Ph.D. Dissertation Proposal

A Semantic Resolution Framework for
Manufacturing Capability Data Integration

10:30am Tuesday, May 14, 2013, ITE 346, UMBC

Yan Kang

Building flexible manufacturing supply chains requires interoperable and accurate manufacturing service capability (MSC) information of all supply chain participants. Today, MSC information, which is typically published either on the supplier’s web site or registered at an e-marketplace portal, has been shown to fall short of the interoperability and accuracy requirements. This issue can be addressed by annotating the MSC information using shared ontologies. However, ontology-based approaches face two main challenges: 1) lack of an effective way to transform a large amount of complex MSC information hidden in the web sites of manufacturers into a representation of shared semantics and 2) difficulties in the adoption of ontology-based approaches by the supply chain managers and users because of their unfamiliar of the syntax and semantics of formal ontology languages such as OWL and RDF and the lack of tools friendly for inexperienced users.

The objective of our research is to address the main challenges of ontology-based approaches by developing an innovative approach that can effectively extract a large volume of manufacturing capability instance data, accurately annotate these instance data with semantics and integrate these data under a formal manufacturing domain ontology. To achieve the objective, a Semantic Resolution Framework is proposed to guides every step of the manufacturing capability data integration process and to resolve semantic heterogeneity with minimal human supervision. The key innovations of this framework includes 1) three assisting systems, including a Triple Store Extractor, a Triple Store to Ontology Mapper and a Ontology-based Extensible Dynamic Form, that can efficiently and effectively perform the automatic processes of extracting, annotating and integrating manufacturing capability data.; 2) a Semantic Resolution Knowledge Base (SR-KB) that incrementally filled with, among other things, rules/patterns learned from errors. This SR-KB together with an Upper Manufacturing Domain Ontology (UMO) provide knowledge for resolving semantic differences in the integration process; 3) an evolution mechanism that enables SR-KB to continuously improve itself and gradually reduce the human involvement by learning from mistakes.

Committee: Yun Peng (chair), Charles Nicholas, Tim Finin, Yaacov Yesha, Boonserm Kulvatunyou (NIST)

The UMBC Cybersecurity Masters in Professional Studies (MPS) program will offer the following courses over the Summer 2013 session:

• CYBR 620: Introduction to Cybersecurity
• CYBR 621: Cyber Warfare
• CYBR 691: Special Topics in Cybersecurity: Application Security Principles/Practices

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.

For those living in Washington, D.C., Northern Virginia, Frederick, MD, and points west, UMBC's Cybersecurity MPS will launch at the Universities at Shady Grove (USG) in Fall 2013.  Courses offered the first semester at that campus will be:

• CYBR 620: Introduction to Cybersecurity
• CYBR 623: Cybersecurity Law & Policy

The deadline to apply for Fall 2013 admission to the UMBC Graduate Cybersecurity Program is August 1, 2013.

Congratulations to Omar Shehab (CS Ph.D.), who has been awarded two NSF travel grants to attend research conferences this June.

First, Omar has received an NSF travel grant to attend the IEEE Conference on Computational Complexity. The conference celebrates research in all areas of computation complexity theory, taking a look at the absolute and relative power of computational models under resource constraints. Specific topics include probalistic and interactive proof systems, proof complexity, and descriptive complexity. The conference will be held in Palo Alto California, June 5-7.

Omar has also received an NSF travel grant to attend the 45^th ACM Symposium on the Theory of Computing (STOC 2013). Here, he will be presenting a poster entited: "Hamiltonian complexity of Trefoil knot transformations." The conference is sponsored by the ACM Special Interest Group on Algorithms and Computation Theory (SIGACT). It will explore original research on the theory of computation. The conference will be held in Palo Alto California, June 1-4.

Omar started UMBC’s Computer Science Ph.D. program in 2010. He is currently pursuing research under the supervision of Dr. Samuel J. Lomonaco Jr. Omar’s doctoral work exlpores adiabatic quantum Hamiltonian complexity, quantum computational simulation of topology and use of quantum optics to understand device independent cryptography. He is currently a Teaching Assistant for CMSC 641: Design and Analysis of Algorithms.

