UMBC CSEE Seminar

A Neural Circuit Deconvolution Approach to Study Motivated Behavior

Dr. Joseph Cheer
Associate Professor, Deptartment of Anatomy and Neurobiology
and Department of Psychiatry
School of Medicine, University of Maryland Baltimore

11:001am-12:00pm, Wednesday, 3 February 2016, ITE 325b

In order to examine relationships between subsecond dopamine signaling and nucleus accumbens (NAc) cell firing during reward-directed behaviors, the ideal experimental approach is to record postsynaptic neuronal firing from the same electrode used to measure dopamine release. We have demonstrated that these measurements are feasible using cylindrical carbon fiber electrodes that can voltammetrically detect the oxidation potential of dopamine and also measure single-units extracellularly or local field potentials (LFPs). Moreover, we have added iontophoresis barrels to the carbon-fiber microelectrode to allow localized, rapid drug delivery to examine the signal transduction utilized by postsynaptic neurons when dopamine release is detected. The drugs to be ejected out of the iontophoresis barrels are selected on the basis of effects of prior microinjections (such as dopamine receptor antagonists). Once a significant behavioral effect is observed following the microinjection, iontophoresis pipettes with the same compounds are loaded for ejection in other animals. Under these conditions ongoing behavior is unaltered allowing for a detailed neurobiological dissection of the particular microanatomical domain during specific times of the behavioral sequence. Finally we can now provide causality between the two simultaneously recorded measures, by applying the above mentioned to animals amenable to optogenetic interrogation of dopaminergic pathways. We will show that dopamine sculpts cue-related patterned neuronal activity as well as the power of the NAc LFP during reward seeking.

Dr. Joseph Cheer graduated from Universidad de los Andes (Bogota, Colombia) with a B.S in Biology and Mathematics in 1996. He joined the Laboratory of Neurobiology and Experimental Microsurgery at the Colombian Neurology Foundation where he worked for 1 year investigating CNS regeneration using oncogene-tranfected cells and sciatic nerve co-grafts in motor cortex-lesioned animals. Joe received his Ph.D from The University of Nottingham (Neuroscience Section of the School of Biomedical Sciences) under the direction of Profs Charles Marsden and Dave Kendall and Dr Rob Mason. Dr. Cheer’s graduate research focused on the behavioral and electrophysiological effects of cannabinoids.

Dr. Cheer’s first postdoc (2000-2002) was spent in Sam Deadwyler’s laboratory (Wake Forest University School of Medicine) where he conducted research on multiple single-unit electrophysiology in freely moving organisms. Joe joined Mark Wightman’s lab as a post doc in fall 2002 at the University of North Carolina (Chapel Hill). There, he established the use of a microelectrode that allows for the simultaneous measurement of single-unit activity and dopamine release via fast-scan cyclic voltammetry.

Dr. Cheer is currently a tenured associate professor at the University of Maryland School of Medicine, where he directs several NIH and private foundation-funded graduate and undergraduate projects related to several neurophysiological and neurochemical aspects of endogenous cannabinoid signaling in intact systems.

Hosts: Professors Fow-Sen Choa () and Alan T. Sherman ()

Big Data/Data Science Programs at NSF

Chaitan Baru
Senior Advisor for Data Science, NSF CISE Directorate
10-11:00am Thursday 28 January 2016, ITE 459, UMBC
postponed

This talk will provide an overview of current programs and activities related to Big Data and Data Science at NSF, and also highlight NSF’s inter-agency engagements in this topic area. The talk will also discuss future directions for Data Science research, education, and infrastructure. Considering that Data Science is a rapidly emerging, evolving field and discipline, ample time will be provided for Q&A and discussions about where the field ought to be going, given what we know today.
Dr. Chaitan Baru is currently a senior advisor for data science in the Computer and Information Science and Engineering Directorate at the National Science Foundation. He is a Distinguished Scientist and Associate Director of Data Initiatives at the San Diego Supercomputer Center (SDSC), UC San Diego where he works on applied and applications-oriented research problems related to data management and data analytics.

Dr. Baru has participated in a number of “data cyberinfrastructure” initiatives, including as Principal Investigator of the OpenTopography project; Cyberinfrastructure Lead, Tropical Ecology, Assessment and Monitoring network; Co-Investigator of the Cyberinfrastructure for Comparative Effectiveness Research project; Member of the founding Senior Management Team of the National Ecologial Observatory Network and Co-PI of the NEON Cyberinfrastructure Testbed; Co-PI of the CUAHSI Hydrologic Information Systems; Director, NEES Cyberinfrastructure Center; PI/Project Director, Geosciences Network; and member of the How Much Information? project.

Baru leads the Advanced Cyberinfrastructure Development Group at SDSC and is also Director of the Center for Large-scale Data Systems research. Prior to joining SDSC in 1996, Baru was at IBM, where he led one of the development teams for DB2 Parallel Edition Version 1 and at the University of Michigan, where he served on the faculty of the EECS Department. He received his B.Tech in Electronics Engineering from the Indian Institute of Technology, Madras, and M.E. and Ph.D. in Electrical Engineering from the University of Florida, Gainesville.

