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检索条件"任意字段=21st International Conference on Distributed Computing and Intelligent Technology"

共 1611 条记录，以下是761-770 订阅

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Proposal of a computer-reproducible model of consciousness

Proposal of a computer-reproducible model of consciousness

引用

international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Toshiki Watanabe Hiroyuki Kameda Graduate School of Bionics Computer and Media Sciences Tokyo University of Technology Tokyo Japan School of Computer Science Tokyo University of Technology Tokyo Japan

In this study, we propose a model of consciousness that can be implemented by computers as a decision-making system based on psychology, with the goal of enabling artificial intelligence to understand human values and... 详细信息

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

关键词： Psychology Computational modeling Ethics Neuroscience Decision making Artificial intelligence Data models

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A latency-sensitive elastic adaptive scheduling in distributed stream computing systems 21

A latency-sensitive elastic adaptive scheduling in distribut...

引用

21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： He, Hanyu Sun, Dawei Yan, Hongbin Gao, Shang School of Science China University of Geosciences Beijing100083 China School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University Waurn PondsVIC3216 Australia

ISBN: (纸本)9781728120584

With the massive growth of big data applications, the requirement for data processing speed is getting higher and higher in stream computing systems. The storm, as one of the most popular distributed stream computing systems, has received more attention. However, the storm's traditional scheduling strategy is not ideal for processing a large volume of streaming data. The resource scheduling in a distributed stream computing system should consider not only node allocation status but also fluctuating input rates of data stream. To address this problem, this paper has completed the following work: (1) A performance model La-stream (latency-sensitive elastic adaptive scheduling) is proposed and built by adopting a quantitative method for calculating the amount of computation required between task map nodes and node communication. (2) A La-stream based algorithm is proposed. The algorithm dynamically plans a resource allocation scheme with minimal data processing latency among available resources to achieve optimal allocation. (3) Three functional modules of La-steam are proposed and implemented: module Monitor, module Optimizer and module Scheduler. The three modules are integrated into the storm platform with minimal overhead. Several sets of experiments are conducted, verifying the feasibility and effectiveness of La-stream. © 2019 IEEE.

关键词： storms

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Linking textual and contextual features for intelligent cyberbullying detection in social media 20

Linking textual and contextual features for intelligent cybe...

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Proceedings of the 18th international conference on Advances in Mobile computing & Multimedia

作者： Nabi Rezvani Amin beheshti Alireza Tabebordbar Macquarie University Sydney Australia

ISBN: (纸本)9781450389242

The rapid and vast adoption of social media and the 21st century technologies is changing the bullying process., i.e., deliberate misuse of power in relationships through social behavior that intends to cause social and psychological harm. In this context, Cyberbullying can be defined as the process of using technology to hurt someone else by sending hurtful messages, pictures, or comments. A successful Cyberbullying detection technique requires an effective combination of big data (generated from social, open, and private data islands), features, and algorithms. While many Cyberbullying detection techniques exist for working with machine learning data and algorithms, support for thinking of new features (to be extracted from images, images' metadata, and textual information generated around images) remains poor. In this paper, we present an intelligent Cyberbullying detection pipeline to: (i) extract features (from an image, image meta-data and textual content generate); (ii) contextualize the extracted features by developing a crowdsourced feedback loop and drawing on human knowledge; and (iii) combining features using a Neural Network to identify and build potentially useful features. We adopt a typical scenario for analyzing social media content generated on Instagram and Twitter to identify Cyberbullying activities. We discuss how the proposed approach significantly improves the quality of extracted knowledge compared to classical natural language analysis techniques.

关键词： cyberbullying detection data curation deep neural networks

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An efficient parallel high utility sequential pattern mining algorithm 21

An efficient parallel high utility sequential pattern mining...

