M.S. Thesis Defense

Neuroevolution-Based Inverse Reinforcement Learning

Karan K. Budhraja

9:00am Wednesday, 2 December 2015, ITE 346

Motivated by such learning in nature, the problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One of the approaches to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations.

This work also extends existing work on Bayesian Non-Parametric Feature construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. Performance of the algorithm is shown to be limited by parameters used, implying adjustable capability. A conclusive performance hierarchy between evaluated algorithms is constructed.

Committee: Drs. Tim Oates, Cynthia Matuszek and Tim Finin

Ph.D. Dissertation Defense
Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Rapid Plan Adaptation Through Offline
Analysis of Potential Plan Disruptors

Robert H. Holder, III

9:00am Wednesday, 9 December 2015, ITE 325b

Computing solutions to intractable planning problems is particularly problematic in dynamic, real-time domains. For example, visitation planning problems, such as a delivery truck that must deliver packages to various locations, can be mapped to a Traveling Salesman Problem (TSP). The TSP is an NP-complete problem, requiring planners to use heuristics to find solutions to any significantly large problem instance, and can require a lengthy amount of time. Planners that solve the dynamic variant, the Dynamic Traveling Salesman Problem (DTSP), calculate an efficient route to visit a set of potentially changing locations. When a new location becomes known, DTSP planners typically use heuristics to add the new locations to the previously computed route. Depending on the placement and quantity of these new locations, the efficiency of this adapted, approximated solution can vary significantly. Solving a DTSP in real time thus requires choosing between a TSP planner, which produces a relatively good but slowly generated solution, and a DTSP planner, which produces a less optimal solution relatively quickly.

Instead of quickly generating approximate solutions or slowly generating better solutions at runtime, this dissertation introduces an alternate approach of precomputing a library of high-quality solutions prior to runtime. One could imagine a library containing a high-quality solution for every potential problem instance consisting of potential new locations, but this approach obviously does not scale with increasing problem complexity. Because complex domains preclude creating a comprehensive library, I instead choose a subset of all possible plans to include. Strategic plan selection will ensure that the library contains appropriate plans for future scenarios.

Committee: Drs. Marie desJardins (co-chair), Tim Finin (co-chair), Tim Oates, Donald Miner, R. Scott Cost

CSEE professor Cynthia Matuszek will teach a new special topics course this spring on Principles of Human-Robot Interaction. The graduate level course (CMSC 691-08) will meet on Tuesday and Thursdays from 4:00 to 5:30pm in 013 Sherman Hall.

Principles of Human-Robot Interaction

An introduction to robots in our daily lives

CMSC691-08, 4:00-5:15pm Tue/Thr, starting 26 January 2016, UMBC

Robots are becoming ubiquitous. From Roombas in our homes, to surgical robots in hospitals, to giant manipulators that assemble cars, robots are everywhere. In the past, robots have only ever interacted with highly trained experts. Now, as they are being deployed more widely, we must address new questions about how our robots can interact day-to-day with end users — non-experts — safely, usefully, and pleasantly. This new area of research is called Human-Robot Interaction, or HRI.

This 3-credit special topics course aims to introduce students to current research in HRI and provide hands-on experience with HRI research. Students will explore the diverse range of research topics in this area, learn to identify HRI problems in their own research, and carry out a collaborative project involving human-robot interactions. Topics to be covered include:

• Social robots: how can robots be social beings? When do we want them to?
• Human-robot collaboration: humans and robots working together on tasks
• Natural-language interactions with robots and human-robot dialog
• Telerobotics: the uses of remote presence and teleoperation
• Expressive robots: how can robots express emotion – and should they?

Students may benefit from having some previous coursework or experience in AI, machine learning, or robotics, but none are necessary. Undergraduate students can enroll with the instructor’s permission. For more information, contact Dr. Matuszek at cmat at umbc.edu.

