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作者： Lu, Shuaibing Wu, Jie Wang, Ning Duan, Yubin Liu, Haiming Zhang, Jiayue Fang, Juan Faculty of Information Technology Beijing University of Technology Beijing China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer Science Rowan University GlassboroNJ United States School of Software Engineering Beijing Jiaotong University Beijing China

Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay-sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited-processor fog nodes (UPFN) and limited-processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP-hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near-optimal solution bounded by (Formula presented.) has been found. m is the number of fog nodes, and (Formula presented.) is the upper bound on the Lipschitz constant of the delay function. Another one is the application-driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms. © 2021 John Wiley & Sons, Ltd.

关键词： Fog

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Privacy-aware Data Fusion and Prediction for Smart City Services in Edge computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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Random Shortening of Linear Codes and Applications

arXiv

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arXiv 2023年

作者： Chen, Xue Cheng, Kuan Li, Xin Mao, Songtao School of Computer Science University of Science and Technology of China China Center on Frontiers of Computing Studies Peking University China Department of Computer Science Johns Hopkins University United States

Random linear codes (RLCs) are well known to have nice combinatorial properties and near-optimal parameters in many different settings. However, getting explicit constructions matching the parameters of RLCs is challenging, and RLCs are hard to decode efficiently. This motivated several previous works to study the problem of partially derandomizing RLCs, by applying certain operations to an explicit mother code. Among them, one of the most well studied operations is random puncturing, where a series of works culminated in the work of Guruswami and Mosheiff (FOCS’ 22), which showed that a random puncturing of a low-biased code is likely to possess almost all interesting local properties of RLCs. In this work, we provide an in-depth study of another, dual operation of random puncturing, known as random shortening, which can be viewed equivalently as random puncturing on the dual code. Our main results show that for any small Ε, by starting from a mother code with certain weaker conditions (e.g., having a large distance) and performing a random (or even pseudorandom) shortening, the new code is Ε-biased with high probability. Our results hold for any field size and yield a shortened code with constant rate. This can be viewed as a complement to random puncturing, and together, we can obtain codes with properties like RLCs from weaker initial conditions. Our proofs involve several non-trivial methods of estimating the weight distribution of codewords, which may be of independent interest. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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A Data-Centric Accelerator for High-Performance Hypergraph Processing 22

A Data-Centric Accelerator for High-Performance Hypergraph P...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Qinggang Wang Long Zheng Ao Hu Yu Huang Pengcheng Yao Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China and Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (纸本)9781665462723

Hypergraph processing has emerged as a powerful approach for analyzing complex multilateral relationships among multiple entities. Past research on building hypergraph systems suggests that changing the scheduling order of bipartite edge tasks can improve the overlap-induced data locality in hypergraph processing. However, due to the complex intertwined connections between vertices and hyperedges, it is almost impossible to find a locality-optimal scheduling order. Thus, these task-centric hypergraph systems often suffer from substantial off-chip *** this paper, we first propose a novel data-centric Load-Trigger-Reduce (LTR) execution model to exploit fully the locality in hypergraph processing. Unlike a task-centric model that loads the required data along with a task, our LTR model invokes tasks as per the data used. Specifically, once the hypergraph data is loaded into the on-chip memory, all of its relevant computation tasks will be triggered simultaneously to output intermediate results, which are finally reduced to update the final results. Our LTR model enables all hypergraph data to be accessed once in each iteration. To fully exploit the LTR performance potential, we further architect an LTR-driven hypergraph accelerator, XuLin, which features with an adaptive data loading mechanism to minimize the loading cost via chunk merging at runtime. XuLin is also equipped with a priority-based differential data reduction scheme to reduce the impact of conflicting updates on performance. We have implemented XuLin both on a Xilinx Alveo U250 FPGA card and using a cycle-accurate simulator. The results show that XuLin outperforms the state-of-the-art hypergraph processing solutions Hygra and ChGraph by 20.47× and 8.77× on average, respectively.

关键词： hypergraph data-centric accelerator

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PRO-HAN: A HETEROGENEOUS GRAPH ATTENTION NETWORK FOR PROFILE-BASED SPOKEN LANGUAGE UNDERSTANDING

arXiv

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arXiv 2024年

作者： Teng, Dechuan Lu, Chunlin Xu, Xiao Che, Wanxiang Qin, Libo Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Computer Science and Engineering Central South University China

Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile information, without considering their interrelationships or excluding irrelevant and conflicting information within them. To address the above issues, we introduce a Heterogeneous Graph Attention Network to perform reasoning across multiple PROfile information, called PRO-HAN. Specifically, we design three types of edges, denoted as intra-PRO, inter-PRO, and utterance-PRO, to capture interrelationships among multiple PROs. We establish a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics. Further analysis experiments also confirm the effectiveness of our method in modeling multi-source profile information. Copyright © 2024, The Authors. All rights reserved.

关键词： User profile

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Pro-HAN: A Heterogeneous Graph Attention Network for Profile-based Spoken Language Understanding

Pro-HAN: A Heterogeneous Graph Attention Network for Profile...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dechuan Teng Chunlin Lu Xiao Xu Wanxiang Che Libo Qin Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Computer Science and Engineering Central South University China

Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile information, without considering their interrelationships or excluding irrelevant and conflicting information within them. To address the above issues, we introduce a Heterogeneous Graph Attention Network to perform reasoning across multiple Profile information, called Pro-HAN. Specifically, we design three types of edges, denoted as intra-Pro, inter-Pro, and utterance-Pro, to capture interrelationships among multiple Pros. We establish a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics. Further analysis experiments also confirm the effectiveness of our method in modeling multi-source profile information.

