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international Joint conference on Rough Sets, IJCRS 2021

International Joint Conference on Rough Sets, IJCRS 2021

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international Joint conference on Rough Sets, IJCRS 2021

ISBN: (纸本)9783030873332

The proceedings contain 25 papers. The special focus in this conference is on Rough Sets. The topics include: Minimizing Depth of Decision Trees with Hypotheses;the Influence of Fuzzy Expectations on Triples of Triangular Norms in the Weighted Fuzzy Petri Net for the Subject Area of Passenger Transport Logistics;possibility Distributions Generated by Intuitionistic L -Fuzzy Sets;feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets;right Adjoint Algebras Versus Operator Left Residuated Posets;adapting Fuzzy Rough Sets for Classification with Missing Values;spark Accelerated Implementation of parallel Attribute Reduction from Incomplete Data;attention Enhanced Hierarchical Feature Representation for Three-Way Decision Boundary processing;an Opinion Summarization-Evaluation System Based on Pre-trained Models;determining Tanimoto Similarity Neighborhoods of Real-Valued Vectors by Means of the Triangle Inequality and Bounds on Lengths;fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets;Three-Way Decisions Based RNN Models for Sentiment Classification;tolerance-Based Short Text Sentiment Classifier;knowledge Graph Representation Learning for Link Prediction with Three-Way Decisions;PNeS in Modelling, Control and Analysis of Concurrent Systems;3rd: A Multi-criteria Decision-Making Method Based on Three-Way Rankings;Rough-Fuzzy Segmentation of Brain MR Volumes: applications in Tumor Detection and Malignancy Assessment;DDAE-GAN: Seismic Data Denoising by Integrating Autoencoder and Generative Adversarial Network;classification of Multi-class Imbalanced Data: Data Difficulty Factors and Selected Methods for Improving Classifiers;general Rough Modeling of Cluster Analysis;possible Coverings in Incomplete Information Tables with Similarity of Values;attribute Reduction Using Functional Dependency Relations in Rough Set Theory.

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8th EAI international conference on Advancement of Science and Technology, ICAST 2020

8th EAI International Conference on Advancement of Science a...

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8th EAI international conference on Advancement of Science and Technology, ICAST 2020

ISBN: (纸本)9783030806170

The proceedings contain 74 papers. The special focus in this conference is on Advancement of Science and Technology. The topics include: Earliest-Arrival Route: A Global Optimized Communication for Networked Control Systems;Impulsive Noise Mitigation Using Hybrid Nonlinear Preprocessor and Turbo Code in OFDM-PLC System;comparative Analysis of Kalman Filtering and Machine Learning Based Cardiovascular Signal processing Algorithms;modeling and Analysis of Three-Phase Inverter for Induction Motor Drive for Three-Wheel Electric Vehicle Application;performance Analysis of Multicarrier Modulation Techniques for Next Generation Networks;synchronous Generator Excitation Loss Detection Based on Reactive Power Flow Limit;estimation of Synchrophasor Parameters in the Presence of 3rd & 5th Harmonics and White Gaussian Noise;Performance Analysis of Vertical Sectorization in Sub-6-GHz Frequency Bands for 4G Mobile Network Under Realistic Deployment Scenario;power Loss Reduction and Voltage Profile Improvement of Radial Distribution System Through Simultaneous Network Reconfiguration and distributed Generation Integration;A Comprehensive Review and Evaluation of Classical MPPT Techniques for a Photovoltaic System;antiviral Activity of Aloe pirottae A. Berger Root Extracts Against Influenza A and B Viruses, Picornaviruses and Dengue Virus: An Endemic Plant Species of Ethiopia;dynamic Performance Evaluation of Type-3 Compensator for Switch-Mode Dc-Dc Converter;inset-Feed Rectangular Microstrip Patch Antenna Array Performance Enhancement for 5G Mobile applications;blockchain Based Green Coffee Supply Chain Management to Improve Traceability and Transparency (Case Study on Sidama Coffee);wireless Local Area Network Intrusion Detection System Using Deep Belief Networks;Direct Adaptive Fuzzy PI Strategy for a Smooth MPPT of Variable Speed Wind Turbines.

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8th EAI international conference on Advancement of Science and Technology, ICAST 2020

8th EAI International Conference on Advancement of Science a...

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8th EAI international conference on Advancement of Science and Technology, ICAST 2020

ISBN: (纸本)9783030806200

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Efficient Stencil Computation with Temporal Blocking by Halide DSL

Efficient Stencil Computation with Temporal Blocking by Hali...

