- Course Name: CSCE 791 - Seminar on Advances in Computing
- Semester: Spring 2016
- Instructor: Greg Gay
- Lecture Hours: Friday, 2:50 - 4:05 PM, 2A27 Swearingen Engineering Center
CSCE 791 is a colloquium series, consisting of talks or seminars given by invited speakers, both from our department and from outside the department and university. The primary goal of this course is to expose students to the "state-of-art" research and development in a variety of computing-related disciplines. CSCE 791 is a great opportunity to see some of the brightest minds from academia and industry and hear their thoughts in person, as well as ask questions and interact with them.
This content is made available in the interest of sharing educational material with any who might find it useful. This page is updated periodically, and may not be in synch with the course itself. For current course students, the latest content, assignment submission, and discussion forums are available on Moodle.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
- January 15, 2016 - Applying Multimodal Sensing to Indoor Localization
- Speaker: He Wang, University of Illinois at Urbana Champaign
- Abstract: Indoor localization has been a tantalizing problem in mobile computing, and despite significant research, there is no solution yet in the mainstream. In this talk, I will discuss the landscape of indoor localization. I will also talk about my own research, UnLoc, which breaks away from pure RF based localization (e.g., cellular, Wi-Fi) and shows the benefits of leveraging smartphone sensors (accelerometers, gyroscopes, magnetometers, etc.) into the solution framework. I will describe additional solutions, VideoLoc, where feeds from surveillance cameras can be leveraged for highly precise localization and customer interaction, without compromising privacy of individual users. I will end with how some of our core techniques are not specific to localization and can be extended to other applications such as augmented reality.
- Bio: He Wang is a PhD candidate in the department of Electrical and Computer Engineering at University of Illinois at Urbana Champaign. His research focuses on designing mobile sensing systems, with an emphasis on indoor localization. His work has been featured in the media such as Scientific American, MIT Technology Review, LA Times, Yahoo News and Daily Mail. He received his master’s degree from Duke University in 2013 and bachelor’s degree from Tsinghua University in 2011.
- January 22, 2016 - An enhanced Metropolis-Hastings algorithm based on Gaussian processes
- Speaker: Asif Jamil Chowdhury, University of South Carolina
- Abstract: Markov Chain Monte Carlo (MCMC) has become the main computational workhorse in scientific computing for solving statistical inverse problems. It is difficult however to use MCMC algorithms when the likelihood function is computational expensive to evaluate.Here, a novel Metropolis-Hastings algorithm is proposed to sample from posterior distributions corresponding to computationally expensive simulations. The main innovation is emulating the likelihood function using Gaussian processes. The proposed emulator is constructed on the fly as the MCMC simulation evolves and adapted based on the uncertainty in the acceptance rate. The algorithm is tested on a number of benchmark problems where it is shown that it significantly reduces the number of forward simulations.
- Bio: Asif Jamil Chowdhury is a graduate student in the department of Computer Science and Engineering at University of South Carolina. His supervisor is Dr. Gabriel Terejanu. His primary research interests lie in the field of uncertainty quantification and model validation. At present he is working on the use of Gaussian Processes in Bayesian optimization and Markov Chain Monte Carlo methods. Before starting his graduate studies he worked as software developer for seven years.
- January 29, 2016 - Maximum Parsimony Analysis of Gene Copy Number Changes in Tumor Phylogenetics
- Speaker: Jijun Tang, University of South Carolina
- Abstract: Evolution of cancer cells are characterized by large scale and rapid changes in the chromosomal landscape. The fluorescence in situ hybridization (FISH) technique provides a way to measure the copy numbers of preselected genes in a group of cells and has been found to be a reliable source of data to model the evolution of tumor cells. Chowdhury recently developed a theoretically sound and scalable model for tumor progression driven by gains and losses in cell count patterns obtained by FISH probes. Their model aims to find the Rectilinear Steiner Minimum Tree (RSMT) that describes progression of FISH cell count patterns over its branches in a parsimonious manner. This model is found to effectively model tumor evolution and is also useful in tumor classification. However the RSMT problem is NP--complete and efficient heuristics are necessary to obtain solutions, especially for large datasets. In this talk we will present a new algorithm for the RSMT problem, based on Maximum Parsimony phylogeny inference. Experimental results from both simulated and real tumor data show that our approach outperforms previous heuristics for the RSMT problem, thus obtaining better models for tumor evolution.
