🗣talk: Internet of Acoustic Things: Challenges, Opportunities & Threats, 10:30 3/28

Internet of Acoustic Things (IoAT):
Challenges, Opportunities, and Threats

Nirupam Roy, University of Illinois, Urbana-Champaign

10:30-11:30am Wed. 28 March 2018, ITE325b, UMBC

The recent proliferation of acoustic devices, ranging from voice assistants to wearable health monitors, is leading to a sensing ecosystem around us — referred to as the Internet of Acoustic Things or IoAT. My research focuses on developing hardware-software building blocks that enable new capabilities for this emerging future. In this talk, I will sample some of my projects. For instance, (1) I will demonstrate carefully designed sounds that are completely inaudible to humans but recordable by all microphones. (2) I will discuss our work with physical vibrations from mobile devices, and how they conduct through finger bones to enable new modalities of short range, human-centric communication. (3) Finally, I will draw attention to various acoustic leakages and threats that arrive with sensor-rich environments. I will conclude this talk with a glimpse of my ongoing and future projects targeting a stronger convergence of sensing, computing, and communications in tomorrow’s IoT, cyber-physical systems, and healthcare technologies.

Nirupam Roy is a Ph.D. candidate in Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign (UIUC). His research interests are in mobile sensing, wireless networking, and embedded systems with applications to IoT, cyber-physical-systems, and security. Roy is the recipient of the Valkenburg graduate research award, the Lalit Bahl fellowship, and the outstanding thesis awards from both his Bachelor’s and Master’s institutes. His recent research on “Making Microphones Hear Inaudible Sounds” received the MobiSys’17 best paper award and was selected for the ACM SIGMOBILE research highlights of the year in 2017.

🗣 talk: Addressing Real-world Societal Challenges: Advanced Game-Theoretic Models and Algorithms 3/29


Addressing Real-world Societal Challenges:
Advanced Game-Theoretic Models and Algorithms


Dr. Thanh H. Nguyen, University of Michigan

1:15-2:15 Thursday, 29 March 2018, ITE 325, UMBC

This talk will cover my research in AI, with a focus on Multi-Agent Systems, for solving real-world societal problems, particularly in the areas of Sustainability, Public Safety and Security, Cybersecurity, and Public Health. In these problems, strategic allocation of limited resources in an adversarial environment is an important challenge which involves complex human decision making, a variety of uncertainties, and exponential action spaces. I will present my research in developing advanced game-theoretic models and algorithms for tactical allocation decisions in these problems. In particular, I will outline three main contributions of my research: (i) learning new behavioral models of human decision-making for adversarial reasoning – I will discuss results from applying these models to both human subjects data from the lab and real-world data; (ii) developing robust game-theoretic algorithms, which handle a variety of uncertainties in security and are applied to domains in which data is not available to generate a prior distribution of uncertainties; and (iii) designing scalable game-theoretic algorithms, which address the challenge of exponential action and state spaces in complex cybersecurity problems. Finally, I will briefly introduce the real-world deployed application PAWS (Protection Assistant for Wildlife Security), which incorporates my models and algorithms, for wildlife protection.

Thanh Nguyen is a Postdoctoral Researcher in the Department of Computer Science & Engineering at the University of Michigan. She received her Ph.D. from the Department of Computer Science at the University of Southern California (USC) in Summer 2016. While at USC, she was part of the USC Center for Artificial Intelligence in Society. Her work in the area of Artificial Intelligence is motivated by real-world societal problems, particularly in the areas of Sustainability, Public Safety and Security, Cybersecurity, and Public Health. Her recent awards include the Deployed Application Award (IAAI 2016) and Runner-up of the Best Innovative Application Paper Award (AAMAS 2016). Thanh has published extensively in several leading conferences in Artificial Intelligence, including IJCAI, AAAI, AAMAS, and GameSec. She has contributed to build the real-world wildlife-protection application PAWS (Protection Assistant for Wildlife Security), which has been extensively used by NGOs in conservation areas in multiple countries.

