Department of Computer Science and Electrical Engineering

The nation's demand for skilled cybersecurity professionals continues to rise. The UMBC Cybersecurity Graduate Program will hold an information session from 6:00pm to 7:30pm on Thursday 21 March in room 102 of the Information Technology and Engineering Building (ITE). Participants will learn how our masters and certificate programs can help you get started or advance in this thriving industry, meet the Graduate Program Director and learn more about our program’s curriculum and flexible class schedules. We are now accepting applications for Fall 2013 with a deadline of 1 August, 2013. RSVP for the Cybersecurity information session online to reserve a seat.

UMBC is certified as a Center for Academic Excellence in Information Assurance Education (CAE) as well as a Center of Academic Excellence in Research (CAE-R) sponsored by the National Security Agency and Department of Homeland Security (DHS). View or download our fact sheet for a summary of the cybersecurity programs.

UMBC Computer Science and Electrical Engineering
Ph.D. Dissertation Proposal

Paper form digitization for information systems strengthening and socio-economic development in developing countries

Huguens Jean

3:00pm Tuesday, 5 March 2013, ITE346, UMBC

In developing countries, people are now more likely to have access to a mobile phone than clean water, making cellular based technology the only viable medium for collecting, aggregating, and communicating local data so that it can be turned into useful information. While mobile phones have found broad application in reporting health, financial, and environmental data, many data collection methods still suffer from delays, inefficiency and difficulties maintaining quality. In environments with insufficient IT support and infrastructure, and among populations with limited education and experience with technology, paper forms rather than electronic methods remain the predominant means for data collection. To meet the digitization needs of paper driven data collection practices, this thesis proposes the development and study of a software platform that automatically converts unknown paper forms into digital structured data and uses human intelligence when necessary to improve its performance.

We begin by identifying a high-level system architecture for dealing with infrastructure constraints and human resources limitations. We then break the architecture into its integral pieces and organize them into three distinct functional and interacting stages: data collection, data conversion, and crowdsourcing. In the collection phase, we focus on visually detecting structurally identical form instances and transmitting the images of their raw input data to a remote server. During this phase, we present a novel framework for identifying specific form types by generating a multipart template for unknown forms and decomposing the form identification problem into three distinct tasks: similar image retrieval, learning, and duplicate matching. The conversion phase uses a mixture of Optical Character Recognition (OCR) and human annotations techniques to convert images into digital information and group structurally identical forms in their respective database table. In crowdsourcing, we investigates how to use low-end smartphones for collecting training information to improve OCR related tasks and verify the accuracy of converted input values. We pay special emphasis on identifying natural interaction forms that lower the technical and knowledge threshold for local residents. Furthermore, because crowdsourcing can also provide money to the mobile workers of its micro-tasking platform, we concurrently explore how systems that facilitate collaboration between humans and machines for improving the quality of intelligent information systems can be used a vehicle for delivering socioeconomic opportunities to developing countries.

Committee: Dr. Timothy Oates (Chair), Dr. Janet Rutledge, Dr. Fow-Sen Choa, Dr. Jesus Caban

Twenty-three CSEE graduate students will present their research at UMBC's 35th Annual Graduate Research Conference (GRC) on Wednesday 20 February 2013. Oral and poster presentations will take place between 9:00am and 12:30pm in the Commons and University Center. There will be a lunch at 1:00pm in the UC Ballroom followed by a panel on civic engagement from 1:30 to 2:15 in which UMBC graduate students, faculty and administrators will discuss activities fostering civic engagement, including graduate level courses with a community engagement component, GSA's Food for Thought project, and Dr. Shaun Kane's Accessibility Hack Day.

Here are the presentations from CSEE students.

