communication systems, cyber-physical security, and economics of e-crime. More generally, he is interested in exploring and improving the security and privacy of large-scale systems.

GRAND Seminar: Approximate Nearest Neighbor Searching and Polytope Approximation

Wednesday, September 26, 2012
1:00pm
ENGR 4201
David M. Mount

Abstract

Nearest neighbor searching is among the most fundamental retrieval problems in geometry. Given a large set of points in d-dimensional space and a query point q, find the closest point of the set to q. Nearest neighbor queries arise in numerous applications of science and engineering, including pattern recognition, machine learning, computer graphics, and geographic information systems. Provably efficient solutions are known in only one- and two-dimensional space, and research over two decades has focused on solving the problem approximately.

In this talk I will survey developments in approximate nearest neighbor searching in Euclidean space of constant dimension. Recent work on establishing the best computational bounds has been based on the solution to a completely different problem, namely approximating a convex polytope with a minimum number of facets. We will see how a number of classical concepts from convex geometry can be applied to obtain the best known bounds for approximate nearest neighbor searching.

Speaker's Bio

David Mount is a professor in the Department of Computer Science at the University of Maryland with a joint appointment in the University's Institute for Advanced Computer Studies (UMIACS). He received his Ph.D. from Purdue University in Computer Science in 1983, and started at the University of Maryland in 1984. In 2001 he was a visiting professor at the Hong Kong University of Science and Technology.

He has written over 140 research publications on algorithms for geometric problems, particularly problems with applications in image processing, pattern recognition, information retrieval, and computer graphics. He currently serves on the editorial boards of ACM Transactions on Mathematical Software, Computational Geometry: Theory and Applications, and the International Journal on Computational Geometry and Applications. He served on the editorial board of Pattern Recognition from 1999 to 2006. He has served on the program committees of many of the major conferences in his area, and he served as the program committee co-chair for the 19th ACM Symposium on Computational Geometry in 2003 and the Fourth Workshop on Algorithm Engineering and Experiments in 2002.

He has won a number of awards for excellence in teaching, including twice winning the University of Maryland's College of Computer, Mathematical and Physical Sciences, Dean's Award for Excellence in Teaching, and the Award for Teaching Excellence Appreciation in 2001 at the Hong Kong University of Science and Technology. He has co-authored the textbook Data Structures and Algorithms in C++ with Mike Goodrich and Roberto Tamassia.

SANG Seminar: vUPS: Virtually Unifying Personal Storage for Fast and Pervasive Data Accesses

Friday, September 28, 2012
12:00pm-1:00pm
ENGR 4201
Mohammed Hassan

Abstract

More and more overlapping functions on all kinds of mobile devices with these on traditional computers have significantly expanded the usage of mobile devices in our daily life. This also causes the demand surge of pervasively and quickly accessing files across different personal devices owned by a user. Most existing solutions, such as DropBox and SkyDrive, come with potential risks of user privacy and data secrecy. In addition, continuously maintaining strong consistency among multiple replicas of a file is very costly. On the other hand, today a common user often owns sufficiently large storage space across her personal home desktop, office computer, and mo- bile devices. In this talk, we present our design and implementation of a system to virtually Unify Personal Storage (vUPS) for fast and pervasive accesses of personal data across different devices. We will discuss additional advantages of vUPS when compared to existing cloud-based services.

Speaker's Bio

Mohammed A. Hassan is a PhD student at George Mason University. His research interest includes mobile-cloud computing. He completed his BSc in CSE from Bangladesh University of Engineering and Technology in 2006.

GRAND Seminar: Embedded Visual Perception for Navigation and Manipulation

Monday, October 01, 2012
11:00am
ENGR 4801
Darius Burschka

Abstract

I will present the work of the Machine Vision and Perception Group at TUM in the field of navigation and object registration on a variety of systems including our flying and manipulation platforms.

Sensing is essential for autonomy in robotic applications. Our focus is on how to provide sensing to low power systems that enables them to cope with the high dynamics of the underlying hardware. A disadvantage of using compact, low-power sensors is often their slower speed and lower accuracy making them unsuitable for direct capture and control of high dynamic motion. On the other hand, the inherent instability of some systems (e.g. helicopters or quadrotors), their limited on-board resources and payload, their multi-DoF design and the uncertain and dynamic environment they operate in, present unique challenges both in achieving robust low level control and in implementing higher level functions. We developed tracking algorithms (AGAST) and localization (Z_inf) techniques that can be used for navigation on embedded systems. I will show their application on OMAP3 processors (BeagleBoard.org system).

Perception of the sensors can be boosted by adding external data in form of sensor data fusion or indexing to external databases. I will present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy.

Speaker's Bio

Darius Burschka received the PhD degree in Electrical and Computer Engineering in 1998 from the Technische Universität München in the field of vision-based navigation and map generation with binocular stereo systems. In 1999, he was a Postdoctoral Associate at the Yale University, New Haven, Connecticut, where he worked on laser-based map generation and landmark selection from video images for vision-based navigation systems. From 1999 to 2003, he has been an Associate Research Scientist at the Johns Hopkins University, Baltimore, Maryland. From 2003 to 2005, he was an Assistant Research Professor in Computer Science at the Johns Hopkins University. Currently, he is an Associate Professor in Computer Science at the Technische Universität München, Germany, where he heads the Machine Vision and Perception group. He was an area coordinator in the DFG Cluster of Excellence ``Cognition in Technical Systems''. He is the head of the virtual institute TUM-DLR on "Telerobotics and Sensor Data Fusion" since 2005.

His areas of research are sensor systems for mobile robots and human computer interfaces. The focus of his research is vision-based navigation and three-dimensional reconstruction from sensor data.

CS Distinguished Lecture Series: Invasion of Privacy: How Bad will it Get?

Wednesday, October 10, 2012
11:00am
Research Hall, Room 163
Keith Ross

Leonard J. Shustek Chair Professor in Computer Science, Polytechnic Institute of NYU.

Abstract

It is well known that Google, Facebook and other Internet companies are gathering a tremendous amount of information about us. Because these brand-name companies have privacy policies and reputations to protect, they will make efforts not to disclose our private information and activities to the world at large. In this talk, we investigate what less-trustworthy individuals and organizations may be able to gather and reveal about us. Specifically, we pose the question: to what extent will third-parties be able to create detailed profiles about Internet users? In this talk, we will survey some of our work at Polytechnic Institute of NYU as well as the work of other research groups working on user profiling. For our own work, we will first describe how an attacker with modest resources can track the geographical whereabouts and file-sharing behavior of Skype users. We will also show how Skype tracking can be combined with P2P crawling to determine the file sharing behavior of targeted individuals. We then turn to Facebook, and describe how third-parties can efficiently exploit Facebook to create detailed profiles about users. We will pay particular attention to profiling minors (under 18 years of age). We will also survey other research groups’ recent efforts to correlate user information across different social media sites. We will conclude the talk by outlining measures that users and social media sites can take to prevent detailed profiling of Internet users by third parties.

Speaker's Bio

Keith Ross is the Leonard J. Shustek Chair Professor in Computer Science at Polytechnic Institute of NYU. He is also the Head of the Computer Science and Engineering Department at NYU-Poly. Before joining NYU-Poly in 2003, he was a professor at University of Pennsylvania (13 years) and a professor at Eurecom Institute (5 years). He received a B.S.E.E from Tufts University, a M.S.E.E. from Columbia University, and a Ph.D. in Computer and Control Engineering from The University of Michigan.

Professor Ross has worked in security and privacy, peer-to-peer networking, Internet measurement, video streaming, multi-service loss networks, content distribution networks, queuing theory, and Markov decision processes. He is an IEEE Fellow, recipient of the Infocom 2009 Best Paper Award (1,435 papers submitted), and recipient of 2008 and the 2011 Best Paper Awards for Multimedia Communications (awarded by IEEE Communications Society). His work has been featured in the New York Times, NPR, Bloomberg Television, Huffington Post, Fast Company, Ars Technia, and the New Scientist.

Professor Ross is co-author (with James F. Kurose) of the popular textbook, Computer Networking: A Top-Down Approach Featuring the Internet, published by Addison-Wesley (first edition in 2000, sixth edition 2012). It is the most popular textbook on computer networking, both nationally and internationally, and has been translated into fourteen languages. Excluding introductory programming textbooks, it is the fifth most popular CS textbook overall. Professor Ross is also the author of the research monograph, Multiservice Loss Models for Broadband Communication Networks, published by Springer in 1995.

He has served on numerous journal editorial boards and conference program committees. He was PC co-chair for ACM Multimedia 2002, ACM CoNext 2008, and IPTPS 2009. From July 1999 to July 2001, Professor Ross took a leave of absence to found and lead Wimba, which develops voice and video applications for online learning. He was the Wimba CEO and CTO during this period. Wimba was acquired by Blackboard in 2010.

VSE Seminar: Scheduling Algorithms and Their Applications

Monday, October 15th, 2012
10:30am
Johnson Center, Gold Room G19
Fei Li

Assistant Professor, CS Department, GMU

Abstract

Scheduling algorithms are concerned with allocating scarce resources to activities over time. In this talk, I will talk about scheduling algorithms and their applications using two examples, online buffer management for Internet routers, and algorithmic power management for advanced computing chips.

The Internet is now the world's dominant information infrastructure. Numerous requests from Internet users and their applications compete for shared resources in multiple ways. I will introduce our robust and insightful online algorithms for network switches forwarding prioritized packets. A new online algorithm analysis scheme will be presented.

The aim of power management is to reduce the energy consumed by electrical devices while maintaining satisfactory performance. I will introduce our approaches of modeling the operation of various system components in the language of combinatorial optimization and of solving these problems using exact or approximate efficient algorithms.

Speaker's Bio

Fei Li is an assistant professor of Computer Science at George Mason University. He received his B.S. degree in Computer Science in July 1997 from Jilin University in China, M.S., M.Phil., and Ph.D. in February 2002,in May 2007 and in February 2008, respectively, all in Computer Science from Columbia University.

Dr. Li's research interests are online and approximation algorithm design and analysis, and applied algorithms for energy-efficient computing, networked systems, and cloud computing.

GRAND Seminar: Cesium: Geo-Scale Data Visualization in a Web Browser

Tuesday, October 23, 2012
12:00pm
ENGR 4201
Patrick Cozzi

Abstract

With WebGL, it is now possible to have hardware-accelerated 3D and 2D graphics in a web browser without a plugin. In this talk, we present Cesium, an open-source virtual globe and map engine for the web built on WebGL. We demo Cesium, including streaming and rendering large real-world datasets of terrain and imagery; streaming and rendering server-side analysis including satellite propagation, sensors, and visibility calculations; and modeling scenarios such as the North Korea Unha-3 launch. We also discuss current platform support and adoption of WebGL, and its future outlook for thin-client visualization.

Speaker's Bio

Patrick is a software developer, teacher, and writer. At Analytical Graphics, Inc., he started Cesium, an open-source virtual globe and map for the web. At the University of Pennsylvania, Patrick teaches C++ and a graduate-level course on GPU Programming and Architecture. He is coauthor of 3D Engine Design for Virtual Globes, coeditor of OpenGL Insights, and active in the graphics community by publishing, reviewing, and generally trying to move the field forward. Patrick has a master's degree in Computer and Information Science from the University of Pennsylvania.

