We were happy to see that ACM is using the Helios online voting system for a number its elections this year, including the 2014 ACM Council election. ACM members, all 100,000 of them, have the option of voting online via the Web or requesting a paper ballot in the election of ACM’s top officers. This demonstrates the confidence that the “world’s largest educational and scientific computing society” has in the technology of online verifiable voting systems.

Helios is an example of an end-to-end verifiable voting system that uses cryptographic techniques that can provide ballot privacy as well as high confidence that errors and fraud will be detected and that the election outcome is correct.  Such systems let voters verify that their votes were not modified and were counted without revealing which candidates were voted for. In some cases, they allow anyone to determine that all of an election’s ballots have been correctly counted and also help prevent coercion and vote selling by making it impossible for a voter to prove how she voted to a third party.

Among the things we like about Helios is that it provides a free service that anyone can use to hold end-to-end verifiable votes on the Web and that its code is open sourced, allowing one to study the (mostly Python)  code and install and run it on their own computers.

Developing verifiable voting systems has been one of the research activities of UMBC’s Center for Information Security and Assurance for more than six years. Professor Alan Sherman and his students contributed to Scantegrity, the first end-to-end verifiable voting system used in a binding municipal election.  The UMBC team oversaw that first use in the Takoma Park, Maryland municipal election in November, 2009.   A subsequent system, Remotegrity, was used to allow Takoma Park residents to submit absentee ballots over the Internet in the November 2011 Takoma Park election.  A current secure voting project in Professor Sherman’s lab is led by Ph.D. student Christopher Nguyen, who is developing techniques to support random-sample elections.

MS Thesis Defense

Learning Hierarchical Workflows Using Community Detection

Akshay Peshave

1:00pm Friday, 18 April 2014, ITE 325b

Workflows identified from user event logs and click-stream data are useful as knowledge bases for behavioral analysis and recommendation systems. In this study we identify abstractions or summaries of event logs modeled as user activity flow networks. The abstractions are identified based on structural properties as well as user activity flow dynamics over the network using community detection methods. We apply a fast modularity optimization and multi-level resolution approach to detect hierarchical community structure in user activity flow networks. The detected communities are compared to those detected by the information-theoretic map equation minimization approach to weigh pros and cons of the fast modularity optimization approach in the workflows context. We further attempt to identify the most probable sources and sinks of user activity in individual communities and trim the network accordingly to reduce entropy of the workflow abstractions.

Committee: Drs. Tim Oates (chair), Matt Schmill and Tim Finin

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

A Rapidly Deployable Image Classification System Using Feature Views

Adrian Rosebrock

9:00am Friday, 18 April 2014, ITE 346, UMBC

Constructing an image classification system using strong, local invariant descriptors is both time consuming and tedious, requiring much experimentation and parameter tunings to obtain an adequate performing model. Furthermore, training a system in a given domain and then migrating the model to a separate domain will likely yield poor performance. As the recent Boston Marathon attacks demonstrated, large, unstructured image databases from traffic cameras, security systems, law enforcement officials, and citizens can be quickly amassed for authorities to review; however, reviewing each and every image is an expensive undertaking, in terms of both time and human effort. Inherently, reviewing crime scene images is a classification task. For example, authorities may want to know if a given image contains a suspect, a suspicious package, or if there are injured people in the photo. Given an emergency situation, these classifications will be needed as quickly and accurately as possible. In this work we present a rapidly deployable image classification system using “feature views”, where each view consists of a set of weak, global features. These weak global descriptors are computationally simple to extract, intuitive to understand, and require substantially less parameter tuning than their local invariant counterparts. We demonstrate that by combining weak features with ensemble methods we are able to outperform current state-of-the-art methods or achieve comparable accuracy with much less effort and domain knowledge. We then provide both theoretical and empirical justifications for our ensemble framework that can be used to construct rapidly deployable image classification systems called “Ecosembles”.

