COLLOQUIUM Glenn T. Starkman COO and Co-founder Soteria, LLC Cyber Security Data and Analytics How do you go from technology to entrepreneurial success? This talk will explore the nuts and bolts, including communication and networking, that go into a successful tech startup. An example of the entrepreneurial process that will be presented is the founding of Soteria. Common themes that recur among entrepreneurs will be examined. Glenn Starkman is the COO and Co-founder of Soteria, a cyber security company that consults businesses with both pre and post breach security incidents. Soteria founders are former NSA elite operatives now based in Charleston, SC. He is a graduate of Boston University with a background in Economics and Math. He is also a Board Member, Co-Founder and Angel Investor in BevBucks, Vixen Enterprises and The Code Lady.. He was previously the Managing Director and Global Head of Sales at UBS, Goldman Sachs and was previously a Partner at Sanford Bernstein, an Asset management company, as well as the Founder and CEO of Starkman Capital LLC. Glenn is also Entrepreneur in Residence at the College of Charleston and is responsible for the Tommy Baker Lecture Series in Entrepreneurial Leadership, is on The College of Charleston Board or Entrepreneurship and leads a weekly lecture series ENTR 445. From this class Glenn will share many of the themes described by speakers dating back to 2014. Glenn will be available for Q and A post his comments.

DISSERTATION DEFENSE Department of Computer Science and Engineering, University of South Carolina Candidate: Emrah Atilgan Advisor: Dr. Jianjun Hu Abstract Developing new materials have historically been time-consuming. Computational material discovery can search large design space to identify promising candidates for experimental verification. Recently, Density Functional Theory (DFT) based first principle calculation has been able to calculate many electrical and physical properties of materials, making them suitable for computational doping based material discovery. In material doping, given a base material, one can change its properties by substituting some elements with new ones or adding additional elements. In computational doping, we have a grid of atoms in a supercell, some of which can be substituted with dopant atoms. There are many possible doping positions for the doped elements in the supercell, among which the most stable supercell with the lowest free electronic energy is the one that most likely appears in experiments. So finding the most stable doped supercell configuration is the first step for computational doping, which is usually done exhaustively nowadays. For each such substitution, the Vienna Ab-Initio Simulation Package is usually used to calculate its energy and higher level physicochemical properties. Free energy calculations take about 15-30 hours for a supercell of 75 atoms for substituting two positions out of 15 with a single dopant element, and it may take days to weeks for multiple dopant elements. This is a typical optimization problem with expensive evaluation functions. Here we first developed a genetic algorithm for finding the most stable structure of the doped material with the lowest free electronic energy for a single dopant element. It can reduce the running time for computational doping by up to 75%. We used SrTiO3 perovskite as the base material and Nb as the substitution element. We also developed another genetic algorithm for multiple dopant elements. Since the search space becomes larger, the genetic algorithm works better and saves up to 85% of calculations for finding the most stable structures. Finally, we developed a genetic programming (GP) algorithm for computational doping which can simultaneously determine multiple dopant elements with different doping ratios. The simultaneous search of dopant elements and their ratios can speed up the search process for large doping spaces.

Gregory Gay, University of South Carolina Abstract: There are two key artifacts necessary to test software, the test data - inputs given to the system under test (SUT) - and the oracle - which judges the correctness of the resulting execution. Substantial research efforts have been devoted towards the creation of effective test inputs, but relatively little attention has been paid to the creation of oracles. Specifying test oracles remains challenging for many domains, such as real-time embedded systems, where small changes in timing or sensory input may cause large behavioral differences. Models of such systems, often built for analysis and simulation before the development of the final system, are appealing for reuse as oracles. These models, however, typically represent an idealized system, abstracting away certain considerations such as non-deterministic timing behavior and sensor noise. Thus, even with the same test data, the model’s behavior may fail to match an acceptable behavior of the SUT, leading to many false positives reported by the oracle. This talk will present an automated framework that can adjust, or steer, the behavior of the model to better match the behavior of the SUT in order to reduce the rate of false positives. This model steering is limited by a set of constraints (defining acceptable differences in behavior) and is based on a search process attempting to minimize a numeric dissimilarity metric. This framework allows non-deterministic, but bounded, behavior differences, while preventing future mismatches, by guiding the oracle—within limits—to match the execution of the SUT. Results show that steering significantly increases SUT-oracle conformance with minimal masking of real faults and, thus, has significant potential for reducing false positives and, consequently, development costs. Bio: Gregory Gay is an Assistant Professor of Computer Science & Engineering at the University of South Carolina. His research interests include automated testing and analysis—with an emphasis on test oracle construction—and search-based software engineering. Greg received his Ph.D. from the University of Minnesota, working with the Critical Systems research group, and an M.S. from West Virginia University. He has previously worked with NASA Ames Research Center and the Chinese Academy of Sciences. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

