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arXiv 2022年

作者： Wang, Ying Yuan, Ye Luo, Xin The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module;b) Building the residual connection and weighted representation propagation to obtain high representation capacity;and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on UWGs emerging from real applications demonstrate that owing to its efficient incorporation of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its good scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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A Bibliometric Analysis of Artificial Intelligence and Electrical Energy Reliability

A Bibliometric Analysis of Artificial Intelligence and Elect...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Samuel-Soma M. Ajibade Kehinde Ehineni Angela Lee Siew Hoong Muhammed Basheer Jasser Oluwadare Joshua Oyebode Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Malaysia Industrial Business Unit Littelfuse The Galleria Saskatoon Canada Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Malaysia Civil and Environmental Engineering Department Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The escalating dependence on artificial intelligence (AI) in energy and electrical power dependability systems has garnered significant interest in both academic and industrial circles. This study provides a bibliometric examination of artificial intelligence (AI) applications in the field of electrical dependability, covering the time frame from 2002 to 2023. The main aim of this study is to examine the patterns of publication, notable authors, institutions, and funding organizations that contribute to research in artificial intelligence for electrical dependability. A further objective of the endeavor is to chart the co-authorship networks, keyword frequencies, and citation trends in the field. Extraction of data from the Scopus database was carried out using an extensive search approach, and subsequently, network visualization was performed using VOSviewer. The findings indicated a substantial rise in research productivity throughout the last ten years, accompanied with a distinguishable expansion in joint research endeavors. China has established itself as the foremost nation in research on electrical reliability powered by artificial intelligence, with the united States and India following closely after. In addition, the National Natural science Foundation of China was recognized as the most productive and influential funding institution. The thematic analysis revealed many prominent subjects in the sector, such as predictive maintenance, problem detection, and energy consumption optimization via artificial intelligence. The paper offers invaluable insights into the present research landscape and emphasizes developing trends, providing a clear plan for future research objectives in the fields of artificial intelligence and electrical dependability.

关键词： Productivity Technological innovation Bibliometrics Organizations Reinforcement learning Market research Robustness Visual databases Artificial intelligence Predictive maintenance

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

arXiv

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arXiv 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 2024, CC BY.

关键词： Latent semantic analysis

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LAS-AT: Adversarial Training with Learnable Attack Strategy

arXiv

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arXiv 2022年

作者： Jia, Xiaojun Zhang, Yong Wu, Baoyuan Ma, Ke Wang, Jue Cao, Xiaochun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyberspace Security University of Chinese Academy of Sciences Beijing China Tencent AI Lab Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial examples (AEs). Most previous methods adopt Projected Gradient Decent (PGD) with manually specifying attack parameters for AE generation. A combination of the attack parameters can be referred to as an attack strategy. Several works have revealed that using a fixed attack strategy to generate AEs during the whole training phase limits the model robustness and propose to exploit different attack strategies at different training stages to improve robustness. But those multi-stage hand-crafted attack strategies need much domain expertise, and the robustness improvement is limited. In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness. Our framework is composed of a target network that uses AEs for training to improve robustness, and a strategy network that produces attack strategies to control the AE generation. Experimental evaluations on three benchmark databases demonstrate the superiority of the proposed method. The code is released at https://***/jiaxiaojunQAQ/LAS-AT. Copyright © 2022, The Authors. All rights reserved.

关键词： Cryptography

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Cybersecurity Threats in FinTech: A Systematic Review

arXiv

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arXiv 2023年

作者： Javaheri, Danial Fahmideh, Mahdi Chizari, Hassan Lalbakhsh, Pooia Hur, Junbeom Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of School of Business University of Southern Queensland SpringfieldQLD4300 Australia School of Business Computing and Social Sciences University of Gloucestershire The Park CampusCheltenham GloucesterGL50 2RH United Kingdom Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University ClaytonVIC3168 Australia