09:00am-10:30am Oral Presentations I (Commons 329)

• Sumeet Bagde, Iterative quantum algorithms
• Jared Dixon, Laser Photothermal Therapy Using Gold Nanorods
• Ben Johnson, A Reference Advisor to an Automatic Text Understanding Engine
• Yatish Kumar Joshi, Autonomous Recovery from Multi-node Failure in Wireless Sensor Networks
• Lisa Mathews, A Collaborative Approach to Situational

9:00am – 10:00am, Poster Presentation I (University Center 312)

• Shihyu Chen, Weighted Radial Basis Function Kernels- Based Support Vector Machines for Multispectral Magnetic Resonance Brain Image Classification
• Prajit Das, Energy efficient semantic context model for managing privacy on smartphones
• Deepal Dhariwal, Text and Ontology driven Information Extraction from Clinical Narratives
• Roshan Ghumare, Distributed Average Consensus in WSN
• Clare Grasso, Identifying Safety Risks Due to Medical Treatment in Patients with Chronic Kidney Disease using KDD
• Neha Sardesai, Develop a System Analysis Model for Trace Gas Detection using a Pulsed Laser and QEPAS
• Puneet Sharma, A Cross-Layer Approach to Detection of Hardware Trojans
• Jennifer Sleeman, Online Unsupervised Coreference Resolution for Semi- Structured Heterogeneous Data
• Shiming Yang, An Adaptive Observation Site Selection Strategy for Road Traffic Data Assimilation

11:00am – 12:15pm, Oral Presentations II (Commons 329)

• Randy Schauer, Reducing Thermal Impact using Probabilistic Energy-Aware Job Scheduling
• Jon Ward, On the Use of Distributed Relays to Increase Base Station Anonymity in Wireless Sensor Networks
• Fahad Zafar, Computational Observer Approach for Assessment of Stereoscopic Visualizations in 3D Medical Data Sets
• Guohao Zhang, Is More Realism Better? Towards Finding the Effectiveness of Visual Realism on Three-Dimensional Streamtube Visualization

11:00am – 12:00pm, Poster Presentation II (University Center 312)

• Arnav Joshi, Generating a linked data resource for software security concepts and vulnerability descriptions
• Vlad Korolev,Machine Learning Methods for Assessment of Risk of Chronic Disease
• Sandhya Krishnan, Social Media Analytics : Digital Footprints
• Ravendar Lal, Information Extraction of Security related entities, concepts and relations from unstructured text
• Varish Mulwad, Exploiting Semantics in Graphical Models for Generating Linked Data from Tables

UMBC alumnus Professor Eric Eaton (BS '03, PhD '09) has positions for undergraduate and graduate summer research internships in Artificial Intelligence and Machine Learning at Bryn Mawr College in suburban Philadelphia. Apply by March 1, 2013 for full consideration.

Spend ten weeks of your summer working on exciting projects in artificial intelligence and machine learning at Bryn Mawr College! We have openings for several undergraduate or graduate research assistants to work on two grant-sponsored research projects this summer. Student participants will join a research team with other students, Prof. Eric Eaton, and one postdoctoral researcher to carry out a detailed program of research toward scholarly publications. Students will present the results of their research during the final week of the program at Bryn Mawr College, and (if appropriate) at their home institutions and/or other academic venues, such as research conferences.

All students who are beginning their junior or senior undergraduate year in Fall 2013 or who will graduate during the Spring 2013 semester, and all graduate students are eligible to apply. To be considered, you should have a background in either computer science, mathematics, physics, or statistics and have strong grades in your major. Although it is not required, it would be beneficial if you have taken and done well in at least one course related to artificial intelligence, machine learning, robotics, statistics, or topology.

On-campus housing and meals are available for student participants, along with a variety of professional development workshops and summer activities. Application instructions and further details are available online.

