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FedTADBench: Federated Time-series Anomaly Detection Benchmark

FedTADBench: Federated Time-series Anomaly Detection Benchma...

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

作者： Fanxing Liu Cheng Zeng Le Zhang Yingjie Zhou Qing Mu Yanru Zhang Ling Zhang Ce Zhu College of Computer Science Sichuan University China School of Information and Communication Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Polymer Science and Engineering Sichuan University China West China School of Public Health Sichuan University China

Time series anomaly detection strives to uncover potential abnormal behaviors and patterns from temporal data, and has fundamental significance in diverse application scenarios. Constructing an effective detection model usually requires adequate training data stored in a centralized manner, however, this requirement sometimes could not be satisfied in realistic scenarios. As a prevailing approach to address the above problem, federated learning has demonstrated its power to cooperate with the distributed data available while protecting the privacy of data providers. However, it is still unclear that how existing time series anomaly detection algorithms perform with decentralized data storage and privacy protection through federated learning. To study this, we conduct a federated time series anomaly detection benchmark, named FedTADBench, which involves five representative time series anomaly detection algorithms and four popular federated learning methods. We would like to answer the following questions: (l)How is the performance of time series anomaly detection algorithms when meeting federated learning? (2) Which federated learning method is the most appropriate one for time series anomaly detection? (3) How do federated time series anomaly detection approaches perform on different partitions of data in clients? Numbers of results as well as corresponding analysis are provided from extensive experiments with various settings. the source code of our benchmark is publicly available at https://***/fanxindiu2020/FedTADBench.

关键词： Data privacy Federated learning Source coding Time series analysis distributed databases Training data Memory

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Abstract Test Execution for Early Testing Activities in Model-Driven Scenarios 8th

Abstract Test Execution for Early Testing Activities in Mode...

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8th international conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Proell, Reinhard Hagemann, Noeel Bauer, Bernhard Univ Augsburg Software Methodol Distributed Syst Augsburg Germany

ISBN: (纸本)9783030674441;9783030674458

the continuous improvement of the performance of computing units makes it possible to cope with increasingly complex tasks. this results in more complex software systems. However, the development of such highly complex systems is difficult to achieve using traditional approaches. Concepts like model-driven software development can weaken this problem in these constructive phases. However, new challenges arise for the testing of development artifacts. In order to be able to perform a real shift left of verification and validation tasks towards early phases of development, we present a semi-formal approach that enables users to execute test cases against the system under development (SUD) on the model-level. Grounded on an Integrated Model Basis which is created and maintained during development, test reports are automatically derived. this opens up a wide range of possibilities for early and targeted troubleshooting.

关键词： Test execution Model-based testing Domain-specific modeling Integrated model basis Model-driven software development

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NETREACT: distributed Event Detection in Sensor Data Streams with Disaggregated Packet Processing Pipelines

NETREACT: Distributed Event Detection in Sensor Data Streams...

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IEEE conference on Network Softwarization (NetSoft)

作者： Csaba Gyö rgyi Ká roly Kecskemé ti Hiba Mallouhi Pé ter Vö rö s Sá ndor Laki ELTE E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd University Budapest Hungary

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

A new phenomenon called in-network computing has recently emerged with the aim of offloading calculations beyond the traditional task of packet forwarding to network switches. One of the most studied in-network computing applications is processing of sensor data streams. Existing works such as FastReact focus on solving this problem using flexible SmartNICs. In this paper, we propose NETREACT: an improved ASIC-oriented design for distributed event detection in sensor data streams to achieve a disaggregated processing pipeline. In contrast to existing approaches, NETREACT distributes the event detection task among a set of switches while leveraging the capabilities of the Intel Tofino platform in terms of boosting throughput and reducing latency. the proposed event-rule disaggregation method has the advantage of overcoming the hardware resource constraints and improving the overall network performance.

关键词： Event detection Filtering conferences Pipelines distributed databases throughput Data processing

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Simulation Platform for Optimal Pricing of a Distribution Utility with distributed Energy Resources Operating in a Residential Community 8

Simulation Platform for Optimal Pricing of a Distribution Ut...

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8th IEEE international conference on Smart Energy Grid Engineering (SEGE)

作者： Atienza, Annie Delos Reyes, Blessie Nicole Pedrasa, Michael Angelo Univ Philippines Diliman Elect & Elect Engn Inst Quezon City Philippines

ISBN: (数字)9781728199122

ISBN: (纸本)9781728199122

the current power grid system in the Philippines allows the consumers to utilize any amount of electrical energy as long as they can pay for it. As a response, the utilities need to meet the demand at all times by purchasing energy from the generating plants whatever the cost may be. Today, these distribution utilities started to explore the usage of distributed energy resources in order to cater these varying demands, especially the peak demand. Moreover, the number of consumers which implemented transactive energy schemes also continues to increase. If this interaction between the utility and the consumers will be well-coordinated, reduction in the electricity price and energy consumption would be possible. this paper describes a computing tool and was able to (1) solve for the electricity tariff, (2) produce schedule of energy dispatch from distributed generators and storage systems and (3) perform proper demand-side management through scheduling of appliance operations, in order to reduce the cost of customer energy consumption at the same time maximize the allowable utility profit.

