- ItemTimingCamouflage+ Decamouflaged(Association for Computer Machinery (ACM), 2023-06-05) Mittu, Priya; Liu, Yuntao; Srivastava, AnkurIn today’s world, sending a chip design to a third party foundry for fabrication poses a serious threat to one’s intellectual property. To keep designs safe from adversaries, design obfuscation techniques have been developed to protect the IP details of the design. This paper explains how the previously considered secure algorithm, TimingCamouflage+, can be thwarted and the original circuit can be recovered . By removing wave-pipelining false paths, the TimingCamouflage+ algorithm is reduced to the insecure TimingCamouflage algorithm . Since the TimingCamouflage algorithm is vulnerable to the TimingSAT attack, this reduction proves that TimingCamouflage+ is also vulnerable to TimingSAT and not a secure camouflaging technique . This paper describes how wave-pipelining paths can be removed, and this method of handling false paths is tested on various benchmarks and shown to be both functionally correct and feasible in complexity.
- ItemUsing Metareasoning on a Mobile Ground Robot to Recover from Path Planning Failures(2023-02) Molnar, Sidney; Mueller, Matt; Macpherson, Robert; Rhoads, Lawrence; Herrmann, Jeffrey W.Autonomous mobile ground robots use global and local path planners to determine the routes that they should follow to achieve mission goals while avoiding obstacles. Although many path planners have been developed, no single one is best for all situations. This paper describes metareasoning approaches that enable a robot to select a new path planning algorithm when the current planning algorithm cannot find a feasible solution. We implemented the approaches within a ROS-based autonomy stack and conducted simulation experiments to evaluate their performance in multiple scenarios. The results show that these metareasoning approaches reduce the frequency of failures and reduce the time required to complete the mission.
- ItemData for: Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions(2021) Brodbeck, Christian; Bhattasali, Shohini; Das, Proloy; Simon, Jonathan Z.This dataset accompanies “Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions” (Brodbeck et al., 2021) and is a derivative of the Alice EEG datasets collected at the University of Michigan Computational Neurolinguistics Lab (Bhattasali et al., 2020), licensed under CC BY (https://creativecommons.org/licenses/by/4.0/) and the original work can be found at DOI: 10.7302/Z29C6VNH. The files were converted from the original matlab format to fif format in order to be compatible with Eelbrain. This dataset includes the EEG data for 33 participants, which were used in the example analyses for the paper. The original Alice dataset included data from all 49 participants and participants were excluded due to artifacts and incorrect behavioral responses (for more details see Bhattasali et al., 2020). You can use the Python script data_grab.py at https://github.com/christianbrodbeck/Alice-Eelbrain to download and unzip these files into a specified destination folder.
- ItemDynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise(PLOS Computational Biology, 2020-08-02) Presacco, Alessandro; Miran, Sina; Fu, Michael; Marcus, Steven; Simon, Jonathan; Babadi, BehtashMEG data used for the "Switching attention" experiment. This set of data refers to the part of the "forced" switching of attention
- ItemDynamic Estimation of Auditory Temporal Response Functions via State-Space Models with Gaussian Mixture Process Noise(PLOS Computational Biology, 2020-08-02) Presacco, Alessandro; Miran, Sina; Fu, Michael; Marcus, Steven; Jonathan, Simon; Babadi, BetashMEG data used for the "Switching attention" experiment