Cyber Defense Lab
University of Maryland, Baltimore County

cMix: Anonymization by High-Performance Scalable Mixing

Farid Javani
Cyber Defense Lab, CSEE Dept., UMBC

11:15am-12:30pm Friday, 29 January 2016, ITE 231

cMix is a cryptographic protocol for mix networks that uses pre-computations of a group-homomorphic encryption function to avoid all real-time public-key operations by the senders, mix nodes, and receivers. Like other mix network protocols, cMix can enable an anonymity service that accepts inputs from senders and delivers them to an output buffer, in a way that the outputs are unlinkable to the inputs. cMix’s high-performance scalable architecture, which results from its unique pre-computation approach, makes it suitable for smartphone-to-smartphone use while maintaining full anonymity sets independently per round.

Each sender establishes a shared key separately with each of the mix nodes, which is used as a seed to a cryptographic pseudorandom number generator to generate a sequence of message keys. Each sender encrypts its input to cMix with modular multiplication by message keys. cMix works by replacing the message keys, which are not known in the pre-computation, in real time with a precomputed random value.

Our presentation includes a detailed specification of cMix and simulation-based security arguments. We also give performance analysis, both modeled and measured, of our working prototype currently running in the cloud.

cMix is the core technology underlying our larger PrivaTegrity system that allows smart devices to carry out a variety of applications anonymously (including sending and receiving chat messages), with little extra bandwidth or battery usage. This talk focuses on cMix.

Joint work with David Chaum (Voting Systems Institute), Aniket Kate (Purdue Univ.), Anna Krasnova (Radboud Univ.), Joeri de Ruiter (Univ. of Birmingham), Alan T. Sherman (UMBC).  See and recent articles in Wired and Fortune for discussion.

Favid Javani is a PhD student working with Dr. Sherman. He earned a MS from the Middle Eastern Technical University, Turkey, with a thesis on lattice-based cryptography.

Host: Alan T. Sherman,

“Alexa, get my coffee”:
Using the Amazon Echo in Research

Megan Zimmerman

10:30am Monday, 7 December 2015, ITE 346

The Amazon Echo is a remarkable example of language-controlled, user-centric technology, but also a great example of how far such devices have to go before they will fulfill the longstanding promise of intelligent assistance. In this talk, we will describe the Interactive Robotics and Language Lab‘s work with the Echo, with an emphasis on the practical aspects of getting it set up for development and adding new capabilities. We will demonstrate adding a simple new interaction, and then lead a brainstorming session on future research applications.

Megan Zimmerman is a UMBC undergrad majoring in computer science working on interpreting language about tasks at varying levels of abstraction, with a focus on interpreting abstract statements as possible task instructions in assistive technology.

The UMBC Cyber Defense Lab presents

Engineering Notes on Homomorphic
Private Information Retrieval

Russ Fink
Johns Hopkins University Applied Physics Lab

11:15am-12:30pm, Friday, 4 December 4 2015, ITE 231

For two years, we have been investigating applications of private information retrieval (PIR) using the additive homomorphic scheme designed by Paillier that forms the basis of a space efficient PIR system by Ostrovsky, Skeith, and Bethencourt. We have implemented a working prototype and gained some insights about the technique, and identified improvements to make it practical to real-world privacy problems. I will present an overview of the technique, present a real world use case, and discuss our technical contributions and ongoing challenges.

Dr. Russ Fink is Chief Engineer of the Enterprise Security Group at APL. He earned the PhD from UMBC with a dissertation on applying trustworthy computing to voting.

Host: Alan T. Sherman,

The UMBC CSEE Seminar Series Presents

Addressing Energy and Big Data
Challenges in Microgrids

Prof. Ting Zhu, UMBC

1-2pm Friday, Dec 4, 2015, ITE 325B

Buildings account for over 75% of the electricity consumption in the United States. To reduce electricity usage and peak demand, many utility companies are introducing market-based time-of-use (TOU) pricing models. In parallel, government programs that increase the fraction of renewable energy are incentivizing residential consumers to adopt on-site renewables and energy storage. Connecting on-site renewables and energy storage between homes forms a sustainable microgrid that is capable of generating, storing, and sharing electricity to balance local generation and consumption in residential areas. In this talk, I will present two pieces of our work in this area. The first work targets at minimizing the electricity cost from a utility company for a microgrid under different market-based TOU pricing models. This work is selected as the best paper runners up at BuildSys 2014. The goals of the second work are real-time energy data gathering, compression, and recovery based on unique features in the energy consumption patterns. In the end of the talk, I will also briefly introduce some of my latest work in indoor localization, networking, and smart health.

Ting Zhu is an assistant professor in the CSEE at UMBC. He received the Computing Innovation Fellowship in 2010. His papers have been selected in the best paper award finalist in multiple conferences (i.e., SenSys ’10, e-Energy ’13, and BuildSys ’14). He has a broad research interest in areas such as internet of things, energy, networking, systems, big data, and security. He is looking for undergraduate, Master and PhD students to work in the above areas.

Hosts: Professors Fow-Sen Choa () and Alan T. Sherman ()

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

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.

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,