关键词： transactive energy demand side management optimization cooperative coevolutionary genetic algorithm

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A Blockchain-based Access and Management System for IoT Devices 8

A Blockchain-based Access and Management System for IoT Devi...

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8th international conference on Internet of things, Systems, Management and Security (IOTSMS)

作者： Valentin, Manuel Pahl, Claus El Ioini, Nabil Barzegar, Hamid R. Free Univ Bozen Bolzano Bozen Bolzano Italy

ISBN: (纸本)9781665458689

Recent developments in distributed ledger technologies have created a whole new set of possibilities in the way of managing trust, security, privacy and traceability in computer-based transactions, principles which are increasingly gaining importance in the world of IoT. Currently, IoT devices are generally based on centralized, client-server systems, where digital service providers have complete control over user data and information generated by their devices. In this paper we present the development of a decentralized access and management system for IoT devices, where operations on these devices, such as the installation and management of apps are handled by a blockchain-based identification and record system. the system prototype consists of a smartphone application acting as a management hub for the whole system, an IoT device API implementation for allowing secure access to management and data functionalities, and a set of smart contracts on the Ethereum blockchain, where all necessary information for the functioning of the system is stored. the system allows app developers to provide their apps as Docker containers for IoT devices without the need to publish them on a centralized app store, and a regular user can subscribe to and access the available applications by paying in cryptocurrency without revealing any private information to the system. A cost and performance evaluation have been performed to assess the feasibility of the proposed solution.

关键词： Blockchain IoT Edge computing Ethereum cryptocurrency smart contracts

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Cloud Resource Demand Prediction to Achieve Efficient Resource Provisioning

Cloud Resource Demand Prediction to Achieve Efficient Resour...

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international conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Sepideh Ebneyousef Saeed Ghazanfari-Rad Department of Electrical and Computer Engineering AleTaha University Tehran Iran

ISBN: (纸本)9781665476249

Cloud computing is a paradigm that uses techniques such as virtualization, time sharing and distributed computing to provide a wide range of hardware and software services for users based on a pay-as-you-go model, on-demand and via ubiquitous network access. Due to the scarcity of important cloud resources such as compute, network and storage, cloud service providers should carefully select, apply and distribute resources within the predefined boundaries of the cloud system meanwhile satisfy the requirements of running applications. One of the most critical components of the cloud computing architecture is resource allocation, where its effectiveness significantly affects the functionality of the entire cloud system. Implementing efficient resource allocation schemes and algorithms in cloud systems is not straightforward and faces several challenges including compromising between the required implementation costs and the utilization of resources, satisfying delay requirements of running applications, effective reallocation of resources, reducing computational complexity and scheduling workloads and tasks appropriately across a data center or in a heterogeneous multicloud environment. One of the goals of the consumers of cloud computing services is to complete tasks at minimal cost. In terms of cost efficiency, the goal of cloud providers is to maximize the resource utilization and thus the profits while cloud users are aimed at achieving the alternative goal which is to reduce the cost without compromising the quality of delivered services. there are various approaches of implementing resource allocation to compromise between maximizing the resource utilization and minimizing the consumer’s costs. In this article, we survey different scheduling algorithms and resource allocation approaches in the cloud computing environment, which include the prediction of resource requests in virtual machines and optimal allocation of resources to reduce costs and increase efficien

关键词： Cloud computing Costs Uncertainty Signal processing algorithms Prediction algorithms Virtual machining Resource management

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distributed Deep Learning of ResNet50 and VGG16 with Pipeline Parallelism 8

Distributed Deep Learning of ResNet50 and VGG16 with Pipelin...

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8th international Symposium on computing and networking Workshops, CANDARW 2020

作者： Takisawa, Naoki Yazaki, Syunji Ishihata, Hiroaki School of Computer Science Tokyo University of Technology Hachioji Tokyo Japan Information Technology Center University of Electro-Communications Chofu Tokyo Japan

ISBN: (纸本)9781728199191

Data parallel distributed deep learning has been used to accelerate the learning speed. the communication is becoming a bottleneck as the computation time is accelerated. therefore, the communication time is needed to be reduced. As a new acceleration method, research activity for pipeline parallelism has increased since 2018. However, the existing methods generally combine data parallelism and pipeline parallelism, and there has been a little analysis of speed improvement in pipeline parallelism alone. this paper analyzes the effect of speed improvement by pipeline *** compared the execution time of data parallelism and pipeline parallelism with VGG16 and ResNet50, respectively. VGG16 includes more parameters comparing to ResNet50 when the size of input data is the same. A comparison of the execution time shows that data parallelism is 3.3 times faster than pipeline parallelism for ResNet50. Pipeline parallelism is 2.45 times faster than data parallelism for *** speed-up of pipeline parallelism was significant in a model with a large number of parameters. therefore, pipeline parallelism is suitable for large-scale models that have a large number of parameters. © 2020 IEEE.

