WalletHub.Com has released its annual survey regarding the states most vulnerable to identity theft.

As a cyber-oriented culture, it’s natural to wonder whether and how a person’s daily habits assist hackers in stealing personal information. Accordingly, the site consulted a panel of cybersecurity professors for answers to such questions and offer advice on how to safeguard data against cybercriminals. Items asked include:

• How should consumers choose among third-party providers  offering services to protect their identity and personal data?
• What can individuals do to guard against identity theft?
• Is the recent expansion of social media facilitating identity thefts?
• Should the federal government intervene to establish a clear process for victims of identity theft looking to clear their name?

Dr. Rick Forno, CSEE’s Cybersecurity Graduate Program Director and Assistant Director of UMBC’s Center for Cybersecurity, was one of those invited to offer comments on this ongoing problem facing Internet users.

NSF Research Experiences for Undergraduates

Interdisciplinary Research on Human Impacts in the Lake Champlain Ecosystem - June 1 to August 5, 2016

Our team-based approach will pair up students working on independent research in different disciplines,with each student serving as an assistant to their teammate, fostering deeper and broader connections across disciplines. For example, a student testing hypotheses about the influence of societal structures on the deposition of pharmaceutical contaminants in Lake Champlain might team with a student testing hypotheses about the impacts of by-products of common pharmaceuticals (e.g., toothpaste, birth control pills) on aquatic organisms, thus linking natural and social sciences through linked societal and ecological processes.

Students will also develop their professional skills and prepare for a career in science through our "Thinking Like A Scientist" Program, weekly journal club, and weekly "after hours" seminar on career paths.

All full-time undergraduate students with an anticipated graduation date after June 2016 are encouraged to apply, including high school seniors entering undergraduate study in 2016. Participants must be U.S. citizens or permanent residents and must be 18 years or older before May 31, 2016. Students from institutions with limited research opportunities and underrepresented groups are especially encouraged to apply.

The University of Vermont (UVM) is located in Burlington, the largest city in Vermont, with an increasingly ethnically and culturally diverse population. Our REU program is based out of the Rubenstein Ecosystem Science Laboratory on the shores of Lake Champlain in downtown Burlington. Students will work with faculty mentors from across the UVM campus and nearby St. Michael’s College.

Burlington is centrally located in the Champlain Valley, nestled between the Green Mountains to the east and the Adirondack Mountains to the west, and conveniently located just hours from Boston, New York City, and Montreal. The Burlington area offers an abundance of recreational activities including biking, camping, swimming, hiking, fishing, and boating, and a rich cultural life including food, music, dance, farmers’ markets, theatre, a vibrant waterfront, and the famous Church Street Marketplace. The area also has an extensive public bus system to which REU students will have free access.

Ph.D. Dissertation Defense

Infrastructure-less Group Data
Sharing using Smart Devices

Ahmed Shahin

2:30 Tuesday, 8 December 2015, ITE-346

Advances in pervasive communication technology have enabled many unconventional applications that facilitate and improve the safety and quality of life in modern society. Among emerging applications is situational awareness where individuals and first-responders receive timely alerts about serious events that could have caused the interruption of the services provided by the communication infrastructure such as cellular networks, Wi-Fi hotspots, etc. Another example is when exchanging road conditions between peer-to-peer networked vehicles without the involvement of roadside units. The popularity of smart portable devices such as iPhone and Android powered phones and tablets has made them an attractive choice that can play a role in the realization of these emerging applications. These devices support multiple communication standards and thus enable Device-to-Device (D2D) data exchange at an increased level of convenience. By using technologies such as Bluetooth, Wi-Fi ad-hoc mode and Wi-Fi Direct, these devices are able to communicate without the need for any communication infrastructures. In addition, many of these devices are equipped with sensors that can provide a wealth of information about the surroundings once their readings are aggregated.

However, most existing protocols for data sharing among devices either require an internet connection, which may not be available and may incur extra costs in some cases, or suffer from the device’s operating system limitations. Actually there is no existing solution that allows a set of devices to start sharing data dynamically without forcing the users to apply an elaborate procedure for setting up a group. These shortcomings render existing solutions unsuitable for emergency cases. In this dissertation proposal, we tackle such a problem by developing a framework for enabling data exchange in a cost-effective and timely manner through the establishment of peer-to-peer links among smart devices. In addition, our framework opts to minimize the user required interaction for setting up a connection and overcome the limitations of the operating system.

