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

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

Translational Bioinformatics Approaches to Evaluate
and Implement Genomic Medicine Programs

Dr. Casey Overby, Assistant Professor

Program for Personalized and Genomic Medicine
University of Maryland – Baltimore

1:00pm Friday, 25 April 2014, ITE 325b, UMBC

There is a growing evidence base to support the use of many genomic applications in healthcare. There are, however, several barriers to healthcare providers making use of genomic data and information on a routine basis. In this talk, I will describe some of our challenges and successes with implementing genomic medicine programs within the Program for Personalized and Genomic Medicine at UMB, introduce one way to conceptualize translational research and translational bioinformatics in this context, describe a proposed model for evaluating and implementing genomic medicine programs, and describe some of my current and planned research in translational bioinformatics.

Casey L. Overby is an Assistant Professor of Medicine in the Program for Personalized and Genomic Medicine and the Center for Health-related Informatics and Bio-Imaging at the University of Maryland School of Medicine. She received her Masters of Biotechnology from the University of Pennsylvania in 2006, her PhD in Biomedical and Health Informatics and a Graduate Certificate in Public Health Genetics from the University of Washington in 2011. In 2013, she completed her post-doctoral training in the Department of Biomedical Informatics at Columbia University and started her position at University of Maryland, Baltimore.

Host: Marie desJardins, Sorry, you need javascript to view this email address.

Translational Bioinformatics Approaches to Evaluate
and Implement Genomic Medicine Programs

Dr. Casey Overby, Assistant Professor

Program for Personalized and Genomic Medicine
University of Maryland – Baltimore

1:00pm Friday, 25 April 2014, ITE 325b, UMBC

There is a growing evidence base to support the use of many genomic applications in healthcare. There are, however, several barriers to healthcare providers making use of genomic data and information on a routine basis. In this talk, I will describe some of our challenges and successes with implementing genomic medicine programs within the Program for Personalized and Genomic Medicine at UMB, introduce one way to conceptualize translational research and translational bioinformatics in this context, describe a proposed model for evaluating and implementing genomic medicine programs, and describe some of my current and planned research in translational bioinformatics.

Casey L. Overby is an Assistant Professor of Medicine in the Program for Personalized and Genomic Medicine and the Center for Health-related Informatics and Bio-Imaging at the University of Maryland School of Medicine. She received her Masters of Biotechnology from the University of Pennsylvania in 2006, her PhD in Biomedical and Health Informatics and a Graduate Certificate in Public Health Genetics from the University of Washington in 2011. In 2013, she completed her post-doctoral training in the Department of Biomedical Informatics at Columbia University and started her position at University of Maryland, Baltimore.

Host: Marie desJardins, Sorry, you need javascript to view this email address.

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

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).

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”.