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Effective and light weight security system for highly confidential cloud data such as PHR 22

Effective and light weight security system for highly confid...

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Proceedings of the 4th international conference on Information Management & Machine Intelligence

作者： Sandeepkumar E V Kayalvizhi Jayavel Networking and Communications SRM University India Department of Networks and Communications School of Computing SRM University India

ISBN: (纸本)9781450399937

The server in a cloud storage system can hold a very large amount of Personal health records (PHR) data or information. The cloud platform's storage servers provide archival services for a lengthy time frame. The third party basically functions as an administrator for the efficiency of cloud storage. This is why we're starting up our cloud storage service. One of the biggest difficulties with the cloud is that it is vulnerable to hacking. Ordinary methods of encryption are used to safeguard the information from prying eyes. All of the secret messages' code words are kept in a system of varying symbols. Deletion coding is carried out in a manner analogous to that which is used to calculate the unequal code word cyphers required for a communication in a distributed setting. When the message symbols are stored in different servers in a dispersed environment, the cryptographic term signs are also calculated independently and stored. For this reason, we introduce and include a threshold proxy re-encryption scheme. Fully Homomorphic Encryption is a promising approach to securing sensitive PHR data by limiting who can view it. When sending encrypted PHR data, the proxy re-encryption mechanism re-encrypts the PHR data again before sending it on to the recipient or storage server. Allocation is completed when secure access control has maximised performance. In light of this, we expect to see the Schmidt-Samoa Public Key Encryption (SSPKE) method developed on the Enhanced v Boosting Algorithm (EBA) by PHR data Hiding Architecture. Additionally, in this initiative, we employ a procedure of multi-party protocol admission control to operate and access the user's PHR data without jeopardising the sensitive cloud PHR data privacy. The results of the experiments show the beneficial effect when various metrics, such as total processing time, server response time, and PHR data decomposition rate, are taken into account for the application of PHR.

关键词： EBA

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Online Automatic Service Composition for Mobile and Pervasive computing

Online Automatic Service Composition for Mobile and Pervasiv...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Zhaoning Wang Bo Cheng Junliang Chen Research Institute China United Network Communications Corporation Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665416597

In the mobile and pervasive computing environment, automatic service composition faces the challenges of the highly dynamic network structure and limited computation resources. Existing approaches create service flows under the assumption that network context changes are slower than the time required to plan the composite service which is no longer set up in the mobile and pervasive computing environment. In this paper, we propose an online automatic service composition approach to dynamically construct service flows and interleave the planning and executing processes. Particularly, it introduces a novel distributed heuristic searching algorithm to determine solutions with limited knowledge bases and execute them in real time. Simulation results show that our proposed online approach reduces the composition time and performs faster composition time and higher composition success rates than state-of-the-art approaches in mobile computing environments.

关键词： Pervasive computing Privacy Simulation Heuristic algorithms Knowledge based systems Real-time systems Planning

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Target-oriented Self Training for Heterogeneous Domain Adaptation

Target-oriented Self Training for Heterogeneous Domain Adapt...

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

作者： An Da Rui Zhang Shuang Li Weipeng Cao China Academy of Electronics and Information Technology China Electronics Technology Group Corporation Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Although the distributed machine learning platform is very successful in the application of structured data, it is still very challenging in the absence of labeled data and a variety of data forms. Therefore, heterogeneous domain adaptation (HDA) emerged at the historic moment, with the goal of transferring knowledge between domains of different features and different distributions. Existing HDA methods only focus on the better alignment of the target domain with the source domain, while ignoring the rich semantic structure information of the target domain data itself, thereby affecting the performance of knowledge transfer. In order to solve the above problems, we propose a simple and effective self-training method oriented to the target domain. Specifically, we use the clustering algorithm to find the prototype of the target domain clusters, and select confident unlabeled data through a novel method of finding pseudo-labels that we propose. Under two self-training mechanisms, that is, single-stage Self-training (TST-SS) and multi-stage self-training (TST-MS), without introducing any additional network parameters, gradually train the transferable model. We conducted extensive experiments on cross-domain and cross-feature tasks to prove that our method is far superior to the existing heterogeneous domain adaptation methods.