Ph.D. Defense

Multi-Source Option-Based Policy Transfer

James MacGlashan

10:00am Friday, 25 January 2013, ITE 325B

Reinforcement learning algorithms are very effective at learning policies (mappings from states to actions) for specific well defined tasks, thereby allowing an agent to learn how to behave without extensive deliberation.  However, if an agent must complete a novel variant of a task that is similar to, but not exactly the same as, a previous version for which it has already learned a policy, learning must begin anew and there is no benefit to having previously learned anything. To address this challenge, I introduce novel approaches for policy transfer. Policy transfer allows the agent to follow the policy of a previously solved, but different, task (called a source task) while it is learning a new task (called a target task). Specifically, I introduce option-based policy transfer (OPT). OPT enables policy transfer by encapsulating the policy for a source task in an option (Sutton, Precup, & Singh 1999), which allows the agent to treat the policy of a source task as if it were a primitive action. A significant advantage of this approach is that if there are multiple source tasks, an option can be created for each of them, thereby enabling the agent to transfer knowledge from multiple sources and to combine their knowledge in useful ways. Moreover, this approach allows the agent to learn in which states of the world each source task is most applicable. OPT's approach to constructing and learning with options that represent source tasks allows OPT to greatly outperform existing policy transfer approaches. Additionally, OPT can utilize source tasks that other forms of transfer learning for reinforcement learning cannot.

Challenges for policy transfer include identifying sets of source tasks that would be useful for a target task and providing mappings between the state and action spaces of source and target tasks. That is, it may not be useful to transfer from all previously solved source tasks. If a source task has a different state or action space than the target task, then a mapping between these spaces must be provided. To address these challenges, I introduce object-oriented OPT (OO-OPT), which leverages object-oriented MDP (OO-MDP) (Diuk, Cohen, & Littman 2008) state representations to automatically detect related tasks and redundant source tasks, and to provide multiple useful state and action space mappings between tasks. I also introduce methods to adapt value function approximation techniques (which are useful when the state space of a task is very large or continuous) to the unique state representation of OO-MDPs.

Committee: Dr. Marie desJardins (Chair), Dr. Tim Finin, Dr. Michael Littman, Dr. Tim Oates, Dr. Yun Peng

It’s not too late to apply for the 2013 CRA-W Graduate Cohort Workshop scheduled for April 5-6, 2013 in Boston, MA. This event brings together women graduate students in their first three years of graduate school for a series of presentations and panels with successful senior women researchers from academic, industrial, and government laboratories about how to succeed in graduate school and in a research career.

Applications will be accepted until 11:59 p.m. (ET) today, 15 January 2013 via an online form. Applicants must be female students in their first, second, or third year of graduate school in computer science and computer engineering or a closely related field at a U.S. or Canadian institution. Past workshops provided support for travel expenses, meals, and lodging for students chosen to participate in this program and we anticipate that similiar support will be available in 2013.

Making Sense

For his master's thesis, Computer Engineering student Kirit Chatterjee worked with scientists from UMBC's Center for Advanced Sensor Technology (CAST) on an innovative temperature sensor for premature babies.

In hospitals, doctors use a thermistor probe to monitor the temperature of a premature baby. But, the glue used to attach it is harmful for the baby, whose skin is as fragile as tissue paper.

“When the probe is removed, there is a high risk of “epidermal stripping” occurring-i.e. the skin of the baby can tear, leaving it open to infection,” explains Kirit Chatterjee, a Computer Engineering graduate student. For his master’s thesis, Kirit helped develop a new temperature sensing device that avoids this problem.

It wasn’t easy. During research, other obvious options had been shot down one by one: Bluetooth sensors had batteries that leaked toxic chemicals. Wireless sensing devices emitted energy that was harmful for the baby.

The solution, supported by an NIH grant, and later commissioned by General Electric (GE), was the result of the combined brain-power of a group of UMBC scientists led by Dr. Govind Rao, Director of UMBC’s Center for Advanced Sensor Technology (CAST). Dr. Yordan Kostov (CAST), Dr. Hung Lam (CAST), and Dr. Ryan Robucci (CSEE)–Kirit’s advisor for the project–were the team’s key players.

Together they created a patch containing a unique fluorescent orange dye. “The intensity of the orange emission depends on the temperature,” explains Dr. Kostov, the senior scientist on the temperature project who also worked closely with Kirit. When the baby’s temperature rises, he explains, the orange patch becomes brighter.