UMBC Department of Information Systems

Is Bigger Better? Comparing User Generated Passwords on
3×3 vs. 4×4 Grid Sizes for Android’s Pattern Unlock

Adam Aviv, USNA

1:00-2:00pm Tuesday, 1 December 2015, ITE 459

Android’s graphical authentication mechanism requires users to unlock their devices by “drawing” a pattern that connects a sequence of contact points arranged in a 3×3 grid. Prior studies have shown that human-generated patterns are far less complex than one would desire; large portions can be trivially guessed with sufficient training. Custom modifications to Android, such as CyanogenMod, offer ways to increase the grid size beyond 3×3, and in this paper we ask the question: Does increasing the grid size increase the security of human-generated patterns?

To answer this question, we conducted two large studies, one in-lab and one online, collecting 934 total 3×3 patterns and 504 4×4 patterns. Analysis shows that for both 3×3 and 4×4 patterns, there is a high incidence of repeated patterns and symmetric pairs (patterns that derive from others based on a sequence of flips and rotations). Further, many of the 4×4 patterns are similar versions of 3×3 patterns distributed over the larger grid space. Leveraging this information, we developed the most advanced guessing algorithm in this space, and we find that guessing the first 20% (0.2) of patterns for both 3×3 and 4×4 can be done as efficiently as guessing a random 2-digit PIN. Guessing larger portions of 4×4 patterns (0.5), however, requires 2-bits more entropy than guessing the same ratio of 3×3 patterns, but the entropy is still on the order of cracking random 3-digit PINs. These results suggest that while there may be some benefit to expanding the grid size to 4×4, the majority of patterns will remain trivially guessable and insecure against broad guessing attacks.

Adam J. Aviv is an Assistant Professor of Computer Science at the United States Naval Academy, receiving his Ph.D. from the University of Pennsylvania under the advisement of Jonathan Smith and Matt Blaze. He has varied research interests including in system and network security, applied cryptography, smartphone security, and more recently in the area of usable security with a focus on mobile devices.

Standing (left to right) are staff: Igor Epshteyn (Coach), CSEE Professor Alan T. Sherman (Director), Joel DeWyer (Business Manager), GM Sam Palatnik (Coach). Sitting (left to right) are the 2015 A Team players: IM Levan “The Georgian Gangster” Bregadze, GM Niclas “The Dark Knight” Huschenbeth (Captain), GM Tanguy “The Belgium Butcher” Ringoir and Dobrynya Konoplev. Photo by Marlayna Demond.

The UMBC chess teams are preparing for the 2015 Pan American Intercollegiate Team Chess Championship which will be hosted by Oberlin College in Cleveland, Ohio on December 27-30. The Pan-Am tournament has been held annually since 1946 and determines the top university chess team in the Americas. UMBC’s chess team has competed in the tournament since 1990 and won or tied for first place ten times, a record only matched by one other college chess team.

UMBC will send a second team to the Pam-Am as well, shown above in a photo by Marlayna Demond.  Its members are Nathaniel Wong, Abhilash Puranik, Jeffrey Carr and Mustapha Diomande.

The top four U.S. schools in the 2015 Pan-Am will advance to the President’s Cup, the Final Four of College Chess, which will take place in spring 2016. The Final Four was started in 2001 and determines the top U.S. college team. UMBC is the only school that has qualified to play in all 15 Final Four tournaments and has won a record six times.

See more pictures of the UMBC chess teams here.

Multiple Tenure-track Faculty Positions Starting Fall 2016

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

UMBC’s Department of Computer Science and Electrical Engineering invites applications for three tenure-track Assistant Professor positions to begin in Fall 2016. Exceptionally strong candidates for higher ranks may be considered. Applicants must have or be completing a Ph.D. in a relevant discipline, have demonstrated the ability to pursue a research program, and have a strong commitment to undergraduate and graduate teaching. Candidates will be expected to build and lead a team of student researchers, obtain external research support and teach both graduate and undergraduate courses.