MS Defense

Modeling Individual Nodes In Dynamic Link Prediction

Maksym Morawski

2:00pm Thursday, 25 April 2013, ITE325b, UMBC

The question of how to predict which links will form in a graph, given the graph’s history, is an open research problem in computer science. There are many different approaches to the link prediction problem, one of which involves building a set of features for pairs of nodes and using supervised learning to build a model that predicts when these pairs of nodes will link. Typically, this model is learned over the entire graph. In this thesis, I investigate building this model over each individual node in an attempt to learn the particular ways in which that node behaves before making predictions about it. In addition, research into link prediction to date lacks intelligent ways of utilizing the graph over large timespans. To address this, I introduce a variety of ways to include temporality into the link prediction process by introducing new ways of using existing features.

Committee: Dr. Marie desJardins (Chair), Dr. Tim Oates, Dr. Tim Finin

MS Thesis Defense

Text and Ontology Driven
Clinical Decision Support System

Deepal Dhariwal

9:00am Tuesday 23 April 2013, ITE325b, UMBC

This thesis discusses our ongoing research in the domain of text and ontology driven clinical decision support system. The proposed framework uses text analytics to extract clinical entities from electronic health records and semantic web analytics to generate a domain specific knowledge base (KB) of patients’ clinical facts. Clinical Rules expressed in the Semantic Web Language OWL are used to reason over the KB to infer additional facts about the patient. The KB is then queried to provide clinically relevant information to the physicians. In the first phase, standard text pre processing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data.

In the second phase, a domain specific medical ontology is used to establish relation between the extracted clinical terms. The output of this phase is a Resource Description Framework KB that stores all possible medical facts about the patient. In the final phase, an OWL reasoner and clinical rules are used to infer additional facts about patient and generate a richer KB. This KB can then be queried for a variety of clinical tasks. To demonstrate a proof of concept of this framework, we have used discharge summaries from the cardiovascular domain and determined the TIMI Risk Score and San Francisco Syncope Score for a patient. The goal of this research is to combine factual knowledge about patients, procedural knowledge (clinical rules), and structured knowledge (medical ontologies) to develop a clinical decision support system.

Committee: Dr. Anupam Joshi (chair), Dr. Michael Grasso, Dr. Tim Finin, Dr. Yelena Yesha

We are now offering the UMBC Cybersecurity MPS program at Shady Grove in Montgomery County, MD.

The Cybersecurity Master’s in Professional Studies degree provides students the essential knowledge required to serve in leadership and operational roles throughout the industry. Through the program, students will learn how to analyze cybersecurity risks and assess available countermeasures. The program will expose students to practical managerial and operational considerations needed to conduct cybersecurity activities for large organizations.

PhD Dissertation Defense

Analysis of brain network connectivity
using spatial information

Sai Ma

1:00pm Thursday, 18 April 2013, ITE 325b

In current functional magnetic resonance imaging (fMRI) research, one of the most active areas involves exploring statistical dependencies among brain regions, known as functional connectivity analysis. Data-driven methods, especially independent component analysis (ICA), have been successfully applied to fMRI data to extract distributed brain networks and offer an opportunity to investigate functional connectivity on a network level, thus at a multivariate level. However, the independence assumption in ICA is neither necessarily nor typically satisfied in real applications and an extension is desirable. Furthermore, most current ICA-based studies focus on the use of temporal information and second-order statistics for functional connectivity analysis. Taking spatial information and higher-order statistics in fMRI data into account is expected to lead to better understanding of the overall brain network connectivity in healthy controls and also in patients with mental disorders, such as schizophrenia.

We develop a dependent component analysis (DCA) framework to generalize the ICA-based connectivity analysis methods by grouping components into maximally independent clusters. First, we define functional network connectivity as the statistical dependence among spatial components, instead of the typically used temporal correlation. Based on this definition, we use a hypothesis test to automatically generate functional connectivity structure for a large number of brain networks. After that, we separate dependent components within a given cluster using prior information, such as sparsity and experimental paradigm information, to achieve a better decomposition. We also combine this DCA-based clustering analysis with graph-theoretical analysis to discover significant group differences in topological properties of functional connectivity structure. To extend the methodologies currently available for functional connectivity, we propose an independent vector analysis (IVA) based scheme to extract and analyze dynamic functional connectivity.

