Abstract: The Internet, which has spanned several networks in a wide variety of domains, is having a significant impact on every aspect of our lives. The next generation of networks will utilize a wide variety of resources with significant sensing capabilities. Such networks will extend beyond physically linked computers to include multimodal-information from biological, cognitive, semantic, and social networks. This paradigm shift will involve symbiotic networks of smart medical devices, and smart phones or mobile personal computing and communication devices (mPCDs). These devices – and the network -- will be constantly sensing, monitoring, and interpreting the environment; this is sometimes referred to as the Internet of Things (IoT). Additionally, we are also witnessing considerable interest in the “Omics” paradigm, which can be viewed as the study of a domain in a massive scale, at different levels of abstraction, in an integrative manner. The IoT revolution combined with the Omics revolution (genomics and sociomics or social networks) will have significant implications on the way health care is delivered in the United States. In this talk I will discuss the following: 1) The P9 concept of smart health care; 2) The evolution of IoT – sensing, monitoring, and interpreting the environment ; 3) The omics revolutions – genomics and sociomics; and 4) Artificial Intelligence/Machine Learning applications.

Biography: Dr. Ram D. Sriram is currently the chief of the Software and Systems Division, Information Technology Laboratory, at the National Institute of Standards and Technology. Before joining the Software and Systems Division, Sriram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Laboratory, where he conducted research on standards for interoperability of computer-aided design systems. He was also the manager of the Sustainable Manufacturing Program. Prior to joining NIST, he was on the engineering faculty (1986-1994) at the Massachusetts Institute of Technology (MIT) and was instrumental in setting up the Intelligent Engineering Systems Laboratory. Sriram has co-authored or authored nearly 250 publications, including several books. Sriram was a founding co-editor of the International Journal for AI in Engineering. In 1989, he was awarded a Presidential Young Investigator Award from the National Science Foundation. In 2011, Sriram received the ASME Design Automation Award for his work on computer-supported collaborative design. Sriram is a Fellow of ASME and AAAS, a member (life) of ACM, a Senior Member of the IEEE, and a member (life) of AAAI. Sriram has a B.Tech. from IIT, Madras, India, and an M.S. and a Ph.D. from Carnegie Mellon University, Pittsburgh, USA.

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Abstract: System-on-Chip (SoC) is the brain behind computing and communication in a wide variety of systems, starting from simple electronic devices in smart homes to complex navigation systems in airplanes. Reusable hardware Intellectual Property (IP) based SoC design has emerged as a pervasive design practice in the industry to dramatically reduce SoC design and verification cost while meeting aggressive time-to-market constraints. Growing reliance on these pre-verified hardware IPs, often gathered from untrusted third-party vendors, severely affects the security and trustworthiness of computing platforms. These IPs may come with deliberate malicious implants to incorporate undesired functionality, undocumented test/debug interface working as hidden backdoor, or other integrity issues. It is crucial to evaluate the integrity and trustworthiness of third-party IPs for designing trustworthy systems. In this talk, I will introduce a wide variety of hardware security vulnerabilities, design-for-security solutions, and possible attacks and countermeasures. I will briefly describe how the complementary abilities of simulation-based validation, formal verification as well as side channel analysis can be effectively utilized for comprehensive SoC security and trust validation. I will conclude with a discussion on application-specific security solutions as well as future hardware security challenges.

Biography: Prabhat Mishra is a Professor in the Department of Computer and Information Science and Engineering and a UF Research Foundation Professor at the University of Florida. He received his Ph.D. in Computer Science from the University of California at Irvine in 2004. His research interests include embedded and cyber-physical systems, hardware security and trust, energy-aware computing, system-on-chip validation, and quantum computing. He has published 8 books and more than 250 research articles in premier international journals and conferences. His research has been recognized by several awards including the NSF CAREER Award, IBM Faculty Award, ten best paper awards and nominations, and EDAA Outstanding Dissertation Award. He currently serves as an Associate Editor of IEEE Transactions on VLSI Systems and ACM Transactions on Embedded Computing Systems. He is an IEEE Fellow and an ACM Distinguished Scientist.

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Change impact analysis evaluates the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. Code changes and bug fixes can have a high impact on code quality by introducing new vulnerabilities or increasing their severity. A recent high-visibility example of this is the code changes in the log4j web software CVE-2021-45105 to fix known vulnerabilities by removing and adding method call change types. This bug fix process may expose further code security concerns. By analyzing the most common set of bug fix change patterns we can better understand the distribution of software changes and their impact on code quality of key importance in software engineering. We describe a Markov Chain approach to measure the software ripple effect, that compares two versions of the code, extracts the changes that have been made, and models how these changes are related to change impact. Using a case study approach we identified the change types for bug-inducing and bug fix changes using the Quixbugs and Defects4J datasets. A positive observation of code change analysis was that carefully made bug fixes can have small ripple effects or low impact on introducing new bugs, confirming that software quality after changes can remain high. On average 90% of the bug fix change types (in Defects4J) are adding and deleting method calls/invocations, adding a new method declaration and changing the method body. We observed a negative correlation between change impact for the change types of adding a new method declaration, changing a method body, adding and deleting a method call. And a positive correlation between the change impact and changing the field type. The log4j bug fixes includes addition and deletion of method calls as well. A common practice in automatic code repair and mitigating input validation vulnerabilities is to add new if statements to patch code and add new method declarations which is consistent with minimizing code ripple effects.