🗣️ talk: Challenges and pitfalls in big data analysis

CHMPR Distinguished Lecture

Challenges and pitfalls in big data analysis

Yoav Benjamini, Tel Aviv University

3:30-5:00 Thursday, 12 April 2018, ITE 325b, UMBC

I shall warn about the pitfalls resulting from the false assurance that “we have all data at hand”, and discuss the challenges that are not commonly recognised such as the validity and replicability of the analysis results. Examples will be given from our work on the Health Informatics part of the European Human Brain Project, as well as from our studies in neuroscience and genomics.

Yoav Benjamini is the Nathan and Lily Silver Professor of Applied Statistics at the Department of statistics and operations research at Tel Aviv University. He holds B.Sc in physics and mathematics and M.Sc in mathematics from the Hebrew University (1976), and Ph.D in Statistics from Princeton University (1981). He is a member of the Sagol School of Neuroscience, and of the Edmond Safra Bioinformatics Center both at Tel Aviv University. He taught as a visiting professor at Wharton, UC Berkeley and Stanford and is currently visiting Columbia University. Prof. Benjamini is a co-developer of the widely used and cited False Discovery Rate concept and methodology. His current research topics are selective and simultaneous inference, replicability and reproducibility in science, model selection, and data mining. His applied research fields are Biostatistics, Bioinformatics, Animal Behavior and Brain Imaging, and as a member of the European Human Brain Project he is involved in health informatics research. Prof. Benjamini served as the president of the Israel Statistical Association, He received the Israel Prize for research in Statistics and Economics at 2012, and is an elected member of the Israel Academy of Sciences and Humanities.

🗣️talk: Computer Vision for Autonomous Underwater Vehicles, 11am Mon 3/12

Computer Vision for Autonomous Underwater Vehicles

Dr. David Chapman, Oceaneering International

11:00-12:00 Monday March 12, 2018, ITE 325, UMBC

Autonomous Underwater Vehicles (AUVs) are unmanned and unteathered submarine vehicles with a variety of applications from bathymetry survey to naval warfare. Attenuation and scattering of light and electromagnetic radiation through water severely restricts wireless communications as well as distorts and attenuates camera imagery. Bandwidth limitations prevent AUVs from being remotely piloted, thus full autonomy is required for operation. Computer vision extends the ability for AUVs to perform advanced behaviors, but must address the unique challenges of underwater photography, underwater lidar, and multibeam sonar sensors. We will discuss recent research and development efforts related to computer vision of AUVs as their applications, including oilfield pipeline survey and inspection, obstacle avoidance and autonomous docking. We will also briefly discuss efforts toward amphibious vehicles, AGVs for factory automation, as well as ongoing research in acoustic signal processing.

Dr. David Chapman is a Senior Software Engineer with Oceaneering International inc., which is the largest producer of subsea Remotely Operated Vehicles (ROVs) and largest operator of Autonomous Underwater Vehicles (AUVs). Dr. Chapman completed his Ph.D. from University of Maryland Baltimore County (UMBC) in 2012 studying remote sensing, image processing, and parallel computing. He also completed a post doctoral fellowship at Columbia University’s Lamont Doherty Earth Observatory studying data analytics for El Nino prediction. At Oceaneering, Dr. Chapman has been a key contributor to computer vision algorithms research for new product development including the Pipeline Inspection AUV (PI-AUV), winner of Oceaneering’s 2017 innovative product award. He is also a contributor to both the proposal and development efforts of a vision-based AUV auto-docking system. Dr. Chapman has studied and applied a variety of computer vision algorithms including the fast Radon transform, wavelet-based feature classification, numerical optimization, and neural networks in order to extend the capabilities of AUVs and related autonomous vehicles.

talk: Creating Educational Cybersecurity Assessment Tools, 12pm Fri 3/9

The UMBC Cyber Defense Lab presents

   Creating Educational Cybersecurity Assessment Tools

Alan T. Sherman
Department of Computer Science and Electrical Engineering
University of Maryland, Baltimore County

12:00–1:00pm Friday, March 9, 2018, ITE 229, UMBC

The Cybersecurity Assessment Tools (CATS) Project provides rigorous evidence-based instruments for assessing and evaluating educational practices. The first CAT will be a Cybersecurity Concept Inventory (CCI) that measures how well students understand basic concepts in cybersecurity (especially adversarial thinking) after a first course in the field. The second CAT will be a Cybersecurity Curriculum Assessment (CCA) that measures how well students understand core concepts after completing a full cybersecurity curriculum. These tools can help identify pedagogies and content that are effective in teaching cybersecurity.