Finally, we recognize the fact that image datasets give us the relatively unique opportunity to extract multiple feature representations through the use of various descriptors. In situations where the original dataset is not available for further feature extraction or in cases where multiple feature views are ambiguous (such as predicting income based on geographical location and census data) the Ecosemble method cannot be applied. In order to extend Ecosembles to arbitrary datasets of diverse modalities, we introduce artificial feature views using kernel approximations. These artificial feature views are constructed from a single representation of the data, alleviating the need to explicitly extract multiple feature views. We then apply artificial feature views to a diverse range of non-image classification datasets to demonstrate our method is applicable to multiple modalities, while still outperforming current state-of-the-art methods.

Committee: Drs. Tim Oates (chair), Jesus Caban, Tim Finin, Charles Nicholas, Jian Chen

UMBC’s ACM student chapter invites nominations from the graduate students in the CSEE department for student officer positions for academic year 2014 – 2015 (Fall 2014 to Spring 2015).

ACM (the Association for Computing Machinery) is a premier organization that promotes computing and technology around the US and the world. On the campus, the ACM student chapter is affiliated and supported by the UMBC graduate students association. The goal of the ACM student chapter is to foster interaction between all students, both graduate and undergraduate, in the CSEE department, provide a forum for student interaction, and opportunities for members to expand their knowledge of computing.

The positions available (and their general responsibilities):

These positions are open to graduate students only. If elected, you would be required to signup as an ACM student member. Membership fee is $19 only.

Please email us the position you would like to run for (there will be elections if we get multiple nominations for a position). Alternatively you can nominate any other person for the positions above. In that case, please send their name, email address and which position you would like to nominate them for.

Please send in your nominations by end of day, Monday, April 14, 2014 to acmofficers at lists dot umbc dot edu. Elections will take place the following week (venue, date and time to be announced later).

A multi-scale approach to analyze large clinical datasets:
Towards the understanding of the complex effects of concussions

Dr. Jesus Caban
National Intrepid Center of Excellence
Walter Reed, Bethesda, MD

Noon Thursday, 10 April 2014, ITE325b

Mild traumatic brain injuries (mTBIs) or concussions are invisible injuries that are poorly understood and their sequelae can be difficult to diagnose. Individuals who have had concussions are at an increased risk of depression, post-traumatic stress disorder (PTSD), headaches, concentration difficulties, and other problems. During the last decade, a significant amount of attention has been given to the acquisition of clinical data from patients suffering from mTBI. Unfortunately, most of the data collection and analysis have focused on individual aspects of the injury, not necessarily on comprehensive and multi-modal analytical techniques to capture the complex biological state of mTBI patients.

This talk will discuss a large-scale informatics database that has been developed to enable interdisciplinary research on mTBI and will introduce a multi-scale approach to mine complex clinical datasets. The millions of multi-modal elements originated from different clinical disciplines are treated as weak features and modeled independently to generate stronger features. Three cases of going from weak to stronger features will be discussed including (a) an inductive/transductive model to extract stable image features from multi-modal MRI scans, (b) a rule-based model used to infer knowledge from blood measurements, and (c) a sentiment analysis-based model to extract behavioral signals from writing samples. Once stronger features are obtained, a relational model is used to integrate the data and extract new knowledge from such a complex dataset.

Dr. Caban is the Acting Chief of Clinical & Research Informatics at the National Intrepid Center of Excellence (NICoE) at Walter Reed Bethesda. He received a Ph.D. in Computer Science from UMBC (2009), his M.S. degree in Computer Science from the University of Kentucky (2005), and his B.S. in Computer Science from the University of Puerto Rico (2002). Over the last eight years Dr. Caban’s research has focused on the design and development of techniques to analyze clinical and imaging data. His research and experience has given him the opportunity to work at top research and healthcare organizations including the National Institutes of Health (NIH), John Hopkins University, the University of Maryland Medical Center, and IBM Research. Dr. Caban is presently an adjunct faculty member at John Hopkins University Applied Physics Lab and a part-time instructor at the Department of Computer Science at UMBC. Recently, he received the 2013-14 JHU/APL Junior faculty award for his commitment to teaching. Currently, he is serving as the Associate Editor of the JAMIA special issue on Visual Analytics in Healthcare and as the contracting officer representative (COR) for the DoD program on “Watson-Like Technologies for TBI/PTSD Clinical Decision Support and Predictive Analytics”.