The methods we develop offer advantages for effective and efficient examination of not only static, but also dynamic functional connectivity among different brain networks. We identify significant differences in functional connectivity structure between healthy controls and patients with schizophrenia, which may prove useful to serve as potential biomarkers for diagnosis. We also find task-induced modulations in functional connectivity when comparing different active states in the brain. Furthermore, we observe temporal variability in functional connectivity structure and physiologically meaningful group differences in dynamic connectivity among several brain networks. Our methods can provide insights to understanding of functional characteristics of the brain network organization in healthy individuals and patients with schizophrenia.

Committee: Dr. Adali (Chair), Dr. Morris, Dr. Rutledge, Dr. LaBerge, Dr. Phlypo, Dr. Calhoun, and Dr. Westlake

PhD Dissertation Defense

Data-driven group analysis of complex-valued fMRI data

Pedro A. Rodriguez

11:00am Tuesday, 16 April 2013, ITE 346, UMBC

Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity of the analysis both for data-driven techniques such as independent component analysis (ICA) and for model-driven techniques. The promise of an increase in sensitivity and specificity in clinical studies provides a powerful motivation for utilizing both the phase and magnitude data; however, the unknown and noisy nature of the phase poses a challenge for successful study of the fMRI data. In addition, complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis and visualization of the results. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The developed methods become key components of a framework that allows the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data.

In this dissertation, we first introduce the methods developed as part of the fully complex framework for ICA of fMRI data. We introduce a physiologically motivated de-noising method that uses phase quality maps to successfully identify and eliminate noisy voxels—3D pixels—in the fMRI complex images so they can be used in individual and group studies. We also introduce a phase correction scheme that can be either applied sub-sequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. Finally, we present two visualization methods that are used to augment the sensitivity and specificity in the detection of activated voxels. We show the benefits of using the developed methods on actual complex-valued fMRI data.

In the remainder of the dissertation, we focus on developing constrained ICA (C-ICA) algorithms for complex-valued fMRI data. C-ICA uses prior information, hence providing a balance between model-based and data-driven approaches such as ICA to improve the source estimation performance and robustness to noise. C-ICA algorithms have been used to improve the estimation performance in real-valued fMRI data, but—to our knowledge—have not been applied to complex-valued fMRI data. We develop the first C-ICA algorithm that uses complex-valued references to constrain either the sources or the mixing coefficients. The designed algorithm is not restricted to having a unitary demixing matrix, which is a major assumption in existing C-ICA algorithms. We show, on both simulated and actual fMRI data, how the performance of ICA improves by using prior information about the fMRI paradigm.

Committee: Dr. Adali (Chair), Dr. Morris, Dr. Rutledge, Dr. Laberge, Dr. Phlypo, and Dr. Calhoun

PhD Dissertation Defense

Independent Vector Analysis:
Theory, Algorithms, and Applications

Matthew Anderson

1:45pm Wednesday, 17 April 2013, ITE 325B

The field of blind source separation (BSS) is a well studied discipline within the signal processing community due to its applicability to a variety of problems when the data observation model is poorly known or difficult to model. For example, in the study of the human brain with functional magnetic resonance imaging (fMRI), a neuroimaging sensor, BSS algorithms are able to provide medical researchers and practitioners with a decomposition of a three-dimensional ‘movie’ of the brain that is amenable to analysis. BSS algorithms achieve this decomposition with only a few justifiable assumptions; this is contrary to methods based on the general linear model, which require prespecified models of the expected or desired response to achieve analysis of fMRI data.

