- Course Name: CSCE 791 - Seminar on Advances in Computing
- Semester: Fall 2017
- Instructor: Greg Gay
- Lecture Hours: Friday, 2:20 - 3:10 PM, 2A14 Swearingen Engineering Center
CSCE 791 is a colloquium series, consisting of talks or seminars given by invited speakers from our department and other departments or universities. 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 hear from academia and industry about the challenges facing our society, and the work being performed to address these challenges.
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.
- August 25, 2017 - Geometric Approaches to Derandomizing Parallel Matching and Matroid Algorithms
- Speaker: Dr. Stephen Fenner, University of South Carolina
- Abstract: I will describe a cluster of recent results that help to derandomize some parallel algorithms for graphs and matroids. Specifically, it has been shown that the problem of finding a perfect matching in a graph is in the complexity class quasi-NC, that is, it is solvable in polylogarithmic time (that is, logO(1)n time) time using quasipolynomially many processors (that is, 2logO(1) n many processors). This was first shown for bipartite graphs by F, Gurjar, and Thierauf in 2015, and a proof for general graphs was announced last spring by Svensson and Tarnawski (to appear in FOCS 2017). In the interim, Gurjar and Thierauf (2016) gave a quasi-NC algorithm for the Linear Matroid Intersection problem. All these results use similar techniques that are based in geometry---for example, discovering lower dimensional faces of the perfect matching polytope of a graph. I will emphasize these geometrical techniques in the talk.
- 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.
- September 1, 2017 - An Application of Natural Language Processing: Analyzing student essays as a big-data project
- Speaker: Dr. Duncan Buell, University of South Carolina
- Abstract: First year students at most large universities take required courses whose purpose is to teach them to write prose essays and make arguments. We have acquired more than 7000 pairs of draft-and-final essays from USC and have been analyzing them. We are not trying to do “machine grading” of essays as an AI project. Rather, we are trying to identify features of writing that can be quantified and thus processed with programs as a big-data analysis. We are interested in the extent to which students revise their draft essays to become final versions. And we are interested in comparing our student writing against other genres of writing. For this last we use the Corpus of Contemporary American English (COCA) as source data. The COCA is a corpus of more than 500 million words of text separated into genres of academic writing, magazine writing, transcripts of spoken English and interviews, and such. Our eventual goal is to situate student writing relative to other genres and thus to help with improving the pedagogy of teaching writing; knowing what the students are actually writing now is key to knowing how to get them to write formal prose effectively. Programming is done in Python. Part of speech tagging is done using the CLAWS package from the University of Lancaster in the UK. Sentence parsing is done using the package from Dan Jurafsky’s lab at Stanford.
- 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.
- September 8, 2017 - Security Challenges for the Internet of Things: A Semantics-Based View
- Speaker: Dr. Csilla Farkas, University of South Carolina
- Abstract: Are you living in a smart home? Are you using smart devices to monitor your health? Is your organization considering to increase automation for sensing and controlling operations? While the concept of Internet of Things (IoT) may mean different things to different people, there is a common theme: the need for cybersecurity. The key security challenges are focused on three areas: 1) device vulnerabilities, 2) communication security and trust, and 3) data integrity, security, and privacy. In this talk I present a semantics-based approach to support IoT data integration and security.
- Bio: Csilla Farkas is a Professor in the Department of Computer Science and Engineering and Director of the Center for Information Assurance Engineering at the University of South Carolina. Dr. Farkas’ research interests include information security, data inference problem, financial and legal analysis of cyber crime, and security and privacy on the Semantic Web. silla Farkas received her PhD from George Mason University, Fairfax. In her dissertation she studied the inference and aggregation problems in multilevel secure relational databases. She received a MS in computer science from George Mason University and BS degrees in computer science and geology from SZAMALK, Hungary and Eotvos Lorand University, Hungary, respectively.
- September 15, 2017 - Human attribute recognition by refining attention heat map
- Speaker: Dr. Song Wang, University of South Carolina
- Abstract: Most existing methods of human attribute recognition are part-based and the performance of these methods is highly dependent on the accuracy of body-part detection, which is a well known challenging problem in computer vision. In this talk, I will introduce a new method to recognize human attributes by using CAM (Class Activation Map) network, as well as an unsupervised algorithm to refine the attention heat map, which is an intermediate result in CAM and reflects relevant image regions for each attribute. The proposed method does not require the detection of body parts and the prior correspondence between body parts and attributes. The proposed methods can achieve comparable performance of attribute recognition to the current state-of-the-art methods.