Talking to Robots: Learning to Ground Human
Language in Robotic Perception

Cynthia Matuszek
University of Washington

1:00pm Monday, 7 April 2014, ITE325b, UMBC

Advances in computation, sensing, and hardware are enabling robots to perform an increasing variety of tasks in ever less constrained settings. It is now possible to imagine robots that can operate in traditionally human-centric settings. However, such robots need the flexibility to take instructions and learn about tasks from nonspecialists using language and other natural modalities. At the same time, learning to process natural language about the physical world is difficult without a robot’s sensors and actuators. Combining these areas to create useful robotic systems is a fundamentally multidisciplinary problem, requiring advances in natural language processing, machine learning, robotics, and human-robot interaction. In this talk, I describe my work on learning natural language from end users in a physical context; such language allows a person to communicate their needs in a natural, unscripted way. I demonstrate that this approach can enable a robot to follow directions, learn about novel objects in the world, and perform simple tasks such as navigating an unfamiliar map or putting away objects.

Cynthia Matuszek is a Ph.D. candidate in the University of Washington Computer Science and Engineering department, where she is a member of both the Robotics and State Estimation lab and the Language, Interaction, and Learning group. She earned a B.S. in Computer Science from the University of Texas at Austin, and M.Sc. from the University of Washington. She is published in the areas of artificial intelligence, robotics, ubiquitous computing, and human-robot interaction.

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Making Physical Inferences to Enhance Wireless Security

Prof. Jie Yang, Oakland University

1:00pm Tuesday, 8 April 2014, ITE 325b

The ubiquity of wireless is redefining security challenges as the increasingly pervasive wireless networks make it easier to conduct attacks for new and rapidly evolving adversaries. There is an urgent need to seek security solutions that can be built into any wireless network stack to defend against attacks across the current heterogeneous mix of wireless technologies, which do not require extensive customization on wireless devices and cannot be undermined easily even when nodes are compromised. In particular, security solutions that are generic across all wireless technologies and can complement conventional security methods must be devised. My research efforts are centered around exploiting physical properties correlated with pervasive wireless environments to enhance wireless security and make inferences for context-aware applications. In this talk, I will present my research work in exploiting spatial correlation as a unique physical property inherited from any wireless device to address identity-based attacks including both spoofing and Sybil. These attacks are especially harmful as the claimed identity of a wireless device is often considered as an important first step in an adversary’s attempt to launch a variety of attacks in different network layers.

Our proposed techniques address several challenges include (1) detecting identity-based attacks in challenging mobile environments, (2) determining the number of attackers, and (3) localizing multiple adversaries. I will also present our work in secret key generation for facilitating secure data communication in the increasing dynamic wireless environments. Our work addressed the problem of collaborative secret key extraction for a group of wireless devices without relying on a key distribution infrastructure. Moreover, in order to provide efficient secret key generation, we exploit fine-grained physical layer information, such as the channel state information made available from OFDM system, to improve the secret key generation rate and make the secret key extraction approach more practical.

Jie Yang received his Ph.D. degree in Computer Engineering from Stevens Institute of Technology in 2011. He is currently an assistant professor in the Department of Computer Science and Engineering at Oakland University. His research interests include cyber security and privacy, and mobile and pervasive computing, with an emphasis on network security, smartphone security and applications, security in cognitive radio and smart grid, location systems and vehicular applications. His research is supported by National Science Foundation and Army Research Office. He is the recipient of the Best Paper Runner-up Award from IEEE Conference on Communications and Network Security 2013 and the Best Paper Award from ACM MobiCom 2011. His research has received wide press coverage including MIT Technology Review, The Wall Street Journal, NPR, CNET News, and Yahoo News.

Hosts: Nilanjan Banerjee and Tim Finin

The UMBC Graduate Cybersecurity Program is offering the following courses over the Summer 2014 session.

Each class will meet one or two days a week in the late afternoon or evening, depending on the length of the session where the course is offered.