In fall 2014, we carried out a Delphi process that identified core concepts of cybersecurity. In spring 2016, we interviewed twenty-six students to uncover their understandings and misconceptions about these concepts. In fall 2016, we generated our first assessment tool—-a draft CCI, comprising approximately thirty multiple-choice questions. Each question targets a concept; incorrect answers are based on observed misconceptions from the interviews. In fall 2017, we began drafting CCA questions. This year we are validating the draft CCI using cognitive interviews, expert reviews, and psychometric testing. In this talk, I highlight our progress to date in developing the CCI and CCA. Audience members will be given an opportunity to answer sample questions.

Presently there is no rigorous, research-based method for measuring the quality of cybersecurity instruction. Validated assessment tools are needed so that cybersecurity educators have trusted methods for discerning whether efforts to improve student preparation are successful.

Joint work with Linda Oliva, David DeLatte, Enis Golaszewski, Geet Parekh, Konstantinos Patsourakos, Dhananjay Phatak, Travis Scheponik (UMBC); Geoffrey Herman, Dong San Choi, Julia Thompson (University of Illinois at Urbana-Champaign)

Alan T. Sherman is a professor of computer science at UMBC in the CSEE Department and Director of UMBC’s Center for Information Security and Assurance. His main research interest is high-integrity voting systems. He has carried out research in election systems, algorithm design, cryptanalysis, theoretical foundations for cryptography, applications of cryptography, and cybersecurity education. Dr. Sherman is also an editor for Cryptologia and a private consultant performing security analyses. Sherman earned the PhD degree in computer science at MIT in 1987 studying under Ronald L. Rivest. www.csee.umbc.edu/~sherman

Support for this research was provided in part by the National Security Agency under grants H98230-15-1-0294 and H98230-15-1-0273 and by the National Science Foundation under SFS grant 1241576.

talk: desJardins on Planning and Learning in Complex Stochastic Domains, 1pm fri 3/8

UMBC ACM Student Chapter

Planning and Learning in Complex Stochastic Domains: AMDPs, Option Discovery, Learning Transfer, Language Learning, and More

Dr. Marie desJardins, University of Maryland, Baltimore County
1-2pm Friday, March 9th, 2018, ITE 456, UMBC

Robots acting in human-scale environments must plan under uncertainty in large state–action spaces and face constantly changing reward functions as requirements and goals change. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level “flat” MDP. AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct.

I will present empirical results in several domains showing significantly improved planning speed, while maintaining solution quality. I will also discuss related work within the same project on automated option discovery, abstraction construction, language learning, and initial steps towards automated methods for learning AMDPs from base MDPs, from teacher demonstrations, and from direct observations in the domain.

This work is collaborative research with Dr. Michael Littman and Dr. Stefanie Tellex of Brown University. Dr. James MacGlashan of SIFT and Dr. Smaranda Muresan of Columbia University collaborated on earlier stages of the project. The following UMBC students have also contributed to the project: Khalil Anderson, Tadewos Bellete, Michael Bishoff, Rose Carignan, Nick Haltemeyer, Nathaniel Lam, Matthew Landen, Keith McNamara, Stephanie Milani, Shane Parr (UMass), Shawn Squire, Tenji Tembo, Nicholay Topin, Puja Trivedi, and John Winder.

Dr. Marie desJardins is a Professor of Computer Science and the Associate Dean for Academic Affairs in the College of Engineering and Information Technology at the University of Maryland, Baltimore County. Prior to joining the faculty at UMBC in 2001, she was a Senior Computer Scientist in the AI Center at SRI International. Her research is in artificial intelligence, focusing on the areas of machine learning, multi-agent systems, planning, interactive AI techniques, information management, reasoning with uncertainty, and decision theory. She is active in the computer science education community, founded the Maryland Center for Computing Education, and leads the CS Matters in Maryland project to develop curriculum and train high school teachers to teach AP CS Principles.

