Artificial Intelligence, Machine Learning, Computer Science

AI and ML for robotics, automated planning, game theory, speech recognition, data visualization, rule-based expert systems and logic programs.

ISR artificial intelligence research on hierarchical task network planning has influenced nearly all subsequent work in this area. We have deep understanding of artificial intelligence planning and the use of mean-field game theory to predict decisions. Our pioneering history in data visualization produced Spotfire, a starfield multidimensional data visualization display tool using dynamic queries and Treemaps, a space-filling method of visualizing large hierarchical collections of quantitative data that gives users the ability to see thousands of data bits in a fixed space that facilitates discovery of patterns, clusters and outliers.  We have expertise in formal methods for description and analysis of concurrent and distributed systems, model checking and abstract interpretation for embedded control and systems biology. Today we are introducing innovations in computer vision and hyperdimensional computing theory for robots, as well as geometric and scientific algorithms for autonomous vehicles, computer graphics, and virtual reality.

Recent publications

2020

A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP

Matthew Evanusa, Cornelia Fermüller, Yiannis Aloimonos

The researchers show that a large, deep layered spiking neural network with dynamical, chaotic activity mimicking the mammalian cortex with biologically-inspired learning rules, such as STDP, is capable of encoding information from temporal data.

arXiv.org

Hybrid Backpropagation Parallel Reservoir Networks

Matthew Evanusa, Snehesh Shrestha, Michelle Girvan, Cornelia Fermüller, Yiannis Aloimonos

Demonstrates the use of a backpropagation hybrid mechanism for parallel reservoir computingwith a meta ring structure and its application on a real-world gesture recognition dataset. This mechanism can be used as an alternative to state of the art recurrent neural networks, LSTMs and GRUs.

arXiv.org

Egocentric Object Manipulation Graphs

Eadom Dessalene, Michael Maynord, Chinmaya Devaraj, Cornelia Fermüller, Yiannis Aloimonos

Introduces Egocentric Object Manipulation Graphs (Ego-OMG):a novel representation for activity modeling and anticipation of near future actions.

arXiv.org

Symbolic Representation and Learning with Hyperdimensional Computing

Anton Mitrokhin, Peter Sutor, Douglas Summers-Stay, Cornelia Fermüller, Yiannis Aloimonos

By using hashing neural networks to produce binary vector representations of images, the authors show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output.

Frontiers in Robotics and AI

Learning Visual Motion Segmentation using Event Surfaces

Anton Mitrokhin, Zhiyuan Hua, Cornelia Fermüller, Yiannis Aloimonos

Presents a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera. Describes spatial and temporal features of event clouds, which provide cues for motion tracking and segmentation.

Computer Vision Foundation

MOMS with Events: Multi-Object Motion Segmentation with Monocular Event Cameras

Chethan M. Parameshwara, Nitin J. Sanket, Arjun Gupta, Cornelia Fermüller, Yiannis Aloimonos

A solution to multi-object motion segmentation using a combination of classical optimization methods along with deep learning and does not require prior knowledge of the 3D motion and the number and structure of objects.

arXiv.org

Following Instructions by Imagining and Reaching Visual Goals

John Kanu, Eadom Dessalene, Xiaomin Lin, Cornelia Fermüller, Yiannis Aloimonos

A novel robotic agent framework for learning to perform temporally extended tasks using spatial reasoning in a deep reinforcement learning framework, by sequentially imagining visual goals and choosing appropriate actions to fulfill imagined goals.

arXiv.org

2020

Convergence of Stochastic Vector Quantization and Learning Vector Quantization with Bregman Divergences

Christos Mavridis, John Baras

The researchers investigate the convergence properties of stochastic vector quantization (VQ) and its supervised counterpart, Learning Vector Quantization (LVQ), using Bregman divergences. We employ the theory of stochastic approximation to study the conditions on the initialization and the Bregman divergence generating functions, under which,the algorithms converge to desired configurations. These results formally support the use of Bregman divergences, such as the Kullback-Leibler divergence, in vector quantization algorithms.