Recording and translating the patch’s fluorescence into a temperature reading that a doctor could understand was Kirit's job. “My part,” explains Kirit, “was to take care of the Engineering side—namely, to build the sensing apparatus.”

Choosing the right camera to monitor the dye was another challenge. The project was bound to a strict budget since the new sensor system was slated for mass production by GE. Therefore, expensive scientific cameras were out of the question.

Instead, Kirit reverse engineered and manipulated a much more affordable camera to serve his purposes. He used a two megapixel camera–the same camera found inside an iPhone 3–to monitor the dye in the patch.

“The dye is just the target for the Computer Engineer,” he says. “To the engineer, it’s just photons being emitted which translate to analog voltage signals inside the camera which then translate to digital bits inside the FPGA and then are analyzed.”

Next, Kirit used an FPGA in order to tap into the camera and retrieve its data, and MATLAB to translate the data into a traditional temperature reading.

The result is a temperature sensing device that is affordable, accurate, and, most importantly, safe for the baby. Dr. Kostov explains that when the GE contract comes to an end this September, the patch system will undergo clinical trials and toxicology tests. If all goes well, the new system should be found in premature baby incubators across the world in as little as two years.

Congratulations to our December Ph.D. graduates! Read on to hear about their Ph.D. dissertation research and their plans for the future. 

