Dr. Shuo Li from the Department of Medical Imaging and Medical Biophysics at the University of Western Ontario will give a talk on Wednesday April 24 at 10:15 - 11:15, in Storey Innovation Center (Room 2277). Medical data analysis is going through great changes with tremendous new opportunities showing up. The rise of machine learning and the rise of big data analytics, have brought wonderful opportunities to invent the new generation of machine learning tools for medical data analytics, not only to solve new problems appearing, but also to solve many years challenges in conventional medical image analysis and computer vision with much more satisfactory solutions. This talk will share our experience in developing the next generation of image analytics tools with newly invented machine learning tools to help physicians, hospital administrative to analyze the growing medical data and help them to make the right decision and early decision at the right timing. Dr. Shuo Li is an associate professor in the department of medical imaging and medical biophysics at the University of Western Ontario and scientist at Lawson Health Research Institute. Before this position he was a research scientist and project manager in General Electric (GE) Healthcare for 9 years. He founded the Digital Imaging Group of London (http://digitalimaginggroup.ca/) in 2006, which is a very dynamic and highly multiple disciplinary collaboration group. He received his Ph.D. degree in computer science from Concordia University 2006, where his PhD thesis won the doctoral prize giving to the most deserving graduating student in the faculty of engineering and computer science. He has published over 100 publications; He is the recipient of several awards from GE, institutes and international organizations; He serves as guest editors and associate editor in several prestigious journals in the field; He serves as program committee members in highly influential conferences; He is the editors of six Springer books; He serves on the board of directors in prestigious MICCAI society. He will be the general chair for MICCAI 2022 conference. His current interest is development intelligent analytic tools to help physicians and hospital administrators to handle the big medical data, centred with medical images.

At Backers & Hackers, EclubSC is excited to share with you all the new apps that students from all majors have developed this semester. Join us for a night of networking, showcasing, and hearing from Laura Boccanfuso. Laura Boccanfuso is the CEO of Vän Robotics. A company that provides students K-8 a robot-assisted tutoring to help enhance the students' learning experience. Hear how she got involved in the world of technology and education, and her valuable insights of her journey to getting where she is today! More Info

Dr. Nihat Altiparmak from the Department of Computer Engineering and Computer Science at the University of Louisville, will give a talk on Friday, April 12, 2019, in the Storey Innovation Center (Room 2277) from 10:15 am - 11:15 am. Abstract Today’s most critical applications, including genome analysis, climate simulations, drug discovery, space observation, and numerical simulations in computational chemistry and high-energy physics, are all data intensive in nature. Storage performance bottlenecks are major threats limiting the performance and scalability of data intensive applications. A common way to address storage I/O bottlenecks is using parallel storage systems and utilizing concurrent operation of independent storage components; however, achieving a consistently high parallel I/O performance is challenging due to static configurations. Modern parallel storage systems, especially in the cloud, enterprise data centers, and scientific clusters are commonly shared by various applications generating dynamic and coexisting data access patterns. Nonetheless, these systems generally utilize one-layout-fits-all data placement strategy frequently resulting in suboptimal I/O parallelism. Guided by association rule mining, graph coloring, bin packing, and network flow techniques, this talk demonstrates a general framework for self-optimizing parallel storage systems that can adaptively alleviate storage performance bottlenecks and continuously provide a high-degree of I/O parallelism. The framework can be applied to a wide range of parallel storage architectures including storage arrays, key-value stores, parallel/distributed file systems, and internal parallelism of solid-state drives. In addition, this talk briefly covers efficient storage, retrieval, and processing strategies for Big Data, and recent advancements in non-volatile memory technology by identifying upcoming challenges in computer systems research to utilize new solid-state storage devices to their full potential. Bio: Dr. Nihat Altiparmak earned his B.S. degree in Computer Engineering from Bilkent University, Ankara, Turkey in May 2007, and his combined M.S. and Ph.D. degrees in Computer Science from the University of Texas at San Antonio in May 2013. He joined the Department of Computer Engineering and Computer Science at the University of Louisville as a tenure-track Assistant Professor in August 2013, and his tenure and promotion to Associate Professor is currently pending approval by the Board of Trustees of the University of Louisville. His research interests lie in the area of computer systems, specifically focusing on data storage systems, parallel and distributed systems, cloud computing, high performance computing, and computer networks. He is particularly interested in researching solid-state storage systems based on new generation non-volatile memory technologies, as well as investigating efficient storage, retrieval, and processing strategies for Big Data using high performance, distributed, and cloud architectures. His recent research findings have appeared in top-tier international journals, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, ACM Transactions on Storage, and ACM Transactions on Sensor Networks, as well as prestigious conferences with competitive acceptance rates. He received multiple grants from the National Science Foundation (NSF) in the PI role, including a prestigious young investigator award (NSF CRII) in 2017 and an NSF MRI award in 2018. He is a senior member of the IEEE and the founding director of the Computer Systems Laboratory at the University of Louisville.

