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arXiv 2023年

作者： Jha, Debesh Durak, Gorkem Sharma, Vanshali Keles, Elif Cicek, Vedat Zhang, Zheyuan Srivastava, Abhishek Rauniyar, Ashish Hagos, Desta Haileselassie Tomar, Nikhil Kumar F.H., Miller A., Topcu A., Yazidi J.E., Håkegård U., Bagci Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University United States Sustainable Communication Technologies SINTEF Digital Norway Department of Electrical Engineering and Computer Science Howard University United States Department of General Surgery Tokat State Hospital Tokat Turkey Lab Oslo Metropolitan University Norway

Artificial intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at *** a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions. © 2023, CC BY.

关键词： Resource allocation

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Real-time Holistic Robot Pose Estimation with Unknown States

arXiv

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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Joint Visual and Haptic Signal Transmission for Immersive Interactions in Human Digital Twin

Joint Visual and Haptic Signal Transmission for Immersive In...

引用

Ubiquitous Communication (Ucom), International Conference on

作者： Kun Wu Jiayuan Chen Changyan Yi Zili Liu Xiaoping Lu Junyi Wang Jun Cai College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China COSMOPlat Institute of Industrial Intelligence (Qingdao) Co. Ltd China Key Lab of Cognitive Radio and Information Processing Ministry of Education Guilin University of Electronic Technology Guilin China Department of Electrical and Computer Engineering Concordia University Montréal QC Canada

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

Human digital twin (HDT) is envisioned as a system interconnecting physical twins (PTs) in the real world with virtual twins (VTs) in the digital world, enabling advanced human-centric applications. In this paper, we study an optimization of quality of experience (QoE) aware multimodal signal transmission, particularly focusing on joint visual and haptic signal feed-back transmissions from VT to its PT, for providing immersive interactions in HDT. To evaluate a synthesized performance of both visual and haptic experiences, we design a comprehensive QoE model, taking into account the video quality, continuous video quality switching rate and average haptic feedback error. Then, to maximize such QoE with a particular guarantee on synchronization between visual and haptic signal transmissions, we dynamically optimize the bandwidth allocation, bitrate and rendering mode of the video, and haptic signal's compression threshold. To this end, we propose a deep reinforcement learning based algorithm, called VisHap, which can produce an adaptive solution. Furthermore, we build an HDT multimodal interactions platform for collecting an authentic dataset, and by using it, we conduct experiments, showing that VisHap is not only feasible but also outperforms the counterparts.

关键词： Visualization Channel allocation Rendering (computer graphics) Haptic interfaces Digital twins Quality assessment Quality of experience Synchronization Optimization Video recording

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Salientr: A Closer Look at Multi-Modal Transformer for Rgb-T Salient Object Detection

SSRN

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SSRN 2023年

作者： Guo, Ruohao Yue, Wenzhen Qu, Liao Qi, Yanyu Niu, Dantong Ying, Xianghua School of Intelligence Science and Technology Peking University Beijing100871 China Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States College of Information and Electrical Engineering China Agricultural University Beijing100091 China BAIR Lab University of California BerkeleyCA94709 United States

Recently, transformer-based methods show impressive performance on challenging dense prediction tasks, yet far from perfect. In this paper, we present SalienTR, a novel and effective transformer-based model for high-quality RGB-T salient object detection. First, it builds on two parallel Swin Transformer encoders to extract hierarchical appearance representations of color maps and spatial information of thermal infrared maps. Next, a well-designed cross-modal fusion transformer, namely ComFormer, is devised to mine and fuse two-modality features. Specifically, the proposed ComFormer consists of three consecutive components: (1) A local cross-modal multi-head self-attention (LoC-MSA) to capture local correlations along space-modal dimensions;(2) A global cross-modal multi-head self-attention (GLoC-MSA) to build long-range dependencies between different modalities;(3) A uni-modal convolution (Uni-Conv) that exploits fused information and further learn latent semantics. Besides, we develop a dual-stream decoder that integrates multi-level features to generate both the final salient map and its corresponding edge map. Extensive experiments show that our SalienTR performs well on widely used RGB-T benchmarks and surpasses other state-of-the-art approaches. In addition, SalienTR exhibits high robustness under various challenging scenarios and transferable generalization on RGB-D benchmark datasets. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop

arXiv

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arXiv 2022年

作者： Zhang, Weixia Li, Dingquan Min, Xiongkuo Zhai, Guangtao Guo, Guodong Yang, Xiaokang Ma, Kede MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Network Intelligence Research Department Peng Cheng Laboratory China Department of Computer Science and Electrical Engineering West Virginia University United States Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely used for performance evaluation and perceptual optimization of man-made vision systems. Here we make one of the first attempts to examine the perceptual robustness of NR-IQA models. Under a Lagrangian formulation, we identify insightful connections of the proposed perceptual attack to previous beautiful ideas in computer vision and machine learning. We test one knowledge-driven and three data-driven NR-IQA methods under four full-reference IQA models (as approximations to human perception of just-noticeable differences). Through carefully designed psychophysical experiments, we find that all four NR-IQA models are vulnerable to the proposed perceptual attack. More interestingly, we observe that the generated counterexamples are not transferable, manifesting themselves as distinct design flows of respective NR-IQA methods. © 2022, CC BY.