VSE Seminar: Decomposition, Approximation and Reconstruction of Large Geometric Data

Wednesday, October 31, 2012
10:30 - 11:30am
Research Hall, Room 163
Jyh-Ming Lien

Abstract

Complex geometric data comprising millions to billions of elements are omnipresent in variety of domains. Examples include point clouds of urban landscapes, polygon soup representing buildings in major cities, and combinatorial data structures induced from the motion of a robot. The unstructured nature of the data and significant amount of noise pose grand challenges in designing efficient and practical geometric algorithms.

In this talk, I will provide an overview of these challenges and algorithmic solutions developed by me and my students for representing, and manipulating massive geometric data of shape and motion. In particular, I will focus the discussion on two of our main contributions: (1) approximate representations via decomposition and (2) robust mesh reconstruction and repair. I will also discuss their applications in the areas of CAD, GIS, and robotics. Our research results have attracted a wide range of interests from academia, open-source software communities and industry.

Speaker's Bio

Jyh-Ming Lien is an Assistant Professor in the Department of Computer Science. He directs the Motion and Shape Computing (MASC) group and is affiliated with the Autonomous Robotics Laboratory at George Mason University. He received his Ph.D. in Computer Science from Texas A&M University in 2006. Prior to joining George Mason in 2007, he was a postdoctoral researcher at UC Berkeley. His recent work focuses on shape decomposition, approximation and reconstruction of complex and dynamic 3D geometries. His research has been supported by NSF, USGS, DOT, AFOSR, and Virginia Center for Innovative Technology. More images, videos, papers, and software about his work can be found at: https://masc.cs.gmu.edu/

VSE Seminar: Engineering Autonomic Software Systems: A Learning-Based Approach

Monday, November 05, 2012
9:30 - 10:30am
Research Hall, Room 163
Sam Malek

Abstract

An autonomic software system is capable of adjusting its behavior at runtime in response to changes in the system, its requirements, or the environment in which it executes. Autonomic capabilities are sought-after to automate the management of software in many computing domains, including service-oriented, mobile, cyber-physical and ubiquitous settings. While the benefits of such software are plenty, the development of it has shown to be much more challenging than the traditional software.

In this talk, I will first provide an introduction to this area of research, followed by an overview of my contributions. Afterwards, I will delve into the details of a particular engineering framework developed in my research group, called FeatUre-oriented Self-adaptatION (FUSION). It brings about two innovations: (1) a feature-oriented approach for representing the adaptation choices that are deemed practical by the engineers, and (2) an online learning-based approach for automatically acquiring the knowledge to troubleshoot and manage a software system. I will present an empirical evaluation of FUSION in the context of a case study. Results demonstrate FUSION’s ability to accurately learn the changing dynamics of the system, while achieving efficient analysis and adaptation. I will conclude the talk with an outline of my future research agenda.

Speaker's Bio

Sam Malek is an Assistant Professor in the Department of Computer Science at George Mason University. He is also a faculty member of the C4I Center. Malek's general research interests are in the field of software engineering, and to date his focus has spanned the areas of software architecture, autonomic software, and software dependability. Malek received his PhD and MS degrees in Computer Science from the University of Southern California, and his BS degree in Information and Computer Science from the University of California, Irvine. His research at Mason has been supported by NSF, DARPA, IARPA, ARO, FBI, AGC, and SAIC. He is a member of the ACM, ACM SIGSOFT, and IEEE.

Inter-Disciplinary Computing Research Seminar: Computational Biomechanics for Subject-specific Simulation

Wednesday, November 07, 2012
2:00pm
Research Hall, Room 163
Qi Wei

Abstract

Subject-specific biomechanical simulation has played an important role in improving our knowledge of human movement and advancing treatment of movement disorders. In this talk, I will present our efforts in developing novel computational models of eye movement biomechanics and musculoskeletal biomechanics.

Contributions of the peripheral ocular plant in accomplishing complex eye movement are under debate. To understand its functions in both normal and pathological conditions, a realistic computational model is needed. I will describe the first three-dimensional biomechanical model of the orbit that can simulate the dynamics of ocular motility interactively. We are especially motivated to use this model to investigate the pulley hypotheses and the mechanical factors of strabismus. I will then present our work on musculoskeletal simulation. Strand-based biomechanical models have been developed to simulate (1) hand movement and (2) muscle actions of the rat hind limb, an important animal model for studying spinal cord injury.

At the end of the talk, I will introduce several ongoing research projects including biomechanical modeling of neck pain and knee kinematics and injuries, in collaboration with other faculty at Mason.

Speaker's Bio

Qi Wei is an Assistant Professor in the Department of Bioengineering. She joined GMU in August 2012, after completing her postdoctoral training in the Department of Physiology in the Feinberg School of Medicine at Northwestern University. Qi Wei received her Ph.D. from Rutgers University and M.Sc. from The University of British Columbia, both in Computer Science.

SWE Seminar: Mining the Execution History of a Software System to Infer the Best Time for its Adaptation

Thursday, November 08, 2012
10:30am
ENGR 4201
Kyle Canavera

Abstract

An important challenge in dynamic adaptation of a software system is to prevent inconsistencies (failures) and disruptions in its operations during and after change. Several prior techniques have solved this problem with various tradeoffs. All of them, however, assume the availability of detailed component dependency models. This talk presents a complementary technique that solves this problem in settings where such models are either not available, difficult to build, or outdated due to the evolution of the software. Our approach first mines the execution history of a software system to infer a stochastic component dependency model, representing the probabilistic sequence of interactions among the system’s components. We then demonstrate how this model could be used at runtime to infer the “best time” for adaptation of the system’s components. We have thoroughly evaluated this research on a multi-user real world software system and under varying conditions.

Speaker's Bio

Kyle Canavera is a student in the Ph.D. program in the Department of Computer Science at George Mason University. Kyle's general research interests are in the fields of software engineering, data mining, and intellectual property law. Kyle received his BS degree in Computer Science from Xavier University in Cincinnati, Ohio.

SWE Seminar: Testing Android Apps through Symbolic Execution

Thursday, November 08, 2012
10:30am
ENGR 4201
Nariman Mirzaei

Abstract

There is a growing need for automated testing techniques aimed at Android apps. A critical challenge is the systematic generation of test cases. One method of systematically generating test cases for Java programs is symbolic execution. But applying symbolic execution tools, such as Symbolic Pathfinder (SPF), to generate test cases for Android apps is challenged by the fact that Android apps run on Dalvik Virtual Machine (DVM) instead of JVM. In addition, Android apps are event driven and susceptible to path-divergence due to their reliance on an application development framework. This talks provides an overview of a two-pronged approach to alleviate these issues. First, we have developed a model of Android libraries in Java Pathfinder (JPF) to enable execution of Android apps in a way that addresses the issues of incompatibility with JVM and path-divergence. Second, we have leveraged program analysis techniques to correlate events with their handlers for automatically generating Android-specific drivers that simulate all valid events.

Speaker's Bio

Nariman is a Computer Science PhD student at George Mason University. He received his Bachelor’s degree in Computer Science from Amirkabir University of Technology in Tehran, Iran in 2007 and then his M.S. Degree from Indiana University--Bloomington in 2009 and became a proud Hoosier. Nariman is currently working toward his dissertation proposal, focusing on test case generation for Android apps under the direction of Dr. Sam Malek.

Information Session: Robocalls: All the Rage

Wednesday, November 14, 2012
12:30pm
Research Hall
FTC Attorney: Kati Daffan

Compete in the Federal Trade Commission’s Public Challenge and You Could Win $50,000.

Abstract

Under the Telemarketing Sales Rule, the vast majority of calls that deliver a prerecorded message trying to sell you something are illegal, unless you’ve given written permission for the caller to call you. Unfortunately, the prevalence of illegal robocalls has increased significantly in recent years due to technological advances that make it easier and cheaper to make large numbers of robocalls to consumers from anywhere in the world and fake Caller ID information in an attempt to evade law enforcement. In addition to the Federal Trade Commission’s law enforcement actions – which have stopped entities responsible for billions of illegal calls – the agency seeks to help stimulate a technological solution. We’ve just announced our first public challenge, in which we offer $50,000 to the individual or small team that creates the best innovation to block illegal commercial robocalls on landlines and mobile phones. The FTC is also releasing de-identified data about millions of consumer complaints regarding robocalls, to solvers who are interested in using it in connection with the challenge. The deadline for the challenge is January 17th. Small student teams are encourage to participate.

At 12:30 on Wednesday, November 14, FTC Attorney Kati Daffan will come to campus to share more information about the problem of illegal robocalls and the challenge itself. Please come meet her and ask your questions in person!

GRAND Seminar: Sparse Linear Methods for Top-N Recommender Systems

Thursday, November 15, 2012
3:00pm
ENGR 4801
Xia Ning

Abstract

Recommender systems represent a set of computational methods that produce recommendations of interesting entities (e.g., products, friends, etc) from a large collection of such entities by retrieving/filtering/learning information from their own properties (e.g., product attributes, personal profiles, etc) and/or the interactions between these entities and other parties (e.g., user-product ratings, friend-friend trust relations, etc). Recommender systems are particularly important for E-commerce applications, where the overwhelming amount of items makes it extremely difficult for users to manually identify those items that best fit their personal preferences. Recommender systems have attracted significant research interests from academia, and meanwhile, they have been functioning as a critical revenue enhancer for major E-commerce websites such as Amazon.com and eBay.com.

In this talk, we will address a core task for recommender systems, that is, top-N recommendation, in which a size-N list of items that most conform to the user's interests and preference is to be generated for recommendation. We have developed 1). a novel sparse linear method for top-N recommendation, which utilizes regularized linear regression with sparsity constraints to model user-item purchase patterns, and it significantly outperforms the current state-of-the-art methods; 2). a set of novel sparse linear methods with side information for top-N recommendation, which use side information to regularize sparse linear models or use side information directly to model user-item purchase behaviors, and they stand for the best performing methods with side information incorporated. These sparse linear methods are particularly suitable for Big-Data environment, where the huge amount of information leaves the conventional computation-intensive recommendation algorithms inapplicable, whereas the sparse linear methods are easy to be distributed on large-scale computing systems like Hadoop.

Speaker's Bio

Dr. Xia Ning is a research staff member at the Autonomic Management Department, NEC Labs America. Dr. Ning received her PhD in Computer Science from the University of Minnesota, Twin Cities, 2012. Her doctoral research focuses on recommender systems for large-scale e-commerce applications and data mining/machine learning methods for drug discovery and medical informatics. She has also been working on applying and developing statistical analytics/data mining algorithms for real “big-data” problems arising in text mining, social network mining and autonomic system management.

CS Distinguished Lecture Series: Mining Big Visual Data

Friday, November 16, 2012
2:00pm
Jajodia Auditorium
Alexei Efros

Abstract

There are an estimated 3.5 trillion photographs in the world, of which 10 percent have been taken in the past 12 months. Facebook alone reports 6 billion photo uploads per month. Every minute, 72 hours of video are uploaded to YouTube. Cisco estimates that in the next few years, visual data (photos and video) will account for over 85 percent of total internet traffic. Yet, we currently lack effective computational methods for making sense of all this mass of visual data. Unlike easily indexed content, such as text, visual content is not routinely searched or mined; it's not even hyperlinked. Visual data is Internet's "digital dark matter" [Perona,2010] -- it's just sitting there!

In this talk, I will first discuss some of the unique challenges that make Big Visual Data difficult compared to other types of content. In particular,I will argue that the central problem is the lack a good measure of similarity for visual data. I will then present some of our recent work that aims to address this challenge in the context of visual matching, image retrieval and visual data mining. As an application of the latter, we used Google Street View data for an entire city in an attempt to answer that age-old question which has been vexing poets (and poets-turned-geeks): "What makes Paris look like Paris?"