All areas of specialization will be considered, but we are especially interested in candidates in the following areas: information assurance and cybersecurity; mobile, wearable and IoT systems; big data with an emphasis on machine learning, analytics, and high-performance computing; knowledge and database systems; hardware systems and experimental methods in circuits, devices, VLSI, FPGA, and sensors; cyber-physical systems; low-power systems; biomedical and healthcare systems; and methods and tools for hardware-software co-design.

The CSEE department is energetic, research-oriented and multi-disciplinary with programs in Computer Science, Computer Engineering, Electrical Engineering and Cybersecurity. Our faculty (34 tenure-track, six teaching and 15 research) enjoy collaboration, working across our specializations as well as with colleagues from other STEM, humanities and the arts departments and external partners. We have 1500 undergraduate CS and CE majors and 400 M.S. and Ph.D. students in our CS, CE, EE and Cybersecurity graduate programs. We have awarded 276 PhDs since our establishment in 1986. Our research supported by a growing and diverse portfolio from government and industrial sponsors with over $5M in yearly research expenditures. We work to help new colleagues be successful by providing startup packages, reduced teaching loads and active mentoring.

UMBC is a dynamic public research university integrating teaching, research and service. As an Honors University, the campus offers academically talented students a strong undergraduate liberal arts foundation that prepares them for graduate and professional study, entry into the workforce, and community service and leadership. UMBC emphasizes science, engineering, information technology, human services and public policy at the graduate level. We are dedicated to cultural and ethnic diversity, social responsibility and lifelong learning. The 2015 US News and World Report Best Colleges report placed UMBC fourth in the Most Innovative National Universities category and sixth in Best Undergraduate Teaching, National Universities. The Chronicle of Higher Education named UMBC as a Great College to Work For, a recognition given to only 86 universities. Our strategic location in the Baltimore-Washington corridor puts us close to many important federal laboratories and agencies and high-tech companies, facilitating interactions, collaboration, and opportunities for sabbaticals and visiting appointments.

UMBC’s campus is located on 500 acres just off I-95 between Baltimore and Washington DC, and less than 10 minutes from the BWI airport and Amtrak station. The campus includes the bwtech@UMBC research and technology park, which has special programs for startups focused on cybersecurity, clean energy, life sciences and training. We are surrounded by one of the greatest concentrations of commercial, cultural and scientific activity in the nation. Located at the head of the Chesapeake Bay, Baltimore has all the advantages of modern, urban living, including professional sports, major art galleries, theaters and a symphony orchestra. The city’s famous Inner Harbor area is an exciting center for entertainment and commerce. The nation’s capital, Washington, DC, is a great tourist attraction with its historical monuments and museums. Just ten minutes from downtown Baltimore and 30 from the D.C. Beltway, UMBC offers easy access to the region’s resources by car or public transportation.

Applicants should submit a cover letter, a brief statement of teaching and research experience and interests, a CV, and three letters of recommendation at Interfolio. Applications received by January 15, 2016 are assured full consideration and those received later will be evaluated as long as the positions remain open. Send questions to and see the CSEE jobs page for more information.

We are committed to inclusive excellence and innovation and welcome applications from women, minorities, veterans, and individuals with disabilities. UMBC is an affirmative action/equal opportunity employer.

Ph.D. Dissertation Defense

Automatic Service Search and Composability
Analysis in Large Scale Service Networks

Yunsu Lee

10:00am Wednesday 25 November 2015, ITE 346, UMBC

Currently, software and hardware system components are trending toward modularized and virtualized as atomic services on the cloud. A number of cloud platforms or marketplaces are available where everybody can provide their system components as services. In this situation, service composition is essential, because the functionalities offered by a single atomic service might not satisfy users’ complex requirements. Since there are already a number of available services and significant increase in the number of new services over time, manual service composition is impractical.

In our research, we propose computer-aided methods to help find and compose appropriate services to fulfill users’ requirement in large scale service network. For this purpose, we explore the following methods. First, we develop a method for formally representing a service in term of composability by considering various functional and non-functional characteristics of services. Second, we develop a method for aiding the development of the reference ontologies that are crucial for representing a service. We explore a bottom-up-based statistical method for the ontology development. Third, we architect a framework that encompasses the reference models, effective strategy, and necessary procedures for the services search and composition. Finally, we develop a graph-based algorithm that is highly specialized for services search and composition. Experimental comparative performance analysis against existing automatic services composition methods is also provided.

