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Violence Detection In Videos via Motion-Guided Global and Local Views

Violence Detection In Videos via Motion-Guided Global and Lo...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Ning Su Lijuan Sun Yutong Gao Jingchen Wu Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. this lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. therefore, in this paper, we design a novel Global-and-Local Cross-Modal network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, the enhanced features are simultaneously fed into two parallel branches of the network. the global branch fuses the visual and audio features into holistic representations. the local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset.

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An Efficient Data Transmission Strategy for Edge-computing-Based Opportunistic Social networks 17th

An Efficient Data Transmission Strategy for Edge-Computing-B...

引用

17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Luo, Jingwen Wu, Jia Wu, Yuzhou School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9783030794774

As wireless network has developed rapidly in recent years, especially with the maturity and wide application of 5G wireless system, millions of mobile users have been able to quickly exchange large amounts of data in social networks. Despite the positive impact of the recent advances in edge computing on opportunistic social networks (OSNs), classical OSN algorithms rely on only a few source nodes to forward data, which often results in data transmission delay, excessive resource consumption and even loss of source nodes. therefore, we propose an efficient edge-computing-based data transmission strategy in OSNs. It classifies nodes into new communities according to their degree of association. Source nodes forward data through relay nodes in community, which effectively reduces the resource consumption of a single node. the experiment results of comparison with the three algorithms show that the proposed method can effectively reduce the resource consumption during data transmission as well as enhance the information transmission efficiency. © 2021, ifip international Federation for Information Processing.

关键词： Information management

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PC-BNA: parallel Convolution Binary Neural network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. the input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. the experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. the image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. the results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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A Configurable Hardware Architecture for Runtime Application of network Calculus 17th

A Configurable Hardware Architecture for Runtime Application...

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17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Hu, Xiao Lu, Zhonghai School of Computer National University of Defense Technology Changsha China KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9783030794774

network Calculus has been a foundational theory for analyzing and ensuring Quality-of-Service (QoS) in a variety of networks including networks on Chip (NoCs). To fulfill dynamic QoS requirements of applications, runtime application of network calculus is essential. However, the primitive operations in network calculus such as arrival curve, min-plus convolution and min-plus deconvolution are very time consuming when calculated in software because of the large volume and long latency of computation. For the first time, we propose a configurable hardware architecture to enable runtime application of network calculus. It employs a unified pipeline that can be dynamically configured to efficiently calculate the arrival curve, min-plus convolution, and min-plus deconvolution at runtime. We have implemented and synthesized this hardware architecture on a Xilinx FPGA platform to quantify its performance and resource consumption. Furthermore, we have built a prototype NoC system incorporating this hardware for dynamic flow regulation to effectively achieve QoS at runtime. © 2021, ifip international Federation for Information Processing.

关键词： Quality of service

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Optimizing and Extending Serverless Platforms: A Survey 8

Optimizing and Extending Serverless Platforms: A Survey

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8th international conference on Software Defined Systems (SDS)

作者： Nazari, Maziyar Goodarzy, Sepideh Keller, Eric Rozner, Eric Mishra, Shivakant Univ Colorado Comp Sci Dept Boulder CO 80309 USA Univ Colorado Elect Comp & Energy Engn Dept Boulder CO 80309 USA Facebook Boulder CO USA

ISBN: (纸本)9781665458207

Serverless computing is a new cloud computing paradigm wherein people in academia and industry are actively proposing either interesting improvements or building excellent applications on top of it. AWS, Google Cloud, Microsoft Azure, and IBM are popular samples of public clouds that offer Function-as-a-Service on top of their Serverless computing platforms. Although this paradigm has had numerous advantages for software developers and programmers, it has introduced new challenges to cloud providers. Factors like fine-grained pricing and pay-as-you-go manner, eliminating the responsibility of resource management on the developer side, promises of elasticity and highly-available service, fault tolerance, auto-scaling, and being able to run embarrassingly parallel jobs make it a suitable platform for developers. On the other hand, efficient resource management, offering low-latency service, and providing proper security/isolation have been partly the main challenges introduced on the cloud provider side. this paper presents a literature review on today's Serverless platform optimizations and extensions that people have proposed and implemented to further capitalize the Serverless infrastructure. In the end, we will provide the current Serverless paradigm's limitations and a few future directions and research opportunities regarding Serverless computing.

关键词： Serverless computing Function-as-a-Service Cloud computing Optimization Extension

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Security Situation Prediction of network Based on Lstm Neural network 17th

Security Situation Prediction of Network Based on Lstm Neura...