关键词： Training Adaptation models Semantics distributed databases Prototypes Clustering algorithms Machine learning

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Large-Area Human Behavior Recognition with Commercial Wi-Fi Devices

Large-Area Human Behavior Recognition with Commercial Wi-Fi ...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Tao Liu Shengli Pan Peng Li The university of Aizu Fukushima Japan Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665406697

Human behavior recognition which is the indispensable technology for Artificial Intelligence(AI) application like smart home and other practical applications, is very challenging as the optimal recognition generally is required to be non-invasive and easy to deploy. An increasing interest has been paid on the human behavior recognition with off-the-shelf Wi-Fi devices. However, most of existing works just limit their focus on the small-scale scene while human behavior recognition will be quite different in large areas for a larger number of antennas and correspondingly a more complex antenna layout. For example, if we want to build a complete behavior awareness system using the distributed Wi-Fi equipment of the entire building, though collecting and using all antennas’ data is feasible maybe, the overhead concerns of computing and bandwidth resources, and the operation complexity will be hard to lessen in practice. In this paper, we first present analyses of the signal performances between different antenna pairs. Then closely following these analyses, we propose a novel scheme for the large-area human behavior recognition. Finally, we conduct extensive confirmatory experiments to verify the validity of our proposed scheme.

关键词： Performance evaluation Layout distributed databases Smart homes Bandwidth Sensors Complexity theory

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Vapor: A GPU Sharing Scheduler with Communication and Computation Pipeline for distributed Deep Learning

Vapor: A GPU Sharing Scheduler with Communication and Comput...

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

作者： Xiaorui Zhu Lei Gong Zongwei Zhu Xuehai Zhou University of Science and Technology of China Suzhou Institute for Advanced Research University of Science and Technology of China

Nowadays, distributed deep learning (DDL) workloads are typically trained on GPU clusters. Modern GPUs support the concurrent execution of multiple jobs to achieve efficient parallelization of computation and communication. While sharing GPU improves resource utilization and cuts down job completion time, interference among co-located jobs results in significant performance degradation. Recent studies such as interference-aware job placement carefully co-locate jobs with the least interference in a best-effort manner and are not ideal. Another challenge in GPU sharing is straggler due to unbalanced workloads in packing. When stragglers occur, GPU resources are wasted, so the training processes are *** present Vapor, a GPU sharing scheduler for distributed deep learning on multi-GPUs featured by two novel scheduling policies: preemptive GPU sharing and adaptive batch redistribution to maximize GPU utilization and improve training efficiency. Firstly, Vapor uses preemptive scheduling that parallels computation and communication of co-located jobs in a pipelined manner. Secondly, Vapor uses an adaptive batch redistribution method to deal with stragglers in packing to improve resource utilization further. Vapor provides an AIMD model to predict the relationship between the batch size of the training data and the model calculation time in each iteration. We evaluate Vapor with other representative schedulers. Experiment on a Kubernetes cluster of 16 Tesla V100 GPU handling with popular DDL jobs shows that Vapor reduces job completion time by 21.8% compared with popular SOTA schedulers.

关键词： Deep learning Training Adaptation models Processor scheduling Computational modeling Graphics processing units Interference

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TriPoll: computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata

TriPoll: Computing Surveys of Triangles in Massive-Scale Tem...

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Supercomputing conference

作者： Trevor Steil Tahsin Reza Keita Iwabuchi Benjamin W. Priest Geoffrey Sanders Roger Pearce Center for Applied Scientific Computing (CASC) Lawrence Livermore National Laboratory (LLNL) Livermore CA USA

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched graphs) are often of interest in this pursuit. In this work, we develop TriPoll, a prototype distributed HPC system capable of surveying triangles in massive graphs containing metadata on their edges and vertices. We contrast our approach with much of the prior effort on triangle analysis, which often focuses on simple triangle counting, usually in simple graphs with no metadata. We assess the scalability of TriPoll when surveying triangles involving metadata on real and synthetic graphs with up to hundreds of billions of edges. We utilize communication-reducing optimizations to demonstrate a triangle counting task on a 224 billion edge web graph in approximately half of the time of competing approaches, while additionally supporting metadata-aware capabilities.

