talk: Oleg Aulov on Human Sensor Networks, 1pm Fri 9/14, UMBC

UMBC CSEE Colloquium

Human Sensor Networks for Improved Modeling
of Natural and Human-Caused Disasters

Oleg Aulov, Computer Science Ph.D. Student, UMBC

1:00pm Friday, 14 September 2012, ITE 227, UMBC

This talk will discuss the importance of different roles that social media can play in management, monitoring, modeling and mitigation of natural and human-caused disasters. We will present a novel approach that views social media data as a human sensor network. These data can serve as a low-cost augmentation to an observing system, which can be incorporated into geophysical models together with other scientific data such as satellite observations and sensor measurements. As a use case scenario, we analyze the Deepwater Horizon oil spill disaster. We gather the social media data that mention sightings of oil from Flickr, geolocate them, and use them as boundary forcings in the General NOAA Oil Modeling Environment (GNOME) software for oil spill predictions. We show how social media data can be incorporated into the GNOME model to obtain improved estimates of the model parameters such as rates of oil spill, couplings between surface winds and ocean currents, diffusion coefficient, and other model parameters. Other social media mining and citizen science projects performed by groups outside of UMBC, on air quality, earthquakes and the Fukushima disaster will also be summarized as related work.

Oleg Aulov received B.S. degree in mathematics from the University of Central Missouri, Warrensburg, MO, in 2004 and M.S. degree in Computer Science with a concentration in Computer Security and Information Assurance from George Washington University, Washington, DC, in 2006. He is currently working toward a Ph.D. degree in the Department of Computer Science and Electrical Engineering at University of Maryland, Baltimore County, Baltimore, MD. His topics of interest include social media mining, citizen science, machine learning, trust establishment and management, information assurance, and social engineering.

For more information and directions see http://bit.ly/UMBCtalks

talk: Mountain on the DoD Advanced Computing Systems Research Program

COLLOQUIUM
Computer Science and Electrical Engineering
University of Maryland, Baltimore County

The Advanced Computing Systems Research Program

David J. Mountain
Technical Director, Center for Exceptional Computing

1:00pm Friday, 7 September 2012, ITE 227, UMBC

The Advanced Computing Systems (ACS) Research Program recently relocated from Adelphi, Maryland, to the Research Park complex next to UMBC. What type of computing research does ACS focus on? How is it organized? What opportunities for collaboration exist for UMBC? This talk will provide answers to these questions, explain how ACS tackles its research challenges, and provide a sneak preview of upcoming ACS presentations on specific research projects.

David J. Mountain is the Technical Director at the Center for Exceptional Computing (CEC), a Department of Defense research laboratory in the UMBC Research Park. The mission of the CEC is to collaborate with industry, academia, and the government to drive innovative research that will impact advanced computing systems at the multi-petaflop scale and beyond. His responsibilities include directing research activities in technical thrusts including power efficiency, chip IO, system level interconnects, file system IO, productivity, and resilience.

Mr. Mountain’s personal research projects have included radiation effects studies, hot carrier reliability characterization, and chip-on-flex process development utilizing ultra-thin circuits. He has been actively involved with 3D electronics research for nearly two decades. Mr. Mountain is the author of seven papers, has been awarded eight patents, and is a Senior Member of the IEEE.

Host: Yaacov Yesha,

Directions and more information on recent and upcoming talks.

Ashwinkumar Ganesan MS defense: Calculating Representativeness of Geographic Sites Across the World

MS Thesis Defense

Calculating Representativeness of
Geographic Sites Across the World

Ashwinkumar Ganesan

11:00am Friday, 31 August 2012, ITE 325b, UMBC

GLOBE is a global correlation engine, a project to study the effects of human activity on land change based on a set of parameters that include temperature, forest cover, human population, atmospheric parameters, and many other variables. The aim of this research is to understand how a land change study or set of studies of specific geographic areas generalizes to other areas of the world. The generic form of the question is – given a set of data points with a set of variables, how can we determine how much a selected subset of points represents the rest of the distribution. The research aims to answer a set of questions which include the definition of representativeness of a geographical site and how the representativeness can be computed. Land change researchers will dynamically select a subset of variables which they would like to study. Hence the method developed not only computes representativeness, but must do so in an efficient manner. For this purpose, we apply dimension reduction techniques to reduce the size of computation and analyze the effectiveness of using these techniques to calculate representativeness.