关键词： Pipelines

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Fog computing based Health Care Applications and Frameworks: A Review

Fog Computing based Health Care Applications and Frameworks:...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Shalu Duggal Parminder Kaur Guru Nanak Dev University Amritsar India

Fog computing has emerged as a distributed computing structure that extends the cloud platform model. this paper presents a literature review of Fog computing-based health care applications and frameworks. the objective of the review is to assimilate intelligent fog computing for health care. It accentuates the features of fog computing and services it can provide in the healthcare system. the usefulness of artificial intelligent enabled fog computing for health care industry is perceived in this review. the review findings provide testimony for mining the power of artificial intelligent fog computing in healthcare system.

关键词： Industries Cloud computing Computational modeling Bibliographies Medical services Edge computing

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Heterogeneous Propagation Graph Representation Learning for Fake News Detection

Heterogeneous Propagation Graph Representation Learning for ...

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

作者： Zhi Zeng Xiang Li Ying Sha College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China

Recently, social network platforms have led to a deluge of fake news, which tends to mislead readers and may arouse public spark panic. thus, detecting fake news on social networks re-mains an important role. Previous researches mainly focused on discovering features from diffusion paths and news content. However, these researches insufficiently exploit dynamic interactions on social networks to refine representations of social roles. therefore, we proposed hetero-geneous propagation graph representation learning (HPGR), which exploits heterogeneous interaction behavior and non-Euclidean propagation preferences of news to improve the detection performance of fake news. Different from previous works, the HPGR transfers spatial propagation activities into a heterogeneous propagation graph. Centered on the news, the graph iteratively traces its publisher representation and user representation to fully learn the static and dynamic representations of nodes. Specifically, we develop a publisher-based attention module to learn publisher representation in the news propagation network, which can capture the shared semantics of publisher's news more accurately and facilitate the fine-grained representation. then, the fine-grained user representations are constructed by user similarity and pref-erences in the news propagation. Further, the representations of news contents are reconstructed through propagation and aggregation of propagation graph neural network, which inter-pretively describes the feedbacks of news propagation process. Finally, the reconstructed representations of news contents are applied for fake news detection task. the experimental results show that the HPGR can improve the accuracy of fake news detection and provide a novel paradigm for fine-grained heterogeneous graph representation learning.

关键词： Representation learning Social networking (online) Semantics Graph neural networks Behavioral sciences Sparks Fake news

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Dynamic Behavior Pattern: Mining the Fraudsters in Telecom Network

Dynamic Behavior Pattern: Mining the Fraudsters in Telecom N...

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

作者： Dengshi Li Lu Zeng Ruimin Hu Zijun Huang Xiaocong Liang Yilong Zang School of Artificial Intelligence Jianghan University Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

In telecom network fraud, the fraud method of the fraudsters varies significantly in different periods. However, the traditional method of telecom fraud is to train the telecom fraud mining model through the user's long-term telephone data. So they can not detect the new fraud method of fraudsters and can not identify those with only have short-term telephone data. To increase the identification precision of telecom fraudsters, the dynamic changes in social structure and behavior pattern features of fraudsters were studied in this paper. Specifically, from the perspective of social structure change, the social structure of fraudsters changes rapidly over time. In contrast, the social structure of normal users is stable over time. From the perspective of user behavior pattern features, we found that the behavior patterns of fraudsters and normal users were very different. the behavior pattern of fraudsters in the different periods also changes significantly. therefore, a telecom network fraud detection framework based on the social relationship evo-lution model was proposed. We designed the feature extraction method of dynamic telephone social behavior patterns based on time slices. the method combines the features of social structure and behavior patterns of users. And the Telephone Network Fraud Detection(TNFD) model is built using LSTM. We set up a sliding time window to enable the model to identify users with only short-term telephone data. this time window dynamically takes the features of the user's telephone social behavior pattern as the input of the TNFD model. Experiments on real dataset show that the precision of TNFD is 7% higher than that of the latest method. Our research may reference public security organs in telecom fraud cases.

关键词： Social networking (online) Telephone sets Feature extraction Data models Fraud Behavioral sciences Telecommunications

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