- Bio: Song Wang received the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana–Champaign in 2002. He received his M.E. and B.E. degrees from Tsinghua University in 1998 and 1994, respectively. In 2002, he joined the Department of Computer Science and Engineering in University of South Carolina, where he is currently a Professor and the director of the Computer Vision Lab. His current research interest is focused on computer vision, image processing and machine learning, as well as their applications to materials science, medical imaging, digital humanities and archaeology. He has published more than 100 research papers in journal and conferences, including top venues like CVPR, ICCV, NIPS, IJCAI, TPAMI, IJCV and TIP. He is currently serving as the Publicity/Web Portal Chair of the Technical Committee of Pattern Analysis and Machine Intelligence of the IEEE Computer Society, and an Associate Editor of Pattern Recognition Letters. He is a senior member of IEEE.
- September 22, 2017 - Generating Effective Test Suites by Combining Coverage Criteria
- Speaker: Dr. Gregory Gay, University of South Carolina
- Abstract: A number of criteria have been proposed to judge test suite adequacy. While search-based test generation has improved greatly at criteria coverage, the produced suites are still often ineffective at detecting faults. Efficacy may be limited by the single-minded application of one criterion at a time when generating suites - a sharp contrast to human testers, who simultaneously explore multiple testing strategies. We hypothesize that automated generation can be improved by selecting and simultaneously exploring multiple criteria. To address this hypothesis, we have generated multi-criteria test suites, measuring efficacy against the Defects4J fault database. We have found that multi-criteria suites can be up to 31.15% more effective at detecting complex, real-world faults than suites generated to satisfy a single criterion and 70.17% more effective than the default combination of all eight criteria. Given a fixed search budget, we recommend pairing a criterion focused on structural exploration - such as Branch Coverage - with targeted supplemental strategies aimed at the type of faults expected from the system under test. Our findings offer lessons to consider when selecting such combinations.
- Bio: Gregory Gay is an assistant professor of Computer Science & Engineering at University of South Carolina. His research interests include automated testing and analysis, and search-based software engineering, with a focus is on the use of coverage criteria in automated test case generation, as well as the construction of effective test oracles for real-time and safety critical systems. He serves on the steering committees for the Symposium on Search-Based Software Engineering and the International Workshop on Search-Based Software Testing, as well as the organizing and program committees of a variety of conferences and workshops. He graduated with a PhD from the University of Minnesota under a NSF Graduate Research Fellowship, working with the Critical Systems research group. He received his BS and MS in Computer Science from West Virginia University. Additionally, he has previously worked at NASA's Ames Research Center and Independent Verification & Validation Center, and served as a visiting academic at the Laboratory for Internet Software Technologies at the Chinese Academy of Sciences in Beijing.
- September 29, 2017 - Error Correction Mechanisms in Social Networks: Implications for Replicators
- Speaker: Dr. Matthew Brashears, University of South Carolina (Sociology)
- Abstract: Humans make mistakes but diffusion through social networks is typically modeled as though they do not. We find in an experiment 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. These results are extended to a nascent general theory of replicators explaining how error correction mechanisms facilitate rapid saturation of a search space. A simulation model and preliminary results are presented that are consistent with this prediction.
- Bio: Matthew E. Brashears is an Associate Professor of Sociology at the University of South Carolina. His work crosses levels, integrating ideas from evolutionary theory, social networks, organizational theory, and neuroscience. 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. He is also engaged in an effort to model values and interactional scripts in an ecological space using cross-national data, with the goal of generating a predictive model of cultural competition and evolution. His work has appeared or is forthcoming in Nature Scientific Reports, the American Sociological Review, the American Journal of Sociology, Social Networks, Social Forces, Advances in Group Processes and Frontiers in Cognitive Psychology, among others. He has received grants from the National Science Foundation, the Defense Threat Reduction Agency, the Army Research Institute, the Army Research Office, and the Office of Naval Research. He is one of two new co-editors for the journal Social Psychology Quarterly, and currently serves as an officer in the American Sociological Association’s Social Psychology Section.
- October 6, 2017 - Enhancement of Hi-C experimental data using deep convolutional neural network
- Speaker: Dr. Jijun Tang, University of South Carolina
- Abstract: Hi-C technology is one of the most popular tools for measuring the spatial organization of mammalian genomes. Although an increasing number of Hi-C datasets have been generated in a variety of tissue/cell types, due to high sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to infer enhancer-promoter interactions or link disease-related non-coding variants to their target genes. To address this challenge, we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. Through extensive testing, we demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while using only as few as 1/16 of the total sequencing reads. We observe that Hi-C interaction matrix contains unique local features that are consistent across different cell types, and such features can be effectively captured by the deep learning framework. We further apply HiCPlus to enhance and expand the usability of Hi-C data sets in a variety of tissue and cell types. In summary, our work not only provides a framework to generate high-resolution Hi-C matrix with a fraction of the sequencing cost, but also reveals features underlying the formation of 3D chromatin interactions.