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

Amey Sane MS defense: Predicting the activities of mobile phone with HMMs

MS Thesis Defense

Predicting the Activities of Mobile Phone Users
with Hidden Markov Models

Amey Sane

9:00am Tuesday, 28 August 2012, ITE 325b, UMBC

Mobile phones are ubiquitous and increasingly capable, with sophisticated sensors, network access, significant storage and processing power and access to a wide range of application data. They can improve the range and quality of their services by acquiring and using models of their context, including the activities in which their users are engaged. This thesis explores the use of supervised machine learning techniques for predicting a smartphone user's activities from available sensor data. We have specifically concentrated on applying classifiers and ensembles using hidden markov models for activity recognition. Our classifiers predict a user's current activity from among a set of conceptual activity classes such as sleeping, traveling, playing, working, and chatting/watching TV. We have experimented with and evaluated the effectiveness of different approaches on data collected on Android smartphones by university faculty and students.

Committee: Drs. Tim Finin (chair), Anupam Joshi and Yelena Yesha

Nikhil Puranik MS defense: Classification of Column Data, 8/24

MS Thesis Defense

A Specialist Approach for Classification of Column Data

Nikhil Puranik

1:30pm Friday 24 August, 2012, 325b ITE, UMBC

Much information is encoded in spreadsheets, databases, and tables on the Web and in documents. Interpreting this content and making its meaning explicit in a representation language like RDF enables many applications. This thesis addresses the problem of identifying the semantic type of the information represented in a table column containing conventionally encoded data such as phone numbers or stock ticker symbols. We describe a ‘specialist’ approach for classification in which different specialists work together to come up with a ranked list for the given input column. We use three types of specialists: those based on regular expressions, dictionaries and classifiers. We discuss a serial and parallel framework for the specialists. We evaluate our system in two ways: by testing individual specialist for accuracy and by testing the performance of the overall system in terms of generation of ranked list. We also discuss the scalability of the system in terms of addition of new specialists and performance impact for systems with hundreds of specialists.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates and Yelena Yesha

PhD Defense: Yasaman Haghpanah

Ph.D. Dissertation Defense

A Trust and Reputation Mechanism Through
Behavioral Modeling of Reviewers

Yasaman Haghpanah

11:00am Tuesday 21 August 2012, ITE 325b, UMBC

Trust and reputation have become important topics in various domains, such as online markets, supply chain management, auctions, social networks, and e-commerce applications. With the significant increase in transactions with people and organizations especially in online markets, people need to interact with strangers with whom they have little or no previous interactions. Reputation information as a form of world of mouth in auctions and supply chain management and as a form of provided reviews and ratings on online websites are two different sources for modeling trust and reputation in order to mitigate the risk of not knowing a stranger before actually start interacting with that stranger.

In providing reputation information, people can have different behavior, such as being biased based on incentives or they can have different preferences and viewpoints. In this dissertation, I introduce a novel trust and reputation mechanism that models and learns a reputation provider’s behavior based on probability theory. This learned behavior is then used to re-interpret the reputation information, thus making use of the entire reputation data effectively, even if they are biased or based on personal viewpoints and preferences.I show the importance of learning the behavior of reputation providers using different patterns of being biased or having different preferences and satisfaction thresholds in three different settings of game-theory, an online rating website, and an online marketplace. My results show that learning the behavior of reputation providers in all three above settings helps individuals to more effectively aggregate and adjust reputation information in order to make decisions, thereby increasing their satisfaction and overall payoffs in their interactions.

