Context-Aware Middleware for Activity Recognition, MS defense, Radhika Dharurkar, 10:30am 5/19

MS Thesis Defense

Context-Aware Middleware for Activity Recognition

Radhika Dharurkar

10:30am Thursday, 19 May 2011, ITE 325B

Smartphones and other mobile devices have a simple notion of context largely restricted to temporal and spatial coordinates. Service providers and enterprise administrators can deploy systems incorporating activity and relations context to enhance the user experience, but this raises considerable collaboration, trust and privacy issues between different service providers. Our work is an initial step toward enabling devices themselves to represent, acquire and use a richer notion of context that includes functional and social aspects such as co-located social organizations, nearby devices and people, typical and inferred activities, and the roles people fill in them.

We describe a system that learns to recognize richer contexts using sensor data from a person's Android phone along with annotations on her calendar and general background knowledge. Geo-social locations include the concepts of 'home' and 'school' and can be extended to others like 'work' or 'a restaurant'.

Our framework combines data from the phone's sensors (GPS, WI-FI, Bluetooth, acceleration, proximity, etc.) with data mined from applications (e.g., calendar) to produce features that can be used in a machine learning system. Training data from several university students and staff was collected using a system that periodically prompted the user for her true geo-social location and activity. The resulting classifier models were used to predict the individual user's context from new sensor data. The data from a set of users was combined to create a generic model.

We report on an evaluation of the individual and generic models in the university setting for predicting context. Finally, we discuss how our extended context notion can be applied to many interesting applications for smart phone users.


  • Dr. Tim Finin (chair)
  • Dr. Anupam Joshi
  • Dr. Yelena Yesha
  • Dr. Laura Zavala

Equation Modeling in Resting State Motor Network in Healthy Subjects, MS defense, Tejaswini Kavallappa

MSEE Thesis Defense

Reliability of Structural Equation Modeling in Examining Resting State Motor Network in Healthy Subjects

Tejaswini Kavallappa

3pm Monday, 16 May 2011, ITE 325

Resting state connectivity studies are of growing significance and interest in the current neuroimaging literature due to their potential in explaining various underlying brain mechanisms and, therefore, their utility in clinical applications. While functional connectivity has been extensively examined in the human brain, effective connectivity is a burgeoning field in functional neuroimaging studies, and there is an increased interest in quantifying effective connectivity that takes into account the directional influences of various brain regions active in a particular functional network. Studies have shown the presence of multiple functional networks in the resting state, which have been shown to be consistent across subjects and between sessions. However, this is not the case with resting state effective connectivity.

In this thesis we evaluate effective connectivity of the resting state motor network in normal subjects using structural equation modeling (SEM), a linear statistical analysis method. It has been shown that signals related to cardiac pulsatality and respiration effects can confound functional MRI results. Thus, we have investigated the effect of various filtering strategies on the reliability of effective connectivity measurements. Our thesis examined the effect of four methods of physiological filtering of resting state data:

  • preprocessed data without any filtering,
  • removal of prospectively recorded cardiac and respiratory fluctuations using RETROICOR,
  • removal of global average signal from all the brain voxels time series,
  • regressing out average signal of the white matter (WM) and cerebrospinal fluid (CSF), and
  • temporal filtering to remove frequencies pertinent to cardiac and respiratory sources.

The resulting effect of each of these methods on the estimation of resting state motor network effective connectivity was examined in this thesis.


  • Dr. Joel M. Morris (chair)
  • Dr. Rao P. Gullapalli (co-advisor)
  • Dr. Tulay Adali
  • Dr. Alan B. McMillan

Group Recognition in Social Networking Systems, MS Defense by Nagapradeep Chinnam

MS Thesis Defense

Group Recognition in Social Networking Systems

Nagapradeep Chinnam

1:30pm Tuesday, 17 May 2011, ITE 325

Recent years have seen an exponential growth in the use of social networking systems, enabling their users to easily share information with their connections. A typical Facebook user, as an example, might have 300-400 connections which include relatives, friends, business associates and casual acquaintances. Sharing information with a such a large and diverse set of people without violating social norms or privacy can be challenging. Allowing users to define groups and restrict information sharing by group reduces the problem but introduces new ones: managing groups and their members, relations and information sharing policies. This thesis addresses the problem of maintaining group membership.

We describe a system that learns to classify a user's new connections into one or more existing groups based on the connection's attributes and relations. We demonstrate the approach using data collected from real Facebook users. The two major tasks are identifying the relevant features for the classification and selecting the learning mechanism that best suits the task. Another significant challenge is posed by hierarchical and overlapping groups. We show that our system classifies new connections into these groups with high accuracy even with only 10-20% of labeled data.


