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检索条件"任意字段=International Conference on Networking and Distributed Computing"

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2016 IEEE/ACIS 17th international conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD 2016

2016 IEEE/ACIS 17th International Conference on Software Eng...

引用

17th IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD 2016

ISBN: (纸本)9781509022397

The proceedings contain 111 papers. The topics discussed include: a self-organizing algorithm for community structure analysis in complex networks;an efficient uniform integrated advection algorithm for finite time Lyapunov exponent field computation on GPU and MIC;an improved model for PM2.5 inference based on support vector machine;ultra high frequency polynomial and cosine artificial higher order neural networks;document representation based on semantic smooth topic model;the optimization of beer recipe based on an improved ant colony optimization;team negotiation based on solidarity behavior: a concession strategy in the team;a hybrid method for bilingual text sentiment classification based on deep learning;facial expression classification using support vector machine based on bidirectional local binary pattern histogram feature descriptor;an empirical study on recovering requirement-to-code links;optimal placement of charging stations for electric taxis in urban area with profit maximization;traffic sign detection based on cascaded convolutional neural networks;and sample reconstruction and secondary feature selection in noisy speech emotion recognition.

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GNSS Aided Precision Multi-clock Synchronization

GNSS Aided Precision Multi-clock Synchronization

引用

IEEE international conference on Hot Information-Centric networking (HotICN)

作者： Lihua Yang Bing Li Yue Yin School of Software Northwestern Polytechnical University Xian China School of Microelectronics Northwestern Polytechnical University Xian China

ISBN: (纸本)9781665405508

Precision Time Protocol (PTP) defined by the IEEE standard is a precision clock synchronization protocol (IEEE1588) for achieving high-precision synchronization in distributed systems by means of network communication and local computing technology. Although PTP system offers sub-microsecond clock synchronization in local area network by software-only implementation, it is difficult to meet the requirement of communication network which requires higher clock synchronization performance. This paper proposes a PTP system aided by Global Positioning System (GPS)/BeiDou Navigation Satellite System (BDS), which attains nanosecond performance.

关键词： Performance evaluation Analytical models Satellites Information-centric networking Stability analysis Main-secondary Synchronization

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Parameter Servers Placement for distributed Deep Learning in Edge computing

Parameter Servers Placement for Distributed Deep Learning in...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Jiaquan Yan Yalan Wu Jigang Wu Long Chen School of Computer Science and Technology Guangdong University of Technology Guangzhou China

The architecture based on global-local parameter servers (PSs) is an appealing framework to accelerate the training of the global model in distributed deep learning. However, the accuracy and training time are influenced by the placement strategy of PSs in distributed deep learning, due to the adopted gradient descent methods and the heterogeneous computation and communication resources. Therefore, this paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of the storage capacity and the number of local PSs. Then, we prove the NP-hardness of the proposed problem. To solve the problem, a parameter servers placement algorithm, followed by an adjustment algorithm, is proposed in this paper, by continuously making decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that, the proposed combined algorithm performs better than existing works for all cases, in terms of the training time of global model. Moreover, the performance of the proposed algorithm is nearly close to that of brute-force approach, in terms of the training time of the global model.

关键词： Training Deep learning NP-hard problem Computational modeling Heuristic algorithms Simulation Optimized production technology

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GGS: General Gradient Sparsification for Federated Learning in Edge computing

GGS: General Gradient Sparsification for Federated Learning ...

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IEEE international conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Li, Shiqi Qi, Qi Wang, Jingyu Sun, Haifeng Li, Yujian Yu, F. Richard Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781728150895

Federated learning is an emerging concept that trains the machine learning models with the distributed datasets, without sending the raw data to the data center. But in an edge computing enviroment where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameters synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy. Gradient sparsification has received increasing attention, which only updates significant gradients and accumulates insignificant gradients locally. However, how to preserve the accuracy after a high ratio sparsification has always been ignored. In this paper, a General Gradient Sparsification (GGS) framework is proposed for adaptive optimizers, to correct the sparse gradient update process. It consists of two important mechanisms: gradient correction and batch normalization update with local gradients (BN-LG). With gradient correction, the optimizer can properly treat the accumulated insignificant gradients, which makes the model converge better. Furthermore, updating the batch normalization layer with local gradients can relieve the impact of delayed gradients without increasing the communication overhead. We have conducted experiments on LeNet-5, CifarNet, DenseNet-121, and AlexNet with adaptive optimizers. Results show that when 99.9% gradients are sparsified, validation datasets are maintained with top-1 accuracy.

关键词： deep learning federated learning distributed training gradient sparsification optimization

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Alleviating Imbalance in Synchronous distributed Training of Deep Neural Networks

Alleviating Imbalance in Synchronous Distributed Training of...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Haiyang Lin Mingyu Yan Duo Wang Wenming Li Xiaochun Ye Zhimin Tang Dongrui Fan State Key Laboratory of Computer Architecture ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

Deep neural networks achieve huge success in computer vision, natural language process and other fields. As larger networks and larger datasets bring better performance, the training time grows rapidly. distributed training uses parallelization strategy to accelerate the training of deep neural network. In the existing distributed training framework of deep neural network, the parameter server architecture has poor scalability and is not suitable for large-scale distributed training. Ring/HD All Reduce algorithm perform well in scalability under ideal circumstances. However, in many realistic scenarios, imbalance will occur frequently in the distributed training system, which is mainly the imbalance of compute ability and network. Due to the structure characteristics of synchronous distributed training and All Reduce algorithm, imbalance will bring serious impact on performance. At present, there is a lack of research on both compute ability imbalance and network imbalance, especially dynamic imbalance. To alleviate this problem, we analyze All Reduce algorithm first, and propose RingDL, which optimizes the performance of distributed training system by alleviating imbalance through customizable dynamic workloads strategy and link strategy. The experimental results show that RingDL significantly reduces idle time in computing phase and synchronous phase, improving the overall performance.