关键词： Social networking (online) Scalability Image edge detection Prototypes Machine learning Metadata Software

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NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

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

作者： Xiandong Huang Qinglin Wang Shuyu Lu Ruochen Hao Songzhu Mei Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China University of Pittsburgh Pittsburgh USA

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in many-core CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases.

关键词： Deep learning Recurrent neural networks Convolution Computer architecture Complexity theory Convolutional neural networks Kernel

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Smart Network Slices Scheduling in Cloud 5

Smart Network Slices Scheduling in Cloud

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5th IEEE international conference on Smart Cloud (IEEE SmartCloud) / 5th IEEE international Symposium on Reinforcement Learning (IEEE ISRL)

作者： Menouer, Tarek Khedimi, Amina Cerin, Christophe Umanis Res & Innovat 7 Rue Paul Vaillant Couturier F-92300 Levallois Perret France Univ Sorbonne Paris Nord LIPN CNRS UMR 7030 F-93430 Villetaneuse France

ISBN: (纸本)9781728165479

Faced to the diversity of the public in using clouds, computer scientists should take care to usages and facilitate access to non-experts i.e. to hiding internal artifacts and complexity of such distributed systems. At the opposite, the cloud provider wants to optimize resource usage, for instance for billing or to keep the cloud as green as possible. These two conflicting objectives are addressed in this paper. We combine a smart view for specifying the Service Level Agreements (SLA) of a user with a strategy of placing the user's jobs, which represents the cloud provider's point of view. We put in place a multi-criteria approach and the experimental results demonstrate the potential of our approach under a different scenario.

关键词： Cloud computing and networking models Resources allocation Network programming Innovations in cloud systems

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An Efficient distributed Approach for Load Balancing in IoT Based on SDN Principles 10

An Efficient Distributed Approach for Load Balancing in IoT ...

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10th international conference on computing, Communication and networking Technologies (ICCCNT)

作者： Eghbali, Zahra Lighvan, Mina Zolfy Beheshti, Arezoo Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

ISBN: (纸本)9781538659069

Internet of things is a new technology which is heavily affecting different aspects of human life. The number of smart devices connecting to IoT and the amount of data gathered or produced by these devices are increasing. In order to organize, secure and analyse the IoT data, novel and creative ideas for IoT design and management are required. Software Defined networking is a new and promising paradigm. Its main idea is to decouple the control functions from physical devices and set them inside a logically centralized control layer. In this paper, a distributed approach is proposed for accelerating data management and balancing the load between IoT devices based on SDN principles. The proposed framework makes it possible to apply different management strategies in a distributed manner. Experimental results indicate that our scheme distributes tasks between devices equally and exhibits a reduction in the average turnaround time and the average waiting time and it will improve the processing performance.

关键词： Data management distributed Internet of things Load balancing Software defined networking

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DALC: distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction 1

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34th international conference on Advanced Information networking and Applications, AINA 2020

作者： Lee, Ming-Chang Lin, Jia-Chun Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik2815 Norway

ISBN: (数字)9783030440411

ISBN: (纸本)9783030440404

Over the past decade, several approaches have been introduced for short-term traffic prediction. However, providing fine-grained traffic prediction for large-scale transportation networks where numerous detectors are geographically deployed to collect traffic data is still an open issue. To address this issue, in this paper, we formulate the problem of customizing an LSTM model for a single detector into a finite Markov decision process and then introduce an Automatic LSTM Customization (ALC) algorithm to automatically customize an LSTM model for a single detector such that the corresponding prediction accuracy can be as satisfactory as possible and the time consumption can be as low as possible. Based on the ALC algorithm, we introduce a distributed approach called distributed Automatic LSTM Customization (DALC) to customize an LSTM model for every detector in large-scale transportation networks. Our experiment demonstrates that the DALC provides higher prediction accuracy than several approaches provided by Apache Spark MLlib. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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