Analyzing Social Media Data
Morgan Madeira
Junior, Computer Science

Social media has increasingly become an outlet for expression for a large part of our society. Literature suggests that analyzing data from these sites can lead to improvements in areas such as health-care and search-ad targeting. Users of these sites often associate with many other users described as “friends,” even if they do not have a strong connection, or what would be described as friendship in daily life. It is valuable to determine the strength of relationships between users and to identify communities within social networks. These communities represent people with similar characteristics, which are used by applications to solve many real-world problems. For instance, it is useful to identify groups that are interested in a specific movie genre. Information about these groups can be used to target movie advertisements towards the people most interested in that genre. These types of problems have similar characteristics to identifying close friends. We have created a system to collect and analyze the data about user characteristics, while being respectful of privacy concerns. The system is composed of a front end Facebook application and a back end machine-learning based tool. The front end component gathers data about a user and their friends. The back end uses the collected data and machine-learning techniques to determine relationships between users.

To learn more about the project, check out an interview with Morgan.

Catch Morgan's poster presentation at URCAD this Wednesday, April 25 in the University Center Ballroom from 10:00 a.m. to 12:30 p.m.

Multiclass Datasets, Their Predictions, and Their Visualization
Wallace Brown and Alexander Morrow with Kevin Winner
Senior, Computer Science
Sophomore, Computer Science

Many datasets contain a wealth of information. For example, a person may be described by their race, age, gender, income, marital status, nationality, level of education, etc. By analyzing this data, we can form educated and accurate predictions about individuals. We can, for instance, determine that a person with a particular race, age, nationality, and income is likely to be a college undergraduate. Our goal is to develop ways to visualize these predictions and the uncertainty associated with the predictions. Displaying data in a scatterplot is a standard means of describing two-dimensional information. However, displaying high-dimensional data (i.e., data that includes many attributes, such as age, race, and income) is significantly more challenging.  We present a means of visualizing high-dimensional data sets and the predictive models derived from the data, using existing dimension reduction techniques and novel glyph-based displays.

To learn more about the project, check out an interview with Wallace and Alexander.

Catch Wallace and Alexander's presentation at URCAD this Wednesday, April 25 in the University Center 312 from 1:15 to 1:30 p.m.

MS Thesis Defense

Towards Implementation of a Pattern Recognition System based on
a Working Theory of the Learning Rule for Dendritic Integration

Shamit Patel

4:00pm Monday 23 April 2012, ITE 346, UMBC

My goal is to develop a working theory of the learning rule for dendritic integration, and to then implement a pattern recognition system based on that learning algorithm so that the algorithm can be evaluated for its generalization ability. In this regard, this thesis presents an implementation of Jeff Hawkins and Dileep George's Hierarchical Temporal Memory (HTM) pattern recognition system that's based on an existing theory of the learning rule for dendritic integration – spike-timing-dependent synaptic plasticity (STDP). The integration of this learning rule is the novel contribution of this thesis. I found that the STDP HTM system achieved much higher probabilistic classification accuracy and better generalization ability than the non-STDP HTM system. Probabilistic classification accuracy is a way of measuring classification accuracy in which a testing pattern is classified correctly if its label appears in the group of labels output by the top-level node of the HTM network.

Committee: Professors Tim Oates (Chair), Yun Peng and Tim Finin

Pay:

·         $9.50/hour, paid bi-weekly through the University of Maryland, Baltimore County

Length of Time:

·         Approximately 40 hours a week, mostly on weekends

·         Runs from mid May, to end of August

Schedule:

·         Friday-Sunday, 11am-11pm

·         4 hours of Job Development/Readiness Shadowing during the week (to be determined based on schedule and availability)

Location:

·         Baltimore’s beautiful Inner Harbor, between the Science Center and the Visitor Center, parallel to Light Street

·         For the weekly 4 hours of Job Development/Readiness Shadowing, the Intern will start at our Cherry Hill office, located on 971 Seagull Ave, 21225

Responsibilities:

·         Provide directions to employees at the stand to ensure smooth operations

·         Keep track of timesheets and hours worked of employees

·         Monitor inventory and cash register receipts to make sure they match up after shifts/cash register switches.

·         Ensure proper customer service and that all customers are satisfied

·         Maintain quality of fruitshakes, grilled cheese sandwiches, and all other menu items

·         Stocking supplies and cleaning as necessary, and ensuring the cleanliness of the stand is being taken care of by employees

A year-long internship in Production Management for the Olney Theatre Center is available for 2012-2013. Located in the Washington, DC theatre community, this internship is a great opportunity for anyone interested in arts administration, specifically union relations, budgets, and other aspects of producing. Duties include generating weekly payroll, assembling contract packets for guest artists, and tracking expenses for the production department. On-campus housing and a weekly stipend is provided.

The ideal candidate would be proficient in Microsoft Excel, Access, and Word, have knowledge of financial terms, have a great sense of humor, be able to roll with the punches, and have a passion for theatre. If you are a student graduating this spring who fits this description, please submit your resume, along with a cover letter and three references to:

Mackenzie Douglas

Olney Theatre Center

mdouglas@olneytheatre.org

Ph.D. Dissertation Defense

DiffServ Assured Forwarded and Robust
Header Compression: Performance Analysis

Houcheng Lee

12:30pm Monday, 23 April 2012, ITE 201b

Performance analysis of network architecture and protocols can be done by using modeling and simulation or using emulation techniques. Modeling and simulation can be used to obtain qualitative results about a network protocol through building models of protocols under drastic abstractions. One major concern of the modeling approach is the fidelity of the results from a model simulation.

This dissertation presents detailed performance analyses of two major Internet standards using emulation techniques. The standards investigated are Differentiated Services (DiffServ) architecture and Robust Compression (ROHC) standard.

The DiffServ architecture was proposed to support quality of services (QoS) implementation in TCP/IP networks. In the first part of dissertation, a new and scalable approach to performance analysis of DiffServ architecture, based on fluid flow modeling approximation to TCP/UDP based traffic flows, is used to emulate millions of competing TCP and UDP flows across networks of various sizes, complexities and link speeds. The emulation involved real-world network constraints, including traffic prioritizations, limited buffers in routers, link congestion, active queue management (AQM) and packet drop policies on congested links. The results provided first quantitative understanding of the interactions of traffic with different drop precedencies in AF1 class traffic in a congested network.

The Robust Header Compression (ROHC) is proposed to implement efficient IP header compression on bandwidth constrained links in TCP/IP networks. In the second part of dissertation, an emulation tool, called CORE, developed at NRL is used to analyze the performance of ROHC. ROHC compresses packet headers at a sender side, and decompresses them at the receiver side to increase efficiency of a communication link. The results provided quantitative understanding of compression gains from using ROHC standard for compressing IPv4/UDP and IPv6/UDP traffic on links.

Committee:

• Dr. Deepinder Sidhu
• Dr. Yelena Yesha
• Dr. Konstantinos Kalpakis
• Dr. Ted M. Foster
• Dr. Edward Zieglar