Speaker's Bio

Alexei (Alyosha) Efros is an associate professor at the Robotics Institute and the Computer Science Department at Carnegie Mellon University. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems which are very hard to model parametrically but where large quantities of data are readily available.

Alyosha received his PhD in 2003 from UC Berkeley under Jitendra Malik and spent the following year as a post-doctoral fellow in Andrew Zisserman's group in Oxford, England. Alyosha is a recipient of CVPR Best Paper Award(2006), NSF CAREER award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), Finmeccanica Career Development Chair (2010), SIGGRAPH Significant New Researcher Award (2010), and ECCV Best Paper Honorable Mention (2010).

CS-Krasnow Seminar: Morphogenetic Engineering, Artificial Development, and the "Shapes" of Brain Dynamics

Friday, November 16, 2012
11:00am
Krasnow Institute, Lecture Room
Rene Doursat

Complex Systems Institute, Paris (ISC-PIF), CNRS and Ecole Polytechnique, France

Abstract

Generally, phenomena of spontaneous pattern formation are random and repetitive, whereas elaborate devices are the product of intentional human design. Yet, multicellular organisms and social insect constructions are proof that some complex systems can be both self-organized and architectured. Can we understand and exploit their precise self-formation capabilities? Can we endow physical systems with information, or embed informational systems in physics, to create truly autonomous machines? I will present a new field of research, Morphogenetic Engineering (ME), which explores "self-architecturing" systems by focusing on the programmability of self-organization—a property underappreciated in complex systems science, while self-organization itself is underappreciated in engineering.

Embryomorphic Engineering (EE) a specific instance of ME, takes its inspiration directly from embryogenesis to build new distributed hardware and software architectures by decentralized multi-agent self-assembly. It combines three key principles of biological development: chemical signaling, gene expression, and cell aggregation. I will illustrate the potential applications of EE in self-forming 2D/3D robot swarms and self-constructing n-D techno-social networks. In all cases, the agents' genotype makes the phenotype's architecture and function modular, programmable and reproducible.

Morphogenetic ideas can also contribute to a new class of models in computational neuroscience. In contrast to traditional neural networks—which essentially assimilate the brain to a passive, feedforward input/output signal processor—it promotes the exploration of active self-organization and emergent (ongoing) activity in recurrent connectivity. In this paradigm, stimuli only trigger or modify existing internal states, imprinted in synaptic contacts by development and learning. In particular, I propose to view the myriads of bioelectrical neural signals arising in a network as a form of "neuron flocking" in phase space. I will show some of my attempts to characterize and exploit these “shapes” of brain dynamics in recognition tasks.

GRAND Seminar: Transformations and Frontiers in Robotics

Tuesday, November 20, 2012
12:00pm
ENGR 4201
Dmitry Berenson

Abstract

Robotics is undergoing three transformations, which are changing not only our research focus but also the way we do research. A new focus on human-robot collaborative systems and the availability of parallel and cloud computing is transforming the topics we study, while new open-source communities and common research platforms are transforming the way we work in the lab and collaborate with others. I will present my contributions to these transformations in the areas of robotic home assistants and medical robotics, where I have developed motion planning algorithms that enable new robotic manipulation capabilities. These include algorithms that plan motion with multiple simultaneous constraints, manage sensor uncertainty in the planning process, and use previous experience to plan faster. I will discuss the theory behind these approaches and show practical applications on real-world robots. I will end by discussing three frontiers that are emerging as a result of these transformations as well as prospects for their exploration.

Speaker's Bio

Dmitry Berenson received a BS in Electrical Engineering from Cornell University and received his Ph.D. degree from the Robotics Institute at Carnegie Mellon University in 2011 . He recently completed a post-doc at UC Berkeley in the Department of Electrical Engineering and Computer Sciences and started as an Assistant Professor in Robotics Engineering and Computer Science at WPI. He founded and directs the Autonomous Robotic Collaboration (ARC) Lab at WPI, which focuses on motion planning, manipulation, and human-robot collaboration.

Inter-Disciplinary Computing Seminar: Finding Expected And Minimum Assets In Markov Network Based Combinatorial Prediction Markets

Wednesday, November 28, 2012
2:00pm-3:00pm
Research Hall, Room 163
Robin Hanson

Abstract

A market-maker-based prediction market lets people aggregate information and judgments by editing a consensus probability distribution, and making a bet to support each edit. However, in general it is not feasible to explicitly representing a joint distribution over many related variables. A factored representation such as a Markov network can make computation tractable. For networks with a low tree-width, standard junction tree algorithms can update a representation of the joint distribution given a change to any local conditional probability, and then cheaply and exactly calculate any conditional marginal. However, in order to let traders reuse asset from prior trades without ever allowing negative assets, a Markov network based prediction market also needs to update a representation of each user's assets, and find the conditional state in which a user has minimum assets. To decide how much to trade, users also find it useful to see conditional expected and minimum assets, and how those would change under proposed edits. We show how to generalize the standard junction tree algorithm to implement all of these computations.

Speaker's Bio

Dr. Hanson is an Associate Professor of economics at GMU and Research Associate at the Future of Humanity Institute at Oxford University. He also serves as Associate Editor of the Journal of Prediction Market. Dr. Hanson received his PhD in 1998 from California Institute of Technology, MS and MA degrees in physics and philosophy of science in 1984 from University of Chicago, and BS in physics in 1981 from University of California at Irvine.

Oral Defense of Doctoral Dissertation: Adversarial Face Recognition and Phishing Detection Using Multi-Layer Data Fusion

Thursday, November 29, 2012
1:00pm-3:00pm
ENGR 1602
Venkatesh Ramanathan

Abstract

This thesis addresses digital identity for biometric / face recognition screening and cyberspace security subject to denial and deception characteristic of adversarial behavior. The adversarial aspect concerns defense and offense operations that involve impostors and identity theft. Denial and deception correspond to occlusion and disguise for biometrics, while for cyberspace security they correspond to spoofing and obfuscation. To prevent or mitigate the impacts of adversarial behavior from offensive attacks this thesis proposes the use of multi-layer data fusion. Multi-layer aspect of fusion refers to features, representations, algorithms, decision-making, adversarial aspects and their purposeful combinations. This novelty, feasibility, and utility of our research is illustrated in the physical and cyber worlds: (i) robust face recognition in the presence of occlusion and disguise, and (ii) phishing detection to prevent identity theft through spoofing and obfuscation. The novel face recognition methodologies include: (i) Adaptive and Robust Correlation Filters (ARCF) built around match filters and recognition-by-parts, and (ii) hybrid anthropometric and appearance based biometric authentication using boosting for feature level fusion and backpropagation learning for decision level fusion. The cluster and strength of the ARCF correlation peaks indicate the confidence in the face authentications. Experimental evidence using the AR benchmark database shows that our methods are highly reliable in the presence of occlusion, disguise, and illumination, expression and temporal variability.

The novel phishing detection methodologies address: (i) phishing email detection using semantic topics and Probabilistic Latent Semantic Analysis (PLSA), boosting, and Co-Training for both labeled and unlabeled examples, (ii) phishing website detection using Latent Dirichlet Allocation (LDA) and boosting, and (iii) impersonated entity discovery using LDA, boosting, and Condition Random Field (CRF). The phishing detection methodology handles the adversarial use of synonyms, polysemy (words with multiple meanings) and other linguistic variations. In addition, the same methodology requires only a small percentage of data to be annotated thus saving time, labor, and avoiding errors incurred during human annotation. The phishing website detection methodology is device and language neutral. The impersonated entity discovery methodology automatically extracts the entity the attacker is trying to spoof. This helps service providers to collaborate with each other to exchange attack information and protect their customers. Experimental results on SPAM Archive, which is one of the largest public corpus, show that our phishing detection methodology outperforms state of the art phishing detection methods.

Speaker's Bio

Bachelor of Science, Birla Institute of Technology and Science, Pilani, India, 1990 Master of Science, University of Maryland, College Park, Maryland, 1994

ACM Distinguished Speaker: Cloud Based Active Archiving Solution for Databases

Thursday, November 29, 2012
4:00pm
ENGR 4201
Mukesh Mohania

Abstract

Cloud computing offers an exciting opportunity to bring on-demand applications to customers and is being used for delivering hosted services over the Internet and/or processing massive amount of data for business intelligence. In this talk, we will discuss the architecture of cloud computing, MapReduce, and Hadoop. We will then discuss how the cloud infrastructure can be used for data management services, how the massive amount of data can be processed over cloud for various businessintelligence applications, and how the cloud can be used for 'Active' Data Archival for near real-time data access. We discuss various issuesconcerning the active archive system including schema modification,query federation, query optimization, access control and data provenance. Using TPC-DS benchmark data, we present evaluation results that shows the ability of our system to seamlessly query archive data along with data stored in the warehouse in order of minutes compared to hours required to move the data into the warehouse from traditional archival systems.

Speaker's Bio

Mukesh Mohania received his Ph.D. in Computer Science & Engineering from Indian Institute of Technology, Bombay, India in 1995. Currently, he is a Senior Technical Staff Member and IBM Master Inventor in IBM Research- India. He has worked extensively in the areas of distributed databases, data warehousing, data integration, and autonomic computing.He has published more than 120 papers and also filed more than 50 patents in these or related areas, and more than 14 have already been granted. He received the best paper awards in CIKM 2004 and CIKM 2005. His work on Data Quality, Information Integration, and Autonomic Computing has led to the development of new products and also influenced several existing IBM products. He has received several awards within IBM, such as "Excellence in People Management", “Outstanding InnovationAward”, "Technical Accomplishment Award", “Leadership By Doing”, and many more. He also received IEEE Meritorious Service Award. He is an ACM Distinguished Scientist, and a member of IBM Academy of Technology.

SWE Seminar: Incremental Lifecycle Validation of Knowledge-Based Systems through CommonKADS

Friday, November 30, 2012
11:00am
ENGR 2901
Feras A. Batarseh

Abstract

Validation is an essential phase in the development lifecycle of knowledge-based systems (KBS). Validation ensures that the system is valid, reliable and that it reflects the knowledge of the expert and meets the specifications. Although many validation methods have been introduced for knowledge-based systems, there is still a need for an incremental validation method based on a lifecycle model. MAVERICK is a novel validation method that aims to satisfy the mentioned goals. Lifecycle models provide a general framework for the developer and a mapping technique from the system to validation and verification. Such models support reusability, modularity and offer guidelines for knowledge engineers to achieve high quality systems. CommonKADS (a lifecycle approach) includes a set of models that helps to represent and analyze knowledge-based systems. It offers a de-facto standard for building knowledge-based systems. Additionally, CommonKADS is a knowledge representation-independent model. It has powerful models that can represent many domains. Defining an incremental validation method based on a conceptual lifecycle model (such as CommonKADS) has a number of advantages such as reducing time and effort, ease of implementation, well-structured design, and better tracking of errors when they occur. Moreover, the validation method introduced is based on case testing and selecting an appropriate set of test cases to validate the system. The intelligent validation method defined makes use of results of prior test cases in an incremental validation procedure. This facilitates defining a minimal set of test cases that provides complete and effective system coverage. CommonKADS doesn't define validation, verification or testing in any of its models. This research seeks to establish a direct relation between validation and lifecycle models, and introduces a validation method for AI systems (such as: KBS) embedded into CommonKADS.