THESIS DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Kimberly Redmond Advisor : Dr. Lisa Luo Date : April 10th , 2019 Time : 11:00 am Place : Meeting room 2267, Bert Storey Innovation Center Abstract Binary code analysis is important for understanding programs without access to the original source code, which is common with proprietary software. Analyzing binaries can be challenging given their high variability: due to growth in tech manufacturers, source code is now frequently compiled for multiple instruction set architectures (ISAs); however, there is no formal dictionary that translates between their assembly languages. The difficulty of analysis is further compounded by different compiler optimizations and obfuscated malware signatures. Such minutiae means that some vulnerabilities may only be detectable on a fine-grained level. Recent strides in machine learning---particularly in Natural Language Processing (NLP)---may provide a solution: deep learning models can process large texts and encode the semantics of individual words into vectors called word embeddings, which are convenient for processing and analyzing text. By treating assembly as a language and instructions as words, we leverage NLP ideas in order to generate individual instruction embeddings. Specifically, we choose to improve upon current models that are only single-architecture, or that suffer from performance issues when handling multiple architectures. This research presents a cross-architecture instruction embedding model that jointly encodes instruction semantics from multiple ISAs, where similar instructions within and across architectures embed closely together. Results show that our model is accurate in extracting semantics from binaries alone, and our embeddings capture semantic equivalences across multiple architectures. When combined, these instruction embeddings can represent the meaning of functions or basic blocks; thus, this model may prove useful for cross-architecture bug, malware, and plagiarism detection.

Monday, April 8, 2019, in the Storey Innovation Center (Room 2277) from 10:15 am - 11:15 am. Abstract: There are two fundamental strategies for finding effective policies to solve stochastic optimization problems and these are policy search and look-ahead policies. Look-ahead policies are mainly used in the context of sequential optimization problems in which the current decision impacts the future ones. There are several types of look-ahead policies depending on the type of the forecast, the problem structure and its dimensionality. In this talk, I will discuss two forms of look-ahead policies, direct look-aheads and value function approximations. In the context of direct look-aheads, we propose a new technique called Primal-Dual Monte Carlo Trees Search that utilizes sampled information relaxation upper bounds on potential actions, creating the possibility of “ignoring" parts of the tree that stem from highly suboptimal choices. This allows us to prove that despite converging to a partial decision tree in the limit, the recommended action from Primal-Dual MCTS is optimal. Then, I will discuss an approximate dynamic programming approach in the context of ride-sharing systems. We extract and prove important properties about the problem structure such as monotonicity and spatial correlation that provide efficient value function approximations. Biography: Lina Al-Kanj is an Associate Research Scholar at the Operations Research and Financial Engineering Department at Princeton University. She received her PhD in Electrical and Computer Engineering from the American University of Beirut. Her research interests include optimal resource allocation and scheduling, stochastic optimization, dynamic programming and optimal learning with applications to energy, communication and transportation systems.

DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Mouiad Al Wahah Advisor : Dr. Csilla Farkas Date : April 5th , 2019 Time : 3:00 pm Place : Conference room 2201, Innovation Center Abstract The appearance of dynamic distributed networks in early eighties of the last century has evoked technologies like pervasive systems, ubiquitous computing, ambient intelligence, and more recently, Internet of Things (IoT) to be developed. Moreover, sensing capabilities embedded in computing devices offer users the ability to share, retrieve, and update resources on anytime and anywhere basis. These resources (or data) constitute what is widely known as contextual information. In these systems, there is an association between a system and its environment and the system should always adapt to its ever-changing environment. This situation makes the Context-Based Access Control (CBAC) the method of choice for such environments. However, most traditional policy models do not address the issue of dynamic nature of dynamic distributed systems and are limited in addressing issues like adaptability, extensibility, and reasoning over security policies. We propose a security framework for dynamic distributed network domain that is based on semantic technologies. This framework presents a flexible and adaptable context-based access control authorization model for protecting dynamic distributed networks' resources. We extend our security model to incorporate context delegation in context-based access control environments. We show that security mechanisms provided by the framework are sound and adhere to the least-privilege principle. We develop a prototype implementation of our framework and present the results to show that our framework correctly derives Context-Based authorization decision. Furthermore, we provide complexity analysis for the authorization framework in its response to the requests and contrast the complexity against possible optimization that can be applied on the framework. Finally, we incorporate semantic-based obligation into our security framework. In phase I of our research, we design two lightweight Web Ontology Language (OWL) ontologies CTX-Lite and CBAC. CTX-Lite ontology serves as a core ontology for context handling, while CBAC ontology is used for modeling access control policy requirements. Based on the two OWL ontologies, we develop access authorization approach in which access decision is solely made based on the context of the request. We separate context operations from access authorization operations to reduce processing time for distributed networks' devices. In phase II, we present two novel ontology-based context delegation approaches. Monotonic context delegation, which adopts GRANT version of delegation, and non-monotonic for TRANSFER version of delegation. Our goal is to present context delegation mechanisms that can be adopted by existing CBAC systems which do not provide delegation services. Phase III has two sub-phases, the first is to provide complexity analysis of the authorization framework. The second sub-phase is dedicated to incorporating semantic-based obligation.

