The ACM Code-A-Thon starts Tonight, Friday November 18 at 7:00PM and will end Tomorrow night, Saturday November 19 at 7:00PM. You are not required to show up at the physical event in order to compete and you can leave whenever you'd like, but there will be pizza and drinks for those who do come by Swearingen 1D11 for a portion of the competition. CSCE145: https://www.hackerrank.com/usc-acm-145-code-a-thon-2016 CSCE146: https://www.hackerrank.com/usc-acm-146-code-a-thon-2016 CSCE240: https://www.hackerrank.com/usc-acm-240-code-a-thon-2016 CSCE350+: https://www.hackerrank.com/usc-acm-350-code-a-thon-2016 A Raspberry Pi will be given to the first place winners for each division. To be eligible to win a prize you MUST compete in the division in which class you are enrolled or the highest corresponding course. For example, if you are enrolled in CSCE240 and CSCE350 this semester you must compete in CSCE350+. However, if you are enrolled in CSCE145 and wish to compete in the CSCE350+ division that is fine, but you should be aware the the questions are much harder and the competition will be more fierce. The contest link for each division will let you sign up at anytime but you will not be able to access the problems until 7PM tonight. Each contest will close at 7PM tomorrow night. Please RSVP to this Facebook event so we can have an accurate number of those who wish to attend/participate: https://www.facebook.com/events/206021163169583/ Email me at vmcquinn@email.sc.edu if you have any questions about the contest in general. If you have questions about the problems during the contest please contest William Hoskins (hoskins.w.h@gmail.com) or Daniel Pade (djpade@gmail.com). Good luck everyone! Tori McQuinn

In this symposium, CSE Ph.D., master and undergraduate students will give poster presentations to report their research progress. You are welcome to attend this event, hear their presentations and discuss with them on their research topics. Food/drinks will be provided. This year, we will have our poster presentations in Faculty Lounge (1A03 Swearingen). Here is the agenda: 1:30 - 2:20 Poster set-up in Faculty Lounge (Swearingen 1A03) 2:20 - 3:20 Poster presentations in Faculty Lounge by CSE Students 3:20 - 4:15 Awards and faculty presentations in Amoco Hall

DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Rufeng Meng Advisor : Dr. Srihari Nelakuditi Abstract Positioning is a basic and important need in many scenarios of human daily activities. With position information, multifarious services could be vitalized to benefit all kinds of users, from individuals to organizations. This dissertation proposes solutions to address the need of positioning in people’s daily life from two aspects: transportation and shopping. All the solutions are smart-device-based (e.g. smartphone, smartwatch), which could potentially benefit most users considering the prevalence of smart devices. In positioning relevant activities, the components and their movement information could be sensed by different entities from diverse perspectives. The mechanisms presented in this dissertation treat the information collected from one perspective as reference and match it against the data collected from other perspectives to acquire absolute or relative position, in spatial as well as temporal dimension. To help drivers improve safety and ease the tension from driving, two correlated systems, Omni View and Driver talk, are provided. These systems infer the relative positions of the vehicles moving together by matching the appearance images of the vehicles seen by each other, which help drivers maintain safe distance from surrounding vehicles and also give them opportunities to precisely convey driving related messages to targeted peer drivers. To improve bus-riding experience for passengers of public transit systems, RideSense is developed. This system correlates the sensor traces collected by both passengers’ smart devices and reference devices in buses to position passengers’ bus-riding, spatially and temporally. With this system, passengers could be billed without any explicit interaction with conventional ticketing facilities in bus system, which makes the transportation system more efficient. For shopping activities, AutoLabel comes into play, which could position customers with regard to stores. AutoLabel constructs a mapping between WiFi vectors and semantic names of stores through correlating the text decorated inside stores with those on stores’ websites. Later, through WiFi scanning and a lookup in the mapping, customers’ smart devices could automatically recognize the semantic names of the stores they are in or nearby. Therefore, AutoLabel-enabled smart device serves as a bridge of information flow between business owners and customers, which could benefit both sides.

THESIS DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Chandrahas Raj G. Venkat Advisor : Dr. Rose ABSTRACT Hidden Markov Models are class of statistical models used in various disciplines for understanding speech, finding different types of genes responsible for cancer and many more. In this thesis, Hidden Markov Models are used to obtain hidden states that can correlate the flow changes in the Wakulla Spring Cave. Sensors installed in the tunnels of Wakulla Spring Cave recorded huge correlated changes in the water flows at numerous tunnels. Assuming the correlated flow changes are a consequence of system being in a set of discrete states, a Hidden Markov Model is calculated. This model comprising all the sensors installed in these conduits can help understand the correlations among the flows at each sensor and estimate the hidden states. In this thesis, using the Baum - Welch algorithm and observations from the sensors hidden states are calculated for the model. The generated model can help identify the set of discrete states for the quantized flow rates at each sensor. The hidden states can predict the correlated flow changes. This document further validates the assumption of the system being in a set of discrete states.

