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Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

computers, Materials & Continua 2022年第12期73卷 4465-4487页

作者： Zia Ullah Muhammad Ismail Mohmand Sadaqat ur Rehman Muhammad Zubair Maha Driss Wadii Boulila Rayan Sheikh Ibrahim Alwawi Department of Computer Science The Brains InstitutePeshawar25000Pakistan School of Natural and Computing Sciences University of AberdeenAberdeenUK Department of Neurosciences KU Leuven Medical SchoolLeuven3000Belgium Security Engineering Laboratory CCISPrince Sultan UniversityRiyadh12435Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science Robert Gordon UniversityAberdeenUK

Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it *** overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature ***,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature ***,the three feature extraction models are utilized using Ensemble Learning techniques for final expression *** representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising *** evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression *** proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and ***,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.

关键词： Ensemble learning emotion recognition feature fusion occlusion

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Fast exact leverage score sampling from khatri-rao products with applications to tensor decomposition 23

Fast exact leverage score sampling from khatri-rao products ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Vivek Bharadwaj Osman Asif Malik Riley Murray Laura Grigori Aydin Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley and Computational Research Division Lawrence Berkeley National Lab Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute and Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Institute of Mathematics EPFL & Lab for Simulation and Modelling Paul Scherrer Institute Computational Research Division Lawrence Berkeley National Lab and Electrical Engineering and Computer Science Department UC Berkeley Electrical Engineering and Computer Science Department UC Berkeley

We present a data structure to randomly sample rows from the Khatri-Rao product of several matrices according to the exact distribution of its leverage scores. Our proposed sampler draws each row in time logarithmic in the height of the Khatri-Rao product and quadratic in its column count, with persistent space overhead at most the size of the input matrices. As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each. When used to sketch the linear least squares problems arising in CANDECOMP / PARAFAC tensor decomposition, our method achieves lower asymptotic complexity per solve than recent state-of-the-art methods. Experiments on billion-scale sparse tensors validate our claims, with our algorithm achieving higher accuracy than competing methods as the decomposition rank grows.

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Large Language Model Driven Interactive Learning for Real-Time Cognitive Load Prediction in Human-Swarm Systems

Large Language Model Driven Interactive Learning for Real-Ti...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Wenshuo Zang Mengsha Hu Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics Engineering Department of Computer Science Kent State University OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The rapid advancements of drones have demonstrated the versatility and promising potential of human-swarm systems (HSS) across various domains. However, human performance within these systems may be impaired by factors such as limited domain knowledge and mental stress, often leading to cognitive overload and hindering the efficiency and effectiveness of human-swarm teaming. Consequently, the accurate monitoring of cognitive load levels is crucial for optimizing HSS performance. To address the challenges of existing measurement methods, which are often expensive, time-consuming, or lack real-time capabilities, we propose a Large Language Model driven cognitive load prediction framework. This framework integrates comprehensive task context, domain knowledge, and behavior analysis to provide fast and cost-effective predictions in complex scenarios. By leveraging the capabilities of Large Language Models and employing reinforcement learning to model the cognitive load generation, our framework aims to offer real-time insights into human-related factors causing high cognitive load and predict cognitive levels over time, ultimately enhancing the performance of HSS teaming.

关键词： Large language models Reinforcement learning Human factors Predictive models Cognitive load Real-time systems Robots Monitoring Load modeling Drones

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Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study

arXiv

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arXiv 2024年

作者： Zhou, Haoying Jiang, Yiwei Gao, Shang Wang, Shiyue Kazanzides, Peter Fischer, Gregory S. Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future. © 2024, CC BY.

关键词： Demonstrations

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Sampling-Based Model Predictive Control for Dexterous Manipulation on a Biomimetic Tendon-Driven Hand

arXiv

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arXiv 2024年

作者： Hess, Adrian Kübler, Alexander M. Forrai, Benedek Dogar, Mehmet Katzschmann, Robert K. Robotics Lab Department of Mechanical and Process Engineering ETH Zurich Switzerland School of Computer Science University of Leeds United Kingdom

Biomimetic and compliant robotic hands offer the potential for human-like dexterity, but controlling them is challenging due to high dimensionality, complex contact interactions, and uncertainties in state estimation. Sampling-based model predictive control (MPC), using a physics simulator as the dynamics model, is a promising approach for generating contact-rich behavior. However, sampling-based MPC has yet to be evaluated on physical (non-simulated) robotic hands, particularly on compliant hands with state uncertainties. We present the first successful demonstration of in-hand manipulation on a physical biomimetic tendon-driven robot hand using sampling-based MPC. While sampling-based MPC does not require lengthy training cycles like reinforcement learning approaches, it still necessitates adapting the task-specific objective function to ensure robust behavior execution on physical hardware. To adapt the objective function, we integrate a visual language model (VLM) with a real-time optimizer (MuJoCo MPC). We provide the VLM with a high-level human language description of the task, and a video of the hand’s current behavior. The VLM gradually adapts the objective function, allowing for efficient behavior generation, with each iteration taking less than two minutes. In our experiments, the hand achieves an average ball rolling speed of 0.35 rad/s, successful ball flips, and catching with a 67% success rate. Our results demonstrate that sampling-based MPC is a promising approach for generating dexterous manipulation skills on biomimetic hands without extensive training cycles. © 2024, CC BY.