The CSE Industrial Advisory Board (IAB), the Department’s industry partners, will hold a Town Hall meeting for CSE undergraduate students: What: Town Hall Meeting (CSE Students and CSE-IAB) When: Friday, April 5 Time: 10 am – 12 pm Location: Storey Innovation Center, Room 2277 Light refreshments will be provided!! This will be an important discussion that requires your participation. Our industry partners need to hear from our students, and this is a great opportunity for an open discussion. We encourage all CSE undergraduate students to attend; note that you do not have to stay for the entire meeting. Discussion topics may include:

• What resources are available to students in the local computing community?
• What computing skills will be in demand over the next five to ten years?
• How has previous student feedback generated positive change in the CSE programs?
• How do we build on the current CSE programs to improve the student experience and foster a stronger sense of community among students, faculty, and alumni?

If you plan to attend the CSE Town Hall meeting at 10 am on April 5, then please email thatche1@cse.sc.edu I would like to have an estimate of how many students will attend. We look forward to seeing everyone at the CSE Town Hall meeting. Sincerely, Matt Matt E. Thatcher, Ph.D. Professor and Chair

Ramtin Zand from the University of Central Florida (UCF), Orlando, will give a talk on Wednesday April 3, 2019 in the Storey Innovation Center (Room 2277) from 10:15 am - 11:15 am. Benefits of alternatives to von-Neumann architectures for emerging applications such as neuromorphic computing include avoidance of the processor-memory bottleneck, reduced energy consumption, and area-sparing computation. However, viable solutions to the challenge of designing theses emerging computing systems span the interrelated fields of machine learning, computer architecture, circuit design, and the potential to leverage the complementary characteristics of emerging device technologies. The objective of this research is to exploit technology-specific advantages to advance new transformative opportunities for leveraging the cooperating benefits of well-established CMOS devices, while simultaneously embracing the strengths of emerging technologies. Moreover, an orthogonal dimension of technology heterogeneity is also non-determinism enabled by either low-voltage CMOS or probabilistic emerging devices. It can be realized using probabilistic devices within a reconfigurable network to blend deterministic and probabilistic computational models. Thus, we leverage the new and powerful prospect of technology heterogeneity both at design-time and at run-time to develop energy-efficient and reliability-aware computing systems. Herein, consider the probabilistic spin logic "p-bit" device as a fabric element comprising a crossbar-structured resistive weighted array. Programmability of the resistive network interconnecting p-bit devices can be achieved by modifying the resistive states of the array's weighted connections. This allows field programmability for a wide range of classification problems and recognition tasks to allow fluid mappings of probabilistic and deterministic computing approaches. In particular, a low-energy Deep Belief Network (DBN) is implemented in the field using recurrent layers of co-processing elements to form an n _ m 1 _ m 2 _ ... _ m i weighted array as a configurable hardware circuit with an n-input layer followed by i _ 1 hidden layers. Cross-layer simulations indicate that the proposed design can achieve approximately three orders of magnitude reduction in energy consumption compared to the most energy-efficient CMOS-only designs, while realizing at least 90X device count reduction for considerable area savings. This area of research provides several possibilities for future work, such as: 1) leveraging evolutionary algorithm-based optimization methodologies to explore the neuromorphic hardware design space in various architecture-to-device granularities to realize an optimized circuit-level implementation of Deep Neural Networks, and 2) realizing robust stochastic neuromorphic architectures with a natural defense mechanism against various types of adversarial attacks. Biography Ramtin Zand received his M.Sc. degree in Digital Electronics from Sharif University of Technology, Tehran, Iran, in 2012. He is a Ph.D. Candidate in Computer Engineering at the University of Central Florida (UCF), Orlando, FL, with the graduation date of May 2019. He has five years of industry experience as Senior Hardware Design Engineer and is currently a senior Graduate Research Assistant of an NSF and SRC jointlysupported project of the Energy-Efficient Computing from Devices to Architectures (E2CDA) program. He has authored or co-authored 17 conference proceedings papers, 13 journal articles (8 Transactions), and one book chapter, and received research recognition from ACM/IEEE including a best paper recognition at ACM GLSVLSI and a featured paper of the issue in IEEE Transactions on Emerging Topics in Computing (TETC) in 2018. Ramtin is the recipient of the Daniel D. Hammond scholarship and the Alireza Seyedi Doctoral Research Innovation Endowed Scholarship. He is a Student Member of IEEE and a reviewer for various IEEE Transactions and conferences. His research interests include: Machine Learning and Neuromorphic Computing, Emerging Nanoscale Electronics including Spin-based Devices, Reconfigurable and Adaptive Computer Architectures, and Low-Power and Reliability-Aware VLSI Circuits.