johnbaras.com

Order Effects of Measurements in Multi-Agent Hypothesis Testing

Aneesh Raghavan, John Baras

This paper pertains to stochastic multi-agent decision-making problems. The authors revisit the concepts of event-state-operation-structure and relationship of incompatibility from literature, and use them as a tool to study the algebraic structure of a set of events. They consider a multi-agent hypothesis testing problem and show that the set of events forms an ortholattice. They then consider the binary hypothesis testing problem wth finite observation space.

arXiv.org

Cooperative Hypothesis Testing by Two Obervers with Asymmetric Information

Aneesh Raghavan, John Baras

This paper pertains to hypothesis testing problems, specifically the problem of collaborative binary hypothesis testing.

arXiv.org

Interpretable machine learning models: A physics-based view

Ion Matei, Johan de Kleer, Christoforos Somarakis, Rahul Rai, John Baras

To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial. In this paper the authors use model-based interpretability, where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed.

arXiv.org

2019

Event-Triggered Add-on Safety for Connected and Automated Vehicles using Roadside Network Infrastructure

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.

arXiv.org

2020

Iterative Pre-Conditioning for Expediting theGradient-Descent Method:The Distributed Linear Least-Squares Problem

Kushal Chakrabarti, Nirupam Gupta, and Nikhil Chopra

This paper considers the multi-agent linear least-squares problem in a server-agent network. The system comprises multiple agents, each having a set of local data points, that are connected to a server. The goal for the agents is to compute a linear mathematical model that optimally fits the collective data points held by all the agents, without sharing their individual local data points. The paper proposes an iterative pre-conditioning technique that mitigates the deleterious effect of the conditioning of data points on the rate of convergence of the gradient-descent method.

arXiv.org

2020

Temporal-Logic Query Checking over Finite Data Streams

Samuel Huang, Rance Cleaveland

This paper describes a technique for inferring temporal-logic properties for sets of finite data streams. Such data streams arise in many domains, including server logs, program testing, and financial and marketing data; temporal-logic formulas that are satisfied by all data streams in a set can provide insight into the underlying dynamics of the system generating these streams. The authors' approach makes use of so-called Linear Temporal Logic (LTL) queries, which are LTL formulas containing a missing subformula and interpreted over finite data streams. Solving such a query involves computing a subformula that can be inserted into the query so that the resulting grounded formula is satisfied by all data streams in the set. The paper describes an automaton-driven approach to solving this query-checking problem and demonstrates a working implementation via a pilot study.

arXiv.org

Timed Automata Benchmark Description

Peter Fontana, Rance Cleaveland

This report contains the descriptions of the timed automata (models) and the prop-erties (specifications) that are used as the “benchmark examples in Data structure choices for on-the-fly model checking of real-time systems” and “The power of proofs: New algorithms for timed automata model checking.” The four models from those sources are: CSMA, FISCHER, LEADER, and GRC. Additionally we include in this re-port two additional models: FDDI and PATHOS. These six models are often used to benchmark timed automata model checker speed throughout timed automata model checking papers.

arXiv.org

Better Automata through Process Algebra

Rance Cleaveland

This paper shows how the use of Structural Operational Semantics (SOS) inthe style popularized by the process-algebra community can lead to a more succinct and useful construction for building finite automata from regular expressions.

arXiv.org

2020

Modeling Feature Representations for Affective Speech using Generative Adversarial Networks

Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson

Implements three auto-encoder and GAN based models to synthetically generate higher dimensional feature vectors useful for speech emotion recognition from a simpler prior distribution pz.

IEEE Transactions on Affective Computing

2019

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.

