FSU CS Expo 2016
November 10th, 2016
organized by
Event Features
Industry Speakers
share their experiences and insight.
Research Presentations
by students and alumni.
Poster Contest
Prizes awarded to the winner.
Grad-Made-Good
Talks given by successful alumni.
Social Gathering
of like-minded professionals.
Awards Ceremony
Celebrate faculty and students.
Software Demo Contest
Prizes awarded to the winner.
Raffles and Door Prizes
Answer trivia in between talks to win.
About the CS Expo
who we are and why we do thisThe Expo Mission
The CS Expo is the premier annual event for students, alumni and faculty of the Computer Science department at the Florida State University to showcase science, technology, and cutting-edge projects, and to exchange opportunities and experiences. This one-day event sets up an open forum that features technical talks, research presentations, demos and posters by computer science students, a "Grad-Made-Good" talk by this year's keynote speaker Dr. John Bowers from James Madison University, and invited talks from Sean Miniclier of Harris Corporation and Dr. Wickus Nienaber of Captiveyes. Alumni and friends of the Computer Science department participate in the event to share post-graduate experiences and opportunities for our students.
The computer was born to solve problems that did not exist before.
– Bill Gates
We can only see a short distance ahead, but we can see plenty there that needs to be done.
– Alan Turing
The question of whether computers can think is like the question of whether submarines can swim.
– Edsger Dijkstra
An algorithm must be seen to be believed.
– Donald Knuth
A language that doesn't have everything is actually easier to program in than some that do.
– Dennis Ritchie
One accurate measurement is worth a thousand expert opinions.
– Grace Hopper
Speakers
Will share their experiences
Dr. John C. Bowers
Assistant Professor of Computer Science, James Madison University
Prof. Bowers received his PhD in Computer Science in 2015 from the University of Massachusetts Amherst and his BS in Mathematics and Computer Science from The Florida State University in 2008. His research interests include discrete geometry, circle packings, and computational origami. When not creating geometric constructions out of ruler and compass, he can most often be found playing with his two kids, mountain biking, or rock climbing in the Shenandoah Valley.
Sean Miniclier
Engineering Manager, Harris Corporation
Mr. Sean Miniclier received his Bachelor of Science in Computer Science from Florida State University in 2001. He is the Engineering Manager for one of the largest software departments in Harris Corporation, a company with over 23,000 employees worldwide. He started his career contributing to programs for the National Geospatial-Intelligence Agency (NGA) through software application development and direct support in the field. As a leader, he successfully implemented one of first Agile Methodology programs for a contracted program for the NGA. He became leader on the VCS21 program, a global, next-generation Voice over Internet Protocol (VoIP) communication system designed for Air Traffic Management communications in support of the Federal Aviation Authority's (FAA) NextGen initiative. He brought many of the collaborative concepts and lessons learned from previous programs to this business area. Mr. Miniclier is a co-inventor on a patent for a "System and Method for Processing Map Images". His talk will discuss his transition from coursework into a career, and the importance of remaining agile and open to change, especially in the field of Computer Science.
Dr. Wickus Nienaber
Lead Software Engineer, Captiveyes
Dr. Wickus Nienaber received his PhD in Computer Science in 2013 from Florida State University. He currently works as CTO/Lead Software Engineer for Captiveyes Group, a location based Media Company, and Bloom Intelligence (a subsidiary of the Captiveyes Group). Recruited to help build Captiveyes' software division, Dr. Nienaber has also been responsible for automating the company's business practices. His accomplishments include automating the lead capture, proposal creation, and sales data tracking functions, and developing other business analytics that measure the health of the business. He leads the team that developed the Bloom Intelligence analytics & marketing platform. This platform is designed for brick and mortar locations that deploy Wi-Fi and other sensors to measure the health of brick and mortar chain locations, provide actionable business intelligence, and then automate marketing function to improve business performance. Bloom Intelligence operates nationwide. A sample of the unique tools developed are machine learning systems that predict user traffic for a business for up to a week in advance; a data mining system that produces real-time business analytics such as daily traffic, first time customer return rates; and customer churn rate, projected lifetime, life time value, and much more.
Student Talks
Curent Research at FSU Computer Science Department
Esra Akbas
Tec-Index for Community Search on Large Graphs
Community detection is a fundamental problem in social network analysis, the goal of which is to group vertices of a graph into densely knitted community with each community being well separated from all the others. Traditional community detection methods aim at identifying all communities in a network. With the increasing size of the networks, local/online community detection problem has attracted much attention in recent years. This problem aims finding meaningful communities that given one or more query vertices belongs to. In this project, we define our communities based on k-truss concept and we study the k-truss community search problem. We design a compact and elegant index structure which supports the efficient search of k-truss communities for a given query node. Our experiments on 4 large real-word networks demonstrate the efficiency and effectiveness of using our index for k-truss community search.
Meysam Ghaffari
BigFCM: Fast, Precise and Scalable FCM on Hadoop
Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitu dereduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering.
Linghan Zhang
VoiceLive: A Phoneme Localization based Liveness Detection for Voice Authentication on Smartphones
VoiceLive is a liveness detection system we proposed for voice authentication on smartphones. It detects a live user by leveraging the user's unique vocal system and the stereo recording of the smartphones. Specifically, while the user is doing voice authentication, VoiceLive captures time-difference-of-arrival(TDoA) changes in a sequence of phoneme sounds to the two microphones of the phone, and uses such unique TDoA dynamic which doesn't exist under replay attacks for liveness detection. VoiceLive doesn't require additional hardware but two-channel stereo recording that is supported by virtually all smartphones. Our experimental results show the VoiceLive achieves over 99% detection accuracy at around 1% Equal Error Rate, also it is robust to phone placements and compatible to different sampling rates and phone models.
Polina Volkova
Automatic Web Page Coloring
We present a web based tool for automatic recoloring of web pages. Automatic application of different color palettes to web pages is essential for both professional and amateur web designers. However existing recoloring tools for images and web pages do not provide full recoloring. We replace colors in .css, .html, and .svg files, and recolor images such as logos, banners, and background tiles to recolor web pages entirely. The new color theme is based on a color guide image provided by user. Evaluation shows a high level of satisfaction with the quality of palettes and results of recoloring. Our tool is publicly available.
Sheng Tan
WiFinger: Leveraging Commodity WiFi for Fine-grained Finger Gesture Recognition
Gesture recognition has become increasingly important in human-computer interaction (HCI) and can support a broad array of emerging applications, such as smart home, virtual reality, and mobile gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using a single commodity WiFi device without requiring user to wear any sensors. Our low-cost system, WiFinger, takes advantages of the fine-grained Channel State Information (CSI) available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. In WiFigner, we devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Our experimental evaluation in both home and office environments demonstrates that our system can achieve over 93% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can work with WiFi beacon signals and provides accurate gesture recognition under NLOS scenarios.