Most BSS algorithms consider just a single dataset, but it also desirable to have methods that can analyze multiple subjects or data collections in fMRI jointly, so as to provide insights beyond that achieved with individual analysis of single datasets. Several frameworks for using BSS on multiple datasets jointly have been proposed. The subject of this dissertation is the study of one of these frameworks, which has been termed independent vector analysis (IVA). IVA is a recent extension of the classical independent component analysis (ICA) model to BSS of multiple datasets and it has been the subject of significant research interest. In this dissertation, we provide a formulation of IVA that accounts for sources which possess properties such as a) following Gaussian or non-Gaussian distributions; b) samples are independently and identically distributed (iid) or are dependent; and c) having either linear or nonlinear dependence of sources between datasets. The proposed IVA formulation utilizes the likelihood to define the objective function. This formulation admits to theoretical analysis. In particular, we provide the identification conditions, i.e., we determine when the sources can be ‘blindly’ recovered by IVA, and give a lower bound on the source separation performance.

Several algorithms exist for achieving IVA. We provide several new approaches to developing IVA algorithms and apply these approaches using a Gaussian distribution source model and a more general Kotz distribution model. The former, in addition to leading to efficient IVA algorithms, serves as the distribution model that directly connects canonical correlation analysis (CCA) and ICA.  

Committee: Dr. Tulay Adali (Chair), Dr. Joel Morris, Dr. Aninyda Roy, Dr. Ronald Phlypo, and Dr. Mike Novey

Dissertation Defense

Digital Forensics for
Infrastructure-as-a-Service Cloud Computing

Josiah Dykstra

10:00am Tuesday, 16 April 2013, ITE 325b

We identify important issues in the application of digital forensics to Infrastructure-as-a-Service cloud computing and develop new practical forensic tools and techniques to facilitate forensic exams of the cloud. When investigating suspected cases involving cloud computing, forensic examiners have been poorly equipped to deal with the technical and legal challenges. Because data in the cloud are remote, distributed, and elastic, these challenges include understanding the cloud environment, acquiring and analyzing data remotely, and applying the law to a new domain. Today digital forensics for cloud computing is challenging at best, but can be performed in a manner consistent with federal law using the tools and techniques we developed.

The first problem is understanding how and why criminal and civil actions in and against cloud computing are unique and difficult to prosecute. We analyze a digital forensic investigation of crime in the cloud, and present two hypothetical case studies that illustrate the unique challenges of acquisition, chain of custody, trust, and forensic integrity. Understanding these issues introduces legal challenges which are also important for federal, state, and local law enforcement who will soon be called upon to conduct cloud investigations.

The second problem is the lack of practical technical tools to conduct cloud forensics. We examine the capabilities for forensics today, evaluate the use of existing tools including EnCase and FTK, and discuss why these tools are incapable of trustworthy cloud acquisition. We design consumer-driven forensic capabilities for OpenStack, including new features for acquiring trustworthy firewall logs, API logs, and disk images.

The third problem is a deficit of legal instruments for seizing cloud-based electronically-stored information. We analyze the application of existing policies and laws to the new domain of cloud computing by analyzing case law and legal opinions about digital evidence discovery, and suggest modifications that would enhance cloud the prosecution of cloud-based crimes. We offer guidance about how to author a search warrant for cloud data, and what pertinent data to request.

This dissertation enhances our understanding of technical, trust, and legal issues needed to investigate cloud-based crimes and offers new tools and techniques to facilitate such investigations.

Committee: Dr. Alan T. Sherman (Chair), Dr. Charles Nicholas, Dr. Richard Forno, Dr. Simson Garfinkel (Naval Postgraduate School), Mr. Donald Flynn, JD (Department of Defense Cyber Crime Center)

The elections for both the IEEE Undergraduate Student Branch and IEEE Graduate Student Branch Executive Boards have been scheduled for Friday, April 19th, 4.00pm to 5.00pm, room TBA. This is our regular meeting time. Below you will find more information on the election process, including the procedure to run for an Officer position.

IEEE Undergraduate Student Branch Officer positions include the following.