Interspeech 2019

2020

A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP

Matthew Evanusa, Cornelia Fermüller, Yiannis Aloimonos

The researchers show that a large, deep layered spiking neural network with dynamical, chaotic activity mimicking the mammalian cortex with biologically-inspired learning rules, such as STDP, is capable of encoding information from temporal data.

arXiv.org

Hybrid Backpropagation Parallel Reservoir Networks

Matthew Evanusa, Snehesh Shrestha, Michelle Girvan, Cornelia Fermüller, Yiannis Aloimonos

Demonstrates the use of a backpropagation hybrid mechanism for parallel reservoir computingwith a meta ring structure and its application on a real-world gesture recognition dataset. This mechanism can be used as an alternative to state of the art recurrent neural networks, LSTMs and GRUs.

arXiv.org

Egocentric Object Manipulation Graphs

Eadom Dessalene, Michael Maynord, Chinmaya Devaraj, Cornelia Fermüller, Yiannis Aloimonos

Introduces Egocentric Object Manipulation Graphs (Ego-OMG):a novel representation for activity modeling and anticipation of near future actions.

arXiv.org

Symbolic Representation and Learning with Hyperdimensional Computing

Anton Mitrokhin, Peter Sutor, Douglas Summers-Stay, Cornelia Fermüller, Yiannis Aloimonos

By using hashing neural networks to produce binary vector representations of images, the authors show how hyperdimensional vectors can be constructed such that vector-symbolic inference arises naturally out of their output.

Frontiers in Robotics and AI

Learning Visual Motion Segmentation using Event Surfaces

Anton Mitrokhin, Zhiyuan Hua, Cornelia Fermüller, Yiannis Aloimonos

Presents a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera. Describes spatial and temporal features of event clouds, which provide cues for motion tracking and segmentation.

Computer Vision Foundation

MOMS with Events: Multi-Object Motion Segmentation with Monocular Event Cameras

Chethan M. Parameshwara, Nitin J. Sanket, Arjun Gupta, Cornelia Fermüller, Yiannis Aloimonos

A solution to multi-object motion segmentation using a combination of classical optimization methods along with deep learning and does not require prior knowledge of the 3D motion and the number and structure of objects.

arXiv.org

Following Instructions by Imagining and Reaching Visual Goals

John Kanu, Eadom Dessalene, Xiaomin Lin, Cornelia Fermüller, Yiannis Aloimonos

A novel robotic agent framework for learning to perform temporally extended tasks using spatial reasoning in a deep reinforcement learning framework, by sequentially imagining visual goals and choosing appropriate actions to fulfill imagined goals.

arXiv.org

2019

Simulation-based algorithms for Markov decision processes: Monte Carlo tree search from AlphaGo to AlphaZero

Michael Fu

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

2020

Automatic Shape Optimization of Patient-Specific Tissue Engineered Vascular Grafts for Aortic Coarctation

Xiaolong Liu, Seda Aslan, Rachel Hess, Paige Mass, Laura Olivieri, Yue-Hin Loke, Narutoshi Hibino, Mark Fuge, Axel Krieger

Develops a computational framework for automatically designing optimal shapes of patient-specific TEVGs for aorta surgery.

42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization

Wei Chen, Mark Fuge

The authors propose a Bayesian optimization approach that only needs to specify an initial search space that does not necessarily include the global optimum, and expands the search space when necessary.

arXiv.org

2020

Fine-Grained Vehicle Perception via 3DPart-Guided Visual Data Augmentation

Feixiang Lu, Zongdai Liu, Hui Miao, Peng Wang, Liangjun Zhang, Ruigang Yang, Dinesh Manocha, Bin Zhou

Holistically understanding an object and its 3D movable parts through visual perception models is essential for enabling anautonomous agent to interact with the world. For autonomous driving, the dynamics and states of vehicle parts such as doors, the trunk, and the bonnet can provide meaningful semantic information and interaction states, which are essential to ensure the safety of the self-driving vehicle. Existing visual perception models mainly focus on coarse parsing such as object bounding box detection orpose estimation and rarely tackle these situations. In this paper, the authors address this important problem for autonomous driving by solving two critical issues using visual data augmentation.

arXiv.org

Self-Illusion: A Study on High-Level Cognition of Role-Playing in Immersive Virtual Environments from Non-Human Perspective

Sheng Li, Xiang Gu, Kangrui Yi, Yanlin Yang, Guoping Wang, Dinesh Manocha

This experiment investigated the occurrence of self-illusion and its contribution to realistic behavior consistent with a virtual role in virtual environments.