- October 13, 2017 - Toward a Theory of Automated Design of Minimal Robots
- Speaker: Dr. Jason O'Kane, University of South Carolina
- Abstract:The design of an effective autonomous robot relies upon a complex web of interactions and tradeoffs between various hardware and software components. The problem of designing such a robot becomes even more challenging when the objective is to find robot designs that are minimal, in the sense of utilizing only limited sensing, actuation, or computational resources. The usual approach to navigating these tradeoffs is currently by careful analysis and human cleverness. In contrast, this talk will present some recent research that seeks to automate some parts of this process, by representing models for a robot's interaction with the world as formal, algorithmically-manipulable objects, and posing various kinds of questions on those data structures. The results include both both bad news (i.e., hardness results) and good news (practical algorithms).
- Bio: Jason O'Kane is Associate Professor in Computer Science and Engineering and Director of the Center for Computational Robotics at the University of South Carolina. He holds the Ph.D. (2007) and M.S. (2005) degrees from the University of Illinois at Urbana-Champaign and the B.S. (2001) degree from Taylor University, all in Computer Science. He has won a CAREER Award from NSF, a Breakthrough Star Award from the University of South Carolina, and the Outstanding Graduate in Computer Science Award from Taylor University. He was a member of the DARPA Computer Science Study Group. His research spans algorithmic robotics, planning under uncertainty, and computational geometry.
- October 27, 2017 - Portable Parallel Programming in the Age of Architecture Diversity for High Performance
- Speaker: Dr. Yonghong Yan, University of South Carolina
- Abstract: Today’s computer systems are becoming much more heterogeneous and complex from both computer architecture and memory system. High performance computing systems and large-scale enterprise clusters are often built with the combination of multiple architectures including multicore CPUs, Nvidia manycore GPUs, Intel Xeon Phi vector manycores, and domain-specific processing units, such as DSP and deep-learning tensor units. The introduction of non-volatile memory and 3D-stack DRAM known as high-bandwidth memory further complicated computer systems by significantly increasing the complexity of the memory hierarchy. For users, parallel programming for those systems has thus become much more challenging than ever. In this talk, the speaker will highlight the latest development of parallel programming models for the existing and emerging architectures for high performance computing. He will introduce the ongoing work in his research team (http://passlab.github.io) for improving productivity and portability of parallel programming for heterogeneous systems with the combination of shared and discrete memory. The speaker will conclude that this is an exciting time for performing computer system research and also share some of his unsuccessful experiences for studying his Ph.D.
- Bio: Dr. Yonghong Yan joined University of South Carolina as an Assistant Professor in Fall 2017 and he is a member of OpenMP Architectural Review Board and OpenMP Language Committee. Dr. Yan calls himself a nerd for parallel computing, compiler technology and high-performance computer architecture and systems. He is an NSF CAREER awardee. His research team develop intra-/inter-node programming models, compiler, runtime systems and performance tools based on OpenMP, MPI and LLVM compiler, explore conventional and advanced computer architectures including CPU, vector, GPU, MIC, FPGA, and dataflow system, and support applications ranging from classical HPC, to big data analysis and machine learning, and to computer imaging. The ongoing development can be found from https://github.com/passlab. Dr. Yan received his PhD degree in computer science from University of Houston and has a bachelor degree in mechanical engineering
- November 3, 2017 - 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.
- November 10, 2017 - Unmanned Systems & Robotics
- Speaker: Nikolaos Vitzilaios, University of South Carolina (Department of Mechanical Engineering)
- Abstract: The area of Unmanned Systems & Robotics has seen a tremendous growth over the last decades with autonomous systems being rapidly developed in many domains resulting in a wide range of applications that we can see in our daily lives (drones, autonomous cars, industrial robots, medical and service robotics, space robotics, etc.). This presentation will show the research of Dr. Nikolaos (Nikos) Vitzilaios in this area over the last 10 years, presenting the developments in specific areas over time and focusing on latest research as long as future research plans.
The presentation will include research in the following areas:
The presentation will focus on the outputs of each research project and will include demos and videos from field experiments and indoor-outdoor testing.
- Aerial Robotics: applications of automatic control in unmanned fixed-wing aircraft and rotorcraft, including theoretical aspects as well as applied hardware and software developments. Several platforms have been developed over the last 10 years while the latest development will be presented based on a patented design of a dual-tilting quadcopter able to perform advanced navigation and control in narrow spaces as well as fault-tolerant control.
