THESIS DEFENSE Author : Shannon Hood Advisor : Dr. Ioannis Rekleitis Abstract This paper proposes a solution to the problem of cooperative exploration using an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). More specifically, the UGV navigates through the free space, and the UAV provides enhanced situational awareness via its higher vantage point. The motivating application is search and rescue in a damaged building. A camera atop the UGV is used to track a fiducial tag on the underside of the UAV, allowing the UAV to maintain a fixed pose relative to the UGV. Furthermore, the UAV uses its front facing camera to provide a birds-eye-view to the remote operator, allowing for observation beyond obstacles that obscure the UGV's sensors. The proposed approach has been tested using a TurtleBot 2 equipped with a Hokuyo laser ranger finder and a Parrot Bebop 2. Experimental results demonstrate the feasibility of this approach. This work is based on several open source packages and the generated code will be available online.

Thesis Defense Author : Nicholas Weidner Advisor : Dr. Ioannis Rekleitis Abstract The proposed work presents a systematic approach for 3-D mapping and reconstruction of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology, which has never before been addressed. The proposed solution employs a stereo camera and a video-light. The approach utilizes the intersection of the cone of video-light with the cave boundaries resulting in the construction of a wire frame outline of the cave. Successive frames produce a scalable accurate point cloud which, through the use of adapted 3-D geometry reconstruction techniques, creates a fully replicated model of the cave system.

People know when they need to hire a dentist or an accountant, but they rarely know when they need to hire a linguist. This talk focuses on the professional opportunities available to people with traditional linguistics and computational linguistics training, from undergraduate to graduate degrees. Laura Walsh Dickey shares her experience transitioning from academia to industry. As part of the talk, she discusses specific problems she’s worked on and the kinds of interesting challenges that linguists might find themselves working on in industry. She talks about how to spot jobs that might be appropriate for linguists and gives practical tips about finding them, applying for them, and deciding what’s right for you. Laura Walsh Dickey is a Program Manager at Google, focusing on machine learning and language technology. She joined Google in 2013 with a PhD in Linguistics from the University of Massachusetts, Amherst and 25 years of experience in academia, consulting, and industry. Her research at the Max Planck Institute for Psycholinguistics and Northwestern University focused on the phonology of liquid consonants, speech perception, and speech production. Her forays into the consulting world opened up a new area of linguistic problems which needed to be solved, from drug name confusability to teaching foreign language pronunciation to understanding what people mean when they type in that Google search box.

IIT Faculty Candidate Seminar Sponsored by Department of Integrated Information Technology Yoris Au Department of Information Systems and Cyber Security College of Business The University of Texas at San Antonio Abstract: This study investigates the effect of observational learning in the crowdsourcing market as an attempt to identify appropriate mechanism(s) for sustaining this increasingly popular business model. Observational learning occurs when crowdsourcing participating agents obtain knowledge from signals they observe in the marketplace and incorporate such knowledge into subsequent actions to improve their participation outcomes. This form of learning is examined in the context of the two-sided crowdsourcing platform in which participating customers’ and professionals’ decisions interact with and influence each other. Two structural models are constructed to capture customer and professional’s probability of success in the presence of various constantly changing market signals. A third model is developed to capture factors that influence market outcomes such as level of participation by professionals. These models will be estimated using the Bayesian approach on a longitudinal dataset that consists of seven years of transaction data in four product categories from a leading crowdsourcing site. We expect to observe learning effect in this crowdsourcing market and to identify various factors that influence the probability of a professional (agent) submitting a bid to a crowdsourcing project and the probability of a customer (principal) selecting a winner through observational learning.