IEEE Transactions on Visualization and Computer Graphics

AutoTrajectory: Label-Free Trajectory Extraction and Prediction from Videos Using Dynamic Points

Yuexin Ma, Xinge Zhu, Xinjing Cheng, Ruigang Yang, Jiming Liu, Dinesh Manocha

A label-free algorithm for trajectory extraction and prediction to use raw videos directly. To better capture the moving objects in videos, the authors introduce dynamic points to model dynamic motions by using a forward-backward extractor to keep temporal consistency and using image reconstruction to keep spatial consistency in an unsupervised manner. The method is the first to achieve unsupervised learning of trajectory extraction and prediction.

2020 European Conference on Computer Vision

CPPM: chi-squared progressive photon mapping

Zehui Lin, Sheng Li, Xinlu Zeng, Congyi Zhang, Jinzhu Jia, Guoping Wang, Dinesh Manocha

This chi-squared progressive photon mapping algorithm (CPPM) constructs an estimator by controlling the bandwidth to obtain superior image quality.

ACM Transactions on Graphics

Sound Synthesis, Propagation, and Rendering: A Survey

Shiguang Liu, Dinesh Manocha

This is a broad overview of research on sound simulation in virtual reality, games, etc. It first surveys various sound synthesis methods,including harmonic synthesis, texture synthesis, spectral analysis, and physics-based synthesis. Then, it summarizes popular sound propagation techniques, namely wave-based methods, geometric-based methods, and hybrid methods. Next, sound rendering methods are reviewed. The authors also highlight some recent methods that use machine learning techniques for synthesis, propagation, and some inverse problems.

arXiv.org

ABC-Net: Semi-Supervised Multimodal GAN-based Engagement Detection using an Affective, Behavioral and Cognitive Model

Pooja Guhan, Manas Agarwal, Naman Awasthi, Gloria Reeves, Dinesh Manocha, Aniket Bera

ABC-Net is a semi-supervised multi-modal GAN framework based on psychology literature that detects engagement levels in video conversations. It uses three constructs—behavioral, cognitive, and affective engagement—to extract various features that can effectively capture engagement levels.

arXiv.org

Generating Emotive Gaits for Virtual Agents Using Affect-BasedAutoregression

Uttaran Bhattacharya, Nicholas Rewkowski, Pooja Guhan, Niall L. Williams, Trisha Mittal, Aniket Bera, Dinesh Manocha

This autoregression network generates virtual agents that convey various emotions through their walking styles or gaits.

arXiv.org

SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments

Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee

An algorithm for self-supervised monocular depth completion in robotic navigation, computer vision and autonomous driving. The approach is based on training a neural network that requires only sparse depth measurements and corresponding monocular video sequences without dense depth labels. Our self-supervised algorithm is designed for challenging indoor environments with textureless regions, glossy and transparent surface, non-Lambertian surfaces, moving people, longer and diverse depth ranges and scenes captured by complex ego-motions.

arXiv.org

LCollision: Fast Generation of Collision-Free Human Poses using Learned Non-Penetration Constraints

Qingyang Tan, Zherong Pan, Dinesh Manocha

LCollision is a learning-based method that synthesizes collision-free 3D human poses. LCollision is the first approach that can obtain high accuracy in handling non-penetration and collision constraints in a learning framework.