Committee: Drs. Marie desJardins (Chair), Tim Oates, Tim Finin, Wolfgang Ketter and David Aha

PhD dissertation proposal: Albert Kir

PhD Dissertation Preliminary Examination

On Optimizing Contrast Quality and Acquisition Time of
SSFP-Sequence-Based techniques for Structural and Functional
MR Imaging via Extended Phase Graph (EPG) Analysis

Albert Kir

1:00pm Wednesday, 15 August 2012, ITE 325b

The extended phase graph (EPG) formalism provides an excellent description of the magnetization evolution (considering the magnetization as a vector quantity in space and its change through time) for steady-state free precession techniques (sequences). This dissertation demonstrates its accuracy by applying it to analyze/optimize structural and functional magnetic resonance imaging. An optimization framework for a structural imaging technique, Magnetization-Prepared RApid Gradient-Echo (MP-RAGE), based on the EPG algorithm is established for obtaining optimal images with respect to image signal strength, contrast, and acquisition time. In addition, a functional imaging technique, True Fast Imaging with Steady-state Precession (TruFISP) with improved quantitative sensitivity is developed using EPG analysis.

Committee: Drs. Joel M. Morris (chair and co-advisor), Alan McMillan (co-advisor), Rao Gullapalli (co-advosor), Janet Rutledge and Tulay Adali

IEEE Colloquium on Energy Harvesting Devices, September 25

The IEEE Baltimore Electron Devices Society chapter, in collaboration with ARL, will be hosting a one day Colloquium on Energy Harvesting Devices at the University of Maryland, College Park on Tuesday, September 25, 10 a.m. to 5 p.m. in the Stamp Student Union Building, Benjamin Banneker Room (Room 2212).

Invited speakers include:

Dr. Edward Shaffer, Army Research Laboratory
Prof. Vikram Dalal, Iowa State University
Prof. Santosh Kurinec, Rochester Institute of Technology
Dr. James Horwitz, Dept. of Energy (DOE)
Prof. Edward Yu, University of Texas,Austin
Prof. Rajendra Singh, Univ. of South Carolina
Prof. Agis IliadisUniv. of MD, College Park

Panel discussion on paths to reliable, efficient and low cost solar cell development (Dr. Anu Kaul/NSF, Chair, Dr. Mike Wraback ARL, Mr. Scott Stephens, DOE).

Attendance is free. To register, please contact: Dr. Naresh C. Das () or Dr. Victor Veliadis ()

For more information, visit the website and download the flyer.

Dinghade MS defense: Approach to Unwrap a 3D Fingerprint to a 2D Equivalent

MS Defense

Approach to Unwrap a 3D Fingerprint to a 2D Equivalent

Ravikiran Dighade

10:00am Thursday, 2 August 2012, ITE 352

Fingerprints are the most widely used biometric feature for human identification because of their accuracy and uniqueness. Traditional fingerprint acquisition techniques are contact based and result in poor quality images. The new generation of non-contact based scanners captures high resolution and detailed 3D fingerprint scan, which addresses many of the problems of traditional fingerprint acquisition techniques. The majority of existing fingerprint databases available today are 2D, so there is a need for backward compatibility for the 3D scans captured. In order to solve this interoperability issue, I present an algorithm to unwrap the 3D fingerprint to its 2D equivalent image to be able used in an Automatic Fingerprint Identification System.

Program Committee: Drs. Marc Olano (Advisor, Chair), Penny Rheingans and Dr. Gymama Slaughter

Venkatesh MS defense: Dynamically Reconfigurable Layered Filesystem

 

MS Thesis defense

Dynamically Reconfigurable Layered Filesystem

Sunil Venkatesh

10:00am Thursday, 26 July 2012, ITE 325b

Traditionally, all files and directories in Linux and UNIX-like systems have been organized in a hierarchical fashion under the root directory “/” adhering to the Filesystem Hierarchy Standard (FHS). Although there is sufficient flexibility in how the filesystem hierarchy is structured given it satisfies the FHS, there is little straightforward means to customize the filesystem structure to suit an individual user’s or a set of users’ needs without affecting rest of the users on a shared system. Our approach aims to eliminate such a restriction by providing isolated environments to individual users with the help of data being organized in the form of layers. Such an environment also provides an important advantage from security perspective by reducing the risk involved in unwarranted access to files by carefully choosing the layers a user has access. Maintainability at the layer level is another key advantage of our approach over the fine-grained approach of dealing with individual files.

Committee: Drs. John Dorband (Chair), Yelena Yesha, Mohamed Younis

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