Dr. Jeremy Blackburn from the Computer Science Department at the University of Alabama at Birmingham will give a talk on Monday April 1, 2019 in the Storey Innovation Center (Room 2277) from 10:15 am - 11:15 am. ABSTRACT: The Web has been one of the most impactful technologies ever, and over the past twenty years or so, has helped advance society in ways no one thought possible. Ubiquitous connectivity has enabled instant communication with anyone in the world. Social media has helped us strengthen existing relationships, and form new ones. The vast amount of content on the Web has broadened our outlook, and let us learn about things we never even knew existed. Unfortunately, along with these benefits has come a set of worrying problems. Powerful new communication mediums have been hijacked to spread hate speech and extremist ideology, and social media has been exploited to wage information warfare. Although these problems are not necessarily new, the scale and speed, coupled with advances in technology, make them fundamentally different than past incarnations. To top it all off, we know very little about these socio-technical problems, making it difficult to even begin to solve them. In this talk, I will present our work towards measuring and understanding these new problems. In particular, I will show how seemingly isolated communities in the Web, where hate speech festers, are not self-contained and perpetrate attacks on mainstream communities. Next, I will show how these seemingly tiny communities have outsized influence in terms of spreading "fake news" throughout the greater Web. Then, I will show how Web born phenomena, i.e., memes, are created, evolve, and are harnessed to spread hateful ideology and propaganda. Finally, I will touch on some of the risks that researchers hoping to address these new socio-technical problems face. BIO: Jeremy Blackburn is an Assistant Professor in the Computer Science Department at the University of Alabama at Birmingham. In a nutshell, Jeremy’s work can be described as studying jerks on the Internet and has been covered in the media by The Washington Post, The Atlantic, Nature News, the BBC, and New Scientist, among others. Although his foundations are in large-scale distributed systems, he has spent the majority of his career measuring and understanding bad behavior on the world’s largest distributed system, the World Wide Web. His research has ranged from studying how cheating behavior spreads like a disease through a global network of online video game players, to understanding and predicting toxic behavior in the world’s most popular multiplayer video game, and more recently, understanding online hate speech, harassment campaigns, and the influence of fringe communities on the greater Web. In addition to this line of work, Jeremy has published on more traditional Computer Science topics like middlebox enabling cryptographic protocols, privacy preserving Web surfing technologies, mobile application performance, and network measurements.

Is your computer or laptop running slow, getting too hot, or riddled with viruses? Students in the USC College of Engineering's Association for Computing Machinery are hosting its annual Fix-It Day on March 30, 2019! On Fix-It Day, student ACM members will repair your laptop or desktop for free, (but you are welcome to make a donation)! You do not have to be affiliated with the university to participate. Computers will be fixed on a first-come first-served basis from 10 a.m. to 3 p.m. If your computer cannot be fixed, the students will recycle it for you, free of charge. Here's a quick note on what to expect: We cannot fix broken hardware such as screens or other damaged components. If you would like a replacement part installed, you need to bring it with you. Examples of tasks that we can do:

• Virus removal
• password reset
• Installation or uninstallation of programs (if you would like a program installed, please bring any software disks and license keys)
• PC tune-up
• Full PC diagnosis
• Simple data recovery
• Some mechanical problems, such as loose hinges, as long as replacement parts are not required
• OS installation (please bring disks, recovery media, and license keys)

PLEASE back up all data before you come. We are not responsible for damage or loss of data or hardware of any kind. You must sign a waiver before your computer will be worked on. https://www.facebook.com/events/367877034044272/