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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

Journal of Computer Science & technology 2023年第4期38卷 807-820页

作者：李若时彭平邵志远金海郑然 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074 China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

关键词： RISC-V vector extension single instruction multiple data(SIMD) computer vision OpenCV

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of Computer Science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Constructing prediction intervals to explore uncertainty based on deep neural networks

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10441-10456页

作者： Yang, Jingling Chen, Liren Chen, Huayou Liu, Jinpei Han, Bing School of Big Data and Statistics Anhui University Hefei China School of Marine Science and Technology Tianjin University Tianjin China School of Business Anhui University Anhui Hefei China Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States School of Mathematical Science Anhui University Hefei China

The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of 'high coverage and narrow width'. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of 'high coverage, narrow width, and small bias' in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. © 2024 - IOS Press. All rights reserved.

关键词： Forecasting

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PSO-based attribute reduction of rough set and its application

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Recent Patents on Computer Science 2017年第4期10卷 275-282页

作者： Bai, Jianchuan Xia, Kewen Zu, Baokai Wu, Panpan School of Electronics and Information Engineering Hebei University of Technology Tianjin China Key Lab of Big Data Computation of Hebei Province 5340 Xiping Road Tianjin300401 China

Background: The rough set theory is a powerful tool to deal with imprecise and incomplete information in the field of data mining. As a core content for rough set theory, attribute reduction aims at removing redundant data and drawing the minimum attributes while maintaining indiscernibility relation. However, traditional rough set theory is available for classical example which has disadvantages of time-consumption, large storage and low recognition accuracy. In this paper, we focus on an attribute reduction based on particle swarm optimization (PSO) to overcome the drawbacks of traditional rough set theory. Firstly, this paper reviews some important concepts of rough set and particle swarm optimization. Then, we establish the model of attribute reduction based on particle swarm optimization. Finally, the proposed method is applied to actual oil logging data, and the reduction results are recognized by Relevance Vector Machine (RVM) and Second Order Cone Programming-Relevance Vector Machine(SOCP-RVM). The experimental results show that the proposed method is efficient and has high recognition accuracy. Methods: Recent publications and patent databases are reviewed to find extraordinary and innovative attribute reduction algorithms for reducing time consumption and accuracy. Results: Two methods which are RVM and SOCP-RVM are applied to recognize the attribute reduction results. The results show that well in 993-997m, 1045-1152.5m and 1236-1255m depth are main oil-layers, the rest are dry-layers (Fig. 2 shows that oil-layers are 995-997m, 1045-1152.5m and 1241.5-1255m;Fig. (3) shows that oil-layers are 993-996m, 1055.5-1143m and 1236-1251m). The recognition results are consistent with the actual oil test results. Conclusion: A novel PSO-Based Attribute Reduction of Rough Set is proposed, and apply it to oil well to deal with actual and complex data. Experimental results show that the proposed algorithm can get effective reduction sets, and the recognition results wi

关键词： Second-order cone programming

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Prediction of Chemical Oxygen Demand in Sewage Based on Support Vector Machine and Neural Network 1

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Zhou, Jian Huang, Qian-Jing Wang, Xiao-Feng Zou, Le Department of Environmental Engineering Hefei University HefeiAnhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China

ISBN: (数字)9783030267636

ISBN: (纸本)9783030267629

Aiming at the problem that the detection accuracy of effluent COD (chemical oxygen demand) in sewage treatment needs to be further improved, a combined model based on support vector machine and neural network is proposed to predict effluent COD. It can reduce the influence of local optimum on the global scope so as to improve the accuracy of prediction. Firstly, the sample data are divided into two categories by support vector machine. Then the BP neural network model and the Echo State Network (ESN) model are established on two sub-samples respectively. Compared with single neural network model, the mean absolute error and root mean square error of combined model are both reduced. Besides, the proposed model has better comprehensive prediction performance and can meet the actual demand of effluent COD prediction in sewage treatment. © 2019, Springer Nature Switzerland AG.

关键词： Support vector machines

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Searching for maximum clique by DNA origami 14

Searching for maximum clique by DNA origami

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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Cui, Jianzhong Yin, Zhixiang Yang, Jing Geng, Xianya School of Electronic and Information Engineering AnHui University of Science and Technology Huainan AnHui232001 China School of Mathematics and Big Data AnHui University of Science and Technology Huainan AnHui232001 China Department of Computer Huainan Union University Huainan AnHui232001 China