Speaker's Bio

Feras A. Batarseh received the BSc degree in Computer Science from Princess Sumaya University for Technology (Amman, Jordan) in 2006. He received the MSc and PhD degrees in Computer Engineering from the University of Central Florida (Orlando, FL) in 2007 and 2011 respectively. His research interests include the field of software engineering, and to date his focus has spanned the areas of software testing, validation and verification, artificial intelligence, cloud computing and e-learning. He is a member of the ACM and IEEE computer societies.

Oral Defense of Doctoral Dissertation: Methods for Improving the Design and Performance of Evolutionary Algorithms

Friday, November 30, 2012
9:00am-11:00am
ENGR 3507
Jeffery K. Bassett

Abstract

Evolutionary algorithms (EAs) can be customized for new problems by incorporating domain specific knowledge, yielding higher quality results and/or faster runtimes. Unfortunately, complex nonlinear interactions between EA components can make it difficult to get such a system to work well. EA theory is of little help here either, since the equations often need to be re-derived to match the new algorithm. This leads most practitioners to use an ad hoc approach to customization instead.

There has been some success at addressing this problem using a biology theory called quantitative genetics. This approach allows one to monitor the behavior of an EA by observing distributions of an outwardly observable phenotypic trait (usually fitness), and thus avoid modeling the algorithm's internal details. Unfortunately, observing a single trait does not provide enough information to diagnose most problems within an EA. It is my hypothesis that using multiple traits will allow one to observe how the population is traversing the search space, thus making more detailed diagnosis possible.

In this work, I adapt a newer multivariate form of quantitative genetics theory for use with evolutionary algorithms and derive a general equation of population variance dynamics. This provides a foundation for building a set of tools that can measure and visualize important characteristics of an algorithm, such as exploration, exploitation, and heritability, throughout an EA run. Finally I provide examples of how these tools can be used to identify and fix problems in two well-known EA variants: Pittsburgh approach rule systems and genetic programming trees.

Speaker's Bio

Bachelor of Science, Rensselaer Polytechnic Institute, 1987 Master of Science, George Mason University, 2003

Oral Defense of Doctoral Dissertation: Computational Mutagenesis Using Transduction, Active Learning, and Association Rule Mining

Thursday, December 06, 2012
8:30am-10:30am
ENGR 3507
Nada Basit

Abstract

Wet laboratory mutagenesis to determine enzyme mutant activity or nsSNP-induced pathology is expensive and time consuming. Automating such prediction tasks motivates in silico computational methods, i.e., computational mutagenesis. The computational methods used in this dissertation are driven by transduction, active learning, and association mining. The specific bioinformatics tasks are linked with the novel computational mutagenesis methods as follows:

• protein function prediction using transduction; • protein function prediction using transduction and active learning • prediction of nsSNP-induced pathology using transduction and active learning combined with association mining.

The feasibility and comparative advantage of these methods are shown on predicting mutant (single amino acid polymorphisms) activity for HIV-1 Protease (HIV-1), Bacteriophage T4 Lysozyme (T4), and Lac Repressor (LAC) proteins; and on predicting non-synonymous Single Nucleotide Polymorphism (nsSNP)-induced pathology on an nsSNP data set composed of a large number of proteins. The problem of unbalanced population, where the proportion of examples in the data set belonging to each class is uneven, is addressed using (a) stratified sampling with cross-validation operating on folds that are identical in class distribution; and (b) random over-sampling to boost the minority class and make it equal in size to the majority class. The annotation problem is a by-product of incremental transduction and active learning. The novel methods proposed in this dissertation perform better than state-of-the-art methods in terms of prediction performance (Tasks 1, 2, and 3), amount of annotation used (size of training data) (Tasks 2 and 3), and explanation (knowledge) gained (Task 3).

Oral Defense of Doctoral Dissertation: Securing Smart Mobile Devices: A Data-Centric Approach

Friday, December 14, 2012
3:00pm-4:30pm
Room 401, Research Hall
Zhaohui Wang

Abstract

The advent of new mobile hand-held devices has fostered the design and development of novel open source operating systems and a wealth of applications for mobile phones and tablet devices. This new generation of smart devices, including Google’s Android, iOS, and Windows Mobile are powerful enough to accomplish tasks previously requiring a personal computer and even go beyond that.

This PhD dissertation will discuss the cyber threats that arise from this new mobile ecosystem and device capabilities including the provisioning process, the multi-homed network capabilities and the online application markets for mobile devices. These threats include exploitation through new communication channels (such as USB, NFC), data exfiltration from portable medium and user/data tracking via the multi-homed network communication interface. I will explain the defense-in-depth and design challenges in dealing with these security issues when the end-goal is to deploy security-enhanced smart phones. To that end, this thesis studies the research efforts to defend against or mitigate the impact of attacks against mobile devices. Our approach is directly applicable in a tactical environments including but not limited to emergency and first responders scenarios. We demonstrate that this is a data-centric model where the protection of the data comes first and we provide solutions that attempt to control and police access to mobile data both in terms of actual user data and executable code. We show that this can be accomplished by controlling the communication mechanisms for synchronizing the user contents with computers and other phones, hardening the Android data storage using encryption, and enforcing unified network communication monitoring and filtering policies using remote Policy Decision Points (PDPs).

Test-out Exam: INFS 501

Tuesday, January 15, 2013
9:00am-10:00am
ENGR 1103

Test-out Exam: INFS 515

Tuesday, January 15, 2013
11:00am-12:00am
ENGR 1103

Test-out Exam: INFS 519

Tuesday, January 15, 2013
1:00pm-2:00pm
ENGR 1103

Test-out Exam: SWE 510

Tuesday, January 15, 2013
3:00pm-4:00pm
ENGR 1103

Computer Science Department: GTA Orientation

Wednesday, January 16, 2013
11:30am - 1:00pm
ENGR 4201

Volgenau School of Engineering: Spring Graduate Student Orientation

Wednesday, January 16, 2013
6:00-9:00pm
Enterprise Hall

Oral Defense of Doctoral Dissertation: Delay-Based Methods for Robust Geolocation of Internet Hosts

Tuesday, January 22, 2013
10:00am-12:00pm
ENGR 2901
Inja Youn

Abstract

Accurate geolocation of IP addresses, is increasingly important in many applications, such as targeted delivery of localized content over Internet, prevention of Internet, detection and prevention of cyberattacks and cyberterrorism, etc. The current geolocation algorithms can be divided into several classes according to the data that is used for determining the geographic location.

Use of round trip delay measurements for geolocation has proved not very reliable in the past, because of the non-linear correlation between distances and delays generated by the network congestion, queuing delay and circuitous routes. This thesis contributes to the advancement of two classes of delay-based geolocation methods. The first contribution is a family of pure delay-based algorithms based on a general class of proximity measures. When such measures are carefully chosen to discard the data which contains little information about the geographical location of a target IP address, the resulting algorithms have improved accuracy over the existing pure-delay based schemes.

The second contribution is the development of a statistical geolocation scheme based on the application of kernel density estimation to delay measurements amongst a set of landmarks. An estimate of the target IP location is then obtained by maximizing the likelihood of the distances from the target to the landmarks, given the measured delays. This is achieved by an algorithm which combines gradient ascent and force-directed methods. We compare the proposed geolocation schemes with the previous methods by developing a measurement framework using thePlanetLab infrastructure. Our experimental results show that the proposed geolocation algorithms have superior accuracy to the prior art.

Oral Defense of Doctoral Dissertation: Detecting Polymorphic and Mutated Malicious Access in Online Ad Serving Systems

Thursday, January 31, 2013
11:00am – 1:00pm
ENGR 4801
Reza Gharavi

Abstract

With the emergence of full-scale electronic commerce, the World Wide Web has been quickly and aggressively realized as an effective advertising medium. Indeed, advertising itself has become an important commodity on the web. The current model allows unethical and dishonest intermediaries to defraud the system by simulating fake traffic to increase revenue.

The development of intelligent data analysis methods for fraud detection can be well motivated from an economic point of view. Additionally, as the number of fraud cases increases, the reputation of Ad Networks suffers. This dissertation focuses on two aspects of the malicious access problem largely unexplored by the research community. These include heuristic analysis and the complexity of detecting polymorphic and mutated fraudulent traffic patterns, as well as the close world assumption generally associated with such problems, where some identities of a given malicious hit are necessarily represented in the training set.

This research investigates the application of novel machine learning techniques to the detection of heterogeneous and complex malicious traffic through a novel heuristic analysis approach aimed at detecting the underlying patterns of malicious hits within a given Click-Stream span.

This thesis proposes a heuristic-based feature selection and classification technique using Artificial Neural Networks (ANN) to detect polymorphic and mutated traffic inflation attacks. A heuristic ANN based feature selector has been investigated. The novelty of the proposed feature selection approach is that it integrates the capability of the wrapper approach to find better feature subsets by combining the filter’s ranking score with the wrapper-heuristic’s score to take advantage of both filter and wrapper heuristics. In addition, hybrid training algorithms for ANN have also been developed to uncover indicators of fraudulent patterns using heuristic analysis and previous conversion data elements. The indicators are used to create a set of patterns which profile legitimate traffic and indicate outliers and anomalies.

Experiments on real life fraud data demonstrate the advantage of the proposed approaches over some of the most frequently used detection techniques. This advantage is demonstrated by a significant decrease in false positive and general stability of the results.

CS Seminar: Dynamically Heterogeneous Cores Through 3D Resource Pooling

Monday, February 11, 2013
2:00pm
ENGR 4201
Houman Homayoun

Abstract

3D die stacking is a recent technological development which makes it possible to create chip multiprocessors using multiple layers of active silicon bonded with low latency, high-bandwidth, and very dense vertical interconnects. 3D die stacking technology provides very fast communication, as low as a few picoseconds, between processing elements residing on different layers of the chip. The rapid communication network in a 3D stack design, along with the expanded geometry, provides an opportunity to dynamically share on-chip resources among different cores. This research describes an architecture for a dynamically heterogeneous processor architecture leveraging 3D stacking technology. Unlike prior work in the 2D plane, the extra dimension makes it possible to share resources at a fine granularity between vertically stacked cores. As a result, each core can grow or shrink resources, as needed by the code running on the core. This architecture, therefore, enables runtime customization of cores at a fine granularity and enables efficient execution at both high and low levels of thread parallelism. This architecture achieves performance gains of up to 2X, depending on the number of executing threads, and gains significant advantage in energy efficiency.

Speaker's Bio

Houman Homayoun is an Assistant Professor of the Department of Electrical and Computer Engineering at George Mason University. He also holds a Courtesy appointment with the Department of Computer Science.Prior to joining George Mason University, He spent two years at the University of California, San Diego, as National Science Foundation Computing Innovation (CI) Fellow awarded by the Computing Research Association (CRA) and the Computing Community Consortium (CCC). Houman's research is on power-temperature and reliability-aware memory and processor design optimizations and spans the areas of computer architecture, embedded systems, circuit design, and VLSI-CAD, where he has published more than 30 technical papers on the subject, including some of the earliest work in the field to address the importance of cross-layer power and temperature optimization in memory peripheral circuits. He is currently leading a number of research projects, including the design of next generation 3D heterogeneous multicores, low power hybrid SRAM-NVM memory hierarchy design, reliability-aware cache design, and power management in data centers. Houman was a recipient of the four-year University of California, Irvine Computer Science Department chair fellowship. He received his PhD degree from the Department of Computer Science at the University of California, Irvine in 2010, an MS degree in computer engineering in 2005 from University of Victoria, Canada and his BS degree in electrical engineering in 2003 from Sharif University of Technology.

