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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

high-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Perceptual Quality Assessment of Virtual Reality Videos in the Wild

arXiv

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arXiv 2022年

作者： Wen, Wen Li, Mu Yao, Yiru Sui, Xiangjie Zhang, Yabin Lan, Long Fang, Yuming Ma, Kede Department of Computer Science City University of Hong Kong Kowloon Hong Kong School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Information Management Jiangxi University of Finance and Economics Jiangxi Nanchang330032 China Multimedia Lab Bytedance Inc China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Investigating how people perceive virtual reality (VR) videos in the wild (i.e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex *** video databases only consider synthetic distortions, assume fixed viewing conditions, and are limited in size. To overcome these shortcomings, we construct the VR Video Quality in the Wild (VRVQW) database, containing 502 user-generated videos with diverse content and distortion characteristics. Based on VRVQW, we conduct a formal psychophysical experiment to record the scanpaths and perceived quality scores from 139 participants under two different viewing conditions. We provide a thorough statistical analysis of the recordeddata, observing significantimpact of viewing conditions on both human scanpaths and perceived quality. Moreover, we develop an objective quality assessment model for VR videos based on pseudocylindrical representation and convolution. Results on the proposed VRVQW show that our method is superior to existing video quality assessment *** have made the database and code available at https://***/ limuhit/VR-Video-Quality-in-the-Wild. Index Terms-Virtual reality, panoramic videos, video quality assessment, psychophysics. Copyright © 2022, The Authors. All rights reserved.

关键词： Virtual reality

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Alzheimer's Disease Detection Model Based on Multimodal Data Early Fusion of Medical Neuroimaging

Alzheimer's Disease Detection Model Based on Multimodal Data...

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IEEE International Conference on high performance computing and Communications (HPCC)

作者： Shangxiao Wu Sheng Qin Kaiyang Wang Su Yang Shiqi Zhang Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China Department of Computer Science Swansea University Swansea UK

Alzheimer's disease (AD) is a long-term neurodegenerative disease. The research in the field of AD is rapidly devel-oping, but most research methods are based on unimodal without considering the complementarity between different modes. In this work, a multimodal-based deep learning model is proposed for AD detection. Specifically, two medical neural image modes (i.e. magnetic resonance imaging and Positron emission computed to-mography) are used to fuse into a feature map. The convolutional block attention modules and convolutional neural networks are utilized to extract the features. A multilayer perceptron is used as a model classifier for the diagnostic classification of the learned AD features. To validate the model, experiments are conducted using the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with existing AD diagnostic models, this work is better, and the accuracy of this model reaches 99.17%. It provides an optional solution for AD detection.

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Speech Emotion Recognition: A Brief Review of Multi-modal Multi-task Learning Approaches

Speech Emotion Recognition: A Brief Review of Multi-modal Mu...

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International Conference on Advanced engineering Theory and Applications, AETA 2022

作者： Pham, Nhat Truong Tran, Anh-Tuan Pham, Bich Ngoc Hong Dang-Ngoc, Hanh Nguyen, Sy Dzung Dang, Duc Ngoc Minh Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City Open University Ho Chi Minh City Viet Nam Department of Telecommunications Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Laboratory for Computational Mechatronics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical–Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam Computing Fundamental Department FPT University Ho Chi Minh Viet Nam

ISBN: (纸本)9789819987023

Speech emotion recognition (SER) has become an attention-grabbing topic in recent years thanks to the development of deep learning in the field of speech processing. However, it is difficult to recognize an accurate emotional state from only speech signals. Therefore, researchers have investigated multi-modalities such as speech, visual, and text inputs to improve the emotional recognition rate of speech. In addition, to enhance the generalized deep learning models for SER, multi-task learning (MTL) strategies have also been applied in the past decade. In this paper, a brief and comprehensive review of multi-modal multi-task learning (3MTL) approaches for recognizing emotional states from speech signals is presented, including multi-modal SER, multi-task learning SER, and multi-modal multi-task learning SER. This paper also discusses about some problems that still need to be solved in 3MTL SER and gives some suggestions for the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System

arXiv

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arXiv 2023年

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023, CC BY-NC-ND.

关键词： Edge computing

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Towards Evidence-Based Cybersecurity Assessment of Programmable Systems to Ensure the Protection of Critical IT Infrastructure

Towards Evidence-Based Cybersecurity Assessment of Programma...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced computing Systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very high-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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Observation of 2D Cherenkov Radiation

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Physical Review X 2023年第1期13卷 011002-011002页

作者： Yuval Adiv Hao Hu Shai Tsesses Raphael Dahan Kangpeng Wang Yaniv Kurman Alexey Gorlach Hongsheng Chen Xiao Lin Guy Bartal Ido Kaminer Andrew and Erna Viterbi Department of Electrical and Computer Engineering Technion Israel Institute of Technology 32000 Haifa Israel School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics (SIOM) Chinese Academy of Sciences (CAS) Shanghai 201800 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China

For over 80 years of research, the conventional description of free-electron radiation phenomena, such as Cherenkov radiation, has remained unchanged: classical three-dimensional electromagnetic waves. Interestingly, in reduced dimensionality, the properties of free-electron radiation are predicted to fundamentally change. Here, we present the first observation of Cherenkov surface waves, wherein free electrons emit narrow-bandwidth photonic quasiparticles propagating in two dimensions. The low dimensionality and narrow bandwidth of the effect enable us to identify quantized emission events through electron energy loss spectroscopy. Our results support the recent theoretical prediction that free electrons do not always emit classical light and can instead become entangled with the photons they emit. The two-dimensional Cherenkov interaction achieves quantum coupling strengths over 2 orders of magnitude larger than ever reported, reaching the single-electron–single-photon interaction regime for the first time with free electrons. Our findings pave the way to previously unexplored phenomena in free-electron quantum optics, facilitating bright, free-electron-based quantum emitters of heralded Fock states.

关键词： Nanophotonics Polaritons Quantum description of light-matter interaction

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Downstream-agnostic Adversarial Examples

arXiv

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arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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