Mohammad Mamduhi, Karl Johansson, Ehsan Hashemi, John Baras
This paper proposes an event-triggered, add-on safety mechanism in a networked vehicular system that can adjust control parameters for timely braking while maintaining maneuverability.
Multi-modal learning for speech emotion recognition: An analysis and comparison of ASR outputs with ground truth transcription
Saurabh Sahu, Vikramjit Mitra, Nadee Seneviratne, Carol Espy-Wilson
The paper leverages multi-modal learning and automated speech recognition (ASR) systems toward building a speech-only emotion recognition model.
The deep neural networks of AlphaGo and AlphaZero can be traced back to an adaptive multistage sampling (AMS) simulation-based algorithm for Markov decision processed published by HS Chang, Michael C. Fu and Steven I Marcus in Operations Research in 2005. Here, Fu retraces history, talks about the impact of the initial research, and suggests enhancements for the future.
Asian-Pacific Journal of Operational Research
Eliot Rudnick-Cohen, Shapour Azarm and Jeffrey Herrmann
The paper presents Scenario Generation and Local Refinement Optimization (SGLRO), a new approach for solving non-convex robust optimization problems.
Journal of Mechanical Design
Kurt Gray, Tanmay Randhavane, Kyra Kapsaskis, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
A data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures.
Rohan Chandra, Tianrui Guan, Srujan Panuganti, Trisha Mittal, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
A novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning.
Qiaoyun Wu, Dinesh Manocha, Jun Wang, Kai Xu
The authors improve the cross-target and cross-scene generalization of visual navigation through a learning agent guided by conceiving the next observations it expects to see. A variational Bayesian model, NeoNav, generates the next expected observations (NEO) conditioned on the current observations of the agent and the target view.
Uttaran Bhattacharya, Christian Roncal, Trisha Mittal, Rohan Chandra,Aniket Bera, Dinesh Manocha
The paper presents an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or from motion-captured data and represented as sequences of 3D poses.
Chaochao Li, Pei Lv, Mingliang Xu, Xinyu Wang, Dinesh Manocha, Bing Zhou, Meng Wang
In many applications such as human-robot interaction, autonomous driving or surveillance, it is important to accurately predict pedestrian trajectories for collision-free navigation or abnormal behavior detection. The authors present a novel trajectory prediction algorithm for pedestrians based on a personality-aware probabilistic feature map.
Uttaran Bhattacharya, Trisha Mittal, Rohan Chandra, Tanmay Randhavane (UNC), Aniket Bera, and Dinesh Manocha
STEP is a novel classifier network able to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network architecture. Given an RGB video of an individual walking, STEP implicitly exploits the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. | Watch a video about STEP |
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha
The GraphRQI algorithm identifies driver behaviors from road agent trajectories. It is 25 percent more accurate over prior behavior classification algorithms for autonomous vehicles. | Watch a video about GraphRQI |
Qingyang Tan, Zherong Pan, Lin Gao, and Dinesh Manocha
A new method bridges the gap between mesh embedding and physical simulation for efficient dynamic models of clothes. The key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies, and a new mesh embedding approach based on physics-inspired loss term. After training, the learned simulator runs10–100 times faster and the accuracy is high enough for robot manipulation tasks. | Watch a video about this method |
William Underwood and Richard Marciano
This paper explores whether the computational thinking practices of mathematicians and scientists in the physical and biological sciences are also the practices of archival scientists. It is argued that these practices are essential elements of an archival science education in preparing students for a professional archival career.
Marciano and 20 students in the Digital Curation Innovation Center developed a reframing model for digital curation through computational thinking. Their case study involves adding metadata to non-digital primary records from the WWII TuleLake Japanese American Internment Camp. Their curation methods led to the discovery of new narratives and connections from this data.