- Bio: Jijun Tang is a professor in the department of Computer Science and Engineering, University of South Carolina, USA. He obtained his Master degree from Tianjin University China and PhD degree from the University of New Mexico, USA. His research interests include computational biology, algorithm design and computer game development, with focus on phylogenetic reconstruction and ancestral genome inference, using higher level genomic data such as genome rearrangements and copy number variations. He has coauthored more than 80 research papers in international conferences and journals. He was program co-chair of 2016 APBC and 2012 WABI conferences and was on the program committees of more than 50 international conferences.
- February 5, 2016 - Visibility-Based Pursuit-Evasion in the Plane
- Speaker: Nick Stifler, University of South Carolina
- Abstract: The speed at which robots begin to enter various application domains is now largely dependent on the availability of robust and efficient algorithms that are capable of solving the complex planning problems inherent to the given
domain. This talk will present a line of research that makes progress towards solving some of the complex planning problems found in target tracking applications where a robot or team of robots seeks to locate and follow a group of moving targets. First, I will describe an algorithm for computing the optimal searcher motion strategy when there exists just a single searcher. Second, I will discuss the multiple searcher scenario and present a deterministic and sampling-based algorithm that coordinates the motion of the searchers. The overall theme is that the design and implementation of robust and efficient planners is imperative for robots to manage the complex tasks we envision for them.
- Bio: Nicholas Stiffler is a Ph.D. candidate in the department of Computer Science and Engineering at the University of South Carolina. His research focuses on on the design and implementation of planners for a variety of complex robotic planning problems. He received his M.S. (2012) and B.S. (2009) degrees from the University of South Carolina.
- February 12, 2016 - Using Computation to Understand Student Writing
- Speaker: 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.
- February 19, 2016 - Robotic System Design for Automated Marine Data Analysis
- Speaker: Gregory Dudek, McGill University
- Abstract: This talk will address the deployment of robotic systems for data collection. This includes task specification, gait learning and data analysis. As a concrete example I will discuss the automated analysis of video data, and specifically video data collected underwater with an amphibious vehicle (the Aqua 2 hexapod). Automated systems can collect data at prodigious rates and the timely analysis of this data is a growing challenge, especially when there are bandwidth constraints between the data source and the people who must examine the data. We are specifically interested in the real-time summarization and detection of the most interesting events in a video sequence, for use by humans who will analyze the data either in real time, or offline. To do this, we are developing methods that adapt to video data streams in real time to collect salient events and using them in the context of a group of vehicles that fly, swim and float.
- Bio: Gregory Dudek is the Director of the School of Computer Science, a James McGill Professor, member of the McGill Research Centre for Intelligent Machines (CIM) and an Associate member of the Dept. of Electrical Engineering at McGill University. He is the former Director of McGill's Research Center for Intelligent Machines, a 25 year old inter-faculty research facility. In 2010 he was awarded the Fessenden Professorship in Science Innovation and also received the prix J. Armand Bombardier for Technological Innovation Robotics from ACFAS, the Association francophone pour le savoir (the French learned society). He is also the recipient of the Canadian Image Processing and Pattern Recognition Award for Research Excellence and the award for Service to the Community at the Conference on Computer and Robot Vision. He directs the McGill Mobile Robotics Laboratory.