Biography: Kannappan Palaniappan is a professor in electrical engineering and computer science. He has received several notable awards, including the National Academies Jefferson Science Fellowship (first in Missouri), the NASA Public Service Medal for pioneering contributions to (Big Data) scientific visualization of petabyte-sized archives, the Air Force Summer Faculty Fellowship, the Boeing Welliver Summer Faculty Fellowship, and MU’s William T. Kemper Fellowship for Teaching Excellence. At NASA’s Goddard Space Flight Center, he co-founded the Visualization and Analysis Lab that has produced a number of spectacular Digital Earth visualizations used by search engines (BlueMarble), museums, magazines and broadcast television. He is co-inventor of the Interactive Image SpreadSheet for handling large multispectral imagery, and he developed the first massively parallel semi-fluid cloud motion analysis algorithm using geostationary satellite imagery. In 2014, his team won first place at the IEEE Computer Vision and Pattern Recognition (CVPR) Change Detection Workshop video analytics challenge. In 2015, the team was a finalist in the CVPR Video Object Tracking Challenge, and in 2016, the team won the best paper award at the CVPR Automatic Traffic Surveillance Workshop and also was selected as a finalist for a best student paper award at the IEEE Engineering in Medicine and Biology Society conference (EMBC 2016). He has several U.S. patents, including one for moving object detection using the flux tensor split Gaussian model and the other for fast bundle adjustment to accurately estimate the pose of airborne camera sensor systems. Research projects have been funded by the National Institutes of Health, the Air Force Research Laboratory, the Army Research Laboratory, NASA, the National Science Foundation and others. His current, multidisciplinary interests in computer vision, high performance computing, data science and biomedical image analysis range across orders of scale from sub-cellular microscopy at the molecular level to aerial and satellite remote sensing imaging at the macro level.

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Abstract: Post Modern AI in the form of its ability to formulate deep ML architectures in novel ways combined with an increasing awareness of novel methods in functional optimization and exploding GPU and CPU power has enabled solving hitherto seemingly intractable problems in Computer Vision and NLP areas. In this talk we would look at how the field of robotics has seen a turn around due to the growth in post modern ML. We look at 3 problem domains, Visual SLAM, Visual Servoing and Vision Language Navigation as examples of such an impact in Robotics.

Biography: K Madhava Krishna heads the Robotics Research Center, RRC, at IIIT Hyderabad for the last 17 years and is also the head of the Kohli Center for Intelligent Systems at IIIT-H. During his tenure RRC gained international visibility and is rated amongst the top 30 centers world wide in Robotics Research and top 5 in Asia according to airankings.org and csrankings.org. The center has been funded over the years by DRDO Labs, govt ministries and a number of private sector companies.

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5G cellular networks support the concept of network slicing that allows diverse network service types to run on a common underlying physical network infrastructure. Network Slicing in 5G networks enables allocation and sharing of the underlying network resources of an infrastructure provider (INP) among multiple tenants of the INP. A slice is defined as an end-to-end independent virtual logical network that is assigned resources by the INP. The INP's Network resources are virtualized and shared among the other slices while maintaining isolation. Each tenant generates slice requests specifying the resources required, and the INP obtains revenue from the tenants for the admitted slices.
The INP uses a Slice Admission Control Module that decides whether to accept or reject the incoming slice requests. This decision helps the INP to manage its underlying resources efficiently, achieve fairness in resource allocation among different slice types and increase its revenue, while minimizing SLA violation among admitted slices. In this work, we have used the Prioritised Experience Replay-based Deep Q-Network with N-step return (N-PERDQN) Reinforcement learning (RL) approach to solve the slice admission control and associated resource allocation problem in a dynamic environment. We have also used Long Short-Term Memory (LSTM) to predict the admitted elastic slices' future resource requirements to share resources among elastic slices more efficiently.

Biography: Krishna M. Sivalingam is an Institute Chair Professor in the Department of CSE, IIT Madras, Chennai, INDIA, where he also served as Head of Department from Feb. 2016 - Feb. 2019. Earlier, he was a Professor in the Dept. of CSEE at University of Maryland, Baltimore County, Maryland, USA; with the School of EECS at Washington State University, Pullman, USA from 1997 until 2002; and with the University of North Carolina Greensboro, USA from 1994 until 1997. He has also conducted research at Lucent Technologies' Bell Labs in Murray Hill, NJ, and at AT&T Labs in Whippany, NJ.

He received his Ph.D. and M.S. degrees in Computer Science from State University of New York at Buffalo in 1994 and 1990 respectively; and his B.E. degree in Computer Science and Engineering in 1988 from Anna University's College of Engineering Guindy, Chennai (Madras), India. While at SUNY Buffalo, he was a Presidential Fellow from 1988 to 1991.

He holds three patents in wireless networks and has published several research articles including more than sixty-five journal publications. He has published an edited book on Next Generation Network Technologies in 2011, on Wireless Sensor Networks in 2004 and on optical WDM networks in 2000 and 2004.

He is presently serving on the Editorial Board of IEEE Networking Letters.

He has served as the Editor-in-Chief for Springer Photonic Network Communications Journal and the EAI Endorsed Transactions on Future Internet. He has served as a member of the Editorial Board for several international journals including IEEE Transactions of Mobile Computing, ACM/Springer Wireless Networks Journal and Elsevier Optical Switching and Networking Journal.

He has served on the Steering Committee of IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) and ICST International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous).

He is a Fellow of the IEEE, Fellow of INAE and an ACM Distinguished Scientist.

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