arXiv.org

StylePredict: Machine Theory of Mind for Human Driver Behavior from Trajectories

Rohan Chandra, Aniket Bera, Dinesh Manocha

Autonomous vehicles behave conservatively in a traffic environment with human drivers and do not adapt to local conditions and socio-cultural norms. However, socially aware AVs can be designed if there exists a mechanism to understand the behaviors of human drivers. In this example of Machine Theory of Mind (M-ToM) the authors infer the behaviors of human drivers by observing the trajectory of their vehicles. "StylePredict" is based on trajectory analysis of vehicles. It mimics human ToM to infer driver behaviors, or styles, using a computational mapping between the extracted trajectory of avehicle in traffic and the driver behaviors using graph-theoretic techniques, including spectral analysis and centrality functions. StylePredict can analyze driver behavior in the USA, China, India, and Singapore, based on traffic density, hetero-geneity, and conformity to traffic rules.

arXiv.org

B-GAP: Behavior-Guided Action Prediction for Autonomous Navigation

Angelos Mavrogiannis, Rohan Chandra, Dinesh Manocha

A learning algorithm for action prediction and local navigation for autonomous driving that classifies the driver behavior of other vehicles or road-agents (aggressive or conservative) and takes that into account for decision making and safe driving.

arXiv.org

BoMuDA: Boundless Multi-Source Domain Adaptive Segmentation in Unconstrained Environments

Divya Kothandaraman, Rohan Chandra, Dinesh Manocha

An unsupervised multi-source domain adaptive semantic segmentation approach for autonomous vehicles in unstructured and unconstrained traffic environments.

arXiv.org

CubeP Crowds: Crowd Simulation Integrated into“Physiology-Psychology-Physics” Factors

Mingliang Xu, Chaochao Li, Pei Lv, Wei Chen, Zhigang Deng, Bing Zhou, Dinesh Manocha

CubeP is a model for crowd simulation that comprehensively considers physiological, psychological, and physical factors. Inspired by the theory of “the devoted actor”, the model determines the movement of each individual by modeling the physical influence from physical strength and emotion. This is the first time that physiological, psychological, and physical factors are integrated in a unified manner, and the relationship between the factors is explicitly determined. The new model is capable of generating effects similar to real-world scenarios and can also reliably predict the changes in the physical strength and emotion of individuals in an emergency situation.

arXiv.org

Deep-Modal: Real-Time Impact Sound Synthesis for Arbitrary Shapes

Xutong Jin, Sheng Li, Tianshu Qu, Dinesh Manocha, Guoping Wang

Model sound synthesis is a physically-based sound synthesis method used to generate audio content in games and virtual worlds. This paper presents a novel learning-based impact sound synthesis algorithm called Deep-Modal. The approach can handle sound synthesis for common arbitrary objects, especially dynamic generated objects, in real time.

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Learning Acoustic Scattering Fields for Highly Dynamic Interactive Sound Propagation

Zhenyu Tang, Hsien-Yu Meng, Dinesh Manocha

A novel hybrid sound propagation algorithm for interactive applications.

arXiv.org

Multi-Window Data Augmentation Approach for Speech Emotion Recognition

Sarala Padi, Dinesh Manocha, Ram Sriram

A novel, Multi-Window Data Augmentation(MWA-SER) approach for speech emotion recognition.MWA-SER is a unimodal approach that focuses on two key concepts; designing the speech augmentation method to generate additional data samples and building the deep learning models to recognize the underlying emotion of an audio signal.

arXiv.org

IR-GAN: Room Impulse Response Generator for Speech Augumentation

Anton Ratnarajah, Zhenyu Tang, Dinesh Manocha

The paper presents a Generative Adversarial Network (GAN) based room impulse response generator for generating realistic synthetic room impulse responses.

arXiv.org

P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using Dynamic Matrix Assembly and Pipelined Implicit Integrators