关键词：

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Medical CT Image Super-resolution Algorithm based on GAN

Medical CT Image Super-resolution Algorithm based on GAN

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computer, Vision and Intelligent technology (ICCVIT), International Conference on

作者： Saihu Tian Taile Peng Zhongan Wang Zhengfeng Li School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei Normal University Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing, and transmitting medical CT images, the clarity of these images can be degraded by non-human factors, resulting in issues such as reduced resolution and blurred texture details, thereby compromising image fidelity. In this study, we present an approach utilizing Generative Adversarial Networks (GANs). that incorporates a grayscale value loss as an additional constraint while maintaining the original loss function constraints. We conduct experiments for 4x super-resolution reconstruction. To evaluate the reconstruction performance, we compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) of the reconstructed images. The experimental results demonstrate ,which the proposed method exhibits promising performance in both quantitative metrics and visual quality.

关键词： Visualization PSNR Computed tomography Computational modeling Superresolution Noise Gray-scale Generative adversarial networks Image reconstruction Medical diagnostic imaging

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FaaSTube: Optimizing GPU-oriented Data Transfer for Serverless computing

arXiv

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arXiv 2024年

作者： Wu, Hao Deng, Junxiao Yu, Minchen Yu, Yue Liu, Yaochen Fan, Hao Wu, Song Wang, Wei National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Hong Kong University of Science and Technology Hong Kong The Chinese University of Hong Kong Shenzhen China

Serverless computing has gained significant traction for machine learning inference applications, which are often deployed as serverless workflows consisting of multiple CPU and GPU functions with data dependency. However, existing data-passing solutions for serverless computing primarily reply on host memory for fast data transfer, mandating substantial data movement and resulting in salient I/O overhead. In this paper, we present FaaSTube, a GPU-efficient data passing system for serverless inference. FaaSTube manages intermediate data within a GPU memory pool to facilitate direct data exchange between GPU functions. It enables fine-grained bandwidth sharing over PCIe and NVLink, minimizing data-passing latency for both host-to-GPU and GPU-to-GPU while providing performance isolation between functions. Additionally, FaaSTube implements an elastic GPU memory pool that dynamically scales to accommodate varying data-passing demands. Evaluations on real-world applications show that FaaSTube reduces end-to-end latency by up to 90% and achieves up to 12x higher throughput compared to the state-of-the-art. Copyright © 2024, The Authors. All rights reserved.

关键词： computer graphics equipment

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Accuracy-Efficiency Optimization for Multi-Stage Small Object Detection in Surveillance Video with Collaborative Frame Sampling

Accuracy-Efficiency Optimization for Multi-Stage Small Objec...

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IEEE International Conference on Cluster computing

作者： Chunhong Du Shanjiang Tang Song Meng Jiekai Gou Ce Yu Yusen Li Hao Fu Ye Tian Ding Yuan College of Intelligence and Computing Tianjin University China School of Computer Science and Technology Nankai University China National Supercomputing Center of Tianjin China

ISBN: (数字)9798350358711

ISBN: (纸本)9798350358728

In video analytics, accuracy and efficiency are two important metrics and there tend to be a tradeoff between each other. In this paper, we consider accuracy-efficiency optimization for small object detection in surveillance video, which is important and has been widely used in many scenarios such as license plate detection in the traffic domain. Given that small objects tend to being attached to big objects, multi-stage object detection is supposed to be an effective approach to achieve high accuracy for small objects by detecting big objects first and then small objects within the ROIs (Region of interests) of big objects. However, existing studies considered the accuracy-efficiency optimization for small object detection only within the single-stage scenario by changing the frame resolution or sampling rate configuration of video data, which are not suitable for multi-stage detection given that its accuracy-efficiency result is determined by the results of all stages jointly. In this paper, we propose an Adaptive and Collaborative frame Sampling approach named ACS for accuracy-efficiency optimization in the multi-stage small object detection. To improve the efficiency significantly while guaranteeing a given accuracy threshold, ACS dynamically adjusts the sampling rates of all stages collaboratively and periodically using the Karush-Kuhn-Tucker (KKT) condition based on the Lagrangian multiplier method. Additionally, we introduce a tuning knob to allow users to flexibly balance accuracy and efficiency, while ensuring a given accuracy threshold λ. Extensive experiments demonstrate the effectiveness of our approach in improving detection efficiency while guaranteeing diverse accuracy requirements.

关键词： Measurement Accuracy Surveillance Visual analytics Collaboration Object detection Traffic control Optimization Tuning License plate recognition

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HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection

HCF-Net: Hierarchical Context Fusion Network for Infrared Sm...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shibiao Xu Shuchen Zheng Wenhao Xu Rongtao Xu Changwei Wang Jiguang Zhang Xiaoqiang Teng Ao Li Li Guo School of Artificial Intelligence Beijing University of Posts and Telecommunications CASIA MAIS School of Artificial Intelligence University of Chinese Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science School of Computer Science and Technology Harbin University of Science and Technology

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet.

关键词： Deep learning Location awareness Convolutional codes computer vision Adaptation models Image recognition Object detection

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