Our framework consists of a set of protocols for group data exchange using Wi-Fi Direct on Android devices. First we present an Efficient and Lightweight protocol for peer-to-peer Networking of Android smart devices over Wi-Fi Direct (ELN). ELN main goal is to overcome the Wi-Fi Direct support limitations in Android, thus allowing the devices in one Wi-Fi Direct group to communicate together. The ELN protocol is validated by implementing a group chatting application. In addition, we present a protocol for Alert Dissemination using Service discovery (ADS) in Wi-Fi Direct. ADS uses the service discovery feature of Wi-Fi Direct for distributing alerts to nearby devices without requiring any prior connections and thus avoids the setup delay in creating Wi-Fi Direct groups and the limitations of multi-group connectivity in Android. ADS is validated by implementing a hazard propagation application for Android. Finally, we present an Efficient Multi-group formation and Communication (EMC) protocol for Wi-Fi Direct. EMC exploits the battery specifications of the devices to qualify potential group owners and enable dynamic formation of efficient groups. Moreover, EMC allows data exchange between different Wi-Fi direct groups. Part of our implementation of EMC in Android involves the modification of the Android source code to allow multi-group support. A chat application is developed to validate EMC.

To complete the dissertation, we plan to extend EMC by replacing the static assignment of devices’ addresses in our current implementation with an IP address negotiation protocol that runs before creating groups. Such an extension would give greater flexibility in adapting EMC. In addition, we plan to define some criteria for selecting proxy members in order to allow maximum coverage and allow the D2D communication to span a larger geographical area. In addition, we will develop a simulator to do large scale testing for the proposed framework. Finally, we would like to explore the use of dual transceivers in order to increase the robustness of D2D connections when the wireless channels are subject to varying level of interference; particularly we like to investigate the integration of Bluetooth Low Energy within our framework to enable group membership of nodes that do not have Wi-Fi Direct or suffer interference that makes the Wi-Fi Direct links unstable.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Chintan Patel, Tinoosh Mohsenin

Ph.D. Dissertation Defense

Infrastructure-less Group Data
Sharing using Smart Devices

Ahmed Shahin

2:30 Tuesday, 8 December 2015, ITE-346

Advances in pervasive communication technology have enabled many unconventional applications that facilitate and improve the safety and quality of life in modern society. Among emerging applications is situational awareness where individuals and first-responders receive timely alerts about serious events that could have caused the interruption of the services provided by the communication infrastructure such as cellular networks, Wi-Fi hotspots, etc. Another example is when exchanging road conditions between peer-to-peer networked vehicles without the involvement of roadside units. The popularity of smart portable devices such as iPhone and Android powered phones and tablets has made them an attractive choice that can play a role in the realization of these emerging applications. These devices support multiple communication standards and thus enable Device-to-Device (D2D) data exchange at an increased level of convenience. By using technologies such as Bluetooth, Wi-Fi ad-hoc mode and Wi-Fi Direct, these devices are able to communicate without the need for any communication infrastructures. In addition, many of these devices are equipped with sensors that can provide a wealth of information about the surroundings once their readings are aggregated.

However, most existing protocols for data sharing among devices either require an internet connection, which may not be available and may incur extra costs in some cases, or suffer from the device’s operating system limitations. Actually there is no existing solution that allows a set of devices to start sharing data dynamically without forcing the users to apply an elaborate procedure for setting up a group. These shortcomings render existing solutions unsuitable for emergency cases. In this dissertation proposal, we tackle such a problem by developing a framework for enabling data exchange in a cost-effective and timely manner through the establishment of peer-to-peer links among smart devices. In addition, our framework opts to minimize the user required interaction for setting up a connection and overcome the limitations of the operating system.

