DISSERTATION DEFENSE Department of Computer Science and Engineering, University of South Carolina Candidate: Nicholas Stiffler Advisor: Dr. Jason O’Kane Abstract As technological advances further increase the amount of memory and computing power available to mobile robots we are seeing an unprecedented explosion in the utilization of deployable robots for various tasks. The speed at which robots begin to enter various domains is largely dependent on the availability of robust and efficient algorithms that are capable of solving the complex planning problems inherent to the given domain. One such domain which is experiencing unprecedented growth in recent years requires a robot to detect and/or track a mobile agent or group of agents. In these scenarios there are typically two-players with diametrically opposed goals. For matters of security, we have a guard and an intruder. The guard's goal is to ensure that if an intruder enters the premises they are caught in a timely manner. Analogously, the intruder wishes to evade detection for as long as possible. Search and rescue operations are often framed as a two-player game between rescuers and survivors. Though the survivors are unlikely to behave antagonistically, an agnostic model is useful for the rescuers to guarantee that the survivors are found, regardless of their movements. Both of these tasks are at their core pursuit-evasion problems. There are many variants of the pursuit-evasion problem, the common theme amongst them is that one group of agents, the ``pursuers'', attempts to track members of another group, the ``evaders''. The visibility-based pursuit-evasion problem requires a pursuer(s) to systematically search an environment to locate one or more evaders ensuring that all evaders will be found by the pursuer(s) in a finite time. This thesis contains four novel contributions that solve various visibility-based pursuit-evasion problems. The first contribution is an algorithm that computes the optimal (minimal path length) pursuer trajectory for a single pursuer. The second contribution is an algorithm that generates a joint motion strategy for multiple pursuers. Motivated by the result of the second contribution, the third result is a sampling-based algorithm for the multiple pursuer scenario. The fourth contribution is a complete algorithm that computes a trajectory for a pursuer that has a very limited sensor footprint.

Jenay Beer, University of South Carolina Abstract: Maintaining one’s independence is a primary goal of older adults and a key component to successful aging and aging-in-place. Technology has the potential to help older adults maintain their independence. In this presentation, Dr. Beer will discuss current and future technology aids, such as robotics and smart homes. For assistive technology to be successful, it is important that the older adult user finds the technology to be simple, user friendly, and useful – a field of study called user-centered design! We will discuss what makes technology user-friendly, how technology might be integrated into the home or healthcare setting, and where the field is headed. Bio: Dr. Beer is an engineering psychologist specializing in human-robot interaction (HRI) for the older adult population. Primary research interests include the application of technology to improve the quality of lives for older adults, as well as the application of assistive technology for older individuals with disabilities. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

Sanjai Rayadurgam, University of Minnesota Abstract: Constructing good test cases and correctly judging their execution on the system under test are particularly challenging for embedded control software in a variety of application domains. Typically, models of these systems are often constructed during development to aid in analysis, simulation, design and code-generation. These models can then also be used as a source for generating test cases and as a reference against which the eventual implementation is to be judged. This talk will cover some recent work along these lines: first, how a notion of observability as a basis for test coverage in concert with dynamic symbolic execution enables an incremental test generation strategy that is efficient and effective; second, how differences between the abstract model and the concrete implementation can be reconciled when judging test executions, using both reactively permissive proactively adaptive strategies. Testing, and more generally, verification activities generate evidence to support important dependability claims about the system being developed. To gain regulatory approval or certification for critical systems, such evidence must be tied to the claims being made through well-justified and structured arguments, often referred to as assurance cases. Demonstrating high confidence that the claims made based on an assurance case can be trusted is crucial to the success of the case. The later part of the talk will cover some recent and ongoing work in the area of quantifying and reasoning about confidence in assurance cases. Bio: Sanjai Rayadurgam is a researcher at the University of Minnesota Software Engineering Center in the Department of Computer Science and Engineering. His research interests are in software testing, formal analysis and requirements modeling, with particular focus on safety-critical systems development and he has co-authored several papers on these topics. He also has ten years of industrial experience in modeling, development and verification of implantable medical devices. His current research deals with problems in assurance, certification, verification and validation of cyber-physical systems, cyber-security and autonomy applications. Rayadurgam received his PhD degree in Computer Science from the University of Minnesota. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

Bryant Walker Smith, University of South Carolina (School of Law) Abstract: This discussion will explore the technologies, applications, and legal aspects of automated driving. Bio: Bryant Walker Smith is an assistant professor in the School of Law and (by courtesy) in the School of Engineering at the University of South Carolina. He is also an affiliate scholar at the Center for Internet and Society at Stanford Law School, chair of the Emerging Technology Law Committee of the Transportation Research Board of the National Academies, and a member of the New York Bar. Bryant's research focuses on risk (particularly tort law and product liability), technology (automation and connectivity), and mobility (safety and regulation). As an internationally recognized expert on the law of self-driving vehicles, Bryant taught the first-ever course on this topic and is regularly consulted by government, industry, and media. His recent article, Proximity-Driven Liability, argues that commercial sellers' growing information about, access to, and control over their products, product users, and product uses could significantly expand their point-of-sale and post-sale obligations toward people endangered by those products. Before joining the University of South Carolina, Bryant led the legal aspects of automated driving program at Stanford University, clerked for the Hon. Evan J. Wallach at the United States Court of International Trade, and worked as a fellow at the European Bank for Reconstruction and Development. He holds both an LL.M. in International Legal Studies and a J.D. (cum laude) from New York University School of Law and a B.S. in civil engineering from the University of Wisconsin. Prior to his legal career, Bryant worked as a transportation engineer.