GRAND Seminar: Heterogeneous Face Recognition

Tuesday, February 12, 2013
12:00pm
ENGR 4201
Brendan Klare

Abstract

A chief benefit of face recognition technology is the extensive collection of face photographs available to populate target galleries. From sources such as driver's licenses, passports, and mug shots, a (generally) high quality gallery seed exists for a large percentage of the developed world's population. While these gallery images are visible light photographs, many face recognition scenarios exist where probe images used to be match against such galleries are only available from some alternate imaging modality. For example, in environments with adverse illumination conditions (such as nighttime), face images must be captured in the infrared spectrum. In other cases, a lack of a face image requires the use of a forensic sketch to depict a subject. The task of matching face images across image modalities is called heterogeneous face recognition. In this talk, the problem of heterogeneous face recognition will be introduced. Different approaches for performing heterogeneous face recognition will be introduced, including methods for (i) directly measuring the similarity between heterogeneous face images, and (ii) using prototype similarities to performing matching without needing a direct comparison. The follow research topics will also be discussed: (i) the effect of demographics (race, gender, and age) on face recognition performance, (ii) studies on training face recognition system for time lapse invariance, and (iii) designing facial features and matching algorithms for matching caricature sketches to photographs.

Speaker's Bio

Brendan Klare is a scientist at Noblis. He received the Ph.D. degree in Computer Science from Michigan State University in 2012, and received the B.S. and M.S. degrees in Computer Science and Engineering from the University of South Florida in 2007 and 2008. From 2001 to 2005 he served as an airborne ranger infantryman in the 75th Ranger Regiment, U.S. Army. Brendan has authored several papers on the topic of face recognition, and was the recipient of the Honeywell Best Student Paper Award at the 2010 IEEE Conference on Biometrics: Theory, Applications and Systems (BTAS). His other research interests include pattern recognition and computer vision.

SWE Seminar: Applying Empirical Software Engineering to Software Architecture: Challenges and Lessons Learned

Thursday, February 14, 2013
10:00am
ENGR 4201
Davide Falessi & Forrest Shull

Abstract

Software architecture community has developed numerous methods, techniques, and tools to support the architecture process (analysis, design, and review). Historically, most advances in software architecture have been driven by talented people and industrial experience, but there is now a growing need to systematically gather empirical evidence about the advantages or otherwise of tools and methods rather than just rely on promotional anecdotes or rhetoric.

The aim of this presentation is to promote and facilitate the application of the empirical paradigm to software architecture. To this end, Davide Falessi will describe several challenges and lessons learned when assessing software architecture research that used controlled experiments, replications, expert opinion, systematic literature reviews, observational studies, and surveys. Before presenting scientific details, Forrest Shull will introduce the Fraunhofer Institute of Experimental Software Engineering, a not-for-profit applied research and technology transfer organization.

Speaker's Bio

Dr. Davide Falessi joined the Fraunhofer Center for Experimental Software Engineering in Maryland (CESE) in 2012 as a Research Scientist in the Measurement and Knowledge Management Division. He currently serves as a program committee member in several international conferences including ESEM, WICSA, ICSR, SEKE, PROFES, EASE, and MTD. His main research interest is in devising and empirically assessing scalable solutions for the development of complex software-intensive systems with a particular emphasis on architecture, requirements, and quality. He received the PhD and the “Laurea” degrees in Computer Engineering from the University of Rome “TorVergata”.

Dr. Forrest Shull is a senior scientist at the Fraunhofer Center for Experimental Software Engineering in Maryland (CESE), a nonprofit research and tech transfer organization, where he leads the Measurement and Knowledge Management Division. At Fraunhofer CESE, he has been a lead researcher on projects for NASA's Office of Safety and Mission Assurance, the NASA Safety Center, the U.S. Department of Defense, the National Science Foundation, the Defense Advanced Research Projects Agency (DARPA), and companies such as Motorola and Fujitsu Labs of America. He is the Editor-in-Chief of IEEE Software.

CS Distinguished Lecture Series: “A Walk in the Dark: Random Walks and Network Discovery”

Friday, February 15, 2013
11:00am
Research Hall, Room 163
Don Towsley

Abstract

We rely on a wide variety of digital networks in our daily lives, to provide a rich set of services in commerce, government, communications, and to feel connected. These networks include the Internet, the World Wide Web, on-line social networks such as Facebook, Twitter, etc., and cellular networks. They are large, complex, very richly structured, and constantly changing over time. Moreover, because of their size and complexity, very little is known about them.

In this talk we focus on the problem of how to discover the structure of these networks. Traditional methods for network discovery and exploration include crawling the network using breadth first search (BFS). We will show, however, that such methods introduce significant biases unless almost all of the network is crawled. Instead, we focus on random walks as a method for exploring the network. We will show that random walks can be used to solve a number of network discovery tasks including characterizing degree distributions, identifying important nodes, finding short paths, locating content, etc., while exploring only a very small portion of network. Last, we do this in the context of networks whose underlying graphs are either directed or undirected.

Speaker's Bio

Professor Towsley's research spans a wide range of activities from stochastic analyses of queueing models of computer and telecommunications to the design and conduct of measurement studies. He has performed some of the pioneering work on the exact and approximate analyses of parallel/distributed applications and architectures. More recently, he pioneered the area of network tomography and the use of fluid models for large networks. He has published extensively, with over 150 articles in leading journals.

PhD Comptuer Science, University of Texas (1975), BA Physics, University of Texas (1971). Professor Towsley first joined the faculty at the University of Massachuseets in the Department of Electrical and Computer Engineering in 1976 and moved to the Department of Computer Science in 1986. He was named University Distinguished Professor of Computer Science in 1998. Professsor Towsley was a Visiting Scientist at the IBM T.J. Watson Research Center, (1982-83, 2003), INRIA and AT&T Labs - Research (1996-97), and Cambridge Microsoft Research Lab (2004); a Visiting Professor at the Laboratoire MASI, Paris, (1989-90).

Computer Science Colloquium: Improving Text Retrieval Applications in Software Engineering: A Case on Concept Location

Tuesday, February 26, 2013
11:00am-12:00pm
Nguyen Engineering 4201
Sonia Haiduc

Abstract

The source code of large scale, long lived software systems is difficult to change by developers. Finding a place in the code where to start implementing a change, also known as concept location, can be particularly challenging. Recent approaches based on Text Retrieval leverage the textual information found in the identifiers and comments found in source code in order to guide developers during this task. In addition, text retrieval has been used to address many other software engineering tasks, but wide adoption in industry and education is still ahead of us. Among the factors that deter broader adoption, researchers observed the difficulties of developers to formulate good queries in unfamiliar software and the quality of identifiers present in software. In order to support developers for writing better queries we propose two query reformulation techniques. One is based on feedback provided by the developers, whereas the other one is completely automated and employs machine learning techniques to learn from past user queries. Empirical validation shows that the queries reformulated using our approaches lead to better results in concept location, compared to the original queries and to previous techniques. In order to improve the quality of identifiers in source code we define a catalog of the most common identifier problems, called lexicon bad smells, and propose a series of refactoring operations in order to correct them. We show that the refactored identifiers lead to an improvement in the results of Text Retrieval-based concept location. In addition, we also investigated how to present to developers the information retrieved from source code during concept location. We studied the use of automated text summarization techniques to

Speaker's Biography

Sonia Haiduc is a PhD candidate at Wayne State University, in Detroit, MI, USA, where she performs research in software engineering. Her research interests include software maintenance and evolution, program comprehension, and source code search. Her work has been published in several highly selective software engineering venues. She has also been awarded the Google Anita Borg Memorial Scholarship for her research and leadership.

Computer Science Colloquium: Solving the Search for Source Code

Thursday, February 28, 2013
10:00am-11:00am
Nguyen Engineering 4201
Kathryn Stolee

Abstract

Programmers frequently use keyword searches to find source code in large repositories. However, to do this effectively, programmers must specify keyword queries that capture implementation details of their desired code. I propose that code search should be about behavior, not about keywords. 

In this talk, I will present an approach to code search that allows programmers to provide inputs and outputs that define the behavior of their desired code. This approach indexes source code repositories by symbolically analyzing the programs and program fragments and transforming them into constraints representing their behavior. Results are identified using an SMT solver, which, given an input/output specification and the constraint representation of a program fragment, determines if the fragment matches the desired behavior. While promoting code reuse, my approach enables reuse where it was not possible before: the constraints can be relaxed, identifying code that approximately matches the specification. Further, the solver can then guide the instantiation of the code to produce the desired behavior. I will illustrate the generality of the approach by showing its instantiation in subsets of three languages, the Java String library, Yahoo! Pipes mashups, and SQL select statements. I will conclude by sharing my vision for new research directions related to this semantic approach to code search. 

Speakers Bio

Kathryn Stolee is a Ph.D. candidate and NSF Graduate Research Fellow in the Department of Computer Science and Engineering at the University of Nebraska-Lincoln. She has been awarded an ESEM Distinguished Paper Award and two departmental outstanding research awards. Her research is in software engineering with a focus on program analysis. Extracting useful information from software artifact repositories is a broad theme of her research, most recently through semantic code search.

SANG Seminar: A Comparative Study of Android and iOS for Accessing Internet Streaming Services

Friday, March 01, 2013
12:00 - 1:00 PM
ENGR 4201
Yao Liu

Abstract

Android and iOS devices are leading the mobile device market. While various user experiences have been reported from the general user community about their differences, such as battery lifetime, display, and touchpad control, few in-depth reports can be found about their comparative performance when receiving the increasingly popular Internet streaming services.

Today, video traffic starts to dominate the Internet mobile data traffic. In this talk, focusing on Internet streaming accesses, we set to analyze and compare the performance when Android and iOS devices are accessing Internet streaming services. Starting from the analysis of a server-side workload collected from a top mobile streaming service provider, we find Android and iOS use different approaches to request media content, leading to different amounts of received traffic on Android and iOS devices when a same video clip is accessed. Further studies on the client side show that different data requesting approaches(standard HTTP request vs. HTTP range request) and different buffer management methods (static vs. dynamic) are used in Android and iOS mediaplayers, and their interplay has led to our observations.

Speaker's Bio

Yao Liu is a Ph.D. student of Computer Science Department at George Mason University.

GRAND Seminar: Discovery of Novel Patterns in Massive Time Series Data

Tuesday, March 05, 2013
12:30pm
ENGR 4201
Jessica Lin

Abstract

Massive amounts of data are generated daily at a rapid rate. As a result, the world is faced with unprecedented challenges and opportunities on managing the ever-growing data, and much of the world's supply of data is in the form of time series. One obvious problem of handling time series databases concerns with its typically massive size—gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. Most classic data mining algorithms do not perform or scale well on time series data due to their unique structure. In particular, the high dimensionality, very high feature correlation, and the typically large amount of noise that characterize time series data present a difficult challenge. As a result, time series data mining has attracted an enormous amount of attention in the past two decades.

This presentation gives an overview of my contributions in the field of time series data mining. The first part of the presentation discusses time series data mining fundamentals - more specifically, the two aspects that hugely determine the efficiency and effectiveness of most time series data mining algorithms: data representation and similarity measure. The second part of the presentation will focus on the discovery of novel and non-trivial patterns in time series data, including frequently encountered (or repeated) patterns, rare (or anomalous) patterns, or latent structure.

