Computer Science Professor Magda El Zarki and History Professor Pat Seed were thrilled to see their game, “Sankofa,” first on the list of Bronze Medal winners for the 2018 International Serious Play Awards. The awards recognize excellence in serious games designed for use in K-12 or higher education, and “Sankofa” is designed to teach history and anthropology in a fun and engaging way by bringing 19th-century Ghana to life through gameplay.
After entering a password, your regular computer keyboard might appear to look the same as always, but a new approach harvesting thermal energy can illuminate the recently pressed keys, revealing that keyboard-based password entry is even less secure than previously thought. Computer Science Ph.D. students Tyler Kaczmarek and Ercan Ozturk in the Donald Bren School of Information and Computer Sciences (ICS), working with Chancellor’s Professor of Computer Science Gene Tsudik, have exploited thermal residue from human fingertips to introduce a new insider attack — the Thermanator.
In a paper to appear at the 2018 European Symposium on Research in Computer Security (ESORICS), a team of researchers from UC Irvine, New York Institute of Technology and University of Padova (Italy) reveal a new attack: Secret Information Leakage from Keystroke Timing Videos (SILK-TV). The UCI researchers include Chancellor’s Professor of Computer Science Gene Tsudik and undergrad exchange students Martin Georgiev and Nikita Samarian.
Chancellor’s Professor of Computer Science Gene Tsudik and two of his Ph.D. students, Tyler Kaczmarek and Ercan Ozturk, have developed a novel technique aimed at mitigating “Lunchtime Attacks.” Such attacks occur when an insider adversary takes over an authenticated state of a careless user who has left his or her computer unattended. Tsudik, Kaczmarek and Ozturk have come up with an unobtrusive and continuous biometric-based “de-authentication,” i.e., a means of quickly terminating the secure session of a previously authenticated user after detecting that user’s absence. They introduce the new biometric, called Assentication, in a paper appearing at the 2018 International Conference on Applied Cryptography and Network Security (ACNS).
Two algorithms developed by the researchers, collectively called Deep Cube, typically can solve the 3-D combination puzzle within 30 moves, which is less than or equal to systems that use human knowledge, according to the team’s research paper. Less than 5.8% of the world’s population can solve the Rubik’s Cube, according to the Rubik’s website.
Read the full story at the Los Angeles Times.
Although Stephen McAleer first became interested in artificial intelligence after reading the book Gödel, Escher, Bach: An Eternal Golden Braid, it wasn’t until Google’s AI program AlphaGo beat the world champion of the game Go that he decided to switch careers from finance to AI research. Now, as a Ph.D. student working with Chancellor’s Professor Pierre Baldi in the Donald Bren School of Information and Computer Sciences (ICS), he has helped tackle a new deep-learning challenge.
UCI Professor of Computer Science Aditi Majumder’s, Ph.D., journey into the virtual/augmented reality world stems back to a young girl growing up in Kolkata, India around her mother, a professional musician and her father, a civil engineer. Both parents unknowingly planted the hybrid seed of art and science, which led Majumder to pursue a blossoming career in virtual/augmented reality that intertwines with her deep appreciation for art and music.
Read the full story at UCI Applied Innovation Tech Currents.
In chess, by contrast, there is a relatively large search space but each move can be evaluated and rewarded accordingly. That just isn’t the case for the Rubik’s Cube.
Enter Stephen McAleer and colleagues from the University of California, Irvine. These guys have pioneered a new kind of deep-learning technique, called “autodidactic iteration,” that can teach itself to solve a Rubik’s Cube with no human assistance. The trick that McAleer and co have mastered is to find a way for the machine to create its own system of rewards.
Read the full story at MIT Technology Review.
Researchers from the University of California, Irvine developed a deep learning-based approach to accelerate drug discovery and cancer research.
“We have developed a convolutional neural network to improve the data analysis processes for high-throughput drug screening using our microphysiological system (MPS),” the researchers stated in their paper.
Read the full story at NVIDIA Developer News Center.