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3rd international conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Mohamed, Omar Hesham Shehab, Mohamed Elemam El Fakharany, Essam Arab Acad Sci Technol & Maritime Transport Informat Syst Dept Cairo Egypt Arab Acad Sci Technol & Maritime Transport Cairo Egypt Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Cairo Egypt

ISBN: (纸本)9783319746906;9783319746890

Big Data analytics are recently coming up as prominent research area in the field of data science. Apache Spark is an open source distributed data processing platform that uses distributed memory abstraction to process large volume of streaming data efficiently. Performance improvement of analytic computational model of streaming big data is important to meet the requirements of many real-time data analysis. Researchers focus on Analytic algorithm improvement to reduce analysis time. This paper presents performance enhancement of in-memory computational model by selecting the most important attributes after caching data to Apache spark. Performance analysis of distributed K-Means clustering algorithm based on in-memory computational model has been conducted. The results show improvement in the performance of the model.

关键词： Big Data Apache Spark Machine learning algorithms K-Means algorithm In-memory computation Big data analytic

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Preface

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CEUR Workshop proceedings 2022年 3304卷

作者： Jia, Weijia Yin, Jian Hu, Shaolin Peng, Shaoliang Niu, Shaozhang Srinivasa Rao, P.C. Lu, Yao Mirsaeidi, Sohrab Cao, Bin Wu, Yilin Wang, Xingbo Subramaniyam, Kannimuthu Boddu, Raja Sarath Kumar Li, Zhonghua Tao, Qian Zhang, Lei Li, Yi Ding, Deqing Wang, Dahan Zhang, Ziyan AlShawabkeh, Mahmoud Aslam, Muhammad Dib, Omar Sahibuddin, Shamsul Bin Wagh, Sanjeev J. Pardalos, Panos M. Al-Sammarraie, Najeeb Abbas Liu, Linfeng Kong, Jin-Heung Borrego, Carlos Tahir, Sabeen Wu, Zhiheng Li, Jiongcheng Xu, Yong Xie, Zhi Wang, Jiahai Cai, Hongmin Yang, Yuedong Qin, Xiaolin Beijing Normal University Zhuhai China Sun Yat-sen University China Guangdong University of Petrochemical Technology China Hunan University China Beijing University of Posts and Telecommunications China India Beijing Jiaotong University China Hebei University of Technology China Guangdong University of Education China Foshan University China Karpagam College of Engineering India Lenora College of Engineerin India Yat-sen University Guangdong High Performance Computing Society China South China University of Technology China Henan University China Wenzhou University China National University of Defense Technology China Xiamen University of Technology China Hainan Tropical Ocean University China Guangxi Normal University for Nationalities China Wuhan University China Wenzhou-Kean University China School of Software Engineering Universiti Teknologi Malaysia Information Technology Government College of Engineering Karad India Department of Industrial & Systems Engineering University of Florida United States Al-Madinah International University Faculty of Computer and Information Technology Malaysia School of Computer Science and Technology Nanjing University of Posts and Telecommunications China School of Computer and Information Engineering Kwangwoon University Korea Republic of Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Information Technology Department King AbdulAziz University Jeddah Saudi Arabia Institute of Manufacturing GDAS China Guangdong Planning and Designing Institute of Telecommunications Co. Ltd China Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences China Cheng Du Information Technology of Chinese Academy of Sciences Co. Ltd China

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proceedings of the 2018 3rd Digital Heritage international Congress, Digital Heritage 2018 - Held jointly with the 2018 24th international conference on Virtual systems.and Multimedia, VSMM 2018

Proceedings of the 2018 3rd Digital Heritage International C...

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3rd Digital Heritage international Congress, Digital Heritage 2018

ISBN: (纸本)9781728102924

The proceedings contain 97 papers. The topics discussed include: intra-site digital documentation of the ancient Maya city of Palenque, Mexico;Vietnam war stories: stories from all sides;madmen"s voices: discovering former psychiatric hospitals via mobile application;capturing the virtual movement of paintings: a game and a tool;developing application based upon an ontology-based modeling of Vietnamese traditional dances;active visitor: augmenting libraries into social spaces;integrating and visualizing humanities and heritage science data;real-world data for virtual reality experiences: interpreting excavations;representation and restoration theories for a digital life of the archival architectural drawings;and digital curation of a World War II Japanese-American incarceration camp collection: implications for sociotechnical archival systems.

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REVIEW OF MACHINE LEARNING APPLICATIONS IN POWDER BED FUSION TECHNOLOGY FOR PART PRODUCTION 3

REVIEW OF MACHINE LEARNING APPLICATIONS IN POWDER BED FUSION...