Speaker's Bio

Dr. Jessica Lin is an Associate Professor in the Department of Computer Science at George Mason University. She received her PhD degree from University of California, Riverside in June, 2005. Her research interests encompass broad areas of data mining, especially data mining for large temporal and spatiotemporal databases, text, and images. Over the years, she has collaborated with researchers from various domains including medicine, earth sciences, manufacturing, national defense, and astronomy. Her research is partially funded by NSF, US Army and Intel Corporation.

Empirical Evaluation of the Statement Deletion Mutation Operator

Thursday, March 07, 2013
10:00am
ENGR 4201
Lin Deng

Abstract

Mutation analysis is widely considered to be an exceptionally effective criterion for designing tests. It is also widely considered to be expensive in terms of the number of test requirements and in the amount of execution needed to create a good test suite. This paper posits that simply deleting statements, implemented with the statement deletion (SDL) mutation operators in Mothra, is enough to get very good tests. A version of the SDL operator for Java was designed and implemented inside the muJava mutation system. The SDL operator was applied to 40 separate Java classes, tests were designed to kill the non-equivalent SDL mutants, and then run against all mutants.

Speaker's Bio

Lin Deng is a first year PhD student at George Mason University. He received the BS degree in Computer Science from Renmin University of China, and the MS degree in Computer and Information Science from Gannon University. He worked as a computer engineer at State Intellectual Property Office of China for four years before joining the MS program of Gannon University. His research interests are in software testing and security.

CS Distinguished Lecture Series: Automatic Annotation of Protein Function

Wednesday, March 20, 2013
11:00am
Research Hall, Room 163
Lydia Kavraki

Speaker's Bio

Lydia E. Kavraki is the Noah Harding Professor of Computer Science and Bioengineering at Rice University. She also holds an appointment at the Department of Structural and Computational Biology and Molecular Biophysics at the Baylor College of Medicine in Houston. Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University working with Jean-Claude Latombe. Her research contributions are in physical algorithms and their applications in robotics (robot motion planning, hybrid systems, formal methods in robotics, assembly planning, micromanipulation, and flexible object manipulation), as well as in computational structural biology, translational bioinformatics, and biomedical informatics (modeling of proteins and biomolecular interactions, large-scale functional annotation of proteins, computer-assisted drug design, and systems biology). Kavraki has authored more than 180 peer-reviewed journal and conference publications and is one of the authors of a robotics textbook titled "Principles of Robot Motion" published by MIT Press. She is heavily involved in the development of The Open Motion Planning Library (OMPL), which is used in industry and in academic research in robotics and bioinformatics. Kavraki is currently on the editorial board of the International Journal of Robotics Research, the ACM/IEEE Transactions on Computational Biology and Bioinformatics, the Computer Science Review, and Big Data. She is also a member of the editorial advisory board of the Springer Tracts in Advanced Robotics. Kavraki is the recipient of the Association for Computing Machinery (ACM) Grace Murray Hopper Award for her technical contributions. She has also received an NSF CAREER award, a Sloan Fellowship, the Early Academic Career Award from the IEEE Society on Robotics and Automation, a recognition as a top young investigator from the MIT Technology Review Magazine, and the Duncan Award for excellence in research and teaching from Rice University. Kavraki is a Fellow of the Association of Computing Machinery (ACM), a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow of the World Technology Network (WTN). Kavraki was elected a member of the Institute of Medicine (IOM) of the National Academies in 2012. She is also a member of the Academy of Medicine, Engineering and Science of Texas (TAMEST) since 2012. Current projects at Kavraki's laboratory are described under http://www.kavrakilab.org and http://www.cs.rice.edu/~kavraki.

Oral Defense of Doctoral Dissertation: Decision Guidance for Sustainable Manufacturing

Thursday, March 21, 2013
2:00pm – 4:00pm
ENGR 4201
Guodong Shao

Abstract

Sustainable manufacturing has significant impacts on a company’s business performance and competitiveness in today’s world. A growing number of manufacturing industries are initiating efforts to address sustainability issues; however, to achieve a higher level of sustainability, manufacturers need methodologies for formally describing, analyzing, evaluating, and optimizing sustainability performance metrics for manufacturing processes and systems. Currently, such methodologies are missing.

This dissertation developed the Sustainable Process Description and Analytics (SPDA) formalism and a systematic decision guidance methodology to fill the research gaps. The methodology provides step-by-step guidance for sustainability performance analysis and decision optimization using the SPDA formalism. The SPDA formalism provides unified syntax and semantics for querying, what-if analysis, and decision optimization; is modular, extensible, and reusable; supports built-in process and sustainability metrics modeling that enable users using data from production, energy management, life cycle assessment reference for modeling and analysis; is easy to use by manufacturing and business users; and also provides a reduction procedure that enables the translations of the SPDA query into specialized models such as optimization or simulation model for decision guidance. Two real world sustainable manufacturing case studies have been performed to demonstrate the use of formalism and the methodology.

CS Colloquium-NEW TIME: Automatic Program Repair Using Genetic Programming

Monday, March 25, 2013
11:30am
ENGR 4201
Claire Le Goues

Abstract

"Everyday, almost 300 bugs appear...far too many for only the Mozilla programmers to handle" --Mozilla developer, 2005  Software quality is a pernicious problem. Although 40 years of software engineering research has provided developers considerable debugging support, actual bug repair remains a predominantly manual, and thus expensive and time-consuming, process.  I will describe GenProg, a technique that uses evolutionary computation to automatically fix software bugs. My empirical evidence demonstrates that GenProg can quickly and cheaply fix a large proportion of real-world bugs in open-source C programs.  I will also briefly discuss the atypical evolutionary search space of the automatic program repair problem, and the ways it has challenged assumptions about software defects.

Speaker's Bio

Claire Le Goues is a Ph.D. candidate in Computer Science at the University of Virginia.  Her research interests lie in the intersection of software engineering and programming languages, with a particular focus on software quality and automated error repair.  Her work on automatic program repair has been recognized with Gold and Bronze designations at the 2009 and 2012 ACM SIGEVO "Humies" awards for Human-Competitive Results Produced by Genetic and Evolutionary Computation and several distinguished and featured paper awards.

CS Colloquium: Searching for Relevant Functions and Their Usages in Millions of Lines of Code

Thursday, March 28, 2013
10:00am
ENGR 4201
Denys Poshyvanyk

Abstract

Different studies show that programmers are more interested in finding definitions of functions and their uses than variables, statements, or ordinary code fragments. Therefore, developers require support in finding relevant functions and determining how those functions are used. Unfortunately, existing code search engines do not provide enough of this support to developers, thus reducing the effectiveness of code reuse. We provide this support to programmers in a code search system called Portfolio that retrieves and visualizes relevant functions and their usages. We have built Portfolio using a combination of models that address surfing behavior of programmers and sharing related concepts among functions. Currently, Portfolio is instantiated on two large source code repositories with thousands of projects spanning 270 Million C/C++ and 440 Million Java lines of code. In order to evaluate Portfolio, we conducted two experiments. First, an experiment with 49 professional C/C++ programmers to compare Portfolio to Google Code Search and Koders using a standard methodology for evaluating Information Retrieval-based engines. And second, an experiment with 19 Java programmers to compare Portfolio to Koders. The results show with strong statistical significance that users find more relevant functions with higher precision with Portfolio than with Google Code Search and Koders. We also demonstrate that by using PageRank, Portfolio is able to rank returned relevant functions more efficiently.

Speaker's Bio

Dr. Denys Poshyvanyk is an Assistant Professor at the College of William and Mary in Virginia. He received his Ph.D. degree in Computer Science from Wayne State University in 2008. He also obtained his M.S. and M.A. degrees in Computer Science from the National University of Kyiv-Mohyla Academy, Ukraine and Wayne State University in 2003 and 2006, respectively. Since 2010, he has been serving on the steering committee of the International Conference on Program Comprehension (ICPC). He has been elected as a chair of the ICPC steering committee in 2012. He serves as a program co-chair for 21st IEEE International Conference on Program Comprehension (ICPC'13). He also served as a program co-chair for the 18th and 19th International Working Conference on Reverse Engineering (WCRE 2011 and WCRE 2012). Dr. Poshyvanyk received NSF CAREER award and several Best Paper Awards, including ICPC’06, ICPC'07, ICSM’10, and SCAM’10. His research interests are in software engineering, software maintenance and evolution, program comprehension, reverse engineering, software repository mining, source code analysis, and metrics.

GRAND Seminar: Multi-target Tracking by Rank-1 Tensor Approximation

Friday, March 29, 2013
12:30pm
ENGR 4201
Haibin Ling

Abstract

Multi-target tracking (MTT) is an important problem in computer vision and has many applications. We introduce a novel framework for MTT using the rank-1 tensor approximation and propose an L1 norm tensor power iteration solution. In particular, a high order tensor is constructed based on trajectories in the time window, with each tensor element as the affinity of the corresponding trajectory. The assignment variables are the L1 normalized vectors, which are used to approximate the rank-1 tensor. Our approach provides a flexible and effective formulation where both pairwise and high-order association energy can be used expediently. We also show the close relation between our formulation and the multi-dimensional assignment (MDA) model. To solve the optimization in the rank-1 tensor approximation, we propose an algorithm that iteratively powers the intermediate solution followed by an L1 tensor normalization. Aside from effectively capturing high-order motion information, the proposed solver runs efficiently with proved convergence. The experimental validations are conducted on two challenging datasets and our method demonstrates promising performances on both of them.

Speaker's Bio

Haibin Ling received the B.S. degree in mathematics and the MS degree in computer science from Peking University, China, in 1997 and 2000, respectively, and the PhD degree from the University of Maryland, College Park, in Computer Science in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia, Beijing, China. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. After that, he joined Siemens Corporate Research, Princeton, NJ, as a research scientist. Since fall 2008, he has been an Assistant Professor at Temple University. Dr. Ling's research interests include computer vision, medical image analysis, human computer interaction, and machine learning. He received the Best Student Paper Award at the ACM Symposium on User Interface Software and Technology (UIST) in 2003.

Inter-Disciplinary Computing Seminar: Using GIS to Optimize Liver Transplant Locations in the US and Other Health GIS Projects

Wednesday, April 17, 2013
2:00pm
ENGR 4201
Nigel Waters

Abstract

The first part of the talk will discuss the main topics covered by health GIS research. This will be illustrated with case studies from my own research. The second half will describe my work with Naoru Koizumi and others at GMU on optimizing the geography of liver transplantation in the US. Specifically this research discusses geographic disparities in access to and outcomes in transplantation that have been a persistent widely discussed problem among transplant researchers and members of the transplant community. One of the alleged causes of these disparities in the United States is the administratively determined organ allocation boundaries that limit organ sharing across regions. This talk will describe the work of our research team in applying mathematical programming models to construct alternative liver allocation boundaries that achieve more geographic equity in access to transplants than the current system. The performance of the optimal boundaries was evaluated and compared to that of current allocation system using discrete event simulation.

CS Distinguished Lecture Series: Machine Learning Approaches to Network and Social Media

Friday, April 19, 2013
11:00am
Research Hall, Room 163
Eric Xing

Abstract

Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time and space. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable.

In this talk, I will present some recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will present new algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; and I will present a family of new Bayesian model and scalable inference algorithms for estimating the trajectories of latent multi-functionality of nodal states, and for learning community structures, in both static and evolving social and biological networks. Case studies of a breast cancer network, and the Facebook network will be presented to highlight current capability and future challenge.