IIT Faculty Candidate Seminar Sponsored by Department of Integrated Information Technology Martin Stytz Chief Research & Technology Officer Calculated Insight Abstract: Healthcare decision-support is a new, crucial, vibrant area of research motivated by the rapid pace of advances in medical information technology and the vast amount of data that a healthcare provider will have to comprehend and prioritize when technology allows a complete patient medical record to be made available. We believe that the amount of data that the healthcare provider will confront will be overwhelming. In a large-scale medical data environment, preventing data overload and protecting the integrity of the data will be important. Clearly, the healthcare provider requires decision-support tools that will aid in retrieving, identifying, displaying, and analyzing the relevant medical data. Our goal is to develop simulation technologies that can be used to build advanced medical decision-support tools that can exploit the large-scale amounts of medical data that will be available. However, as noted the data must be trustworthy. A cyber-attack upon medical data can disrupt information, sow confusion, thwart situational awareness, increase decision time, and delay reaction to events. Because of the seriousness of the consequences of a cyber-attack, we contend that medical decision-makers must be prepared to operate within environments where information is compromised. A safe method for preparing for the cyber-attacks is to acclimate medical decision-makers to information compromised environments using simulation systems. The cyber-attack simulation environments can cause the information uncertainty and confusion that cyber-attacks produce. These same cyber simulation environments can be used to develop intelligent cyber defense systems that react to preserve the medical information environment. Additionally, the cyber-attack simulation environment can be used to develop and test cyber defense strategies and technologies. In the talk, we will discuss simulation to improve healthcare delivery through the development of better decision-support tools and the use of simulation to improve cyber security, and medical infrastructure cyber resilience. Biography: Dr. Martin Stytz received his PhD form University of Michigan in 1989. His research interests encompass secure systems, secure software development, cybersecurity, high-confidence data analysis, and cyber situational awareness.. Dr. Stytz has published 28 journal articles, over 300 technical articles and holds two patents. Dr. Stytz has conducted $12.8 Million ($8 Million as PI) in research for the US Government.

COLLOQUIUM Selcuk Uluagac Abstract Cyber space is expanding fast with the introduction of new Internet of Things (IoT) and CPS devices. Wearables, smart watches, glasses, fitness trackers, medical devices, Internet-connected house appliances and vehicles have grown exponentially in a short period of time. Our everyday lives will be dominated by billions of connected smart devices by the end of this decade. Similarly, our nation's critical infrastructure (e.g., Smart Grid) also deploys a myriad of CPS and IoT equipment. Given the increasingly critical nature of the cyberspace of these CPS and IoT devices and the CPS infrastructure, it is imperative that they are secured against malicious activities. In this talk, I will briefly introduce three different current research topics related to the security of CPS and IoT devices and the CPS infrastructure: (1) The first topic will introduce the sensory channel threats to CPS and IoT systems. I will discuss how using sensory channels (e.g., light, temperature, infrared), an adversary can successfully attack IoT/CPS applications and devices. (2) The second topic will introduce the design of a novel IoT device fingerprinting and identification framework (IFF) to complement existing security solutions (e.g., authentication and access control) in identifying CPS and IoT devices (i.e., ensuring the devices are actually who they are). Finally, (3) The third topic will focus on the threat of counterfeit smart grid devices (e.g., PMUs, IEDs). Such devices with corrupted hardware components may exist in the deployment region without a priori knowledge and may leak important information to malicious entities. Dr. Selcuk Uluagac is currently an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Florida International University (FIU). Before joining FIU, he was a Senior Research Engineer in the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Technology. Prior to Georgia Tech, he was a Senior Research Engineer at Symantec. He earned his Ph.D. with a concentration in information security and networking from the School of ECE, Georgia Tech in 2010. He also received an M.Sc. in Information Security from the School of Computer Science, Georgia Tech and an M.Sc. in ECE from Carnegie Mellon University in 2009 and 2002, respectively. He obtained his BS in Computer Science and Engineering and BA in Naval Science from the Turkish Naval Academy in 1997. The focus of his research is on cyber security topics with an emphasis on its practical and applied aspects. He is interested in and currently working on problems pertinent to the security of Internet of Things and Cyber-Physical Systems. In 2015, he received a Faculty Early Career Development (CAREER) Award from the US National Science Foundation (NSF). In 2015, he was also selected to receive fellowship from the US Air Force Office of Sponsored Research (AFOSR)?s 2015 Summer Faculty Fellowship Program. In 2016, he received the Summer Faculty Fellowship from the University of Padova, Italy. In 2007, he received the ?Outstanding ECE Graduate Teaching Assistant Award? from the School of ECE, Georgia Tech. He is an active member of IEEE (senior grade), ACM, USENIX, and ASEE and a regular contributor to national panels and leading journals and conferences in the field. Currently, he is the area editor of Elsevier Journal of Network and Computer Applications and serves on the editorial board of the IEEE Communication Surveys and Tutorials. More information can be obtained from: http://web.eng.fiu.edu/selcuk.