DISSERTATION DEFENSE Department of Computer Science and Engineering, University of South Carolina Candidate: Yuewei Lin Advisor: Dr. Song Wang Date: March 16, 2016 Time: 12:00 P.M. Place: Swearingen 3A75 Abstract Interest detection is to detect an object, event, or process which causes attention. In this work, we focus on the interest detection in the image, video and multiple videos. Interest detection in an image or a video, is closely related to the visual attention. However, the interest detection in multiple videos needs to consider all the videos as a whole rather than considering the attention in each single video independently. In this work, we first introduce a new computational visual-attention model for detecting region of interest in static images and/or videos. This model constructs the saliency map for each image and takes the region with the highest salient value as the region of interest. Specifically, we use the Earth Mover's Distance (EMD) to measure the center-surround difference in the receptive field. Furthermore, we propose to take two steps of biologically-inspired nonlinear operations for combining different features. Then, we extend the proposed model to construct dynamic saliency maps from videos, by computing the center-surround difference in the spatio-temporal receptive field. Motivated by the natural relation between visual saliency and object/region of interest, we then propose an algorithm to detect infrequently moving foreground, in which the saliency detection technique is used to identify the foreground (object/region of interest) and background. Finally, we focus on the task of locating the co-interest person from multiple temporally synchronized videos taken by the multiple wearable cameras. More specifically, we propose a co-interest detection algorithm that can find persons that draw attention from most camera wearers, even if multiple similar-appearance persons are present in the videos. We built a Conditional Random Field (CRF) to achieve this goal, by taking each frame as a node and the detected persons as the states at each node.

More info at: https://www.facebook.com/events/943148319099082/

Stephen Fenner, University of South Carolina Abstract: We show that the bipartite perfect matching problem is in quasi-NC. In particular, it has uniform circuits of quasi-polynomial size and O(log^2 n) depth. Previously, only an exponential upper bound was known on the size of such circuits with poly-logarithmic depth. We obtain our result by an almost complete derandomization of the Isolation Lemma of Mulmuley, Vazirani, & Vazirani, which was used to yield an efficient randomized parallel algorithm for the bipartite perfect matching problem. Time permitting, we describe an RNC algorithm to find a perfect matching in a bipartite graph using O(log^2 n) random bits. Bio: Stephen Fenner is a professor of Computer Science and Engineering at the University of South Carolina. His research interests are in theoretical computer science and include computational complexity, computability, algorithms, and quantum informatics. Be aware, this talk will be held in an earlier timeslot (1:45 - 3:00) in a different room (2A19) than usual. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

Duncan Buell, University of South Carolina Abstract: The pedagogical center of many university First Year Composition programs is the revision of essays, the notion that the draft of a student’s English 101 essay should be revised before being turned in as a final version. Most of the established research on FYC concludes that students revise in a shallow way, correcting minor grammatical errors and doing minor word substitution. This research has, however, been conducted by human beings examining small sets of FYC essays. Dr. Buell, together with Dr. Chris Holcomb, Director of First Year English at USC, have been looking at revision in the ENGL 101/102 as a “big data” computation. They have written Python code to compare draft and final versions on specific and targeted features that can be examined by computer. Based on an early corpus of 439 papers from 2014-2015, it would seem that the established conclusions about student revision are just wrong, and that student revision is much different thing. Buell and Holcomb, with a team of graduate and undergraduate students funded by the Center for Digital Humanities, are working to collect all 10,000 (plus or minus) essays from ENGL 101 and 102 in the spring 2016 and following semesters, and to process them all to examine revision and writing characteristics. This is thus a combination of a “big data” and a “natural language” computation. We emphasize that although we use natural language packages, this is not software to “grade” or “assess” the writing. Rather, we have targeted characteristics thought to be typical of student (and compared against “academic”) writing, and we are computing quantified measurements of these characteristics. Bio: Duncan A. Buell is a Professor in the Department of Computer Science and Engineering at the Unviversity of South Carolina. His Ph.D. is in mathematics from the University of Illinois at Chicago (1976). He was from 2000 to 2009 the department chair at USC, and in 2005-2006 was interim dean. He has done research in document retrieval, computational number theory, and parallel computing, and has more recently turned to digital humanities as one of the emerging “marketplace” applications for computing. He is engaged with First Year English at USC on the analysis of freshman English essays, searching for an understanding of actual student writing in an effort to improve pedagogy for first year English instruction. He has team taught four times with Dr. Heidi Rae Cooley on the presentation of unacknowledged history on mobile devices, and he and Dr. Cooley are actively engaged in ways to go beyond text to fully enable the use of visual media in mobile applications that present humanities content, especially content that might normally remain unacknowledged by institutional authority. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

Matthew Brashears, University of South Carolina (Department of Sociology) Abstract: Humans make mistakes but diffusion through social networks is typically modeled as though they do not. We find in an experiment that high entropy message formats (text messaging pidgin) are more prone to error than lower entropy formats (standard English). We also find that efforts to correct mistakes are effective, but generate more mutant forms of the contagion than would result from a lack of correction. This indicates that the ability of messages to cross “small-world” human social networks may be overestimated and that failed error corrections create new versions of a contagion that diffuse in competition with the original. Bio: Matthew E. Brashears is an Associate Professor of Sociology at the University of South Carolina. His current research focuses on linking cognition to social network structure, studying the effects of error and error correction on diffusion dynamics, and using ecological models to connect individual behavior to collective dynamics. His work has appeared in Nature Scientific Reports, the American Sociological Review, Social Networks, Sociological Science, and Social Psychology Quarterly, among others. He has received grants from the National Science Foundation, the Defense Threat Reduction Agency, and the Army Research Office. He currently serves on the editorial board for Social Psychology Q This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.