Speaker's Bio

Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional and dynamic possible worlds; and for building quantitative models and predictive understandings of biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley.

His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) application of statistical learning in social networks, data mining, vision. Professor Xing has published over 150 peer-reviewed papers, and is an associate editor of the Journal of the American Statistical Association, Annals of Applied Statistics, the IEEE Transactions of Pattern Analysis and Machine Intelligence, the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning journal. He is a recipient of the NSF Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Faculty Award.

Inter-Disciplinary Computing Seminar: Motor Primitives with Spindle-like Properties Predict the Ability to Learn Different Types of Motor Adaptations

Wednesday, April 24, 2013
2:00pm
ENGR 4201
Wilsaan Joiner

Abstract

The mammalian brain generates motor commands to initiate movement. Through interactions with our environment this motor output is adapted in order to reduce the error between the planned and actual movement. One readily learned motor adaptation involves the predictive compensation for changes in the physical dynamics of the environment. These dynamics result in time-varying physical perturbations that are a function of the motion state, such as velocity. Early in compensating for velocity-dependent perturbations, subjects exhibit a force profile with a shape resembling a muscle spindle firing pattern (a transient, velocity-dependent peak followed by a static, position-dependent offset). As adaptation progresses, this force profile transforms into one that is almost entirely velocity-dependent (task goal specific), as the static ‘tail’ fades and the transient peak increases. Analysis of the initial and final force profiles suggests that these high-dimensional force-profiles can be represented as simple combinations of position and velocity signals linearly modified with stiffness and viscosity gains. We therefore constructed a simple neural network model with spindle-like primitives (having positive position and velocity dependence) and a simple gradient descent learning rule to test whether such a model would show primitive-dependent early learning but task-dependent late learning. This model accurately predicts that (1) the direction of initial learning is generally biased towards the center of the primitive distribution (2) the asymptotic learning state mostly reflects the task goal and (3) the adaptation rate for learning goals in the directions parallel or orthogonal to the center of the primitive distribution is enhanced or slowed, respectively, compared to other directions. These results suggest that adaptation to predictable force perturbations takes place in a spindle-like coordinate system that dictates the degree of difficulty for learning different types of perturbations and predicts the evolution of adaptive responses.

Speaker's Bio

Dr. Wilsaan Joiner is an Assistant Professor in the Department of Bioengineering. He received his PhD in Biomedical Engineering from the Johns Hopkins University School of Medicine in 2007. From 2007-2012, he was a postdoctoral fellow at Harvard University and the National Eye Institute (The Laboratory of Sensorimotor Research). Dr. Joiner’s Sensorimotor Integration Laboratory at the Volgenau School of Engineering conducts translational research investigating human sensory integration, motor learning and control using computational and experimental approaches. Ongoing projects include the influence of eye movements and internal monitoring signals in guiding goal-directed movements and the neural processes underlying motor adaptation and memory consolidation.

Oral Defense of Doctoral Dissertation: Towards Power-Efficient Internet Streaming to Mobile Devices

Wednesday, April 24, 2013
10:00am-12:00pm
ENGR 4201
Yao Liu

Abstract

Internet streaming services are very popular today. As a fact, video traffic now accounts for more than 51 percent total Internet traffic. With the pervasive adoption of various mobile devices in practice in the past several years, today Internet streaming services are receiving a rapidly growing number of requests from various mobile devices. As a result, more than 50 percent of the data consumed by mobile devices is video streaming traffic. However, streaming delivery to mobile devices is more challenging than to its desktop counterpart.

In this dissertation, we first empirically investigate Internet mobile streaming practices. We investigate mobile streaming from various perspectives, including hardware and software heterogeneity, different characteristics of mobile videos, and different user access patterns. The results provide us in-depth understanding on the current Internet mobile streaming services. A critical constraint on mobile devices for receiving Internet streaming services is that they have limited battery capacities. Among different power-consuming sources, transmission power consumption is very significant: for a mobile device receiving streaming services, about 30 percent to 40 percent of the power is consumed by the WNIC for streaming data transmission. So in order to prolong the battery lifetime, it is important to save the battery power consumed by the WNIC. For this purpose, we design and implement new schemes that can effectively save battery power consumption while maintaining good streaming quality to mobile devices. In particular, we focus on P2P streaming and client-server streaming as they are widely used. We aim to save battery power consumption from two aspects: (1) how the data is received, and (2) how much data is received. Our techniques have been implemented and experimental results show they are effective in reducing the battery power consumption on mobile devices without degrading the streaming quality.

Oral Defense of Doctoral Dissertation: A Cost-Effective Distributed Architecture for Content Delivery and Exchange Over Emerging Wireless Technologies

Thursday, April 25, 2013
10:00am-12:00pm
ENGR 3507
Khondkar R. Islam

Abstract

Opportunities in education are lacking in many parts of the developed nations and are missing in most parts of the developing nations. This is, in significant part, due to shortages of classroom instructional resources such as quality teaching staff, hardware and software. Distance education (DE) has proved to be a successful teaching approach and overcomes some of the barriers imposed by classroom instruction, primarily due to the shortage of teachers. Many DE software tools have been developed and are in use today. Most require high network capacity, not supported by common long distance wireless network infrastructures in many places of the world. To address obstacles related to network infrastructures of developing countries for content delivery and exchange, this research develops the design of a cost-effective distributed architecture for content delivery and exchange over emerging limited capacity wireless technologies. The design of the proposed target architecture includes an overlay network with distributed peer-to-peer systems. Simulation is used to explore parameters and metrics in order to validate the effectiveness and scalability of this architecture. An n-tier hierarchical training model to train local instructors by experienced instructors, using DE resources supported by this architecture, is discussed as a way to mitigate the teacher shortages in developing countries. The situation in Bangladesh is used to provide examples, based on the author’s familiarity with it.

Oral Defense of Doctoral Dissertation: Data Mining Framework for Metagenome Analysis

Tuesday, April 30, 2013
2:00pm–4:00pm
ENGR 4801
Zeehasham Rasheed

Abstract

Advances in biotechnology have dramatically changed the manner of characterizing large populations of microbial communities that are ubiquitous across several environments. The process of “metagenomics” involves sequencing of the genetic material of organisms, co-existing within ecosystems ranging from ocean, soil and human body. Researchers are trying to determine the collective microbial community or population of microbes that coexist across different environmental and clinical samples. Several researchers and clinicians have embarked on studying the pathogenic role played by the microbiome (i.e., the collection of microbial organisms within the human body) with respect to human health and disease conditions.

There is a critical need to develop new methods that can analyze metagenomes and correlate heterogeneous microbiome data to clinical metadata. Lack of such methods is an impediment for the identification of the function and presence of microbial organism within different samples, reducing our ability to elucidate the microbial-host interactions and discover novel therapeutics. From another perspective, comparing metagenomes across different ecological samples allows for the characterization of biodiversity across the planet. The goals of this dissertation are to develop novel data mining algorithms that allow for the accurate and efficient analysis of metagenome data obtained from different environments.

Specific contributions have included the development of a suite of clustering algorithms for handling large-scale targeted and whole metagenome sequences. We develop a novel locality sensitive hashing (LSH) based method for clustering metagenome sequence reads. Our method achieves efficiency by approximating the pairwise sequence comparison operations by using a randomized hashing technique. We incorporate this clustering approach within a computational pipeline (LSH-Div) to estimate the species diversity within an ecological sample. We also developed an algorithm called MC-MinH that uses the min-wise hashing approach, along with a greedy clustering algorithm to group 16S and whole metagenome sequences. We represent unequal length sequences using contiguous subsequences or k-mers, and then approximate the computation of pairwise similarity using independent min-wise hashing. Further, MC-MinH was extended as a distributed algorithm implemented within the Map-Reduce based Hadoop platform. The distributed clustering algorithm can perform a greedy iterative clustering as well as an agglomerative hierarchical clustering and can handle large volumes of input sequences.

We also developed a novel sequence composition-based taxonomic classifier using extreme learning machines referred to as TAC-ELM. This algorithm uses the framework of extreme learning machines to quickly and accurately learn the weights for a neural network model. TAC-ELM when combined with BLAST (Basic Local Alignment Search Tool) has shown improved taxonomy classification results.

In order to make these developed computational tools accessible to a broad group of researchers, we have also developed a web-based analysis portal. The portal implements a LIMS database using the open source Drupal content management system to store and retrieve the multi-modal microbiome data. For analysis and development of workflows, we use the Galaxy platform. This provides a web-based user friendly platform for integrating the tools developed to create user-customized pipelines and a batch-based job submission system.

To summarize, this dissertation has contributions in the area of metagenome sequence clustering and classification which can be easily integrated within computational workflows for species diversity estimation and large-scale microbiome analysis.

Oral Defense of Doctoral Dissertation: An Autonomic Framework for Integrating Security and Quality of Service Support in Databases

Thursday, May 02, 2013
10:00am-12:00pm
ENGR 3507
Firas Alomari

Abstract

The back-end databases of multi-tiered applications are a major data security concern for enterprises. The abundance of these systems and the emergence of new and different threats require multiple and overlapping security mechanisms. Therefore, providing multiple and diverse database intrusion detection and prevention systems (IDPS) is a critical component of the defense-in-depth strategy for DB information systems. At the same time, an e-business application is expected to process requests with a certain service quality to maintain current customers and attract new ones. To meet both objectives it is necessary to use a combination of IDPSs that best meets the security and QoS requirements of the system stakeholders for each workload intensity level. Due to the dynamic variability of the workload intensity, it is not feasible for human beings to continuously reconfigure the system. It is therefore important that current systems be built with adaptive capabilities that can dynamically respond to changes in it is surroundings. This research presents a self-optimizing and self-protecting database system environment that captures dynamic and fine-grained tradeoffs between security and QoS by using a multi-objective utility function. The utility functions considers the performance impact of IDPSs on the overall system under a certain workload, the detection and false detection rates of the IDPSs, and high level stakeholder preferences and constraints. The model was validated in a simulated environment. The feasibility of the approach is also demonstrated in an experimental environment.

Oral Defense of Doctoral Dissertation: Emergency Communications via Handheld Devices

Thursday, May 02, 2013
1:30pm-3:30pm
ENGR 4201
Paul Ngo

Abstract

Ensuring effective communications during emergencies is an important issue for any functional government. One way to address this issue is to ensure the availability of the emergency responders capable of making the appropriate decisions and taking timely actions with sufficient resources. Many XML-based languages such as the Emergency Data Exchange Language (EDXL) and associated Common Alert Protocol (CAP) have been de- signed to provide a basis for such communications.

To ensure that messages are delivered in a timely manner, I propose some role- and task-based ontological enhancements for these languages. I address this availability problem further by proposing a Role-based model Availability Emergency Responder Framework (AERF). This AERF ensures that a list of personnel for a particular role in an organization is always reachable to handle an emergency call. I develop a working prototype of the AERF framework for a local hospital that provides emergency cases. The prototype demonstrates the feasibility and security of the AERF framework and addresses the availability of emergency responders based on their assigned roles.

In order to inform the general public of nearby emergencies, the Department of Homeland Security initiated the Commercial Mobile Alert System (CMAS), which utilized existing commercial telecommunication infrastructures to broadcast emergency alert text messages to all mobile users in an area affected by an emergency. One of the limitations of the Cell Broadcast Service (CBS) is that the smallest area that CMAS can broadcast its message is a cell site, which is, in most cases, quite large for small-scale emergencies.