关键词： Image quality

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

引用

SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Algarni, Abeer D. Soliman, Naglaa F. El-Samie, Fathi E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

关键词： Emotion Recognition

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Topology Rewiring Strategies to construct robust scale-free medical Internet of Things Networks

引用

Simulation Modelling Practice and Theory 2025年 142卷

作者： Khan, Muhammad Awais Javaid, Nadeem Alrajeh, Nabil Bouk, Safdar Hussain Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan ComSens Lab International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Old Dominion University 5115 Hampton Blvd NorfolkVA23529 United States

The Internet of Things (IoT) network topologies are now most commonly impacted by cyberattacks. The scale-free network topologies have demonstrated great robustness against random attacks by preserving the connectedness of the nodes. The scale-free network topologies’ susceptibility to malicious attacks, however, is a significant worry. It is due to the significance of the scale-free networks in different fields of life like medical, transportation, education, agriculture, etc. Also, high-degree node removal diminishes the network's resiliency and compromises the connection of the majority of nodes. In this study, we offer several rewiring techniques for building scale-free, reliable Medical Internet of Things (MIoT) networks that can withstand malicious attacks. Initially, the scale-free MIoT network's performance optimization is ensured using a heuristic algorithm known as the Great Deluge Algorithm (GDA). Then, four rewiring strategies are formulated. The initial approach is degree dissortativity, which rewires the network if all nodes have high maximum connectivity to other neighbors with a similar degree. For the second strategy, we introduced a degree difference operation based on degree dissortativity to ensure that the edges that are connected possess low dissortativity and degree difference. Meanwhile, the remaining two strategies take into account the node load bound and enhanced GDA to increase network robustness. The performance of the proposed rewiring strategies is validated through simulations. The results prove that the proposed strategies increase network robustness by up to 25% compared to Hill Climbing (HC) and Simulated Annealing (SA). Additionally, the strategies show great success in improving network connectivity and graph density. However, their computational time is higher compared to HC and SA. © 2025 Elsevier B.V.

关键词： Network topology

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Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers

arXiv

引用

arXiv 2023年

作者： Czech, Johannes Blüml, Jannis Kersting, Kristian Steingrimsson, Hedinn Artificial Intelligence and Machine Learning Lab TU Darmstadt Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States HoustonTX United States

While transformers have gained recognition as a versatile tool for artificial intelligence (AI), an unexplored challenge arises in the context of chess — a classical AI benchmark. Here, incorporating Vision Transformers (ViTs) into AlphaZero is insufficient for chess mastery, mainly due to ViTs’ computational limitations. The attempt to optimize their efficiency by combining MobileNet and NextViT outperformed AlphaZero by about 30 Elo. However, we propose a practical improvement that involves a simple change in the input representation and value loss functions. As a result, we achieve a significant performance boost of up to 180 Elo points beyond what is currently achievable with AlphaZero in chess. In addition to these improvements, our experimental results using the Integrated Gradient technique confirm the effectiveness of the newly introduced features. © 2023, CC BY.

关键词： Distribution transformers

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Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems

arXiv

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arXiv 2021年

作者： Wang, Fen Cheung, Gene Li, Taihao Du, Ying Ruan, Yu-Ping Department of Artificial Intelligence Zhejiang Lab Hangzhou China Department of Electrical Engineering & Computer Science York University Canada

Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations. In this paper, we propose a fast sampling algorithm to place sensors. Specifically, assuming that the field signal f is represented by a linear model f = Φg, it can be estimated from partial noisy samples via an unbiased least-squares (LS) method, whose expected mean square error (MSE) depends on chosen samples. First, we formulate an approximate MSE problem, and then prove it is equivalent to a problem related to a principle submatrix of ΦΦ indexed by sample set. To solve the formulated problem, we devise a fast greedy algorithm with simple matrix-vector multiplications, leveraging a matrix inverse formula. To further reduce complexity, we reuse results in the previous greedy step for warm start, so that candidates can be evaluated via lightweight vector-vector multiplications. Extensive experiments show that our proposed sensor placement method achieved the lowest sensor sampling time and the best performance compared to state-of-the-art schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Mean square error

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