关键词： Visual languages

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Learning-on-the-Drive: Self-supervised Adaptive Long-range Perception for High-speed Offroad Driving

Learning-on-the-Drive: Self-supervised Adaptive Long-range P...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Eric Chen Cherie Ho Mukhtar Maulimov Chen Wang Sebastian Scherer Department of Computer Science and Mathematics Harvey Mudd College USA Robotics Institute Carnegie Mellon University USA Spatial AI & Robotics Lab University at Buffalo NY USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous offroad driving is essential for applications like emergency rescue, military operations, and agriculture. Despite progress, systems struggle with high-speed vehicles exceeding 10m/s due to the need for accurate long-range (> 50m) perception for safe navigation. Current approaches are limited by sensor constraints; LiDAR-based methods offer precise short-range data but are noisy beyond 30m, while visual models provide dense long-range measurements but falter with unseen scenarios. To overcome these issues, we introduce ALTER, a learning-on-the-drive perception framework that leverages both sensor types. ALTER uses a self-supervised visual model to learn and adapt from near-range LiDAR measurements, improving long-range prediction in new environments without manual labeling. It also includes a model selection module for better sensor failure response and adaptability to known environments. Testing in two real-world settings showed on average 43.4% better traversability prediction than LiDAR-only and 164% over non-adaptive state-of-the-art (SOTA) visual semantic methods after 45 seconds of online learning.

关键词： Adaptation models Visualization Laser radar Accuracy Soft sensors Semantics Predictive models Robot sensing systems Robustness Testing

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The Fused Multiply-Add and Global Atmospheric Models: A Distributional Investigation into a Surprising Correctness Scenario

The Fused Multiply-Add and Global Atmospheric Models: A Dist...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Price-Broncucia, Teo Baker, Allison H. Duda, Michael University of Colorado Department of Computer Science BoulderCO80303 United States NSF National Center for Atmospheric Research Computational and Information Systems Lab BoulderCO80305 United States NSF National Center for Atmospheric Research Mesoscale and Microscale Meteorology Lab BoulderCO80305 United States

ISBN: (纸本)9798350355543

In a series of related works developing an ensemble consistency testing approach for multiple popular global climate models (GCMs), one test scenario has repeatedly stood out. Why does the use of the Fused Multiply-Add (FMA) operation result in model configurations getting flagged as failures, while changes to compiler choice, optimization level, processor type and number, etc. are passed as expected? This work explores the impacts of FMA on GCM simulation output from a distributional perspective and provides directions for future work to enable model developers and users to use numerical optimization techniques with confidence. © 2024 IEEE.

关键词： climate-models correctness ensemble-methods

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Multimodal Episodic Analysis of Human Personality Traits for Personalized Behavior in Social Robots

Multimodal Episodic Analysis of Human Personality Traits for...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sarwar Paplu Bhalachandra Gajanana Bhat Karsten Berns Department of Computer Science University of Kaiserslautern-Landau Kaiserslautern Germany Department of Computer Science Chair of the Robotics Research Lab University of Kaiserslautern-Landau Kaiserslautern Germany

This paper presents a longitudinal analysis of hu-man personality for socially interactive robots. A combination of visual, verbal, and paralinguistic cues has been employed to predict human personality and generate a robot's personalized behavior in an episodic manner. Low-level perceptual features have been extracted with a stereo vision camera sensor and a wireless microphone. A higher-level interpretation of these features provides us with suitable cues for personality assessment. A Random Forest supervised learning algorithm has been applied to classify the personality label of each individual interacting with the social robot, Emah. Three personality traits: Extraversion-Introversion, Agreeableness-Self centered, and Openness-Traditionalist are considered in this paper. We utilized the long-term memory of the interactive robot to enhance the likelihood of personality estimation. Suitable per-sonalized robot behavior has been generated in the form of speech, gestures, and facial expressions. The proposed system has been validated in real-world human-robot interaction sce-narios. The evaluation results show promising improvement in the adaptability of the robot's behavior, provided multimodal as well as episodic personality assessment is employed.

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Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

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2nd International Conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 IEEE.

关键词： Sensitive data

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Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yanzhou Wang Lidia Al-Zogbi Jiawei Liu Lauren Shepard Ahmed Ghazi Junichi Tokuda Simon Leonard Axel Krieger Iulian Iordachita Department of Mechanical Engineering and the Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Laboratory of Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Brady Urological Institute Johns Hopkins University Baltimore MD USA Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston MA USA Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation.

关键词： Uncertain systems Uncertainty Parameter estimation Medical robotics Shape Biological system modeling Biopsy Needles Prostate cancer Standards

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