The rapid evolution of the Smart-everything movement and Artificial Intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services. This paper introduces a novel and refined taxonomy of security threats in FinTech and conducts a comprehensive systematic review of defensive strategies. Through PRISMA methodology applied to 74 selected studies and topic modeling, we identified 11 central cyber threats, with 43 papers detailing them, and pinpointed 9 corresponding defense strategies, as covered in 31 papers. This in-depth analysis offers invaluable insights for stakeholders ranging from banks and enterprises to global governmental bodies, highlighting both the current challenges in FinTech and effective countermeasures, as well as directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Implementing termination analysis on quantum programming

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science China(information sciences) 2019年第12期62卷 215-222页

作者： Shusen LIU Kan HE Runyao DUAN School of Data and Computer Science Sun Yat-sen University Institute for Quantum Computing Baidu Beijing Institute for Advanced Study Tsinghua University College of Mathematics & College of Information and Computer Science Taiyuan University of Technology

Termination analysis is an essential part in programming. Especially quantum programming concerning measurement, entanglement and even superposition are the foundations of bizarre behaviours in quantum programs. In this paper, we analyse and extend the theoretical theorems on termination analysis proposed by Ying et al. into computational theorems and algorithms. The new algorithm without the Jordan decomposition process has a significant acceleration with polynomial complexity both on terminating and almost-surely terminating programs. Moreover, the least upper bound of termination programs steps is studied and utilized to output the substituted matrix representation of quantum programs. We also implement four groups of experiments to illustrate the advantages of the new algorithm in case of processing a simplified quantum walk example comparing with the original counterpart.

关键词： termination analysis quantum while-language quantum programming quantum experiment terminating steps

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Feature Rotation Invariance Learning for Point Cloud Analysis

Feature Rotation Invariance Learning for Point Cloud Analysi...

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Smart World Congress (SWC), IEEE

作者： Lu Shi Qi Cao Guoqing Zhang Jin Yi Yansen Huang Yigang Cen State Key Laboratory of Advanced Rail Autonomous Operation the School of Computer Science and Technology and Visual Intellgence +X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University Beijing China the School of Computing Science University of Glasgow Singapore With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education the State Key Laboratory of Advanced Rail Autonomous Operation and the School of Computer and Information Technology Beijing Jiaotong University Beijing China the College of Civil Engineering Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. LTD Guizhou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks.

关键词： Point cloud compression Representation learning Analytical models Solid modeling Visualization Three-dimensional displays Semantics Cloning Feature extraction data models

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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

arXiv

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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

arXiv

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arXiv 2022年

作者： Xu, Fei Xu, Jianian Chen, Jiabin Chen, Li Shang, Ruitao Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference;(2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Searching for a new probability distribution for modeling non-scale-free heavy-tailed real-world networks

TechRxiv

引用

TechRxiv 2022年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Kumar, Uttam Jayavelu, Senthilnath Spatial Computing Laboratory Center for Data Sciences International Institute of Information Technology Bangalore560100 India Machine Intelligence Unit Indian Statistical Institute Kolkata700108 India Department of Data Science S P Jain School of Global Management Mumbai400070 India Institute for Infocomm Research Agency for Science Technology and Research 138632 Singapore

Perhaps the most controversial topic in network science research is whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] claimed that the degree distributions of real-world networks are rarely power-law under statistical tests. Such complex networks include social, biological, information, temporal, and brain networks are often heavy-tailed where the assumption on scale-free nature of real-world heavy-tailed networks becomes insignificant so as the complex system evolves over time. The failure of power-law distribution in fitting the degree distribution data is mainly due to the presence of an identifiable non-linearity in the entire degree distribution in a log-log scale of a heavy-tailed complex network. Here, we attempt to address this issue by proposing a new class of heavy-tailed probability distributions, for modeling the entire degree distributions of complex networks and capturing the non-linearity of these heavy-tailed networks. The generalized Lomax Model is introduced to fit the degree distributions of non-scale-free real-world networks and applied to model a wide variety of large-scale real-world complex networks. Several statistical properties of the proposed model, such as extreme value and inferential statistical properties are derived into this context. Rigorous experimental analysis showcases the excellent performance of the proposed family of distributions while fitting the heavy-tailed real-world complex networks over fifty real-world data sets in comparison with the state-of-the-art. © 2022, CC BY.

关键词： Statistical mechanics

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