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

作者： Hiroki Aikawa Toshio Endo Tomoya Yuki Takahiro Hirofuchi Tsutomu Ikegami Tokyo Institute of Technology Tokyo Japan AIST Tokyo Japan

The stencil computation is a memory centric computation, where memory access becomes a bottleneck. Therefore locality improvement method like temporal blocking is essential to improve the speed. Introducing temporal blocking, however, introduces heavy programming costs. This paper proposes a methodology to implement efficient stencil software with temporal blocking easier using the domain-specific language Halide. Generally, boundary conditions, such as Neumann and Dirichlet boundary conditions, are needed in stencil computations. Also user-defined boundary computations may be needed in general fluid dynamics applications. We indicate a pitfall in combining temporal blocking and boundary conditions on Halide and show a solution to improve performance. We also apply our methodology to the Hotspot3D bench-mark from Rodinia benchmark suite and demonstrate 3.5 to 5.0 times performance improvement over the original OpenMP implementation.

关键词： Costs Fluid dynamics Software algorithms Graphics processing units Benchmark testing Programming Boundary conditions

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Cloud-Based Smart Energy Framework for Accelerated Data Analytics with parallel Computing of Orchestrated Containers: Study Case of CU-BEMS 21

Cloud-Based Smart Energy Framework for Accelerated Data Anal...

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Proceedings of the 3rd international conference on Advanced Information Science and System

作者： Kittipat Saengkaenpetch Chaodit Aswakul System Database Management Department Electricity Generating Authority of Thailand (EGAT) Thailand and Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Thailand Wireless Network and Future Internet Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Thailand

ISBN: (纸本)9781450385862

This paper proposes a practical smart energy framework for data analytic on energy management system at Chulalongkorn University, called CU-BEMS. This serves as an example of demand-sided smart energy application that copes with the challenges of big data analytic and real-time processing needs. The framework is based on the divide and conquer paradigm to accelerate data analytics with parallel computing. The workload is containerized and deployed on the Kubernetes cloud facility of our internationally collaborated IoTcloudServe@TEIN playground. With this playground, the workload scalability and portability can be achieved. Applying the proposed framework, this paper reports on a practical data log analysis to determine the wasted energy consumption. Based on the experimental result, the wasted energy consumption of the whole data set of CU-BEMS's communication research laboratory area from March 2014 to August 2017 can be computed within 81 seconds by using 32 cores running in parallel. The framework is expected to serve as a basis template for further research ongoing at CU-BEMS and smart energy applications that can be computationally enhanced by data analytic pipelining with containerized services as orchestrated by Kubernetes.

关键词： Data Analytics

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A Metadata Model to Connect Isolated Data Silos and Activities of the CAE Domain 33rd

A Metadata Model to Connect Isolated Data Silos and Activiti...

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33rd international conference on Advanced Information Systems Engineering (CAiSE)

作者： Ziegler, Julian Reimann, Peter Keller, Florian Mitschang, Bernhard Univ Stuttgart Grad Sch Adv Mfg Engn GSaME Stuttgart Germany Univ Stuttgart Inst Parallel & Distributed Syst IPVS Stuttgart Germany MANN HUMMEL GmbH Ludwigsburg Germany

ISBN: (纸本)9783030793821;9783030793814

Computer-aided engineering (CAE) applications support the digital transformation of the manufacturing industry. They facilitate virtual product development and product testing via computer simulations. CAE applications generate vast quantities of heterogeneous data. Domain experts struggle to access and analyze them, because such engineering data are not sufficiently described with metadata. In this paper, we characterize the CAE domain and identify unsolved challenges for a tailored data and metadata management. For instance, work activities in product development projects and their relationships to data are not represented explicitly in current metadata models. We propose a metadata model that addresses all challenges and provides a connected view on all CAE data, metadata, and work activities of development projects. We validate the feasibility of our metadata model through a prototypical implementation and its application to a real-world use case. This verifies that our metadata model addresses the CAE-specific challenges and this way eases the task of domain experts to exploit relevant data.

关键词： Metadata models Graphs Computer-aided engineering

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Performance measurement of processes and threads controlling, tracking and monitoring based on shared-memory parallel processing approach 3

Performance measurement of processes and threads controlling...