Cheng Li, Min Tang, Ruofeng tong, Ming Cai, Jieyi Zhao, Dinesh Manocha

Cloth simulation is an active area of research in computer graphics, computer-aided design (CAD) and the fashion industry. Over the last few decades many methods have been proposed for solving the underlying dynamical system with robust collision handling. The paper presents a novel parallel algorithm for cloth simulation that exploits multiple GPUs for fast computation and the handling of very high resolution meshes. It is the first approach that can perform almost interactive complex cloth simulation with wrinkles, friction and folds on commodity workstations.

arXiv.org

PerMO: Perceiving More at Once from a Single Image for Autonomous Driving

Feixiang Lu, Zongdai Liu, Xibin Song, Dingfu Zhou, Wei Li, Hui Miao, Miao Liao, Liangjun Zhang, BinZhou, Ruigang Yang, Dinesh Manocha

The paper presents a robust and effective approach to reconstruct complete 3D poses and shapes of vehicles from a single image. It introduces a novel part-level representation for vehicle segmentation and 3D reconstruction, which significantly improves performance.

arXiv.org

SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning

Andrew Best, Sahil Narang, Dinesh Manocha

Sense-Plan-act (SPA) is a new approach for generating plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. It extends prior work in propositional planning and natural language processing to enable agents to plan with uncertain information, and leverage question and answer dialogue with other agents and avatars to obtain the needed information and complete their goals. The agents are additionally able to respond to questions from the avatars and other agents using natural-language enabling real-time multi-agent multi-avatar communication environments.

arXiv.org

RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments

Rohan Chandra, Uttaran Bhattacharya, Tanmay Randhavane, Aniket Bera, Dinesh Manocha

RoadTrack is a realtime tracking algorithm for autonomous driving that tracks heterogeneous road-agents in dense traffic videos. The approach is designed for dense traffic scenarios that consist of different road-agents such as pedestrians, two-wheelers, cars, buses, etc. sharing the road.

GAMMA website

DGaze: CNN-Based Gaze Prediction in Dynamic Scenes

Zhiming Hu, Sheng Li, Congyi Zhang, Kangrui Yi, Guoping Wang, Dinesh Manocha

DGaze is a CNN-based model that combines object position sequence, head velocity sequence, and saliency features to predict users' gaze positions in HMD-based applications. The model can be applied to predict not only real-time gaze positions but also gaze positions in the near future and can achieve better performance than prior method.

IEEE Transactions on Visualization and Computer Graphics

RANDM: Random Access Depth Map Compression UsingRange-Partitioning and Global Dictionary

Srihari Pratapa, Dinesh Manocha

RANDM is a random-access depth map compression algorithm for interactive rendering. The compressed representation provides random access to the depth values and enables real-time parallel decompression on commodity hardware. This method partitions the depth range captured in a given scene into equal-sized intervals and uses this partition to generate three separate components that exhibit higher coherence. Each of these components is processed independently to generate the compressed stream.

GAMMA website

CMetric: A Driving Behavior Measure Using Centrality Functions

Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Aniket Bera, Dinesh Manocha

CMetric classifies driver behaviors using centrality functions. The formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. This approach is designed for real-time autonomous driving applications, where the trajectory of each vehicle or road-agent is extracted from a video.

arXiv.org

Emotions Don’t Lie: A Deepfake Detection Method using Audio-Visual Affective Cues

Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha

The paper presents a learning-based method for detecting fake videos. The authors use the similarity between audio-visual modalities and the similarity between the affective cues of the two modalities to infer whether a video is “real” or “fake.”

arXiv.org

EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege’sPrinciple

Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha

EmotiCon is a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. It uses multiple modalities of faces and gaits, background visual information and socio-dynamic inter-agent interactions to infer the perceived emotion. EmotiCon outperforms prior context-aware emotion recognition methods.

arXiv.org

MCQA: Multimodal Co-attention Based Network for Question Answering

Abhishek Kumar, Trisha Mittal, Dinesh Manocha

MCQA is a learning-based algorithm for multimodal question answering that explicitly fuses and aligns the multi-modal input (i.e. text, audio, and video) forming the context for the query (question and answer).