Mobile Robotics: several ground robotic platforms will be presented, including customized commercial mobile robots as well as in-house built robots (including a patented one). These platforms are built for different applications and projects and the presentation will focus on the collaboration with aerial robots in critical missions
UAV Aerodynamics: novel research in the area of circulation control wings for fixed-wing aircraft will be presented.
Marine Robotics: a novel propulsion system will be presented for low speed propeller less robots that are required to be used in extreme environments (nuclear reactors).
Medical Robotics: the latest research on the modelling of the human thumb will be presented accompanied by a new kinematic model that shows the importance of the thumb in grasping and how this will affect our perception for the development of future robotic hands.
Mechatronic Systems: the development of an automatic bike gear shifter (patent pending).
Modeling and control of complex and highly nonlinear systems.
Future trends and planned research in perception and control.
- Bio: Dr. Nikolaos (Nikos) Vitzilaios is an Assistant Professor at the Department of Mechanical Engineering, University of South Carolina, since August 2017. He holds a PhD in Mechanical Engineering from the Technical University of Crete (2010) and his PhD thesis was on the development of autonomous controllers for helicopter UAVs. Prior to joining USC, he was a Senior Lecturer in Robotics at Kingston University, London, UK. From 2011-2012, he was a Postdoctoral Fellow at the Department of Electrical Engineering, University of Alberta, Canada, working in the Applied Nonlinear Controls Laboratory and developing a helicopter UAV for power line inspection, funded by the Canadian government (NSERC). From 2012-2015, he was a Research Scientist at the University of Denver Unmanned Systems Research Institute (Department of Electrical Engineering), leading the Aerial Robotics Team and working on several projects in the area of unmanned systems funded by various agencies (NSF, ARL, NASA).
Dr. Vitzilaios has more than 10 years of research and more than 5 years of teaching experience in the areas of Robotics and Controls, with notable presence in the Robotics & Automation society, more than 30 publications, one US patent and successful grant applications both in US and UK. His background is interdisciplinary from the areas of Mechanical Engineering, Electrical Engineering and Computer Science. His research interests span the broad area of Autonomous Unmanned Systems where he has significant hands-on experience in all kinds of robotic applications (aerial, ground, marine, industrial, biomedical). His research is mainly experimental and his interests include prototype development and commercialization of research outcomes. He is a Fellow of the Higher Education Academy in UK and a member of IEEE, AIAA, AUVSI and IFAC. He is a Chartered Mechanical Engineer in the Technical Chamber of Greece since March 2005.
- December 1, 2017 - Deep Learning and its Application in Bioinformatics: Case Study on Protein-peptide Binding Prediction
- Speaker: Dr. Jianjun Hu, University of South Carolina
- Abstract: Deep learning has led to tremendous progress in computer vision, speech recognition, and natural language processing. It has now crossed the boundary and has brought breakthroughs also in the area of bioinformatics. One interesting problem is developing accurate models for predicting peptide binding affinities to protein receptors such as MHC(Major Histocompatibility complex), which can shed understanding to adverse drug reaction and autoimmune diseases and lead to more effective protein therapy and design of vaccines.
We proposed a deep convolutional neural network (CNN) based peptide binding prediction algorithm for achieving substantially higher accuracy as tested in MHC-I peptide binding affinity prediction. Our model takes raw binding peptide sequences and affinity scores or binding labels as input without needing any human-designed features. The back-propagation training algorithm allows it to learn nonlinear relationships among the amino acid positions of the peptides. It also can naturally handle the peptide length variation, MHC polymorphasim, and unbalanced training samples of MHC proteins with different alleles via a simple amino acid padding scheme. Our experiments showed that DeepMHC can achieve the state-of-the-art prediction performance on most of the IEDB benchmark datasets with a single model architecture and without using any consensus or composite ensemble classifier models.
- Bio: I joined CSE department of the University of South Carolina in August 2007. I am now working on integrative functional genomics and especially integrative analysis of microarray data. I am also interested in motif discovery for understanding gene expression mechanisms involved in diseases. I got my Ph.D. in Computer Science in the area of machine learning and particularly evolutionary computation at the Genetic Algorithm Research and Application Group (GARAGe) of Michigan State University. My dissertation focuses on sustainable evolutionary computation algorithms and automated computational synthesis. I have worked on the DNA motif discovery problem as Postdoc at Kihara Bioinformatics Lab, Purdue University and microarray analysis at the Computational Molecular Biology Division at the University of Southern California (another USC).
- December 8, 2017 - TBD
- Speaker: TBD