THESIS DEFENSE Department Of Computer Science and Engineering University of South Carolina Sourav Das ABSTRACT Time-series analysis is used heavily in modelling and forecasting weather, economics, medical data as well as in various other fields. Change point detection (CPD) means finding abrupt changes in the time-series when the statistical property of a certain part of it starts to differ. CPD has attracted a lot of attention in the artificial intelligence, machine learning and data mining communities. In this thesis, a novel CPD algorithm is introduced for segmenting multivariate time-series data. The proposed algorithm is a general pipeline to process any high dimensional multivariate time-series data using non-linear non-parametric dynamic system. It consists of manifold learning technique for dimensionality reduction, Gaussian process regression to model the non-linear dynamics of the data and predict the next possible time-step, as well as outlier detection based on Mahalanobis distance to determine the change points. The performance of the new CPD algorithm is assessed on synthetic as well as real-world data for validation. The pipeline is used on economic data to predict recession. Finally, functional magnetic resonance imaging (fMRI) data of larval zebrafish is used to segment regions of homogeneous brain activity.

Speaker: Jack Beasley (Managing Director) Affiliation: USC/Columbia Technology Incubator Location: SWGN 2A31 When: Friday, November 4th @ 2:20 - 3:10 PM Abstract: Join us Friday, November 4th at 2:20PM in SWGN 2A31 to learn about Startup Weekend. Startup Weekend is a global, grassroots movement designed to give participants a taste of the startup life and teaches the basics of launching successful ventures. The USC/Columbia Technology Incubator will be here to give us the details, and talk with us about how they help startups turn their ideas into sustainable businesses.

Speaker: Jack Beasley (Managing Director) Affiliation: USC/Columbia Technology Incubator Title: Turning Your Skills into a Business in 54 Hours Location: SWGN 2A31 When: Friday, November 4th @ 2:20 - 3:10 PM Join us Friday to learn about Startup Weekend. Startup Weekend is a global, grassroots movement designed to give participants a taste of the startup life and teaches the basics of launching successful ventures. The USC/Columbia Technology Incubator will be here to give us the details, and talk with us about how they help startups turn their ideas into sustainable businesses.

ACM will be having it's second meeting of the semester on Thursday, Novemeber 3 at 7:00PM in Amoco Hall. November 8 is election day so Dr. Duncan Buell will be speaking about voting machines and their relevance to the upcoming election. There will also be pizza for those who attend. More Details and RSVP

Speaker: Juan Caicedo Affiliation: Department of Civil and Environmental Engineering, USC Location: SWGN 2A31 Time: 2:20 - 3:10 PM

DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Muhammad Nazmus Sakib Advisor: DR. Chin-Tser Huang Date : Oct 26th 2016 Time : 8:30 am Place : 3A75 Swearingen ABSTRACT In this age of ubiquitous communication in which we can stay constantly connected with the rest of the world, for most of the part, we have to be grateful for one particular invention - the Internet. But as the popularity of Internet connectivity grows, it has become a very dangerous place where objects of malicious content and intent can be hidden in plain sight. In this dissertation, we investigate different ways to detect and capture these malicious contents hidden in the Internet. First, we propose an automated system that mimics high-risk browsing activities such as clicking on suspicious online ads, and as a result collects malicious executable files for further analysis and diagnosis. Using our system we crawled over the Internet and collected a considerable amount of malicious executables with very limited resources. Malvertising has been one of the major recent threats against cyber security. Malvertisers apply a variety of evasion techniques to evade detection, whereas the ad networks apply inspection techniques to reveal the malicious ads. However, both the malvertiser and the ad network are under the constraints of resource and time. In the second part of this dissertation, we propose a game theoretic approach to formulate the problem of inspecting the malware inserted by the malvertisers into the Web-based advertising system. During malware collection, we used the online multi-AV scanning service VirusTotal to scan and analyze the samples, which can only generate an aggregation of antivirus scan reports. We need a multi-scanner solution that can accurately determine the maliciousness of a given sample. In the third part of this dissertation, we introduce three theoretical models, which enable us to predict the accuracy levels of different combination of scanners and determine the optimum configuration of a multi-scanner detection system to achieve maximum accuracy. Malicious communication generated by malware also can reveal the presence of it. In the case of botnets, their command and control (C&C) communication is a good candidate for it. Among the widely used C&C protocols, HTTP is becoming the most preferred one. However, detecting HTTP-based C&C packets that constitute a minuscule portion of everyday HTTP traffic is a formidable task. In the final part of this dissertation, we present an anomaly detection based approach to detect HTTP-based C&C traffic using statistical features based on client generated HTTP request packets and DNS server generated response packets.