  • Dr. Tim Finin (chair)
  • Dr. Anupam Joshi
  • Dr. Tim Oates

Community Detection in Twitter, MS defense by Mohit Kewalramani, 1pm Mon 5/16

MS Thesis Defense

Community Detection in Twitter

Mohit Kewalramani

1:00pm Monday, 16 May 2011, ITE 346

Twitter has evolved into a source of social, political and real time information in addition to being a means of mass-communication and marketing. Monitoring and analyzing information on Twitter can lead to invaluable insights, which might otherwise be hard to get using conventional media resources. An important task in analyzing highly networked information sources like twitter is to identify communities that are formed. A community on twitter can be defined as a set of users that are more similar to other members than to non-members.

We present a technique to devise a similarity metric between any two users on twitter based on the similarity of their content, links and metadata. The link structure on Twitter can be characterized using the twitter notion of followers, being followed and the @Mentions, @Reply and @RT tags in tweets. Content similarity is characterized by the words in the tweets combined with the hash-tags they are annotated with. Meta-data similarity includes similarity based on other sources of user information such as location, age and gender. We then use this similarity metric to cluster users into communities using spectral and bottom-up agglomerative hierarchical clustering. We evaluate the performance of clustering using different similarity measures on different types of datasets. We also present a heuristic to find communities in twitter that take advantage of the network characteristics of twitter.


  • Dr. Tim Finin (chair)
  • Dr. Anupam Joshi
  • Dr. Tim Oates

MS defense: Lohr on Semantic Light, 2:15 Thu 5/12

MS Thesis Defense

Semantic Light: Building Blocks

Charles Lohr

2:15pm Thursday, 12 May 2011, ITE 346

The concept of Semantic Light is simply that lighting systems can be aware of what they are lighting. This offers a number of potential advantages over conventional lighting in quality and efficiency. Semantic Light requires fine grained control of the output of many lights and requires sensors to take in information about what is being lit. It uses this information to control the output lighting in great detail. By running various algorithms, Semantic Light can provide information to the user and has a number of applications including augmented reality.

Traditional lighting that is currently in wide use has limited control of quality and quantity of the light produced. Few lights for large-scale use are intended to control their output in any kind of detailed manner. Most area lighting only has a switch that must be manually turned on or off. While there are many commercial systems that allow for more fine grained control, they are typically limited to remote control, motion control and extra manual controls. These systems can be wasteful, or they may provide inappropriate amounts of light, or they may be on when no one is using them.

While other Semantic Lighting systems may focus on "green" or powern saving aspects, we concentrate instead on innovative roles Semantic Light could play as well as on the technology to make it possible to fill those roles. By emphasizing new utility and maximizing our speed to prototype, we have made several tradeoffs that will cause our system to be less efficient than it could be, even less efficient than traditional lighting systems. The ideas and concepts covered, however, could be adapted to different underlying technologies to produce a product that could provide considerable power saving over conventional lighting.

It is important to think of the many concepts covered as primary building blocks, rather than a complete commercial system. A number of refinements and extensions will be needed to produce a commercial viable product. We demonstrate all of the needed building blocks in a concise, prototyped system.


  • Mark Olano
  • Yelena Yesha
  • Zary Segall (advisor)

MS defense: Mahale on Group Centric Information Sharing, 10am Tue

MS Thesis Defense

Group Centric Information Sharing
using Hierarchical Models

Amit Mahale

10:00am Tuesday, 10 May 2011, ITE 346, UMBC

Traditional security policies are often based on the concept of “need to know” and are typified by predefined and often rigid specifications of which principals and roles are pre-authorized to access what information. A recommendations of the 9/11 commission was to find ways to move from this traditional perspective toward one that emphasizes the “need to share”. Ravi Sandhu and his colleagues have developed the Group centric secure information sharing model (gSIS) as a new model that is more adaptible to highly dynamic situations requiring information sharing. We present an implementation of gSIS and demonstrate its usefulness to usecases in information sharing in social media. Our contributions include the prototype implementation, extension to the model such as hierarchical groups and necessary and sufficient conditions, and the use of the semantic Web language OWL for representing the central gSIS concepts and associated data. Our framework uses a pragmatic approach of using semantic web technology to represent and reason about the hierarchy and procedural method to compute access decisions relying on the gSIS semantics.

Thesis Committee:

  • Dr. Tim Finin (chair)
  • Dr. Anupam Joshi
  • Dr. Yelena Yesha
  • Dr. Laura Zavala

MS defense: Detection of Unsafe Action in Laparoscopic Cholecystectomy Video

MS defense

Detection of Unsafe Action in Laparoscopic Cholecystectomy Video

Ashwini Lahane

10:00am Thursday, 28 April 2011, ITE 346

Wellness and healthcare are central to the lives of all people. Information technology has already contributed in significant ways towards enhancement of healthcare delivery and to improving the quality of life. And it will continue to do so with the development of “smarter” technologies and environments. Recent years have seen context awareness as one of the most important aspects in the emerging pervasive computing paradigm. We focus our work on situation awareness; a more holistic variant of context awareness where situations are regarded as logically aggregated contexts. We demonstrate an application of situation aware computing in healthcare. We primarily focus on laparoscopic cholecystectomy, a complex yet commonly performed surgical procedure. The outcome of the surgery is influenced greatly by the training, skill, and judgment of the surgeon. Many surgical simulators have been developed to meet the training and practice needs of the surgeons. However few systems provide feedback during the actual surgery. We present a method to detect a situation, that shows possibility of injury to an artery by analyzing the laparoscopic cholecystectomy surgical video. The system can be used to provide feedback to the operating surgeon in case of a possible risk. We have also built a prototype to demonstrate the use of our system in telemedicine, in the form of a web service.