	Dr. Karuna Joshi Computer Science Semantically Rich, Policy Based Framework to Automate Lifecycle of Cloud Based Services Mentors: Yelena Yesha and Tim Finin Thesis Topic: Dr. Joshi developed a new framework to automate the acquisition, composition, and consumption/monitoring of virtualized services delivered on the cloud. The lifecycle consists of five phases of requirements, discovery, negotiation, composition, and consumption. She has developed ontologies to represent the concepts and relationships for each phase using Semantic Web languages. She has also developed a protocol to automate the negotiation process when acquiring virtualized services. "I chose to concentrate on Cloud Services automation for my Ph.D. thesis since I was able to draw upon my extensive experience as an IT Project Manager to determine open issues that need to be addressed for broader adoption of cloud computing." Future plans: Dr. Joshi has received funding from NIST to continue her research on Cloud Computing and Big Data management. As part of this funding, she will be working as a research faculty member in the CSEE Department. In the spring, Dr. Joshi will teach a course on Software Design and Development.
	Dr. Phuong Nguyen Computer Science Data Intensive Scientific Compute Model For Multicore Clusters Mentors: Milton Halem and Yelena Yesha Thesis Topic: Dr. Phuong developed a scalable workflow system on top Apache Hadoop for orchestrating data intensive scientific workflows. New scheduling algorithms have been developed in the workflow system to manage and reduce latency of the workflow executions. The evaluations of the workflow system on the climate data processing and analysis application (several TB dataset) showed that it is feasible and improved. The scientific results of the application provide new global climate change indicators for the decade of 2002-2012. "The Ph.D. topic came from the motivations related to our NASA and NOAA projects which need to process and analyze very large datasets to study climate change. My research contributions provide new tools for accelerating scientific discoveries from very large datasets and the scientific results." Future plans: Work on research and development related to building large distributed systems or applications.
	Dr. David Chapman Computer Science A Decadal Gridded Hyperspectral Infrared Record for Climate Mentors: Milton Halem Yelena Yesha, Shujia Zhou, John Dorband, Joel Susskind (NASA) Thesis Topic: Dr. Chapman helped improve our understanding of Global Climate Change by creating a Climate Data Record (CDR) of Outgoing Longwave Radiation (OLR) from 55 terabytes of NASA satellite weather observations from the Atmospheric Infrared Sounder (AIRS). He developed a parallel data-intensive scientific workflow infrastructure making use of Large Array Storage (LAS) in order to show the complete derivation these climate trending results. "Global Climate Change and Global Warming are very important and controversial issues, and we need to measure if they have actually happened. AIRS is the first of its kind because it measures hyperspectral radiation. The trick is to take a Big Dataset, and squeeze it into something meaningful. This takes a lot of hardware, and typically a large software team to develop the processing system. I showed how the Large Array Storage (LAS) paradigm can simplify these calculations along with their derivation." Future plans: Dr. Chapman has applied for a post doc in Climate Modeling at Columbia University. It would allow him to do interdisciplinary work to develop Big Data Analytics infrastructure alongside the statistical validation of climate models.
	Dr. Niyati Chhaya Computer Science Joint Inference for Extracting Soft Biometric Text Descriptors from Patient Triage Images Mentors: Tim Oates Thesis Topic: Dr. Chhaya's research was a combination of Soft biometrics, Probalistic Graphical Models, and Natural Language Processing techniques. The aim was to extract soft biometric text labels (using computer vision techniques) from images of mass disaster victims. The main contributions of the work include soft biometric feature extractors, a probalistic graphical model that exploits related appearance-related features, and a novel study of natural human descriptors using NLP techniques that help understand 1) how people describe other people and 2) order and structure of free text human descriptions. "Socially, this work aims at addressing the issue of providing victim information to the public in a post disaster situation. It forms an important contribution to anonymize available image data using text labels to facilitate efficient search. Technically, this is the first work of its kind that aims at using Probabilistic Graphical Models to relate Soft biometric features, and in turn improve the overall accuracy of text label extraction. Also, the NLP study is a significant contribution along with the datasets gathered for this research. The key contribution is the use of techniques from computer vision, machine learning, and NLP to build a robust system that extracts soft biometric features." Future Plans: Dr. Chhaya has moved back to India and will work as a Computer Scientist with Adobe Research Labs starting in January.
	Dr. Yasaman Haghpanah Jahromi Computer Science A Trust and Reputation Mechanism Through Behavioral Modeling of Reviewers Mentors: Marie desJardins Thesis Topic: Dr. Haghpanah introduced a novel mechanism to represent trust and reputation using behavioral modeling of online reviewers. Her approach helps decision makers utilize reputation information more effectively. "Evidence shows that people are now relying more and more on other people's posted opinions for making decisions about which product to buy, which movie to watch, etc. So, I modeled the raters' or in general information providers' behavior and showed how we can improve our decisions by knowing the behavior of the online raters." Future Plans: Dr. Haghpanah is currently interviewing for postdoctoral positions at universities and research labs to extend and broaden her knowledge.
	Dr. Ganesh Saiprasad Electrical Engineering Automatic Detection of Adrenal Gland Abnormality Using The Random Forest Classification Framework combined with Histogram Analysis Mentors: Chein-I Chang Thesis Topic: Dr. Saiprasad proposed a new, more accurate way to detect adrenal abnormalities: rather than using the popular Region of Interest (ROI) method, Dr. Saiprasad suggests segmenting the adrenal gland automatically using the random forest classification framework and then performing histogram analysis. "Working with radiologists and surgeons at the University of Maryland Medical Center on my Master's research helped me pick a topic for my Ph.D. research. Adrenal gland abnormality detection is a very challenging problem and we have some preliminary results now to show that it can be done automatically. This is a very important step forward in using such systems as decision support tools and also the same methodology can be used for other smaller organs to detect abnormalities which are challenging to detect on CT." Future Plans: Postdoc at the National Institute of Standards and Technology (NIST)
	Dr. Kevin Fisher Computer Engineering Real-Time Progressive Band Processing for Linear Spectral Unmixing and Endmember Extraction Mentors: Chein-I Chang Milton Halem (NASA) Thesis Topic: Dr. Fisher developed three algorithms that work on hyperspectral images–pictures (often taken by satellites or airborne cameras) where each pixel is a spectograph of the materials in that part of the image. His algorithms work to reduce the amount of irrelevant data in the image, detect samples of pure materials in the image, and then estimate the abundance of those materials in each pixel in the image. "In 2006, I finished a Master's thesis with Prof. Alan Sherman on electronic voting systems. It was an engaging project in a hot topic in computing, but it was not related to the work I was doing as an intern at NASA Goddard Space Flight Center. I sat down with my supervisor and some NASA technologists, and looked for common areas of interest between UMBC and NASA. Hyperspectral image processing was on the short list and that's when I contacted Prof. Chein-I Chang about potential research projects." Future Plans: Dr. Fisher will continue working at NASA as a software systems engineer working on the ground antenna system for the Geostationary Operational Environmental Satellite, R-Series (GOES-R) spacecraft, a new line of weather satellites due to launch in 2015.
	Dr. Joel Sachs Computer Science Supporting Citizen Science and Biodiversity Informatics on the Semantic Web Mentors: Tim Finin Thesis Topic: Dr. Sachs introduces an approach to constructing ontologies by layer, designed to make it easier for both data publishers and application developers to tailor-fit semantics to use cases.