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作者： Zhang, Chunkai Zu, Yiwen School of Computer Science and Technology Harbin Institute of Technology Shenzhen HIT Campus Shenzhen University Town Xili Shenzhen China

ISBN: (纸本)9781728120584

High utility sequential pattern mining (HUSPM) is an emerging research topic in pattern mining. Aiming at this problem, many efficient algorithms have been proposed in recent years. However, most of them are serial algorithms, which assume that the entire database (and the data structures) can completely fit into memory. Thus, two conspicuous drawbacks attract our attention. Firstly, in the big data era, it is hard to put the large scale database completely into the memory. Secondly, the serial algorithms are unable to process the real-time data quickly. To address these shortcomings, in this paper, we propose an efficient distributed algorithm for HUSPM. The proposed algorithm takes the advantages of multiple machines and multi-core processer, so it can quickly compete the mining task. The major contributions are summarized as follows. First, we propose an algorithm based on the SLst strategy to partition the original database. Second, we propose a multi-threading algorithm for HUSPM. Finally, based on the two points, an efficient distributed algorithm, named PHUSP, is proposed. Our experiments show the proposed algorithm is much faster than the state-of-the-art algorithms. © 2019 IEEE.

关键词： Data mining

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statistical analysis for detection of sensitive data using hadoop clusters 21

Statistical analysis for detection of sensitive data using h...

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作者： Adhikari, Binod Kumar Zuo, Wanli Maharjan, Ramesh Han, Xuming Amatya, Prakash Bahadur Ali, Wajid College of Computer Science and Technology Jilin University Changchun130012 China Amrit Campus Tribhuvan University Kathmandu Nepal

ISBN: (纸本)9781728120584

The omnipresence of internet technology and the advent of smart devices accumulate varieties of voluminous, viscous real-time data in a network from varieties of sources and also facilitates a way for criminal, intruders to attack the network which persuades information theft, financial loss, cyber-attack, and cyberwar. It is the major challenge for researchers to determine sensitive data from large scale real-time data so that the right action can be taken at the right time. Therefore, it is important to purpose a framework to handle massive data and to detect sensitive data from big data. In this regard, the collected data from the network are stored in cloud drives, other storage devices and then transferred to Hadoop distributed file system and processed with MapReduce processing using distributed computing concepts, Machine learning algorithms, and advanced statistical methods. statistical analytical tools like Descriptive Analysis, Regression, ANOVA, Parametric Levene's test, Pearson Correlation, and Kruskal-Wallis Test present that recent big data analytical tools are effective than the traditional method for retrieval of sensitive data from Big Data. © 2019 IEEE.

关键词： Learning algorithms

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Design of 3D Animation Builder with HLA Based distributed Simulations 21

Design of 3D Animation Builder with HLA Based Distributed Si...

引用

21st international conference on Advanced Communication technology (ICACT)

作者： Choi, Sangha Kim, Wooshik Yoon, Sugjoon Sejong Univ Dept Aerosp Engn Dept Elect Informat & Commun Engn Seoul Gunja Dong South Korea

ISBN: (纸本)9791188428021

MATLAB has been widely used for providing a variety of functions and tools in engineering computing, numerical analysis, modeling and visualization. In particular, through 3D animation, MATLAB has advantages for M&S (Modeling & Simulation). High Level Architecture (HLA) is a general standard for interoperability and reusability in M&S. 3D animation has been used in the distributed simulations for the simulation analysis. In the distributed simulations, objects or entities continuously occur changes. Each change requires modifying its 3D animation objects or entities. It would be a problem in larger simulation environment. This paper proposes the method of an automatic-generate-3D-animation builder in MATLAB. The 3D-animation builder supports advantages of the HLA based distributed simulations. In this paper, we introduce design of a 3D-animation builder. We also present the example of an HLA-based simulation with the 3D-animation builder that could fully support the advantages of HLA simulation.

关键词： 3D Animation distributed Simulation Modeling & Simulation HLA analysis

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Hardware-Supported Remote Persistence for distributed Persistent Memory

Hardware-Supported Remote Persistence for Distributed Persis...

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Supercomputing conference

作者： Zhuohui Duan Haodi Lu Haikun Liu Xiaofei Liao Hai Jin Yu Zhang Song Wu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA prim-itives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.