关键词： Training Deep learning Degradation Computer vision Heuristic algorithms Scalability Neural networks

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Hieff: Enabling Efficient VNF Clusters by Coordinating VNF Scaling and Flow Scheduling 39

Hieff: Enabling Efficient VNF Clusters by Coordinating VNF S...

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39th IEEE international Performance computing and Communications conference (IPCCC)

作者： Wang, Zenan Zhang, Jiao Wei, Haoran Huang, Tao BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9781728198293

A cluster of Virtual Network Functions (VNF) can serve massive fluctuating traffic by managing VNF instances and distributing flows. However, how to schedule the flows and manage VNF scaling efficiently in a VNF cluster is still an open question. Existing solutions such as hash based schemes encounter imbalance and passive flow remapping obstacles while flow table-based scheme suffers from high processing latency and flow entries overflow challenges. In this paper, we design and present Hieff, an efficient NFV system that coordinates VNF scaling and flow scheduling within a VNF cluster. The key idea of Hieff is to precisely manage the heavy flows with a flow table while simply allowing light flows be distributed by hash. Though this idea has been explored in previous work, we are the first to apply it with the VNF scaling process. We mathematically model the Hieff system and propose a heuristic algorithm to determine the optimized VNF scaling and flow scheduling strategies. We implement Hieff based on BESS and Click and use real-world tracing to evaluate the system. Results show that Hieff can handle co-existing massive flows efficiently with low latency while balancing the load of VNF instances at low cost.

关键词： Network function virtualization VNF cluster scaling flow scheduling

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Random verifiable multi-server searchable encryption scheme

Random verifiable multi-server searchable encryption scheme

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international conference on networking and Network Applications (NaNA)

作者： Yu He Youliang Tian Hua Xu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang P. R. China Institute of Cryptography and Data Security Guizhou University Guiyang P. R. China

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time.

关键词： Data privacy Cloud computing distributed databases Games Search problems Data models Encryption

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A Pervasive Multi-physiological Signal-Based Emotion Classification with Shapelet Transformation and Decision Fusion 17th

A Pervasive Multi-physiological Signal-Based Emotion Classif...

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17th EAI international conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Shichao Zheng, Xiangwei Zhang, Mingzhe Guo, Gengyuan Ji, Cun School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9783030926342

Emotion classification is a hot pot at present. Since physiological signals are objective and difficult to hide, physiological signals are commonly used in emotion classification methods. However, traditional emotional classification based on physiological signals faced the following challenges: low accuracy and low interpretability. For this, this paper proposes a pervasive multi-physiological signal-based emotion classification with shapelet transformation and decision fusion (PMSEC). In PMSEC, the shapelet transformation and feature extraction are carried out for ECG, GSR, and RA, respectively. Following by, six sub-classifiers are constructed for different physiological signals. Lastly, decision-level fusion is implemented to obtain final emotion results. Experimental results show that the proposed PMSEC is not only highly competitive but also has a broad application prospect compared with EEG-based classification method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Electrocardiograms

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MediNER: Understanding Diabetes Management Strategies Based on Social Media Discourse

MediNER: Understanding Diabetes Management Strategies Based ...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Oladapo Oyebode Rita Orji Faculty of Computer Science Dalhousie University Halifax Canada

Over the years, researchers have leveraged patients' discourse on social media to inform clinical and digital interventions. We contribute to these research efforts by mining and analyzing diabetes-related public posts on two social networks (online forums) with the aim of identifying management strategies adopted by diabetes patients and suggesting appropriate interventions. We develop a medical named entity recognition framework, MediNER, to identify named entities related to diabetes management and classify them into Food, Medication, Therapeutic Procedure, and Supplement. Our analysis shows that food-related strategies are most prevalent among both Type 1 and Type 2 diabetes patients, followed by medication-related strategies. Strategies involving supplements (such as vitamins) are the least utilized. We also investigate for gender differences in the strategies employed by diabetes patients. Finally, we offer design recommendations for digital interventions aimed at diabetes self-management based on our findings.

关键词： Social networking (online) Machine learning Diabetes

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A Path Allocation Method for Opportunistic Networks with Limited Delay 10th

A Path Allocation Method for Opportunistic Networks with Lim...

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10th international conference on Computer Engineering and Networks, CENet 2020

作者： An, Zhiyuan Liu, Yan Wang, Lei Zhang, Ningning Dong, Kaili Liu, Xin Xiao, Kun Communication and Dispatching Center State Grid Henan Information and Telecommunication Company Zhengzhou450000 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811584619

In the future heterogeneous integrated wireless network environment, delay limited network has the characteristics of distributed self-organization, multi hop transmission, delay tolerance and intermittent link connection. Although it has great application value in many scenarios, it still needs to provide certain QoS guarantee according to the business requirements. Therefore, how to effectively transmit information to meet the needs of different services in terms of transmission rate and delay is one of the key issues in the research of path allocation in delay constrained networks. In this paper, a path allocation method based on service priority is proposed. Network nodes use local information to evaluate the network state and calculate the initial service priority, and then select relay nodes according to congestion degree and encounter probability. Simulation results show that the algorithm improves the quality of service of high priority services in congestion, and provides high performance services for low priority services when the network resources are sufficient. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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