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3rd international conference on Progress in Additive Manufacturing (Pro-AM)

作者： Huang, De Jun Li, Hua Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Ctr 3D Printing 50 Nanyang Ave Singapore 639798 Singapore

Additive manufacturing (AM) has become a viable option for production of industrial parts to meet the growing demands for customized components with complex geometry within a short lead time. Powder bed fusion (PBF) technology is often favored for its superior geometrical resolution and system stability. In the perspective of PBF machine users, the part production cycle can be broadly divided to three stages: the preparation stage, the printing stage and the postprocessing stage. The complexity in each stage gives rise to a challenging task for process control and quality assurance. The rising of machine learning in recent years sheds light on tackling this multi-factorial challenge in order to improve the overall performance of AM for part production. This article provides a review of machine learning techniques that are applied in or relevant to the part production cycle in PBF systems. The studies to date have showed segmented applications of machine learning techniques in different processes related to AM. To gain insight into the system behavior of PBF machines, more efforts could be put into constructing effective data representations and performing holistic analyses for the entire production cycle.

关键词： Machine Learning Additive Manufacturing Powder Bed Fusion Review

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Partitioning-Insensitive Watermarking Approach for distributed Relational Databases 3rd

Partitioning-Insensitive Watermarking Approach for Distribut...

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3rd international conference on Future Data and Security Engineering (FDSE) / 10th international conference on Advanced computing and Applications (ACOMP)

作者： Rani, Sapana Koshley, Dileep Kumar Halder, Raju Indian Inst Technol Patna Patna Bihar India

ISBN: (纸本)9783662562666;9783662562659

This paper introduces an efficient watermarking approach for distributed relational databases, which is generic enough to support database outsourcing and hybrid partitioning. Various challenges, like partitioning and distribution of data, existence of replication etc., are addressed effectively by watermarking different partitions using different sub keys and by maintaining a meta-data about the data distribution. Notably, the embedding and detection phases are designed with the aim of making embedded watermarks partitioning-insensitive. That means, database partitioning and its distribution do not disturb any embedded watermark at all. To the best of our knowledge, this is the first proposal on watermarking of distributed relational databases supporting database outsourcing, its partitioning and distribution in a distributed setting.

关键词： Watermarking distributed databases Security

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Performance of Dynamic Networks: A Measurement Consists of Flow, Timing and Reliability 3

Performance of Dynamic Networks: A Measurement Consists of F...

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3rd international conference on Computer and Communication systems.(ICCCS)

作者： Laih, Yih-Wenn Jane, Chin-Chia Ling Tung Univ Dept Finance 1 Lingtung Rd Taichung 40852 Taiwan Ling Tung Univ Dept Business Adm 1 Lingtung Rd Taichung 40852 Taiwan

ISBN: (纸本)9781538663509

Dynamic network models are widely employed for system analysis and performance evaluation of computer and communication systems. In dynamic networks each arc is weighted with a capacity and a transit time. The former is the maximum amount of flow that can travel through the arc per unit time, the later is the time period needed for flow to travel through the arc. In this article, an additional weight operation probability, is assigned to each arc to note the capability of the arc's successful transition. The performance of dynamic networks is defined by the probability that the maximum dynamic flow from source to sink within required time horizon is no less than specified threshold. To the best of our knowledge, no existing article had considered dynamic flow and network reliability simultaneously. Based on facts that there is always a maximum dynamic flow among the temporally repeated flows and the maximum temporally repeated flow can be obtained via an equivalent minimum cost maximum flow problem, we present a pioneering algorithm to compute performance of dynamic networks by solving minimum cost maximum flow and two-terminal capacity reliability at the same time.

关键词： performance dynamic networks reliability algorithm

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Performance Measurements of Narrowband-IoT Network in Emulated and Field Testbeds

Performance Measurements of Narrowband-IoT Network in Emulat...

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IEEE international conference on Intelligent Data Acquisition and Advanced computing systems

作者： Jubin Sebastian E Axel Sikora Institute of Reliable Embedded Systems and Communication Electronics (ivESK) Offenburg University of Applied Sciences

ISBN: (纸本)9781728140704

Narrowband IoT (NB-IoT) as a radio access technology for the cellular Internet of Things (cIoT) is getting more traction due to attractive system parameters, new proposals in the 3rd Generation Partnership Project (3GPP) Release 14 for reduced power consumption and ongoing world-wide deployment. As per 3GPP, the low-power and wide-area use cases in 5G specification will be addressed by the early NB-IoT and Long-Term Evolution for Machines (LTE-M) based technologies. Since these cIoT networks will operate in a spatially distributed environment, there are various challenges to be addressed for tests and measurements of these networks. To meet these requirements, unified emulated and field testbeds for NB-IoT-networks were developed and used for extensive performance measurements. This paper analyses the results of these measurements with regard to RF coverage, signal quality, latency, and protocol consistency.