Our framework consists of a set of protocols for group data exchange using Wi-Fi Direct on Android devices. First we present an Efficient and Lightweight protocol for peer-to-peer Networking of Android smart devices over Wi-Fi Direct (ELN). ELN main goal is to overcome the Wi-Fi Direct support limitations in Android, thus allowing the devices in one Wi-Fi Direct group to communicate together. The ELN protocol is validated by implementing a group chatting application. In addition, we present a protocol for Alert Dissemination using Service discovery (ADS) in Wi-Fi Direct. ADS uses the service discovery feature of Wi-Fi Direct for distributing alerts to nearby devices without requiring any prior connections and thus avoids the setup delay in creating Wi-Fi Direct groups and the limitations of multi-group connectivity in Android. ADS is validated by implementing a hazard propagation application for Android. Finally, we present an Efficient Multi-group formation and Communication (EMC) protocol for Wi-Fi Direct. EMC exploits the battery specifications of the devices to qualify potential group owners and enable dynamic formation of efficient groups. Moreover, EMC allows data exchange between different Wi-Fi direct groups. Part of our implementation of EMC in Android involves the modification of the Android source code to allow multi-group support. A chat application is developed to validate EMC.

To complete the dissertation, we plan to extend EMC by replacing the static assignment of devices’ addresses in our current implementation with an IP address negotiation protocol that runs before creating groups. Such an extension would give greater flexibility in adapting EMC. In addition, we plan to define some criteria for selecting proxy members in order to allow maximum coverage and allow the D2D communication to span a larger geographical area. In addition, we will develop a simulator to do large scale testing for the proposed framework. Finally, we would like to explore the use of dual transceivers in order to increase the robustness of D2D connections when the wireless channels are subject to varying level of interference; particularly we like to investigate the integration of Bluetooth Low Energy within our framework to enable group membership of nodes that do not have Wi-Fi Direct or suffer interference that makes the Wi-Fi Direct links unstable.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Chintan Patel, Tinoosh Mohsenin

The Population Division of the U.S. Census Bureau has paid internship opportunities for students in geography and related fields.  The application deadline for this posting is Wednesday, December 9.

We are looking for well-qualified people with training in geographic information systems, population geography or demography, remote sensing/satellite image analysis, and urban geography, among other specialties.  Interns will work on projects related to international and domestic population mapping.

Salaries reflect the level of education completed.  All positions require U.S. citizenship.

Paid internships are available both during the academic year and in the summer.  Census HQ is in the Washington, DC, area and is located next to a subway station.

We plan follow-up messages about permanent employment opportunities.  These will offer the opportunity to work on a range of projects related to international and domestic population geography and mapping.

This is an exciting time for the U.S. Census Bureau, and specifically the Population Division.  Please forward this email to students (or colleagues with students) who may be interested in working at the Census Bureau.  For more information about the agency, including position opportunities, look under “About Us” on our website at Census.gov.  

To apply for the internships, please visit USAJOBS no later than Wednesday, December 9:

https://www.usajobs.gov/GetJob/ViewDetails/422689700

If you or your students have questions about work in Population Division, please contact us: Joshua.Comenetz@census.gov or James.D.Fitzsimmons@census.gov. 

For specific questions about available positions and ways of applying for them, please contact David Zaslow at David.C.Zaslow@census.gov or 301-763-6195.

M.S. Thesis Defense

Neuroevolution-Based Inverse Reinforcement Learning

Karan K. Budhraja

9:00am Wednesday, 2 December 2015, ITE 346

Motivated by such learning in nature, the problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One of the approaches to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations.

This work also extends existing work on Bayesian Non-Parametric Feature construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. Performance of the algorithm is shown to be limited by parameters used, implying adjustable capability. A conclusive performance hierarchy between evaluated algorithms is constructed.

Committee: Drs. Tim Oates, Cynthia Matuszek and Tim Finin

M.S. Thesis Defense

Neuroevolution-Based Inverse Reinforcement Learning

Karan K. Budhraja

9:00am Wednesday, 2 December 2015, ITE 346

Motivated by such learning in nature, the problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One of the approaches to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations.

This work also extends existing work on Bayesian Non-Parametric Feature construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. Performance of the algorithm is shown to be limited by parameters used, implying adjustable capability. A conclusive performance hierarchy between evaluated algorithms is constructed.

Committee: Drs. Tim Oates, Cynthia Matuszek and Tim Finin