I propose an enhancement to CMAS by using CMAS as a transport protocol to distribute small-scale emergency alerts to areas that are smaller than a cell site. I also suggest a proper enhancement to the CAP 1.2 message structure for CMAS emergency alerts. Another limitation of CMAS messages is the maximum message size of 90 characters of clear text. I propose an enhancement to CMAS by using a combination of different encoding techniques and emergency protocol standard including the Common Alerting Protocol (CAP 1.2) to provide alert messages with meaningful and rich content. I show the viability of our solution using a prototype implementation that can generate and broadcast CMAS emergency alerts through Emergency Response Alert System (ERAlert) to Android phones where an Emergency Response Application (ERApp) will intercept, decode, and display meaningful alerts to users. Lastly, I propose a Navigation Assistance Framework (NAF) that allows emergency organizations to provide emergency information that can be filtered through the traffic patterns in order to assist victims navigate out of the emergency and reach their intended destinations in a reasonable amount of time. I develop a ERSimMon to simulate this capability in a small scale to show the effectiveness of my solution.

SWE Seminar: GuideArch: Guiding the Exploration of Architectural Solution Space Under Uncertainty

Thursday, May 02, 2013
11:00am
ENGR 4201
Naeem Esfahani

Abstract

A system's early architectural decisions impact its properties (e.g., scalability, dependability) as well as stakeholder concerns (e.g., cost, time to delivery). Choices made early on are both difficult and costly to change, and thus it is paramount that the engineer gets them "right". This leads to a paradox, as in early design, the engineer is often forced to make these decisions under uncertainty, i.e., not knowing the precise impact of those decisions on the various concerns. How could the engineer make the "right" choices in such circumstances? This is precisely the question we have tackled in this talk. We present GuideArch, a framework aimed at quantitative exploration of the architectural solution space under uncertainty. It provides techniques founded on fuzzy math that help the engineer with making informed decisions.

Speaker's Bio

Naeem Esfahani is a Ph.D. candidate in Computer Science Department, Volgenau School of Engineering. He got his B.Sc. degrees on Electrical and Computer Engineering from University of Tehran in 2005. He also received a M.Sc. degree in Computer Engineering from Sharif University of Technology in 2008. His current research mainly focuses on Software Architecture, Self-Adaptive Software Systems, and Software Quality of Service Analysis & Improvement.

Project Presentation for Engineer Degree: A TeC Implementation on the Android Platform

Friday, May 03, 2013
1:00pm-3:00pm
ENGR 4801
Xiang Shen

Abstract

This thesis describes a software system developed based upon the Team Computing (TeC) model. TeC belongs to Ubiquitous computing, which emphasizes the coordination of teams comprised of software components, devices and human operation.

This particular system is implemented on the popular Android platform using Java. It allows the teams designed by end users to be deployed and operating on the Android devices. The system also implemented a decentralized, lightweight protocol for self- healing.

Department of Computer Science: Graduation Celebration and Awards Dinner

Wednesday, May 15, 2013
6:00pm
JC Dewberry Hall
By Invitation Only

Volgenau School of Engineering: Convocation

Thursday, May 16, 2013
2:00pm
Patriot Center

Event Info

IN CASE OF RAIN students should report directly to the Patriot Center floor, via the lower entrance on the "East" side of the Patriot Center (i.e. loading dock door), to be directed to their seats. There will be no procession in the event of rain.

Graduates traditionally assemble at 1:15 p.m. in the Patriot Center parking lot at your department's designated area (indicated by department signs). It is very important that you be at the appropriate location by 1:15 p.m. so we can line up all graduates by degree program for the processional.

Department of Computer Science: Post-Convocation Reception

Thursday, May 16, 2013
4:00pm
Atrium, Engineering Building

Event Info

Immediately following the Volgenau School of Engineering Convocation Ceremony. All graduates from the Computer Science Department and their guests are welcome.

Oral Defense of Doctoral Dissertation: An Extensible Framework for Generating Ontology from Various Data Models

Thursday, May 30, 2013
10:00am-12:00pm
ENGR 4801
Khalid Albarrak

Abstract

In the Information Technology field, Ontology is concerned with the use of formal representation to describe concepts and relationships in a domain of knowledge. Using ontologies, organizations can facilitate processes such as integrating heterogeneous systems, assessing data quality, validating business rules, and discovering hidden facts. Ontology engineering, however, is not a trivial process. Developing ontologies is highly dependent on the availability and knowledge of ontology modelers and domain experts. Moreover, the development process is often lengthy and error-prone. In this dissertation, I developed an extensible framework for generating ontologies from data models. For this dissertation, the framework is limited to generating ontology from two types of data models: the Relational Database (RDB) and Object-Relational Database (ORDB) models. The framework, however, is extensible to support the generation of ontologies from other types of data models (e.g. XML). The derived ontology is expressed in the OWL Web Ontology Language, a W3C recommendation.

The proposed framework has been validated by implementing it as a prototype, and by examining the ontologies it generates from a syntactic and semantic perspective. For the semantic examination, domain requirements were used to compute the recall and precision for the ontologies generated by my framework and that of a similar tool. Moreover, the relative amount of terminological content (which I call the relative explicitness) of these ontologies was measured as well using a methodology that I developed in my research. The results showed the ability of my framework to generate ontologies that are closely aligned with the domain.

SWE Seminar: Adaptive Program Repair via Program Equivalence: A Duality With Mutation Testing

Wednesday, June 05, 2013
11:00am
ENGR 4201
Westley Weimer

Abstract

Software bugs remain a compelling problem. Automated program repair is a promising approach for reducing cost, and many methods have recently demonstrated positive results. However, success on any particular bug is variable, as is the cost to find a repair.

This talk focuses on generate-and-validate repair methods that enumerate candidate repairs and use test cases to define correct behavior. We formalize repair cost in terms of test executions, which dominate most test-based repair algorithms. Insights from this model lead to a novel deterministic repair algorithm that computes a patch quotient space with respect to an approximate semantic equivalence relation. Generate-and-validate program repair is shown to be a dual of mutation testing, directly suggesting possible cross-fertilization. Evaluating on 105 real-world bugs in programs totaling 5MLOC and involving 10,000 tests, our new algorithm requires an order-of-magnitude fewer test evaluations than the previous state-of-the-art and is over five times more efficient monetarily. This talk presents work that is currently under submission.

Speaker's Bio

Westley Weimer is an Associate Professor of Computer Science at the University of Virginia. His main research interest lies in advancing software quality by using both static and dynamic programming language approaches. http://www.cs.virginia.edu/~weimer/

Oral Defense of Doctoral Dissertation: Scheduling Algorithms Optimizing Throughput and Energy for Networked Systems

Wedensday, June 26, 2013
11:00am – 1:00pm
ENGR 4201
Zhi Zhang

Abstract

Scheduling problems consider allocating limited resources under constraints among competing requests in order to fulfill their obligations. Practical resource management algorithms with provable performance guarantees are of great importance. In this dissertation, we study scheduling algorithms for resource management in networked systems. Mainly, we design, analyze, and implement two types of scheduling algorithms: (1)throughput-aware scheduling algorithms, and(2)energy-aware scheduling algorithms.

Throughput is a main metric that scheduling algorithms are designed to optimize. We study algorithms for network routers to schedule weighted packets with time constraints over a wireless fading channel. We design both offline and online algorithms to maximize weighted throughput, which is defined as the total value of the packets successfully sent before their respective deadlines.

Energy consumption has become an important performance metric in designing scheduling algorithms for computing systems and networked systems. For network routers, we first study a problem of scheduling jobs with values and deadlines to maximize net profit, which is defined as the difference between the revenue obtained from the jobs sent before their respective deadlines and the cost of total energy consumption during this course. Another model for network routers that we study is the trade-off between energy consumption and jobs’ flow time or stretch in an online setting. We design bi-criteria power-down strategies optimizing both and analyze their performance using competitive ratio. For Point-of-Presence (PoP) design, current IP core networks operate at a nearly constant power rate independent of the traffic load. Thus, the gap between the available network capacity and the temporal traffic demand presents opportunities for reducing network power consumption by deactivating network components without noticeably affecting network performance. We study a theoretical model for PoP design. The objective is to find out an assignment between traffic links and chassis within a PoP, such that the total energy cost of the PoP is minimized. We analyze the hardness of this model and design several approximation algorithms with provable near-optimal performance. For a given processor, each speed change involves time and energy overhead, as well as a negative impact on its lifetime reliability. Motivated by this observation, we study theoretical energy-aware scheduling problems by considering the number and the cost of processor’s speed changes. Four related problems based on this framework are studied.

Oral Defense of Doctoral Dissertation: On Using Meta-Modeling and Multi-Modeling to Address Complex Problems

Wednesday, July 03, 2013
2:00pm – 4:00pm
ENGR 3507
Ahmed Abu Jbara

Abstract

Models, created using different modeling techniques, usually serve different purposes and provide unique insights. While each modeling technique might be capable of answering specific questions, complex problems require multiple models interoperating to complement/supplement each other; we call this Multi-Modeling. To address the syntactic and semantic challenges of this multi-modeling approach for solving complex problems, a systematic methodology for developing multi-modeling workflows is presented. The approach is domain specific: Identification of the domain and the supporting modeling techniques is the first step. Then a Domain Specific Multi-Modeling Workflow Language (DSMWL), supported by a Domain Ontology, is developed and then used to construct workflows that capture interoperations between various models. The domain ontology provides semantic guidance to effect valid model interoperation.

The approach is illustrated using a case study from the Drug Interdiction and Intelligence domain. The Joint Inter-Agency Task Force (JIATF) - South, an agency well known for interagency cooperation and intelligence fusion, receives large amounts of disparate data regarding drug smuggling efforts. Analysis of such data using various modeling techniques is essential in identifying best Courses of Action (COAs). The proposed methodology is applied to the Drug Interdiction domain by performing domain analysis, developing a Domain Specific Multi-Modeling Workflow Language (DSMWL) and a Domain Ontology, and then using the DSMWL and the Domain Ontology to create workflows of model interoperations involving Social Networks, Timed Influence Nets, and Geospatial models.

Test-Out Exam: INFS 501

Monday, August 19, 2013
9:00am-10:00am
ENGR 1110
Registration Required

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*If fall 2013 will be your first semester as a student. If this is not your first semester as an MS student, please explain why you are are taking the test out exams now.

Test-Out Exam: INFS 515

Monday, August 19, 2013
11:00am-12:00pm
ENGR 1110
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*Your full name and G number.

*Your program and academic advisor.

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*If fall 2013 will be your first semester as a student. If this is not your first semester as an MS student, please explain why you are are taking the test out exams now.

Test-Out Exam: INFS 519

Monday, August 19, 2013
1:00pm-2:00pm
ENGR 1110
Registration Required

Registration

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*Your full name and G number.

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*If fall 2013 will be your first semester as a student. If this is not your first semester as an MS student, please explain why you are are taking the test out exams now.

Test-Out Exam: SWE 510

Monday, August 19, 2013
3:00pm-4:00pm
ENGR 1110
Registration Required

Registration

Please register by sending an email to csadmin@cs.gmu.edu with the following REQUIRED information:

*Your full name and G number.

*Your program and academic advisor.

*Which exams you wish to register for.

*If fall 2013 will be your first semester as a student. If this is not your first semester as an MS student, please explain why you are are taking the test out exams now.