• CYBR 620: Introduction to Cybersecurity (Parr)
  T/TH 6-8:45PM
  Summer I (6 weeks) May 27th-July 3rd, 2014
• CYBR 691:  Special Topics in Cybersecurity: “Software Security” (Coman)
  M/W 7:10-9:40PM
  Summer I (6 weeks) May 27th-July 3rd, 2014
  Pre-Req: CYBR 620 or equivalent

Offered to students enrolled at the Universities at Shady Grove campus ONLY

• CYBR 691: Special Topics in Cybersecurity: “Cybersecurity Risk Management” (Shariati)
  M/W 7:10-9:40PM
  Summer I (6 weeks) May 27th-July 3rd, 2014
  PreReq: CYBR 620 and enrollment at the Universities at Shady Grove

The deadline to apply for Fall 2014 admission to the UMBC Graduate Cybersecurity Program is August 1, 2014.

Computer Engineering graduate student Alexander Nelson received an honorable mention for the National Science Foundation (NSF) Graduate fellowship. Alex’s research is focused on developing innovative cyber-physical systems that can dramatically improve a person’s standard of living in an impactful way. His current and past projects have involved emergency communications, assistive devices and home automation.

His prior research in assistive devices has gained acceptance to the 2013 IEEE Sensors Conference and 2013 Real Time Systems Symposium and contributed to a Microsoft Software Engineering Innovation Foundation award. His current research gained acceptance to a Works-in-Progress session at the 2013 Real Time Systems Symposium. His research co-mentors are Professors Nilanjan Banerjee and Ryan Robucci.

The NSF honorable mention designation is considered a significant national academic achievement and provides access to cyberinfrastructure resources through NSF’s Extreme Science and Engineering Discovery Environment (XSEDE) computing infrastruture. XSEDE is the most advanced, powerful, and robust collection of integrated advanced digital resources and services in the world. It is a single virtual system that scientists can use to interactively share computing resources, data, and expertise.

Center for Hybrid Multicore Productivity Research
Distinguished Computational Science Lecture Series

Image Registration for Multisource Remote Sensing

Dr. Jacqueline Le Moigne
NASA Goddard Space Flight Center
Software Engineering Division – Code 580

3:00pm Thursday, 3 April 2014, ITE 456, UMBC

Satellite remote sensing systems provide large amounts of global coverage and repetitive measurements representing simultaneous or multi-temporal observations of the same features by different sensors; for example over the last 40 years, Landsat satellites have been acquiring more than three million images representing about one petabyte of data. Furthermore, most sensors are carried on separate platforms, resulting in a tremendous amount of data that must be combined. In meeting some of the Earth System Science objectives, the combination of all these data at various resolutions — spatial, radiometric and temporal — will facilitate a better understanding of Earth and space science phenomena, and image registration enables the first step towards this integration.

In this talk, we will describe the image registration challenge in the context of Earth and space remote sensing. Then, we will review a subset of the methods that are being utilized to tackle this challenge, and finally we will describe some of our work that utilizes multiscale representations, in particular wavelets and over-complete representations, as well as more recent work dealing with the registration of Martian data based on crater extraction and matching.

Dr. Le Moigne is the Assistant Chief for Technology in the Software Engineering Division at NASA Goddard, and was Goddard Center Associate for ESTO/Advanced Information Systems Technology Program, from 2009 to 2012. Dr. Le Moigne received a B.S. and a M.S. in Mathematics, and a Ph.D. in Computer Science from the University Pierre and Marie Curie, France. While her Ph.D. thesis dealt with biomedical imagery, her post-doctoral work at the UMD Computer Vision Lab focused on the development of a visual navigation system for the first DARPA Autonomous Land Vehicle project. At NASA Goddard since 1990, Dr. Le Moigne has performed extensive work in the processing and the analysis of remote sensing data. Her work particularly focuses on image registration, utilizing multiscale representations as well as high-performance and on-board processing. More recent work dealt with creating web-based access to benchmark data for Image Processing education and research (imageseer.nasa.gov). Currently, Dr. Le Moigne is the PI of a Goddard Internal project focused on Distributed Spacecraft Missions. She has published over 120 publications, including 23 journal papers, holds one patent, and has co-edited a book on “Image Registration for Remote Sensing” published by Cambridge University Press in 2011. She is a NASA Goddard Senior Fellow, an IEEE Senior Member and an ABET Program Evaluator. She was a NATO Science for Peace and Security Committee Panel Member from 2008 to 2011. In 2012, she received the NASA Exceptional Service Medal and the Goddard Information Science and Technology Award.