Recent technological advances have enabled deployments of pervasive sensing and actuation in our physical world, which has led to the emergence of cyber-physical systems where computing and sensing interact with the physical world and humans in unique and exciting ways. Such systems are increasingly being deployed in smart city domains such as energy, transportation, health, grids, and agriculture.
In this talk, I will argue that the rich and vast amounts of data generated by smart city applications necessitate a data-driven approach where AI and systems techniques are employed in a symbiotic manner to tackle smart city challenges. I will present two smart city applications from the energy domain as examples of such a symbiotic approach. I will first present WattHome, a city-scale machine-learning-based approach that can determine the least efficient buildings within a large city or region. I will present the results of a city-scale evaluation performed in collaboration with a local utility, where WattHome successfully identified causes of energy inefficiency for thousands of buildings. Second, I will present SolarClique, a sensor-less data-driven approach that is designed to detect anomalies in power generation of large number of existing solar sites without requiring any additional sensor instrumentation. I will conclude my talk by describing a number of open challenges in designing data-driven approaches for smart cities.
Prashant Shenoy is currently a Professor and Associate Dean in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He received the B.Tech degree in Computer Science and Engineering from the Indian Institute of Technology, Bombay and the M.S and Ph.D degrees in Computer Science from the University of Texas, Austin. His research interests lie in distributed systems and networking, with a recent emphasis on cloud and green computing. He has been the recipient of several best paper awards at leading conferences, including a Sigmetrics Test of Time Award. He serves on editorial boards of the several journals and has served as the program chair of over a dozen ACM and IEEE conferences. He is a fellow of the IEEE and the AAAS and a distinguished member of the ACM.
The February meeting of the Data Works MD Meetup features a talk by UMBC Professor Tim Oates on Two Algorithms for Weakly Supervised Denoising of EEG Data, 6:30-9pm Thursday, February 28, 2019 at UMBC’s South Campus. Join the meetup and register to attend this free talk and network with members of the Maryland data science community. The talk abstract and Dr. Oates’s biosketch are given below.
Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However, these approaches are artifact specific. In this talk, I will discuss two algorithms for solving this problem that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. In the first algorithm, the EEG data is decomposed into independent components using Independent Components Analysis, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. The second algorithm is a novel Generative Adversarial Network (GAN) formulation. I’ll present empirical results on EEG data gathered by the Army Research Lab, and discuss pros and cons of both algorithms.
Dr. Tim Oates is an Oros Family Professor in the Computer Science Department at the University of Maryland, Baltimore County. His Ph.D. from the University of Massachusetts Amherst was in the areas of artificial intelligence and machine learning with a focus on situated language learning. After working as a postdoctoral researcher in the MIT Artificial Intelligence Lab, he joined UMBC where he has taught extensively in core areas of Computer Science, including data structures, discrete math, compiler design, artificial intelligence, machine learning, and robotics. Dr. Oates has published more than 150 peer-reviewed papers in areas such as time series analysis, natural language processing, relational learning, and social media analysis. He has developed systems to determine operating room state from video streams, predict the need for blood transfusions and emergency surgery for traumatic brain injury patients based on vital signs data, detect seizures from scalp EEG, and find story chains (causal connections) joining news articles, among many others. Recently Dr. Oates served as the Chief Scientist of a Virginia-based startup where he developed architectures and algorithms for managing contact data, including entity linking, fuzzy record matching, and connected components on billion node graphs stored in a columnar database. He has extensive knowledge of machine learning algorithms, implementations, and usage.
The past decade has seen explosive growth in the use and deployment of IoT (Internet of Things) devices. According to Gartner there will be about 20.8 billion IoT devices in use by 2020. These devices are seeing wide spread adoption as they are cheap, easy to use and require little to no maintenance. In most cases, setup simply requires using a web or phone app to configure Wi-Fi credentials. Digital home assistants, security cameras, smart locks, home appliances, smart switches, toys, vacuum cleaners, thermostats, leakage sensors etc are examples of IoT devices that are widely used and deployed in home and enterprise environments.
The threat landscape is constantly evolving and threat actors are always on the prowl for new vulnerabilities they can exploit. With traditional attack methods yielding fewer exploits due to the increased focus on security testing, frequent patches, increased user awareness, Threat actors have turned their attention on IoT devices and are exploiting inherent vulnerabilities in these devices. The vulnerabilities, always ON nature, and autonomous mode of operation allow attackers to spy on users, spoof data, or leverage them as botnet infrastructure to launch devastating attacks on third parties. Mirai, a well known IoT malware utilized hundreds and thousands of enslaved IoT devices to launch DDoS attacks on Dyn affecting access to Netflix, Twitter, Github and many other websites. With the release of the Mirai source code numerous variants of the malware are infecting IoT devices across the world and using them to carry out attacks.
These attacks are made possible because the devices are manufactured without security in mind!. In this talk I will demonstrate how one can hack a widely available off-the-shelf IP Camera and router by exploiting the vulnerabilities present in these devices to get on the network, steal personal data, spy on a user, disrupt operation etc. We will also look at what can be done to mitigate the dangers posed by IOT devices.
So attend hack & defend!
Dr. Yatish Joshi is a software engineer in the Firepower division at Cisco Systems where he works on developing new features for Cisco’s security offerings. Yatish has a PhD in Computer Engineering from UMBC. Prior to Cisco Yatish worked as a lecturer at UMBC, and was a senior software engineer developing TV software at Samsung Electronics. When not working, he enjoys reading spy thrillers and fantasy novels.
In this talk, we present some major trends in compute, memory/storage, and networking, and for each we will discuss how OpenCAPI Memory Interface (OMI) and related interface technologies are set to transform how we build, program, and think about our computer systems. For the first of these trends, it allows us to compensate for the reduced growth in processor performance (per dollar) and performance per Watt. Accelerators are sharing memory and other resources over NVLink or OpenCAPI with conventional IBM POWER cores and are driving performance in the world’s largest supercomputers and IBM’s systems are targeting AI and other workloads. The second addresses the reduced improvement in memory cost and capacity. OMI allows us to use technologies other than DRAM as memory, and because many of these technologies are nonvolatile, the line between memory and storage is becoming blurred. The third, OpenCAPI-based networking leverages the rapid improvements in cost per Gb/s and allows us to contemplate systems that extend memory beyond the node using commodity infrastructure.
Harm Peter Hofstee is a Dutch physicist and computer scientist who currently is a distinguished research staff member at the IBM Austin Research Laboratory, USA, and a part-time Professor in Big Data Systems at Delft University of Technology, Netherlands. Hofstee is best known for his contributions to Heterogeneous computing as the chief architect of the Synergistic Processor Elements in the Cell Broadband Engine processor used in the Sony PlayStation 3, and the first supercomputer to reach sustained Petaflop operation. His early research work on coherently attached reconfigurable acceleration on POWER7 paved the way for the new coherent attach processor interface on POWER8. Hofstee is an IBM Master Inventor with more than 100 issued patents and a member of the IBM Academy of Technology. Hofstee was born in Groningen and obtained his master’s degree in theoretical physics of the University of Groningen in 1988. He continued to study at the California Institute of Technology where he wrote a master’s thesis Constructing Some Distributed Programs in 1991 and obtained a Ph.D. with a thesis titled Synchronizing Processes in 1995. He joined Caltech as a lecturer for two years and moved to IBM in the Austin, Texas, Research Laboratory, where he had staff member, senior technical staff member and distinguished engineer positions.