He has been on the organizing and/or program committees of Robotics: Systems and Science, the IEEE International Conference on Robotics and Automation (ICRA), the IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS), the International Joint Conference on Artificial Intelligence (IJCAI), Computer and Robot Vision, IEEE International Conference on Mechatronics and International Conference on Hands-on Intelligent Mechatronics and Automation among other bodies. He is president of CIPPRS, the Canadian Information Processing and Pattern Recognition Society, an ICPR national affiliate.
He was on leave in 2000-2001 as Visiting Associate Professor at the Department of Computer Science at Stanford University and at Xerox Palo Alto Research Center (PARC). During his sabbatical in 2007-2008 he visited the Massachusetts Institute of technology and co-founded the company Independent Robotics Inc. He obtained his PhD in computer science (computational vision) from the University of Toronto, his MSc in computer science (systems) at the University of Toronto and his BSc in computer science and physics at Queen's University.
He has published over 200 research papers on subjects including visual object description and recognition, robotic navigation and map construction, distributed system design and biological perception. This includes a book entitled "Computational Principles of Mobile Robotics" co-authored with Michael Jenkin and published by Cambridge University Press. He has chaired and been otherwise involved in numerous national and international conferences and professional activities concerned with Robotics, Machine Sensing and Computer Vision. His research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering.
- February 26, 2016 - Error Correction Mechanisms in Social Networks can Reduce Accuracy and Encourage Innovation
- Speaker: 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 Quarterly.
- March 4, 2016 - Bipartite Perfect Matching is in quasi-NC
- Speaker: 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.
- March 18, 2016 - Law and Technology of Automated Driving
- Speaker: 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.
- March 25, 2016 - Testing and Assurance of Software for Critical Systems
- Speaker: 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.
- April 1, 2016 - Assistive Robotics and Technology
- Speaker: 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.
- April 8, 2016 - "Sophisticated Robots": Balancing Liability, Regulation, and Innovation
- Speaker: 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.
- April 15, 2016 - Automated Steering of Model-Based Test Oracles to Admit Real Program Behaviors
- Speaker: 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.
- April 22, 2016 - New Trends of Mobile Health (mHealth) and Secure Integration with Electronic Health Records (EHR)
- Speaker: Chin-Tser Huang, University of South Carolina
- Abstract:Mobile Health (mHealth), which refers to the use of mobile technologies to improve the quality of health care, has attracted increasing attention thanks to the continuous growth of mobile devices and smartphones. It is desirable to integrate mHealth with Electronic Health Record (EHR), the preferred new method to store patients’ health records. However, several security properties need to be satisfied to make the integration practical, such as data privacy, fine-grained access control and scalable access between different clouds. In this talk, we first introduce latest trends of mHealth technologies and applications, and present a secure and scalable framework for EHR data sharing, which combines Identity-based Encryption and Attribute-based Encryption together to enforce a fine-grained access control scheme on EHR and to enable scalable access between multiple clouds.
- Bio:Dr. Chin-Tser Huang is an Associate Professor in the Department of Computer Science and Engineering at University of South Carolina at Columbia. He received the B.S. degree in Computer Science and Information Engineering from National Taiwan University, Taipei, Taiwan, in 1993, and the M.S. and Ph.D. degrees in Computer Sciences from the University of Texas at Austin in 1998 and 2003, respectively. His research interests include network security, network protocol design and verification, and distributed systems. He is the director of the Secure Protocol Implementation and Development (SPID) Laboratory at the University of South Carolina. He is the author (along with Mohamed Gouda) of the book ‘‘Hop Integrity in the Internet,’’ published by Springer in 2005. His research has been funded by DARPA, AFOSR, AFRL, and NSF. He received the US Air Force Summer Faculty Fellowship Award from 2008 to 2010, and also worked as a Visiting Faculty Researcher with Air Force Research Lab in the summers of 2011 to 2015. He served as the President of The Chinese-American Academic and Professional Association in Southeastern United States (CAPASUS) in 2014-2015.