DISSERTATION DEFENSE Department of Computer Science and Engineering, University of South Carolina Candidate: Annamaria Victoria Kish Advisor: Dr. Cilla Farkas Date: April 14, 2016 Time: 12:00 P.M. Place: Swearingen 3A75 Abstract Our aim is to provide efficient partitioning and allocation of data for web service compositions. Web service compositions are represented as partial order database transactions. We accommodate a variety of transaction types (such as read-only and write-oriented transactions) to support workloads in cloud environments. We introduce an approach that partitions and allocates small units of data, called micropartitions, to multiple database nodes. Each database node stores only the data needed to support a specific workload. Transactions are routed directly to the appropriate data nodes. Our approach guarantees serializability and efficient execution. In Phase1, we cluster transactions based on data requirements. We associate each cluster with an abstract query definition. An abstract query represents the minimal data requirement that would satisfy all the queries that belong to a given cluster. A micropartition is generated by executing the abstract query on the original database. We show that our abstract query definition is complete and minimal. Intuitively, completeness means that all queries of the corresponding cluster can be correctly answered using the micropartition generated from the abstract query. The minimality property means that no smaller partition of the data can satisfy all of the queries in the cluster. We also aim to support efficient web services execution. Our approach reduces the number of data accesses to distributed data. We also aim to limit the number of replica updates. Our empirical results show that the partitioning approach improves data access efficiency over standard partitioning of data. In Phase 2, we investigate the performance improvement via parallel execution. Based on the data allocation achieved in Phase I, we develop a scheduling approach. Our approach guarantees serializability while efficiently exploiting parallel execution of web services. We achieve conflict serializability by scheduling conflicting operations in a predefined order. This order is based on the calculation of a minimal delay requirement. We use this delay to schedule services to preserve serializability without the traditional locking mechanisms.

Come join WiC and Marie Chatfield of Square! Marie graduated recently from Rice University in Texas and moved to Silicon Valley. She’ll give a talk about how web applications work and then talk about her journey into software engineering. The meeting is Tuesday, April 12th at 6:00-7:30pm in Swearingen 2A24. There will be free pizza, Square swag, and as always, all are welcome to attend! RSVP here.

Patrick Hubbard, University of South Carolina (School of Law) Abstract: Our lives are being transformed by large, mobile, "sophisticated robots" with increasingly higher levels of autonomy, intelligence, and interconnectivity among themselves. For example, driverless automobiles are likely to become commercially available within a decade. Many people who suffer physical injuries from these robots will seek legal redress for their injury, and regulatory schemes are likely to impose requirements on the field to reduce the number and severity of injuries. This talk addresses the issue of whether the current liability and regulatory systems provide a fair, efficient method for balancing the concern for physical safety against the need to incentivize the innovation that is necessary to develop the robots. The talk provides context for analysis by reviewing innovation and robots' increasing size, mobility, autonomy, intelligence, and interconnections in terms of safety - particularly in terms of physical interaction with humans - and by summarizing the current legal framework for addressing personal injuries in terms of doctrine, application, and underlying policies. This talk argues that the legal system's method of addressing physical injury from robotic machines that interact closely with humans provides an appropriate balance of innovation and liability for personal injury. It critiques claims that the system is flawed and needs fundamental change and concludes that the legal system will continue to fairly and efficiently foster the innovation of reasonably safe sophisticated robots. Bio: Professor Hubbard has been a member of the University of South Carolina School of Law since 1973. He retired from full time teaching in 2015. He currently teaches Legal Theory and Land Use Planning. In recent years, he also taught Torts, Products Liability, Evidence, and Criminal Law. Before joining the faculty, Professor Hubbard was an associate at Mudge, Rose, Guthrie, and Alexander (New York City) and was a staff attorney with Community Legal Services Program (Austin, TX). He graduated Phi Beta Kappa from Davidson College. He received a JD from New York University School of Law and a LLM from Yale Law School. Professor Hubbard has written books on tort law and criminal law and has published dozens of articles and book chapters on criminal law, legal theory, torts, and land use planning. As a legal realist, he actively related his scholarship to the world outside the law school. For example, his interest in land use planning includes working on a drafting committee for recent amendments to the South Carolina zoning enabling act, serving as chair of the Columbia Planning Commission in the 1990s and as vice-chair of the Board of Zoning Appeals currently, and working with a taskforce revising the Columbia Zoning Code, and assisting neighborhood organizations in zoning matters. Professor Hubbard has been a visiting professor of law at University of Southampton U.K., at University of Birmingham, U.K., and at Florida Coastal School of Law. Professor Hubbard and his wife have been happily married since 1968. They have two sons, both of whom are married, and have five grandchildren. This seminar is open to anyone who is interested, not just students enrolled in the CSCE 791 class. Please consider attending.

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.