Thesis Committee:

  • Dr. Yelena Yesha (chair)
  • Dr. Anupam Joshi
  • Dr. Milton Halem
  • Dr. Michael Grasso


Complex-valued Adaptive Signal Processing: Applications in Medical Imaging

EE Graduate Seminar

Complex-valued Adaptive Signal Processing:
Applications in Medical Imaging

Prof. Tulay Adali
Machine Learning for Signal Processing Laboratory

1-2pm Friday, 29 April 2011, ITE 237

Complex-valued signals arise frequently in applications as diverse as communications, radar, geophysics, optics, and biomedicine, as most practical modulation formats are of complex type and applications such as radar and magnetic resonance imaging lead to data that are inherently complex valued. The complex domain, however, presents unique challenges for signal processing, in particular for adaptive nonlinear processing, and as a result, until recently, most algorithms derived for the complex domain have taken engineering shortcuts limiting their usefulness. The most common one among those has been assuming the circularity of the signal, thus ignoring the information conveyed by the phase. Similarly when taking gradients in the complex domain, a "split" approach that performs optimization separately with respect to the real and imaginary variables has been the dominant practice.

There have been important advances in the area within the last decade that clearly demonstrate that noncircularity is an intrinsic characteristic of many signals of practical interest, and when taken into account, the methods developed for their processing may provide significant performance gains. Similarly, it has been shown that using Wirtinger calculus, all calculations can be carried out in a manner similar to real-valued calculus while keeping all the computations in the complex domain.

In this talk, after a brief introduction to optimization using Wirtinger calculus and statistics in the complex domain, and then I will give examples from some of the recent work conducted at the MLSP-Lab.

MS defense: Boosting Base Station Anonymity in Wireless Sensor Networks

MS Defense

Exploiting Architectural Techniques for Boosting
Base Station Anonymity in Wireless Sensor Networks

Zhong Ren

2:00pm Thursday, 28 April 2011, ITE 346

Wireless Sensor Networks (WSNs) can be deployed to serve mission-critical applications in hostile environments such as battlefield and territorial borders. In these setups, the WSN may be subject to attacks in order to disrupt the network operation. The most effective way for an adversary to do so is by targeting the Base-Station (BS), where the sensor data are collected in the field. By identifying and locating the BS, the adversary can launch attacks to damage or disrupt the operation of the BS. Therefore, maintaining the BS anonymity is of utmost importance in WSNs.

In this thesis we propose three novel approaches to boost the anonymity of the BS nodes to protect them from potential threats. We first explore the deployment of more BS nodes. We compare the BS anonymity of one versus multiple stationary BS under different network topologies. Our results show that having more base-stations can boost both the average and max anonymity of BS nodes. We further provide guidelines on a cost versus anonymity trade-off to determine the most suitable BS count for a network. Second we exploit the mobility of base-stations and explore the effect of relocating some of the existing BS nodes to the lowest anonymity regions. Our results show that having one mobile BS can dramatically boost the anonymity of the network and moving multiple BS does not provide much value. Finally, we propose to pursue dynamic sensor to cluster re-association to confuse the adversary. This can be employed when base-stations cannot safely move.

Committee members:

  • Mohamed Younis (Chair)
  • Yun Peng
  • Charles Nicholas

Two bioinfomatics talks, Wed 27 April

The UMBC Biological Sciences Department will host two talks on bioinformatics on Wednesday, April 27.

Bioinformatics: Illustrations from 20 years at NCB*I, Dr. Jim Ostell, NCBI, NIH, 11:00am Wednesday 27 April, BS-004

Jim Ostell is one of the founders of NCBI, he will give a general audience talk, ideal for students and faculty from Biology, CS, Chemistry, Statistics and Math that would like to learn what Bioinformatics is about and the history of one of the main bioinformatic center in the world.

Network and state space models: science and science fiction approaches to cell fate predictions, John Quackenbush, Harvard, 12:00pm Wednesday 27 April BS-004

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. ‘Omic technologies were once heralded as providing a window into those networks, but so far their success has been limited, in large part because the high-dimensional they produce cannot be fully constrained by the limited number of measurements and in part because the data themselves represent only a small part of the complete story. To circumvent these limitations, we have developed methods that combine ‘omic data with other sources of information in an effort to leverage, more completely, the compendium of information that we have been able to amass.Here we will present a number of approaches we have developed, with an emphasis on the how those methods have provided into the role that particular cellular pathways play in driving differentiation, and the role that variation in gene expression patterns influences the development of disease states. Looking forward, we will examine more abstract state-space models that may have potential to lead us to a more general predictive, theoretical biology.

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