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3rd international conference on Engineering Technology and its applications, IICETA 2020

作者： Sharif, Karzan H. Zeebaree, Subhi R. M. Haji, Lailan M. Zebari, Rizgar R. University of Human Development Computer Science Department Sulaymaniyah Iraq Duhok Polytechnic University Duhok Iraq University of Zakho Computer Science Department KRG Iraq

ISBN: (纸本)9781728182315

In this paper, a professional integrated operating system performance measurement system is proposed, designed, and implemented in order to monitor processes and threads. Because monitoring itself is not enough, so this system provides full control of the CPU forcing, Priority-changing, and pausing-resuming-killing of these processes/threads. During these complex operations, it provides the ability to measure/compute the details of Total-execution-time, CPU-time, User-time, Kernel-time, context switching, CPU -usage, and the priority of processes/threads. The proposed system can be used with any architecture of multicore processors. Despite that measuring all these parameters in one system is very difficult and complex especially the Kernel-time and Context-switching, but all of them have been applied and determined successfully. Adding to the above features that never been integrated (by any previous work) in one system, and in order to illustrate the real abilities of this system, a shared-memory parallel processing approach has been proposed and applied. The proposed system prepared to run all possible cases of processes and threads that construct under-tested-program which may be one of the: Single-Process-Single-Thread, Single-Process-Multi- Thread, Multi-Process-Single-Thread, Multi-Process-Multi- Thread, and Multi- Process-Single-Multi-Thread. The algorithms of this system are designed and implemented via C++ programming language(using C++ Standard Library Thread) which is the nearest programming to the OSs provides a more increasing processing speed. © 2020 IEEE.

关键词： Software testing

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A C++ Library for Memory Layout and Performance Portability of Scientific applications 28th

A C++ Library for Memory Layout and Performance Portabilit...

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28th international European conference on parallel and distributed Computing , Euro-Par 2022

作者： Incardona, Pietro Gupta, Aryaman Yaskovets, Serhii Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden01307 Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig Germany

ISBN: (纸本)9783031312083

We present a C++14 library for performance portability of scientific computing codes across CPU and GPU architectures. Our library combines generic data structures like vectors, multi-dimensional arrays, maps, graphs, and sparse grids with basic, reusable algorithms like convolutions, sorting, prefix sum, reductions, and scan. The memory layout of the data structures is adapted at compile-time using tuples with optional memory mirroring between CPU and GPU. We combine this transparent memory mapping with generic algorithms under two alternative programming interfaces: a CUDA-like kernel interface for multi-core CPUs, Nvidia GPUs, and AMD GPUs, as well as a lambda interface. We validate and benchmark the presented library using micro-benchmarks, showing that the abstractions introduce negligible performance overhead, and we compare performance against the current state of the art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Application Characterization for Near Memory processing

Application Characterization for Near Memory Processing

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Maryam S. Hosseini Masoumeh Ebrahimi Pooria Yaghini Nader Bagherzadeh Center for Pervasive Communications and Computing (CPCC) University of California Irvine KTH Royal Institute of Technology Sweden

Data movement between memory subsystem and processor unit is a crippling performance and energy bottleneck for data-intensive applications. Near Memory processing (NMP) is a promising solution to alleviate the data movement bottleneck. The introduction of 3D-stacked memories and more importantly hybrid memory systems enable the long-wished NMP capability. This work explores the feasibility and efficacy of having NMP on the hybrid memory system for a given set of applications. In this paper, we first redefine a set of NMP-centric performance metrics in order to analyze the efficacy of a given processing unit. Leveraging the proposed metrics, we characterize various sets of applications to assess the suitability of a processing unit in terms of performance. Specifically, in this work we motivate the efficiency of NMP subsystems to process memory-intensive applications when 3D-NVM technologies are employed.

关键词： Measurement Nonvolatile memory Memory management Performance analysis Central processing Unit

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Performance-sensitive Data Distribution Method for distributed Stream processing Systems 20

Performance-sensitive Data Distribution Method for Distribut...

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4th High Performance Computing and Cluster Technologies conference, HPCCT 2020 and the 3rd international conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Chen, Yanqiu Zheng, Linjiang Liu, Weining College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9781450375603

In the distributed stream processing, data skew and dynamics can result in imbalanced load distribution at downstream tasks and affect the throughput of the systems. Efficient data distribution method is urgently needed to solve the problem of imbalanced load distribution to improve system throughput. Existing researches cannot better balance the semantic correctness and the throughput of distributed stream processing systems, and it is hard to apply to distributed clusters with different node performance. In this paper, we propose a performance-sensitive data distribution method. Firstly, we propose a load balancing framework that considers the performance of nodes. Then we present a data redistribution algorithm, which aims to balance the execution delay between parallel nodes and follows the principle of "Less Migration key, Less Partition key"(LMLP). The proposed algorithm can reduce the extra cost caused by the data redistribution process and it can be applied to distributed clusters with different performance. Experimental results show that our method is better than the similar methods in terms of load imbalance degree, complete latency, routing table size, etc. And we also use real data to prove that our method can obtain higher throughput and resource utilization. © 2020 ACM.

关键词： Sensitive data

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