Graduate

Smartwatch app could inspire more frequent physical activity, Virginia Tech study finds

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An interdisciplinary study conducted by researchers at Virginia Tech suggests the secret to obtaining your summertime fitness goals might not be the amount of weight you’re bench pressing or how many miles you run, but generating friendly competition to keep you one step ahead of your fitness buddies.

The concept of friendly competition in group exercise being explored by the researchers uses a smartwatch app that could help people in a group exercise program get —and stay — more active.

At the heart of the group exercise research is a Fitbit-like smartwatch and its software developed by Andrey Esakia, a Ph.D. candidate in Virginia Tech’s Department of Computer Science who worked on the project to study the effects of technology in group physical activity. The work is supported by a multidisciplinary seed grant from Virginia Tech’s Institute for Creativity, Arts, and Technology.

Esakia collaborated with the Department of Human Nutrition, Foods, and Exercise in the College of Agriculture and Life Sciences and the Department of Communication in the College of Liberal Arts and Human Sciences to incorporate the hardware and software of the watch into an existing initiative, FitEx, from the Physical Activity Leadership Team of Virginia Cooperative Extension and the Physical Activity Research and Community Implementation Laboratory. FitEx is an eight-week physical activity and fruit and vegetable consumption program delivered in community settings.

 

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Overlap in computer modeling holds key to next-generation processing, Virginia Tech researchers find

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Exascale computing — the ability to perform calculations at 1 billion billion per second — is what researchers are striving to push processors to do in the next decade. That’s 1,000 times faster than the first petascale computer that came into existence in 2008.

Achieving efficiency will be paramount to building high-performance parallel computing systems if applications are to run in environments of enormous scale and also limited power.

A team of researchers in the Department of Computer Science in Virginia Tech’s College of Engineering discovered a key to what could keep supercomputing on the road to the ever-faster processing times needed to achieve exascale computing — and what policymakers say is necessary to keep the United States competitive in industries from everything to cybersecurity to ecommerce.

“Parallel computing is everywhere when you think about it,”said Bo Li, computer science Ph.D. candidate and first author on the paper being presented about the team’s research this month. “From making Hollywood movies to managing cybersecurity threats to contributing to milestones in life science research, making strides in processing times is a priority to get to the next generation of supercomputing.”

Li will present the team’s research on June 29 at the Association for Computing Machinery’s 26th International Symposium on High Performance Parallel and Distributed Computing in Washington, D.C. The research was funded by the National Science Foundation.

 

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PhD student Cory Bart wins Best Research Paper award at SIGCSE

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Cory Bart, computer science PhD student, won the Best Research Paper award at the SIGCSE conference in March.  The SIGCSE Technical Symposium is the largest computing education conference worldwide organized by ACM SIGCSE.  It attracts around 1,300 researchers, educators, and others interested in improving computing education in K-12 and higher education. Ryan Whitcomb, computer science undergraduate student, along with CS faculty members: Dennis Kafura, Cliff Shaffer, and Eli Tilevich are co-authors of the paper.

ABSTRACT
To successfully bring introductory computing to non-CS majors, one needs to create a curriculum that will appeal to students from diverse disciplines. Several educational theories emphasize the need for introductory contexts that align with students’ long-term goals and are perceived as useful. Data Science, using algorithms to manipulate real-world data and interpreting the results, has emerged as a eld with crossdisciplinary value, and has strong potential as an appealing context for introductory computing courses. However, it is not easy to nd, clean, and integrate datasets that will satisfy a broad variety of learners. The CORGIS project (https://think.cs.vt.edu/corgis) enables instructors to easily incorporate data science into their classroom. Specifically, it provides over 40 datasets in areas including history, politics, medicine, and education. Additionally, the CORGIS infrastructure supports the integration of new datasets with simple libraries for Java, Python, and Racket, thus empowering introductory students to write programs that manipulate real data. Finally, the CORGIS web-based tools allow learners to visualize and explore datasets without programming, enabling data science lessons on day one. We have incorporated CORGIS assignments into an introductory course for non-majors to study their impact on learners’ motivation, with positive initial results. These results indicate that external adopters are likely to nd the CORGIS tools and materials useful in their own pedagogical pursuits.

 

 

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Liang Zhao, CS alumnus, named in the Top 20 New Stars in Data Mining by Microsoft

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Liang Zhao, computer science Ph.D. alumnus, has been named one of the Top 20 New Stars in Data Mining, provided by Microsoft searching.  Microsoft searching mines the past 6 years of Knowledge Discovery and Data Mining (KDD) submissions and combines the big data from Microsoft to then achieve the ranking by an automatic algorithm.

Zhao was advised by Dr. Chang-Tien Lu, professor in the department of computer science at Virginia Tech.

Liang Zhao is an assistant professor in the Department of Information Sciences and Technology at the Volgenau School of Engineering. He is also affiliated with the Department of Computer Science. His research interests include data mining and machine learning, with particular emphasis on social media modeling, feature selection, and text mining. He has led the papers in prestigious conferences and journals including ACM SIGKDD, IEEE ICDM, SIAM Data Mining, PLoS One, and IEEE BigData, and served as the reviewer for leading conferences and journals such as ACM SIGKDD, ACM TKDD, IEEE ICDM, SIAM Data Mining, ACM TIST, ACM SIGSPATIAL, and Geoinformatica. He also owns two US IP discloses on social media mining.