关键词： High performance computing Throughput Proposals Network interfaces Guidelines

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Research of distributed computing framework for foreign exchange market monitoring 21

Research of distributed computing framework for foreign exch...

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作者： Cheng, Wenliang Wang, Zhihong Zhou, Yu Guo, Yi Zhao, Junfeng Co. Ltd Shanghai China East China University of Science and Technology Shanghai China National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai China Shihezi University Xinjiang China

ISBN: (纸本)9781728120584

Foreign exchange market monitoring is an important part of financial regulation, which amends the failures of market mechanism, protect the legal rights of market participants and maintain the equality, efficiency, transparency and stability of the foreign exchange market. This paper analyzes and summarizes the business characteristics and existing technology framework of foreign exchange market monitoring, and proposes a novel distributed computing framework for this task based on Spark. Considering the business features of single-market multi-indicator and multi-market multi-indicator, this framework involves market-level directed acyclic graph model (M-DAG) and Market-level resource allocation strategy (M-YARN) based on Spark's job scheduling mechanism and YARN's resource scheduling pool isolation mechanism, respectively. The experimental results prove the performance of our proposed framework in foreign exchange market monitoring, which effectively guarantee the completeness, accuracy and timeliness of foreign exchange market monitoring indicators under the background of big data. © 2019 IEEE.

关键词： Yarn

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Metaflow: A better traffic abstraction for distributed applications 21

Metaflow: A better traffic abstraction for distributed appli...

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作者： Shi, Yang Fei, Jiawei Wen, Mei Huang, Qun Wu, Nan National University of Defense Technology Changsha China Institute of Computing Technology Chinese Academy of Sciences Beijing China Netforward Shenzhen China

ISBN: (纸本)9781728120584

distributed applications usually feature a set of correlated flows between two consecutive computation stages. The scheduling of these flows has a crucial influence on job completion time. Coflow improves performance by optimizing the finish time of the entire set of flows. However, the flows and computing tasks in one application have more complex relationships that exceed the coflow's harrier assumption. In this context, scheduling via coflow abstraction may hurt application performance. Accordingly, we propose metaflow, a traffic abstraction derived from the computation graph of the application. Metaflow reveals the detailed flow requirements of the application and makes it easier to reduce the job completion time. Based on metaflow, we devise a scheduling heuristic called MSA. MSA is able to find shorter jobs more accurately for inter-job scheduling, and maximize the overlap between communication and computation for intra-job scheduling. To demonstrate the effectiveness of our work, we have conducted extensive simulations with both synthetic single jobs and production traces containing multiple jobs. The simulation results verify that MSA adapts well to different jobs and can achieve a significant increase in average speed of over 1.98x on a real-life workload. © 2019 IEEE.

关键词： Abstracting

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SwFLOW: A dataflow deep learning framework on sunway taihulight supercomputer 21

SwFLOW: A dataflow deep learning framework on sunway taihuli...

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作者： Lin, Han Lin, Zeng Diaz, Jose Monsalve Li, Mingfan An, Hong Gao, Guang R. School of Computer Science and Technology University of Science and Technology of China Hefei China University of Delaware Delaware United States

ISBN: (纸本)9781728120584

Deep learning technology is widely used in many modern fields and a number of deep learning models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems with specialized architectures such as Sunway TaihuLight. In this paper, we propose swFLOW: a TensorFlow-based dataflow deep learning framework on Sunway TaihuLight. Based on the performance analysis results on convolutional neural network (CNN), we optimize the convolution layer, reduce the data layout transpose operation and get 10.42x speedup compared to single management processing element (MPE) version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication and use data prefetch to avoid data fetch being a performance bottleneck. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8. Limited by the maximal executable batch size, the current performance of swFLOW is far from optimal. It is very necessary to further optimize using technology like remote direct memory access (RDMA) and model parallelism. © 2019 IEEE.

关键词： Supercomputers

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