Jan 10 2020
Title: Path planning for Continuum arms
Speaker/s: Brandon Meng & Jiahao Deng
Abstract: Continuum robotic arms replicate the functions of biological appendages like elephant trunks. The compliance inherent to these devices renders them human-friendly and more adaptable to their surroundings at the same time complicates smooth and reliable path planning. In this talk, we discuss two approaches for continuum arm path planning that is both reliable and generates smooth trajectories for environments with and without obstacles. The first approach uses Machine-Learning for path planning that greatly improved the speed and stability of the generated path. The second approach uses an anticipatory path planning scheme implemented through temporal graphs to accommodates moving obstacles in a dynamic environment.
Bio: Jiahao Deng is a doctoral candidate in Computer Science at DePaul CDM. He finished his Bachelor’s degree in Information Systems at the University of Iowa and a Master’s degree in Computer Science at DePaul University. His areas of interests include Robotics, Motion Planning, Artificial Intelligence and Machine Learning. He is currently working at the Robotics and Medical Engineering (RoME) lab at DePaul University.
Brandon Meng is a doctoral candidate and GAANN fellow at DePaul University. After receiving his Bachelor’s and Masters degree in Computer Science from DePaul, he now studies continuum arms in the RoME Lab.
Jan 17 2020
Title: Meet Malexa, Alexa’s Malicious Twin: Ambient Tactical Deception Attacks on Intelligent Voice Assistants
Speaker/s: Filipo Sharevski
Abstract: Malexa is an intelligent voice assistant with a simple and seemingly legitimate third-party skill that delivers news briefings to users. The twist, however, is that Malexa covertly rewords these briefings to introduce misperception about the reported events intentionally. This covert rewording is what we call an Ambient Tactical Deception (ATD) attack. It differs from squatting or invocation hijacking attacks in that it is focused on manipulating the “content” instead of the “invocation logic.” Malexa dynamically manipulates news briefings to make a government response sound more accidental or lenient than the original news delivered by Amazon Alexa. A study with 220 participants was conducted to assess Malexa’s effect on inducing misperceptions and covert manipulation of reality. We found that users who interacted with Malexa perceived that the government was less friendly to working people and more in favor of big businesses, regardless of their political ideology or frequency of interaction. These findings express the potential of Malexa becoming a covert “influencer” aiming to disrupt the current political climate, particularly the build-up to the 2020 presidential elections in the United States.
Bio: Filipo Sharevski is a cybersecurity researcher and tactician who constructs and manipulates reality as it unfolds across the cyber-physical spaces and within power structures, particularly focused on social engineering, reality interventions, resistances, and low-intensity cyberwarfare. His academic work has been published internationally, including a book on cellular network forensics, cybersecurity curriculum under the Cybersecurity National Action Plan (CNAP), and academic articles in renewed cybersecurity journals and conferences. His research areas include; Ambient Tactical Deception; malicious user experience design; secure design, divergence and deception in human communication and interaction; psychological operations; cyberwarfare; behavioral security in cellular and cyber-physical systems. Dr. Sharevski holds a Ph.D. in Interdisciplinary Cybersecurity from Purdue University, West Lafayette. He is currently an Assistant Professor in the College of Computing and Digital Media at DePaul University, where he co-founded and co-directs the Divergent Design Lab. He also leads the 5G De-Mobile Lab focused on behavioral security and forensics research in future cellular networks.
Jan 24 2020
Title: Accurate and efficient methods for reduced-complexity modeling in fluid mechanics
Abstract: This talk will explore several developments that improve upon the efficiency, accuracy, and theoretical understanding of methods for decomposition and reduced-order modeling of systems in fluid mechanics. The increasing size and complexity of problems that are now tractable for researchers to study heightens the need to develop methods capable of extracting pertinent information and accurate reduced-order models from large systems and datasets. I will start by talking about how such data-driven algorithms can be adversely affected by noisy data, and will discuss methods by which their robustness to noise can be improved. Next, I will focus on identifying optimal energy-amplification mechanisms within fluids systems from the decomposition of physics-based operators. In particular, I will show how the numerical computations typically required for such analysis can be sped up through the use of randomized algorithms, or eliminated entirely through the use of appropriately-chosen analytic approximations. The utility of the methods described in this talk will be demonstrated across a number of examples in fluid mechanics and aerodynamics.
Bio: Scott Dawson is an assistant professor in the Mechanical, Materials, and Aerospace Engineering Department at the Illinois Institute of Technology. Prior to this, he was a postdoctoral scholar at the California Institute of Technology, and he completed his Ph.D. in Mechanical and Aerospace Engineering at Princeton University. His research interests include modeling, optimization, and control in fluid mechanics, with a particular focus on turbulent shear flows, unsteady aerodynamic systems, and data-driven modeling approaches.
Jan 31 2020
Feb 7 2020
Feb 14 2020
Feb 21 2020
Feb 28 2020
Mar 6 2020
Mar 13 2020