COLLOQUIUM Xueqing Huang Mobile and wireless systems are bracing for a massive penetration of Internet of Things (IoT) devices and experiencing an exponential growth in wireless applications. To achieve the expected service requirements, the networking resources are being pushed to the edge, such that each edge node can function as a standalone local unit, which has its own green energy harvester, cache and computing resources. Since resources available at the edge nodes are limited and dynamic, network cooperation is critical to guarantee the smooth operation and security of the wireless access networks. In this talk, I will present the network cooperation framework that allows the ?connected? edge nodes to share their networking resources. The first part of this talk concentrates on secure cooperative data transmission. By exploring the broadcasting nature of wireless links, the radio resources in terms of energy can be shared by allowing base stations to transmit data to the same user. I will present cooperative data transmission schemes to improve the network performance in terms of energy efficiency and data confidentiality. The second part of this talk focuses on secure data crowdsourcing. By leveraging multiple data paths at the mobile edge, distributed storage resources can be used to facilitate data sharing among a crowd of users. Data privacy is paramount in assuring users in crowdsourcing; I will present a multi-party data transmission scheme to improve the data sharing latency and data privacy. Xueqing Huang received the B.E. degree from the Hefei University of Technology in 2009, and the M.E. degree from the Beijing University of Posts and Telecommunications in 2012. She is currently a Ph.D. candidate from the New Jersey Institute of Technology. Her research interests are in internet of things, physical layer and network security, and cooperative mobile edge.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Damindra Bandara Abstract Safety objectives of Positive Train Control (PTC) are to avoid train to train collisions, train derailments and ensure railroad worker safety. Under published specifications of Interoperable Electronic Train Management System (I-ETMS), the on-board PTC controller communicates with two networks; the Signaling Network and the Wayside Interface Unit (WIU) network to gather navigational information such as the positions of other trains, the status of critical infrastructure and any hazardous conditions along the train path. PTC systems are predicated on having a reliable radio communication network. Secure Intelligent Radio for Trains (SIRT) is an intelligent radio that is customized for train operations with the aim of improving the reliability and security of the radio communication network. SIRT system can (1) operate in areas with high train congestion, different noise levels and interference conditions, (2) withstand jamming attacks, (3) improve data throughput and (4) detect threats and improve communication security. My work includes (1) Analyzing the PTC system to identify communication constraints and vulnerabilities, (2) Designing SIRT to overcome them, (3) Developing a prototype of SIRT using Software Defined Radios and (4) Testing it under varying channel conditions, noise levels and attackers. My experiments show that SIRT dynamically chooses the best modulation schemes based on the channel noise level and switches channels in response to channel jamming. Also, it changes cryptographic key values using a scheme like Lamport scheme and detects replay and forgery attacks with an accuracy more than 93%. Damindra Bandara is a Ph.D. candidate at George Mason University, Fairfax, Virginia. She will defend her Ph.D. by the end of March 2017. She received her Bachelor's degree in Electrical and Electronic Engineering from University of Peradeniya, Sri Lanka and Master degree in Information Security and Assurance from George Mason University. Previously she has worked as a Wireless Quality Assurance intern at Time Warner Cable, Herndon Virginia and as a lecturer in Department of Electrical and Electronic Engineering, University of Peradeniya, Sri Lanka. Her research interests are network security, wireless controlled trains and Software Defined Radio.