Dr. desJardins has published over 125 scientific papers in journals, conferences, and workshops. She will be the IJCAI-20 Conference Chair, and has been an Associate Editor of the Journal of Artificial Intelligence Research and the Journal of Autonomous Agents and Multi-Agent Systems, a member of the editorial board of AI Magazine, and Program Co-chair for AAAI-13. She has previously served as AAAI Liaison to the Board of Directors of the Computing Research Association, Vice-Chair of ACM’s SIGART, and AAAI Councillor. She is a AAAI Fellow, an ACM Distinguished Member, a Member-at-Large for Section T (Information, Computing, and Communication) of the American Association for the Advancement of Science, the 2014-17 UMBC Presidential Teaching Professor, a member and former chair of UMBC’s Honors College Advisory Board, former chair of UMBC’s Faculty Affairs Committee, and a member of the advisory board of UMBC’s Center for Women in Technology.

talk: Circuit Complexity of One-Way Boolean Functions, 12pm Fri 2/23, ITE229

The UMBC Cyber Defense Lab presents

Experimentally Measuring the Circuit Complexity
of One-Way Boolean Functions

Brian Weber, CSEE, UMBC

12:00–1:00pm, Friday, 23 February 2018, ITE 229

I present preliminary results from an exhaustive search for one-way functions in certain classes of small Boolean functions.   One-way functions are functions that are easy to compute but hard to invert.  They are vital for cryptography, yet no one has proven their existence for arbitrary input sizes.  For any bounded circuit model of computation, it is possible to search exhaustively over all possible Boolean functions of restricted size and thereby determine for the searched class the maximum disparity between the complexity of any function and its inverse.  Throughout, we assume a circuit model in which each gate has fan-in 2 and fan-out 1.

In his 1985 dissertation at MIT, Steven Boyack carried out the first such search.  For any positive integers n and M, let Fn,M denote the set of Boolean functions with n inputs and Moutputs. Using circuit size as the complexity measure, Boyack searched the space of every combinatorial function in F3,3 by searching each of 52 equivalency classes of functions in this space.  He found that every function class in this space has an identically sized inverse.  He was able to prove that functions do exist with more complex inverses outside the space he searched, but not by more than a constant factor.

In spring 2017, using circuit depth as the complexity measure, I searched all injective functions up to F8,8 whose coordinate functions are in F2,1.  A coordinate function in this context refers to the function that computes an individual output bit.  In addition, I searched up to F4,4 allowing coordinate functions in F3,1.  In the space I searched, the most one-way function has fixed depth of 1, and an inverse depth exactly equal to the input size of the function. That is, for each 2 < n < 9, the hardest inverse in the space I searched has a depth of n, where n is the number of input bits. In addition, a search space allowing a larger fan-in for the coordinate functions did not yield functions less invertible than were found in the original search space.

Brian Weber is a senior BS/MS computer engineering student and SFS scholar at UMBC.  He hopes to extend the work presented here into his Master’s thesis next year.  Email: 

Host: Alan T.  Sherman, Support for this research was provided in part by the National Science Foundation under SFS grant 1241576.

The UMBC Cyber Defense Lab meets biweekly Fridays.  All meetings are open to the public.

talk: Semi-supervised Learning for Visual Recognition, 1pm Fri 2/23, ITE325, UMBC

ACM Faculty Talk Series

Semi-supervised Learning for Visual Recognition

Dr. Hamed Pirsiavash, Assistant Professor, CSEE

1:00-2:00pm Friday, February 23, 2018, ITE 325, UMBC

We are interested in learning representations (features) that are discriminative for semantic image understanding tasks such as object classification, detection, and segmentation in images. A common approach to obtain such features is to use supervised learning. However, this requires manual annotation of images, which is costly, time-consuming, and prone to errors. In contrast, unsupervised or self-supervised feature learning methods exploiting unlabeled data can be much more scalable and flexible. I will present some of our efforts in this direction.

Hamed Pirsiavash is an assistant professor at the University of Maryland, Baltimore County (UMBC). Prior to joining UMBC in 2015 he was a postdoctoral research associate at MIT and he obtained his PhD at the University of California Irvine. He does research in the intersection of computer vision and machine learning.