引用

17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Chen, Liqiong Fan, Guoqing Guo, Kun Zhao, Junyan Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201400 China Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9783030794774

As an emerging technology that blocks network security threats, network security situation prediction is the key to defending against network security threats. In view of the single source of information and the lack of time attributes of the existing methods, we propose an optimal network security situation prediction model based on lstm neural network. We employ the stochastic gradient descent method as the minimum training loss to establish a network security situation prediction model, and give the model implementation algorithm pseudo code to further predict the future network security situation. the simulation experiments based on the data collected from Security Data dataset show that compared with other commonly used time series methods, the prediction accuracy of the model is higher and the overall situation of network security situation is more intuitively reflected, which provides a new solution for network security situation. © 2021, ifip international Federation for Information Processing.

关键词： network security

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Reducing the Time of Live Container Migration in a Workflow 17th

Reducing the Time of Live Container Migration in a Workflow

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17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Di, Zhanyuan Shao, En He, Mujun University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China Chongqing University Chongqing China

ISBN: (纸本)9783030794774

As a lightweight virtualization solution, container technology can provide resource limiting capabilities and can run multiple isolated process sets under a single kernel instance. Multi-tenant preemption leads to competition in computing, storage, and network resources, resulting in degraded computing service performance. Virtualization service migration can provide a solution to the problem of resource shortage in supercomputing systems. However, the resource overhead and delay in the migration process also reduce the efficiency of high-performance computers. To solve this problem, this dissertation proposes a method of container migration and a tool for supporting container migration. Also, this paper optimizes the startup of containers from checkpoints and proposes a multi-container migration strategy, reducing migration time by 30% compared to sequential migration. the migration method in this paper provides valuable experience for service migration in supercomputers and data centers. © 2021, ifip international Federation for Information Processing.

关键词： Containers

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An Adaptive Delay-Limited Offloading Scheme Based on Multi-round Auction Model for Mobile Edge computing 17th

An Adaptive Delay-Limited Offloading Scheme Based on Multi-r...

引用

17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Qi, Huamei Wang, Yiran Jiang, Su Yang, Chunmeng Wu, Jia School of Computer Science and Engineering Central South University Changsha China Information Technology Department China Life Ecommerce Company Limited Changsha Regional Branch Changsha Hunan China

ISBN: (纸本)9783030794774

Mobile edge computing (MEC) has been widely used in many scenarios due to its advantages such as low latency, high bandwidth, and strong real-time performance. For mobile edge computing in networks with multiple mobile users and multiple MEC servers, this paper proposes an adaptive delay-limited offloading scheme based on a multi-round auction model. First, a multi-round auction model is adopted. Users and servers select each other to achieve a globally optimal match. the proposed evaluation function is based on the delay and energy consumption that can be reduced by offloading, and weights the two parts based on the user’s optimization needs. At the same time, a mechanism for adaptive delay limitation is proposed. the threshold is dynamically updated according to the unloading feedback, which makes the algorithm dynamically adapt to the load changes in the network. Experimental results show that the proposed offloading scheme has obvious advantages in reducing the total delay and the total energy consumption of the system, effectively improving the system performance. © 2021, ifip international Federation for Information Processing.

关键词： computation offloading

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Comparison of Hardware Accelerator of Matrix Multiplication with Approximate Adders

Comparison of Hardware Accelerator of Matrix Multiplication ...

引用

20th international conference on Electronics, Information, and Communication (ICEIC)

作者： Chung, Yunchul Cho, Manhee Kim, Youngmin Hongik Univ Sch Elect & Elect Engn Seoul 04066 South Korea

ISBN: (纸本)9781728191614

Approximate computing is attracting attention as an energy-efficient design, especially suitable for Deep Neural network and Image Processing. In this study, a variety of approximate adders are applied to a hardware accelerator of 8 by 8 Matrix Multiplication in parallel structure to compare Error Distance and Error Rate. In the simulations, each shows different error figures according to metrics for the reliability, and AMA1, InXA2 and VAXA show the fewest errors according to the indicators. In addition, it shows that various approximate adders have few errors that can be used for matrix multiplication, and that parallel structures and approximate computing can be used appropriately for matrix operations.

关键词： approximate computing approximate adder hardware accelerator error distance error rate

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Aware: Adaptive Distributed Training with Computation, Communication and Position Awareness for Deep Learning Model

Aware: Adaptive Distributed Training with Computation, Commu...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Yan Zeng Guangzheng Yi Yuyu Yin Jiyang Wu Meiting Xue Jilin Zhang Jian Wan Yunquan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

the accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. this paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. this paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. the results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence.

关键词： Deep learning Training Adaptation models Runtime Computational modeling Neural networks Reinforcement learning

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