arXiv.org

Scene-aware Sound Rendering in Virtual and Real Worlds

Zhenyu Tang, Dinesh Manocha

Modern computer graphics applications including virtual reality and augmented reality have adopted techniques for both visual rendering and audio rendering. While visual rendering can already synthesize virtual objects into the real world seamlessly, it remains difficult to correctly blend virtual sound with real-world sound using state-of-the-art audio rendering. When the virtual sound is generated unaware of the scene, the corresponding application becomes less immersive, especially for AR. The authors present their current work on generating scene-aware sound using ray-tracing based simulation combined with deep learning and optimization.

2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops

Interactive Geometric Sound Propagation and Rendering

Micah Taylor, Anish Chandak, Lakulish Antani, Dinesh Manocha

An algorithm and system for sound propagation and rendering in virtual environments and media applications. The approach uses geometric propagation techniques for fast computation of propagation paths from a source to a listener and takes into account specular reflections, diffuse reflections, and edge diffraction.

Intel

Reactive Navigation Under Non-Parametric Uncertainty Through Hilbert Space Embedding of Probabilistic Velocity Obstacles

SriSai Naga Jyotish Poonganam, Bharath Gopalakrishnan, Venkata Seetharama Sai Bhargav Kumar Avula, K. Madhava Krishna, Arun Kumar Singh, Dinesh Manocha

A new model predictive control framework that improves reactive navigation for autonomous robots. The framework allows roboticists to compute low cost control inputs while ensuring some upper bound on the risk of collision.

IEEE Robotics and Automation Letters

RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments

Dinesh Manocha, Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Tanmay Randhavane

The authors' RoadTrack algorithm could help autonomous vehicles navigate dense traffic scenarios. The algorithm uses tracking-by-detection approach to detect vehicles and pedestrians, then predict where they are going.

IROS 2019

2019

The Liar’s Walk: Detecting Deception with Gait and Gesture

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.

arXiv.org

Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

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.

arxiv.org

NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations

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.

PDF

Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping

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.

arxiv.org

Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians

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.

arxiv.org

STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

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 |

arxiv.org

GraphRQI: Classifying Driver Behaviors Using Graph Spectrums

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 |

arxiv.org

Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding

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 |

arxiv.org

2020

Using Data Partitions and Stateless Servers to Scale Up Fedora Repositories

Gregory Jansen, Aaron Coburn, Adam Soroka, Richard Marciano

Describes the development and testing of the next-generation Trellis Linked Data Platform with Memento versioning support.

dcicblog.umd.edu

2019

Computational thinking in archival science research and education

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.

Reframing Digital Curation Practices through a Computational Thinking Framework

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.

2020

Integrating acting, planning, and learning in hierarchical operational models

Sunandita Patra, Amit Kumar, James Mason, Malik Ghallab, Paolo Traverso, Dana Nau

New planning and learning algorithms for Refinement Acting Engine (RAE), which uses hierarchical operational models to perform tasks in dynamically changing environments. 

2020 International Conference on Automated Planning and Scheduling (ICAPS)

2019

APE: Acting and Planning Engine

Sudanita Patra, Malik Ghallab, Dana Nau, Paolo Traverso

An integrated acting and planning system that addresses the consistency problem by using the actor’s operational models both for acting and for planning.

2020

Metamorphic filtering of black-box adversarial attacks onmulti-network face recognition models

Rohan Mekala, Adam Porter, Mikael Lindvall

The authors build a black box attack against robust multi-model face recognition pipelines and test it against Google’s FaceNet. They present a novel metamorphic defense pipeline relying on nonlinear image transformations to detect adversarial attacks with a high degree of accuracy. They further use the results to create probabilistic metamorphic relations that define efficient decision boundaries between safe and adversarial examples.