ISBN: (纸本)9781538680971

Scaffolded DNA origami, a long single-stranded DNA is folded into pre-designed pattern with the guidance of short staple strands, enables the fabrication of sophisticated nanodevice. Inspired by this nanotechnology, we presented searching the maximum clique in an undirected graph with six vertices and eleven edges. We initially folded a unique type of scaffolds into a pool of strands with n hairpin-like structures, or hairpin for convenience, which corresponded to the total ensemble of six vertices cliques. Followed by, re-folded scaffolds by staples to perform a series of selection processes according to edges in the complementary graph. Scaffolds encoding correct cliques were selected via gel electrophoresis. When this selection processes terminated, all cliques were found. Scaffold with the maximum number of in-clique hairpins was selected by unfolding simultaneously all in-clique hairpins, followed by gel electrophoresis. Then a series of unfolding out-of-clique hairpins one by one via unfolding strands, each vertex in the given graph was decided whether it is in the maximum clique or not. When deciding processes terminated, the maximum clique in the given graph was eventually found. The proposed method employed relatively reliable biological operations. Gel electrophoresis guarantees the accuracy of each step of the proposed method. Therefore, the method proposed is reliable, error tolerant, and feasible. Our work demonstrates, with simple designs for scaffold, staples, and unfolding strands, NP-complete searching problem can be readily and reliably solved by using DNA origami. The work extends application fields of DNA origami a step further. © 2018 IEEE.

关键词： DNA

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Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

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Plant Phenomics 2024年第2期6卷 349-363页

作者： Yandong Wu Weiliang Wen Shenghao Gu Guanmin Huang Chuanyu Wang Xianju Lu Pengliang Xiao Xinyu Guo Linsheng Huang National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Nongxin Science&Technology(Beijing)Co. LtdBeijing 100097China

The 3-dimensional(3D)modeling of crop canopies is fundamental for studying functional-structural plant *** studies often fail to capture the structural characteristics of crop canopies,such as organ overlapping and resource *** address this issue,we propose a 3D maize modeling method based on computational *** initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture.

关键词： intelligence modeling based canopies computational dimensional maize three

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Object Detection with YOLO Version 3 for big data 1

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5th International Conference on Information Systems and Management Science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Karna, Vishma Kumar Big Data Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeth Vishwavidyalaya Indore India Big Data Lab Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Object detection is the most vital task for the application of computer vision. The You only Look Once (YOLO) version3 is the most promising technique used for deep learning-based object detection. It is the k-means cluster method, which estimates initial weight and height of the predicting bounding boxes that are sensitive to the initial cluster centers. This paper focuses on study of approaches for efficient object detection techniques in the field of computer vision. The improvement in the accuracy had been studied based on the deep learning techniques. It aims to develop an online tool for a better study about the object detection techniques as a platform to review the object detection in both online and offline images. A popular state of art object detection algorithm called YOLOv3 which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Information encryption scheme based on chaotic map and hybridization chain reaction 2

Information encryption scheme based on chaotic map and hybri...

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2021 2nd International Conference on Computer Science and Communication technology, ICCSCT 2021

作者： Tang, Zhen Yin, Zhixiang Yang, Jing Xu, Rujie Wang, Xiyuan Pang, Xue Liu, Congcong School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan232001 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China

Chaotic maps are characterized by randomness, unpredictability and sensitivity to initial values, and are suitable for information encryption. The natural characteristics of DNA molecules have advantages over conventional methods in the process of mass information storage and processing. In this paper, we propose an information encryption scheme based on Logistic map and hybridization chain reaction. Firstly, the plaintext is XOR-operated by chaotic sequences generated by Logistic map, and it is scrambled by hybridization chain reaction, and finally the plaintext is transformed into encrypted text. The analysis of an example shows that the information encryption scheme has good encryption effect. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Cryptography

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Multi-fuzzy-constrained graph pattern matching with big graph data

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Intelligent data Analysis 2020年第4期24卷 941-958页

作者： Liu, Guliu Li, Lei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Anhui 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China

Graph pattern matching has been widespread used for protein structure analysis, expert finding and social group selection, ect. Recently, the study of graph pattern matching using the abundant attribute information of vertices and edges as constraint conditions has attracted the attention of scholars, and multi-constrained simulation has been proposed to address the problem in contextual social networks. Actually, multi-constrained graph pattern matching is an NP-complete problem and the fuzziness of constraint variables may exist in many applications. In this paper, we introduce a multi-fuzzy-constrained graph pattern matching problem in big graph data, and propose an efficient first-k algorithm Fuzzy-ETOF-K for solving it. Specifically, exploration-based method based on edge topology is adopted to improve the efficiency of edge connection, and breadth-first bounded search is used for edge matching instead of shortest path query between two nodes to improve the efficiency of edge matching. The results of our experiments conducted on three datasets of real social networks illustrate that our proposed algorithm Fuzzy-ETOF-K significantly outperforms existing approaches in efficiency and the introduction of fuzzy constraints makes our proposed algorithm more efficient and effective. © 2020 - IOS Press and the authors. All rights reserved.

关键词： big graph data Graph pattern matching multi-fuzzy-constrained

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