COLLOQUIUM Sherif Abdelwahed Abstract Modern computing systems support a range of mission-critical information technology applications crucial to commerce and banking, transportation, and command and control systems, to name just a few. Consequently, their reliable design and operation have significant economic and social impact. To operate such systems effectively while maintaining their availability and security multiple operational data and parameters must be analyzed in real-time and dynamically tuned to adapt to abnormal conditions such as failures or cyber-attacks. As system and application scales increase, ad hoc heuristic-based approaches to system adaptation and management quickly become ineffective. Model-based technologies help address this problem by enabling design-time and run-time analysis, and providing means to automate the development, verification, deployment and real-time adaptation of computing systems. This presentation introduces recent work on developing model-based approaches for systematic design of reliable and secure self-managing computing systems. The developed approaches use mathematical models to represent the system reaction to both control and environment inputs. In these approaches, the system management problems of interest are posed as a sequential and discrete optimization under uncertainty. Results of this work show that model-based techniques can be effectively applied to maintain the security and reliability of complex modern computing systems. The presentation introduces several implementations of this model-based technology and discusses future related research directions. Sherif Abdelwahed is an Associate Director of the Distributed Analytics and Security Institute (DASI) and an Associate Professor in the Electrical and Computer Engineering Department at Mississippi State University (MSU) where he teaches and conducts research in the area of computer engineering, with specific interests in cyber-security, autonomic computing, real-time systems, modeling and analysis of discrete-event and hybrid systems, model-integrated computing, and formal verification. He received his Ph.D in 2002 from the Department of Electrical and Computer Engineering at the University of Toronto. Prior to joining Mississippi State University, he was a research assistant professor at the Department of Electrical Engineering and Computer Science and senior research scientist at the Institute for Software Integrated Systems, Vanderbilt University, from 2001-2007. From 2000-2001 he worked as a research scientist with Rockwell Scientific Company. He established, collaboratively, the first NSF I/UCRC center at Mississippi State University, the Center for Autonomic Computing. He is currently the co-director of this center. He co-chaired several international conferences and conference tracks, and has served as technical committee member at various national and international conferences. He received the StatePride Faculty award for 2010 and 2011, the Bagley College of Engineering Hearin Faculty Excellence award in 2010, and recently the 2016 Faculty Research Award from the Bagley College of Engineering at MSU. Dr. Abdelwahed has more than 140 publications and is a senior member of the IEEE.

COLLOQUIUM Department of Computer Science and Engineering University of South Carolina Shuai Li Abstract With the advances of mechanics, electronics, computer engineering, using autonomous robots, or a collection of them, to perform various tasks is becoming increasingly popular in both industry and our daily lives. Control plays an important role for stable and accurate task execution while learning is outstanding in dealing with unknowns or uncertainties. Recent advances in machine learning provide us with an opportunity to employ innovative learning structures for efficient adaptation. However, it remains challenging on how to efficiently integrate learning with control efficiently to reach provable and guaranteed stability even in the worst case. This talk will present our recent results along this research direction. Shuai Li received the B.E. degree in electrical engineering from the Hefei University of Technology, Hefei, China, in 2005, the M.E. degree in control engineering from the University of Science and Technology of China, Hefei, in 2008, and the Ph.D. degree in electrical and computer engineering from the Stevens Institute of Technology, Hoboken, NJ, USA, in 2014. He joined Hong Kong Polytechnic University after graduation and directed his group to do research in robotics, cyber physical systems, intelligent control, etc. Dr. Li is an associate editor of the International Journal of Advanced Robotic Systems, Frontiers in Neurorobotics, and Neural Processing Letters.