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CS PhD Students Attend CRA-W Grad Cohort Workshop

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The CS Department sent a group of eight women PhD students to the 2016 CRA-W Grad Cohort Workshop this year.  Doaa Altarawy, Sorour Amiri, Mai Dahshan, Michelle Dowling, Zahra Ghaed, Azam Moosavi, Elaheh Raisi, and Ziqian Song traveled to San Diego in April to participate in this two-day event organized by the Computing Research Association.  The annual workshop is aimed at building the pipeline of senior women in computing-related studies and research.  Presentations and discussions led by senior researchers and professionals offered advice on a variety of topics such as graduate school survival skills, networking, publishing, etc.  According to VT attendee Elaheh Raisi, “… resources introduced related to different skills such as presentation, writing, and research for graduate studies.  There were some female professionals as mentors and we could talk to them and ask for their help about any problem we might have.”  Doaa Altarawy reported that she was able “ … to learn more about networking and to actually practice it by knowing new women in computing, [and] to see examples of successful women who were able to overcome difficulties and boundaries and reach their goals.”   Michelle Dowling added, “the atmosphere was wonderfully relaxed and focused heavily on interacting with the audience and addressing specific concerns that people had, all while promoting networking and mentoring opportunities.”

 

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CRA-W Grad Cohort

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Virginia Tech Presenting Smartphone Security Work at Top Security Conference

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Smartphone security has become a topic of paramount importance in both academic and commercial communities.  Currently, the most widely used processors in mobile devices are the ARM processors.  ARM processors employ TrustZone, a hardware security extension, to protect sensitive code and data in a privilege and isolated execution environment. Although TrustZone design is effective against many malicious software-based attacks, smartphones may be lost or stolen.  Once in the possession of unfriendly hands, sensitive information in a smartphone may be retrieved through physical memory disclosure attacks such as cold boot attack, in which an attacker can bypass all software protection and gain unrestricted access to the contents in the dynamic random access memory.

Ning Zhang, a CS Ph.D. candidate, under the supervision of Prof. Wenjing Lou, presented his work on how to protect smartphones against physical-level memory disclosure attacks in a paper at the 37th IEEE Symposium on Security and Privacy, the top security conference in the field.  In the paper, Zhang, Lou and their co-authors presented a novel system that combines hardware-assisted security protection of TrustZone and unique cache features in ARM.  The system offers an isolated execution environment that can protect sensitive tasks against both malicious software and hardware memory disclosure attack. The system uses the on-chip cache as the program execution environment and applies cryptography to protect the sensitive program context that is stored in the physical memory. Through experimentation on a prototype, the new system was found to be effective and is capable of providing unprecedented protection with little performance impact.

Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field. The 2016 Symposium marks the 37th annual meeting of this flagship conference.  Among over 400 submissions, only 55 papers were accepted for presentation at this year’s symposium.

 

Wenjing Lou story submission for June 2016 newsletter

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Dissertation Defenses

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Dr. Daphne Yao’s doctoral student Hao Zhang successfully defended his dissertation on October 8, 2015. Hao’s research lies in the area of network security, and more specifically, traffic dependence analysis for network anomaly detection. His thesis work detects stealthy malware activities through analyzing causal relations among network requests.  Applications of this analysis include the detection of data exfiltration, bot and spyware activities. Tools developed in Hao’s thesis research provide rich contextual and structural information associated with network events. They can be used by security analysts to help reason about the security alerts. He and Dr. Yao have a pending continuous-in-part (CIP) patent on this anomaly detection technology. Hao Zhang will join the database security team at Oracle, Redwood Shores CA, starting from January 2016.

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Southeast Women in Computing Conference (SEWIC)

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Dr. Barbara G. Ryder and 18 CS@VT undergraduate and graduate students attended the Southeast Women in Computing Conference (SEWIC) in Atlanta, Georgia November 13-15.  Congratulations to Sorour Ekhtiari Amir who won first place in the Graduate Research Poster category.  Her poster is entitled “DASSA: Automatic Segmentation of General Time-Stamped Data Sequences “.  Sorour along with Liangzhe Chen and B. Aditya Prakash co-authored the poster.  For more information about the conference, please visit SEWIC.

 

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ACM Student Research Competition at MobileSoft 2015

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Computer Science graduate student Zheng (Jason) Song won 2nd place in the ACM Student Research Competition at MobileSoft 2015.  Jason’s project was entitled: “Programming Support for Seamless Resource Sharing across Heterogeneous Mobile Devices.”  The MobileSoft conference (ACM International Conference on Mobile Software Engineering and Systems) took place May 16-17 in Florence, Italy.  The event was co-hosted by ICSE, http://mobilesoftconf.org/2015/.  Professor Eli Tilevich is Jason’s advisor.

 

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