Ph.D. Dissertation Defense

Supporting Citizen Science and
Biodiversity Informatics on the Semantic Web

Joel Sachs

10:00am Friday, 14 December 2012, ITE 325b

It is common for Semantic Web documents to use terms from multiple ontologies, with no expectation that the full semantics of each ontology will be imported by consuming applications. This makes sense, because importing all ontologies referenced by a document causes both practical and logical problems. But it has the drawback of leaving it to the consuming application to determine appropriate semantics for the terms being used. We describe an approach to constructing ontologies by layer, designed to make it easier for both data publishers and application developers to tailor-fit semantics to use cases.

The layers that we develop correspond to patterns in the RDF graph. This contrasts with typical approaches to modular ontology development, where the layers are domain based. The three primary motivations for this approach are i) preserving computational tractability; ii) enabling easy coupling and decoupling with foundational ontologies and iii) maintaining cognitive tractability. This third motivation is still under-studied in semantic web development; we consider it in relation to reducing the ease with which ontology users can publish data that accidentally implies things that they do not mean. This is important always, but becomes especially so in citizen science, where users will naturally bring intuitive semantics to the terms that they encounter.

We describe case studies that involved deploying our approach in the context of citizen science activities, and which provided opportunities to assess its capabilities and limitations. We also describe subsequent work aimed at addressing these limitations, and, by applying newly defined layers over the underlying data, show that we are able to improve the competency of our knowledge base. More generally, we show that appropriately combining triple-pattern-based layers allows us to support a wide variety of use cases with varied (and occasionally conflicting) requirements.

In addition to our approach to semantic layering, contributions include an improved understanding of how to blend social and semantic computing to support citizen science, and a collection of layers for representing biodiversity information in RDF, with a focus on invasive species. Compared with other proposed “semanticizations” of the Darwin Core standard for representing biodiversity occurrence data, these layers involve minimal modification to the Darwin Core vocabulary, and make maximal use of the Darwin Core namespace, thereby simplifying the transition of current practices onto the semantic web.

Committee: Drs. Tim Finin (Chair), Anupam Joshi, Tim Oates, Cynthia Parr, Yelena Yesha, Laura Zavala

MS Thesis Defense

Simultaneous Feature Acquisition and Cost Estimation

Zachary Kurtz

11:00am Thursday, 6 December 2012, ITE 325b

This thesis will address classification problems with two sources of cost: the cost of acquiring feature values and the cost of incorrect classifications. In particular, I address problems with feature costs and instance-dependent misclassification costs. Many real-world applications, such as medical diagnosis, contain both feature acquisition costs and instance-dependent misclassification costs. The goal of my research is to minimize the total cost of classifying an unknown instance. This goal is accomplished with a new approach: Simultaneous Feature Acquisition and Cost Estimation (SFACE), which combines feature acquisition methods with a regression algorithm that estimates misclassification costs. The estimated cost values are used to estimate the expected cost reduction for the acquisition of each feature. SFACE is evaluated by comparing the total cost of operation to the cost incurred by existing cost-insensitive, cost-sensitive, and feature acquisition algorithms. The results show that SFACE results in lower total cost for the tested datasets.

Committee: Dr. Marie desJardins (Chair), Dr. Tim Oates and Dr. Michael Grasso