ICSEW'20 May 2020, Seoul, South Korea

2020

A New Aging Sensor for the Detection of Recycled ICs

Zhichao Xu, Aijiao Cui, Gang Qu

The electronics industry has become the main target of counterfeiting. Integrated circuits (ICs) are highly vulnerable to various types of counterfeiting such as recycling. The recycled ICs do not have the performance and service lifetime of the genuine ones, which poses a threat to reliability of electronic systems. This paper proposes a novel recycled IC detection method. An authentication mechanism and a parallel circuit unit structures, as an aging sensor, are used to distinguish recycled ICs from fresh ICs.

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

Is It Approximate Computing or Malicious Computing?

Ye Wang, Jian Dong, Qian Xu, Zhaojun Lu, Gang Qu

Approximate computing (AC) is an attractive energy efficient technique that can be implemented at almost all the design levels including data, algorithm, and hardware. The basic idea behind AC is to deliberately control the trade-off between computation accuracy and energy efficiency. However, with the introduction of AC, traditional computing frameworks are having many potential security vulnerabilities. This paper analyzes these vulnerabilities and the associated attacks as well as corresponding countermeasures.

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

Privacy Threats and Protection in Machine Learning

Jiliang Zhang, Chen Li, Jing Ye, Gang Qu

This article reviews recent research progress on machine learning privacy. First, the privacy threats on data and models in different scenarios are described in detail. Then, typical privacy protection methods are introduced. Finally, the limitations and future development trends of ML privacy research are discussed.

GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI

2019

Research on the impact of different benchmark circuits on the representative path in FPGAs

Jiqing Xu, Zhengjie Li , Yunbing Pang , Jian Wang , Gang Qu, Jinmei Lai

Under the premise of selecting a large number of typical benchmark circuits, a representative path delay can well represent the overall timing performance of FPGAs.

2019 IEEE 13th International Conference on ASIC (ASICON)

2020

Human-Centered Artificial Intelligence: Three Fresh Ideas

Ben Shneiderman

A commentary that reverses the current emphasis on algorithms and AI methods, by putting humans at the center of systems design thinking. It offers three ideas: (1) a two-dimensional HCAI framework, which shows how it is possible to have both high levels of human control AND high levels of automation, (2) a shift from emulating humans to empowering people with a plea to shift language, imagery, and metaphors away from portrayals of intelligent autonomous teammates towards descriptions of powerful tool-like appliances and tele-operated devices, and (3) a three-level governance structure that describes how software engineering teams can develop more reliable systems, how managers can emphasize a safety culture across an organization, and how industry-wide certification can promote trustworthy HCAI systems.

AIS Transactions on Human-Computer Interaction

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

Ben Shneiderman

Proposes a two-dimensional framework alternative to autonomous AI systems called Human-Centered Artificial Intelligence that clarifies how to design for high levels of human control and high levels of computer automation to increase human performance, understand the situations in which full human control or full computer control are necessary, and avoid the dangers of either excessive human control or excessive computer control.

ACM International Journal of Human-Computer Interaction

2020

Coded Distributed Computing with Partial Recovery

Emre Ozfatura, Sennur Ulukus, Deniz Gunduz

Introduces a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both computation time and decoding complexity by allowing a trade-off between the accuracy and the speed of computation. The approach is extended to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated.

2019 IEEE International Conference on Acoustics, Speech and Signal Processin; arXiv.org

Age-Based Coded Computation for Bias Reduction in Distributed Learning

Deniz Gunduz, Emre Ozfatura, Sennur Ulukus, Baturalp Buyukates

The age of information (AoI) metric is used to track the recovery frequency of partial computations in distributed gradient descent, the most common approach in supervised machine learning, a new solution to the problem of “straggling” worker machines.

arXiv.org

Channel-Aware Adversarial Attacks against Deep Learning-based Wireless Signal Classifiers

Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

Presents over-the-air adversarial attacks against deep learning-based modulation classifiers, accounting for realistic channel and broadcast transmission effects. A certified defense method using randomized smoothing is also included.

arXiv.org

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