This talk is sponsored by the UMBC Student Chapter of the ACM. Contact with any questions regarding this event.

talk: Towards Hardware Cybersecurity, 11am Tue 2/20, ITE325, UMBC

hardware cybersecurity

Towards Hardware Cybersecurity

Professor Houman Homayoun
George Mason University

11:00am-12:00pm Tuesday, 20 Febuary 2018, ITE 325, UMBC

Electronic system security, trust and reliability has become an increasingly critical area of concern for modern society. Secure hardware systems, platforms, as well as supply chains are critical to industry and government sectors such as national defense, healthcare, transportation, and finance.

Traditionally, authenticity and integrity of data has been protected with various security protocol at the software level with the underlying hardware assumed to be secure, and reliable. This assumption however is no longer true with an increasing number of attacks reported on the hardware. Counterfeiting electronic components, inserting hardware trojans, and cloning integrated circuits are just few out of many malicious byproducts of hardware vulnerabilities, which need to be urgently addressed.

In the first part of this talk I will address the security and vulnerability challenges in the horizontal integrated hardware development process. I will then present the concept of hybrid spin-transfer torque CMOS look up table based design which is our latest effort on developing a cost-effective solution to prevent physical reverse engineering attacks.

In the second part of my talk I will present how information at the hardware level can be used to address some of the major challenges of software security vulnerabilities monitoring and detection methods. I will first discuss these challenges and will then show how the use of data at the hardware architecture level in combination with an effective machine learning based predictor helps protecting systems against various classes of hardware vulnerability attacks.

I will conclude the talk by emphasizing the importance of this emerging area and proposing a research agenda for the future.

Dr. Houman Homayoun is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. He also holds a courtesy appointment with the Department of Computer Science as well as Information Science and Technology Department. He is the director of GMU’s Accelerated, Secure, and Energy-Efficient Computing Laboratory (ASEEC).  Prior to joining GMU, Houman spent two years at the University of California, San Diego, as NSF Computing Innovation (CI) Fellow awarded by the CRA-CCC. Houman graduated in 2010 from University of California, Irvine with a Ph.D. in Computer Science. He was a recipient of the four-year University of California, Irvine Computer Science Department chair fellowship. Houman received the MS degree in computer engineering in 2005 from University of Victoria and BS degree in electrical engineering in 2003 from Sharif University of Technology. Houman conducts research in hardware security and trust, big data computing, and heterogeneous computing, where he has published more than 80 technical papers in the prestigious conferences and journals on the subject. Since 2012 he leads ten research projects, a total of $7.2 million in funding, supported by DARPA, AFRL, NSF, NIST, and GM on the topics of hardware security and trust, big data computing, heterogeneous architectures, and biomedical computing. Houman received the 2016 GLSVLSI conference best paper award for developing a manycore accelerator for wearable biomedical computing. Since 2017 he has been serving as an Associate Editor of IEEE Transactions on VLSI. He is currently serving as technical program co-chair of 2018 GLSVLSI conference.

talk: Nonnegative Binary Matrix Factorization on a D-Wave Quantum Annealer, 1:30 2/15


CHMPR Distinguished Lecture Series

Nonnegative Binary Matrix Factorization
with a D-Wave Quantum Annealer

Dr. Daniel O’Malley
Los Alamos National Laboratory

1:30 15 February 2018, ITE325, UMBC


D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output — one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave is mixed. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.

Daniel O’Malley is a scientist in the Computational Earth Science group at Los Alamos National Laboratory (LANL). Prior to that, he held postdoctoral positions at LANL and in the Department of Earth, Atmospheric and Planetary Sciences at Purdue University. He studied at Purdue University, receiving a B.S. degree in computer science and mathematics (2004), an M.S. in mathematics (2006) and a Ph.D. in applied mathematics (2011). His research interests include computational science (with an emphasis on subsurface flow and transport), quantum computing, uncertainty quantification, and machine learning. He has won numerous awards including a Director’s Postdoctoral Fellowship from LANL (2014), the InterPore-Fraunhofer Award for Young Researchers from the International Society for Porous Media (2012), a Charles C. Chappelle Fellowship from Purdue University (2004), and the Meyer E. Jerison Memorial Award in Analysis from the Department of Mathematics at Purdue University (2004).

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