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arXiv 2024年

作者： Wu, Jiaqi Chen, Simin Wang, Zehua Chen, Wei Tian, Zijian Yu, F. Richard Leung, Victor C.M. Beijing100083 China Department of Electrical and Computer Engineering University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada The university of Texas at dallas 800 W Campbell Rd RichardsonTX75080 United States The Ministry of Emergency Management Big Data Center Beijing100013 China The Department of Electrical and Computer Engineering The University of British Columbia Vancouver 2332 Main Mall VancouverBCV6T 1Z4 Canada

As the volume of collected video data continues to grow, data-oriented cloud computing in Internet of Video Things (IoVT) systems faces increasing challenges, such as system latency and bandwidth pressure. Task-oriented edge computing, by shifting data analysis to the edge, enables real-time visual inference. However, the limited computational power of edge devices presents challenges for effectively executing computationally intensive visual tasks. Existing methods struggle with the trade-off between high model performance and low resource consumption. For instance, lightweight neural networks often deliver limited performance, while Neural Architecture Search (NAS)-based model design incurs high computational and commercial costs at training Inspired by hardware/software co-design principles, we propose, for the first time, a co-design framework at both the model and system levels to optimize neural network architecture and deployment strategies during inference. This framework maximizes the computational efficiency of edge devices to achieve high throughput, enabling real-time edge inference. Specifically, we implement a dynamic model structure based on the re-parameterization principle, coupled with a Roofline-based model partitioning strategy to synergistically enhance the computational performance of heterogeneous devices. Furthermore, we employ a multi-objective co-optimization approach for the above strategies to balance throughput and accuracy. We also conduct a thorough analysis of the mathematical consistency between the partitioned and original models, in addition to deriving the convergence of the partitioned model. Experimental results show that, compared to baseline algorithms, our method significantly improves throughput (12.05% on MNIST, 18.83% on ImageNet) while also achieving higher classification accuracy. Additionally, it ensures stable performance across devices with varying computational capacities, highlighting its generalizability and practi

关键词： Model structures

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Dynamic neural network approach to human emotion: An analysis based on sliding time windows 20

Dynamic neural network approach to human emotion: An analysi...

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20th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2021

作者： Wang, Jingqi Shi, Gen Ma, Ning Sun, Yang Li, Xuesong Sui, Jie Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Children's Hospital Capital Medical University Big Data and Engineering Research Center Department of Echocardiography Beijing China Shenyang Normal University School of Educational Science Shenyang China University of Aberdeen School of Psychology Aberdeen United Kingdom

ISBN: (纸本)9781665421195

Emotion is a key motivational factor of a person strivings for health and well-being. Understanding neural networks supporting different types of emotion bears far-reaching implications for mental health. Recent studies suggest that emotional processing is associated with a large number of brain regions. However, the precise functional connectivity (FC) of these regions in investigations of emotional processing are largely unknown. To address this issue, we recruited 359 participants who completed emotional-related measures including the Positive and Negative Affect Schedule (PANAS) the Self-Compassion Scale, while scanned with resting-state functional magnetic resonance images (fMRI). Here, we proposed a novel psychological characteristics analysis framework by using a dynamic sliding window method to characterize the nature of resting-state functional connectivity in the human brain, in relation to the static FC method. The comparison results showed that the dynamic FC method produced the better performance, compared to the static FC method. The global network analyses across all 6 possible connectivity matrices further demonstrated that the dynamically hemispheric asymmetry best predicted emotional processing. The dynamic FC method was evaluated on the three emotional labels-positive emotion, negative emotion, self-compassion and the best prediction performance was consistently observed in the dynamically hemispheric asymmetric FC. © 2021 IEEE.

关键词： Magnetic resonance

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

arXiv

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arXiv 2024年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Jiaxing314001 China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Localization of Dummy data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

arXiv

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arXiv 2024年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong Zang, Jiye Xu, Qi School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China School of Computer Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

关键词： Convolution

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Privacy Preserving Ultra-Short-term Wind Power Prediction Based on Secure Multi Party Computation

arXiv

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arXiv 2023年

作者： Fan, Hang Fan, Xiaoyu Hao, Tianyi Wei, Wei Chen, Kun Wang, Guosai Jia, Xiaofeng Li, Yidong Xu, Wei PBC School of Finance Tsinghua University Beijing100084 China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Department of Electrical Engineering Tsinghua University Beijing100084 China Tsingjiao Information Technology Co. Ltd. Tsinghua Science and Technology Park Beijing100084 China Data Management Department Beijing Big Data Center Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100025 China