关键词： NB-IoT Cellular IoT Test and measurements 5G IoT LPWAN Access to technology Testing and Measurement Power consumption 3GPP system parameter measurement result Performance metrics Long Term Evolution

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RPC 2018 - proceedings of the 3rd Russian-Pacific conference on Computer Technology and Applications

RPC 2018 - Proceedings of the 3rd Russian-Pacific Conference...

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3rd Russian-Pacific conference on Computer Technology and Applications, RPC 2018

ISBN: (纸本)9781538675311

The proceedings contain 118 papers. The topics discussed include: use of agent-based modeling for wildfire situations simulation;modeling of a solar arrays deployment process at ground tests of mechanical devices on active gravity compensation systems.design concept of the intelligent information and telecommunication system of university security;object identification on raster images by user query;model synthesis and model-oriented programming: a new technology for high performance agent-based modeling;services and cloud infrastructure to support interdisciplinary scientific research;ontology-based model of object field concerning individual's capabilities for self-development;application of the case-based reasoning approach for identification of acoustic-emission control signals of complex technical objects;automatic error detection and hint generation in the teaching of formal languages syntax using correctwriting question type for moodle LMS;and decision support system for designing an effective configuration of a computing network for distributed complex problem solving.

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Bristrita: Namespace and Metadata Distribution in Large-Scale distributed Cloud Storage systems 3

Bristrita: Namespace and Metadata Distribution in Large-Scal...

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3rd IEEE international conference on Smart Cloud (SmartCloud)

作者： Dewan, Hrishikesh Hansdah, R. C. Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

ISBN: (纸本)9781538680001

The volume of data being processed today in various applications is phenomenally enormous. While the computational speed of the processors to process these data has increased many folds since the last few decades, the storage systems.designs to handle the processing of these data do not have adequate features. A crucial entity to support processing of such enormous volume of data is a file system. Until, a few years back, most of the file system designs relied on a single metadata and namespace server and the emergence of distributed metadata and namespace designs is seen only recently. However, most of these distributed namespace designs are based on cluster-based systems.where the underlying environment is predictive and can be controlled. In this paper, we present Bristrita, a distributed file system metadata and namespace design layered on top of a peer-to-peer network. Bristrita allows varieties of file system operations, supports files with unlimited size with no limit on the depth of the namespace tree, the number of files in each directory and is resilient to failures. The paper includes the design decisions, the algorithms, and their analysis and shows that it can be used to support users across multiple different geographical regions encompassing a wide area network of computing nodes, storage clusters with support for ad-hoc nodes joining and leaving the network.

关键词： distributed file storage system distributed metadata cloud computing fault-tolerance load balancing

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Fault-tolerant scheduling for scientific workflow with task replication method in cloud 3

Fault-tolerant scheduling for scientific workflow with task ...

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3rd international conference on Internet of Things, Big Data and Security, IoTBDS 2018

作者： Li, Zhongjin Yu, Jiacheng Hu, Haiyang Chen, Jie Hu, Hua Ge, Jidong Chang, Victor School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Key Laboratory for Novel Software Technology Software Institute Nanjing University China International Business School Suzhou Xi'an Jiaotong Liverpool University Suzhou China

ISBN: (纸本)9789897582967

Cloud computing has become a revolutionary paradigm by provisioning on-demand and low cost computing resources for customers. As a result, scientific workflow, which is the big data application, is increasingly prone to adopt cloud computing resources. However, internal failure (host fault) is inevitable in such large distributed computing environment. It is also well studied that cloud data center will experience malicious attacks frequently. Hence, external failure (failure by malicious attack) should also be considered when executing scientific workflows in cloud. In this paper, a fault-tolerant scheduling (FTS) algorithm is proposed for scientific workflow in cloud computing environment, the aim of which is to minimize the workflow cost with the deadline constraint even in the presence of internal and external failures. The FTS algorithm, based on tasks replication method, is one of the widely used fault tolerant mechanisms. The experimental results in terms of real-world scientific workflow applications demonstrate the effectiveness and practicality of our proposed algorithm. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Cloud computing

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