• Chair: Will be responsible for leading the IEEE Undergraduate Student Branch overall. S/he will also be responsible for representing the IEEE USB in General Body meetings and Executive Board meetings, and delegating responsibilities for various task. Events and activities must be approved by the Chair before sent to committees and funds must be approved before being forwarded to the Treasurer.
• Vice-Chair: Will be responsible for assisting the Chair in leading the IEEE USB and helping to run the branch smoothly. If the Chair is not present, the VP will take upon her/his responsibilities.
• Secretary: Will be responsible for taking minutes at meetings, keeping track of undergraduate student attendance and reporting the activities to the branch. S/he will be the point person for external relationships.
• Treasurer: Will manage the accounts and funds for the IEEE USB. S/he’ll be responsible for attending the SGA treasurer training session, and working with the Executive Board to generate a budget plan.
• Member At Large: Will support the other executive board members by facilitating their responsibilities as well as managing member recruitment/retention, managing fundraising activities in cooperation with the Treasurer and developing and managing projects.

IEEE Graduate Student Branch Officer positions include the following.

• Chair: Will be responsible for leading the IEEE Graduate Student Branch overall. S/he will also be responsible for representing the GSO in the GSA Senate meetings and external events. Funds must be approved by the Chair before being forwarded to the Treasurer.
• Vice-Chair: Will be responsible for assisting the Chair in leading the IEEE GSB and helping her/him to run the branch smoothly.
• General Secretary: Will be responsible for reporting the activities to the IEEE SB GSA. S/he will be the point person for external relationships.
• Treasurer: Will manage the accounts and funds for the IEEE GSB. S/he’ll be responsible for the annual budget report along with the payments.

To run for one of the above positions you must be a grad/undergrad student in good academic status, be subscribed to our mailing list, and send me () a small description (no more than 100 words) about you and why should other members vote for you, no later than next Thursday, April 11th, by 12.00pm (noon). You can only run for one position.

These descriptions will be compiled and sent out to the entire mailing list no later than the following Friday, April 12th. Each candidate will be given two minutes during that Friday's meeting to make a brief speech.

The election process will be supervised by our advisor, Dr. Choa.

IMPORTANT

• if you win, you MUST be an official IEEE member or become one within a week after the election date.
• to vote, you must present your UMBC ID and the email that you used to subscribe to our mailing list.

Please come and vote on the 19th to ensure that your voice is heard, and consider running for one of the positions. If you have any concerns or questions about the election process, please let Jorge Teixeira () know ASAP.

On behalf of the UMBC IEEE GSB and UMBC IEEE USB Executive Boards,

UMBC IEEE Student Branch, Chair
Jorge Teixeira

The Fourth Trusted Infrastructure Workshop (TIW 2013) will be held at Penn State University in University Park, PA from Sunday afternoon, June 2 to Thursday, June 6.

TIW 2013 is a free premier educational meeting for graduate students with a focus on computer systems security research and research that builds on trusted computing foundations. TIW 2013 will consist of lectures and hands-on labs, enabling the students to learn concepts and apply them in practice. Speakers include world-class experts in their respective fields from industry, government, and academia. See the preliminary program for more information.

The workshop is designed for graduate students with a research interest in computer security. Although the workshop is free for students, students must apply to be selected for TIW 2013. Applications received by April 25 will also receive full consideration for travel support. Applications will continue to be received until May 20 based on space and funds. The workshop may have space for a small number of other attendees, but a fee will be required for other attendees.

Doctoral students, postdocs and established researchers working in the intersection of technology and social systems might consider applying to the 2013 Digital Societies and Social Technologies Summer Institute, 7/28 to 8/1 at the University of Maryland College Park.

"MOOCs, Education and learning; personal health and well-being; open innovation, eScience, and citizen science; co-production, open source, and new forms of work; cultural heritage and information access; energy management and climate change; civic hacking, engagement and government; disaster response; cybersecurity and privacy – these are just a few problem domains where effective design and robust understanding of complex sociotechnical systems is critical. To meet these challenges a trans-disciplinary community of scholars has come together from fields as wide ranging as CSCW, HCI, social computing, organization studies, information visualization, social informatics, sociology, information systems, medical informatics, computer science, ICT for development, education, learning science, journalism, and political science."

Lodging, meals, and other onsite costs will be covered for all Summer Institute participants. Applications are due by April 5.

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.

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

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.

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.