Mining the spatial and temporal correlation of wind farm output data is beneficial for enhancing the precision of ultra-short-term wind power prediction. However, if the wind farms are owned by separate entities, they may be reluctant to share their data directly due to privacy concerns as well as business management regulation policies. Although cryptographic approaches have been designed to protect privacy in the process of data sharing, it is still a challenging problem to encrypt the original data while extracting the nonlinear relationship among multiple wind farms in the machine learning process. This paper presents pwXGBoost, a technique based on the machine learning tree model and secure multi-party computation (SMPC) that can successfully extract complicated relationships while preserving data privacy. A maximum mean discrepancy (MMD) based scheme is proposed to effectively choose adjacent candidate wind farms to participate in the collaborative model training, therefore improving the accuracy and reducing the burden of data acquisition. The proposed method was evaluated on real world data collected from a cluster of wind farms in Inner Mongolia, China, demonstrating that it is capable of achieving considerable efficiency and performance improvements while preserving privacy. Copyright © 2023, The Authors. All rights reserved.

关键词： data acquisition

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Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping

arXiv

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arXiv 2023年

作者： Li, Xinrui Dong, Zhenjun Zeng, Yong Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In this paper, we investigate multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, where modular extremely large-scale uniform linear array (XL-ULA) is deployed at the base station (BS) to serve multiple single-antenna users. By exploiting the unique modular array architecture and considering the potential near-field propagation, we develop sub-array based uniform spherical wave (USW) models for distinct versus common angles of arrival/departure (AoAs/AoDs) with respect to different sub-arrays/modules, respectively. Under such USW models, we analyze the beam focusing patterns at the near-field observation location by using near-field beamforming. The analysis reveals that compared to the conventional XL-MIMO with collocated antenna elements, modular XL-MIMO can provide better spatial resolution by benefiting from its larger array aperture. However, it also incurs undesired grating lobes due to the large inter-module separation. Moreover, it is found that for multi-user modular XL-MIMO communications, the achievable signal-to-interference-plus-noise ratio (SINR) for users may be degraded by the grating lobes of the beam focusing pattern. To address this issue, an efficient user grouping method is proposed for multi-user transmission scheduling, so that users located within the grating lobes of each other are not allocated to the same time-frequency resource block (RB) for their communications. Numerical results are presented to verify the effectiveness of the proposed user grouping method, as well as the superior performance of modular XL-MIMO over its collocated counterpart with densely distributed users. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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K-Space-Aware Cross-Modality Score for Quality Assessment of Synthesized Neuroimages

arXiv

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arXiv 2023年

作者： Xie, Guoyang Wang, Jinbao Huang, Yawen Lyu, Jiayi Zheng, Feng Zheng, Yefeng Jin, Yaochu Southern University of Science and Technology Department of Computer Science Shenzhen518055 China CATL Department of Intelligent Manufacturing Ningde352000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Tencent Youtu Lab Jarvis Research Center Shenzhen510310 China University of Chinese Academy of Sciences the School of Engineering Science Beijing100049 China Westlake University the School of Engineering Hangzhou310030 China

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS. To forster reproducibility and accessibility, the source codeis uploaded to the website: https://***/M-3LAB/K-CROSS © 2023, CC BY.

关键词： Magnetic resonance imaging

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Performance tuning analysis of spatial operations on Spatial Hadoop cluster with SSD

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Procedia computer Science 2020年 167卷 2253-2266页

作者： Prafullata Auradkar Tejas Prashanth Suraj Aralihalli Sreeniketh Pradeep Kumar Dinkar Sitaram Center for Cloud Computing and Big Data Department of Computer Science and Engineering PES University Bangalore Karnataka India

Solid State Drives have shown promising results in improving the performance of MapReduce jobs. Previous work in the usage of SSD consisted of evaluation of its incorporation in Hadoop using benchmarking tests, such as WordCount, TeraGen, TeraSort and so on. It was empirically observed that the type of Map Reduce implementation, specifically the nature of the processing performed during the shuffle phase, has a major role in the overall outcome. This finding opens a plethora of possibilities of exploring the impact of storage media on spatial data processing in big data frameworks. Spatial big data frameworks, such as Spatial Hadoop, have been successfully used for several spatial operations, but the effect of SSD utilization in these operations is yet to be evaluated. This paper evaluates the feasibility of adopting SSD in a Spatial Hadoop cluster. The contributions presented are 1) Characterization of Spatial Hadoop jobs that would benefit from the usage of SSD. 2) Identification of the optimal mix of Solid State Drives and Hard Disk Drives for HDFS for each type of job. 3) Evaluation of cluster configuration parameters that influence the speed-up. The cluster exhibited a speed-up of up to 15% for I/O intensive spatial operations, while other compute-intensive operations could not harness a similar gain.

关键词： Spatial Hadoop Solid State Drives Performance analysis Global indexes Computational geometry

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