Commitee: Drs. Yun Peng (chair), Tim Finin, Yelena Yesha, Milton Halem, Nenad Ivezic (NIST) and Boonserm Kulvatunyou (NIST)

The UMBC Cyber Defense Lab presents

Security Review of the MyUMBC Mobile App

Mikhail Aleksander, Enis Golaszewski, Gavin Lebo and Daniel Whitt

11:15am-12:30pm Friday, 20 November 2015, ITE 231

Our team will present preliminary findings and lead an informal discussion on its project to carry out a security review of new custom software for mobile devices in the UMBC enterprise. Using Highpoint, this custom software allows users to connect from IOS and Android mobile devices to application services including Peoplesoft (registration and administrative functions), Blackboard (instructional support), and Cashnet (campus financial transactions). Focusing on the custom software, the review includes an adversarial model, summary of the data and resources to be protected, analysis of the system design and architecture, and static and dynamic analysis of the source code using a variety of tools. Among other questions, the review addresses the following: What are potential vulnerabilities? How might an adversary exploit these vulnerabilities? What attacks are possible, how difficult would it be to carry out such attacks, what would their consequences be, and what is the risk of such attacks? Are appropriate cryptography and protocols used, are they used appropriately, and are the key lengths appropriate? Is the key management sound, and where are keys stored? Does the design and implementation follow best practices? The final report will include constructive recommendations.

Mikhail Aleksander, Enis Golaszewski, Gavin Lebo, and Daniel Whitt are students in Dr. Sherman’s CMSC-491/691 Cybersecurity Research class of the NSF-funded INSuRE project.  Aleksander, Golaszewski, and Lebo are BS students in computer science; Whitt is a MPS student in Cyber. Lebo and Whitt are also SFS Scholars.

Host: Alan T. Sherman,

MS Defense
UMBC Computer Science and Electrical Engineering

Distance Adaptation of Diffuse Reflectance
and Subsurface Scattering

Elizabeth Baumel

1:30pm Friday, November 20, ITE 352, UMBC

Objects in the world around us are made of a myriad of materials, both metallic and non-metallic. Most non-metallic materials scatter light in varying amounts within their surfaces, giving softer, more saturated diffuse colors and softer-edged shadows. This effect, subsurface scattering, is important to make translucent objects look realistic. Non-metallic objects that are opaque also scatter light, just at a very small distance. These non-metallic materials may look somewhat translucent at very close viewing distances, but from farther away they exhibit a more opaque, but still soft diffuse appearance. To shade these objects realistically from all distances, a method is needed to model subsurface scattering effects at close ranges and to smoothly transition to a soft diffuse reflection at larger viewing distances. We present a method that takes advantage of graphics processor texture filtering hardware to linearly filter maps that encode diffuse reflection and translucency information and to interpolate between a close-range subsurface scattering effect and a long-range reflectance function.

Committee: Drs. Marc Olano (Advisor, Chair), Penny Rheingans, Jian Chen

UMBC’s Information Systems Security Association Chapter and Cyber Scholars & Affiliates Program will host a panel on Women and Leadership in IT followed by hors d’oeuvres and networking with the panelists and representatives from Northrop Grumman.  The event will take place from 5:00 to 6:30pm on Wednesday, 18 November 2015 in room 312 of the University Center at UMBC. Panelists include:

• Deborah Bonanni: Former Chief of Staff of NSA & VP of Intelligent Decisions, Inc.
• Diane Howard: VP of Cyber Operations of Northrop Grumman
• Belinda Coleman: President/CEO the Coleman Group Inc.
• Brenda Martineau : Organizational Leadership & Management Skill Community Director of NSA
• Jennifer R. Walker : President/CEO Resolute Technologies, LLC

Everyone is welcome. See the event announcement  for more information and to optionally RSVP.