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11th international Joint conference on Rough Sets, IJCRS 2025

作者： Li, Hangying Xu, Changlin Shen, Juhong School of Mathematics and Information Science North Minzu University Ningxia Yinchuan750021 China Key Laboratory of Intelligent Information and Big Data Processing Ningxia Yinchuan750021 China

ISBN: (纸本)9783031927461

To improve the recommendation accuracy of collaborative filtering algorithms, this paper proposes a similarity measurement method based on the concept of normal cloud. First, starting from the characteristic curve of the normal cloud, combining the expected entropy curve and the outer envelope curve of the normal cloud, the paper uses the Bhattacharyya distance to characterize the similarity of probability distributions, and proposes a Bhattacharyya distance-based normal cloud similarity measurement method. then, numerical simulation experiments are conducted to compare and analyze the performance of the proposed method with existing methods. the experimental results show that the proposed method has a better similarity differentiation capability. Finally, the method is applied to a collaborative filter recommendation system, and experiments are carried out on the MovieLens 100k movie review dataset. the results demonstrate that the proposed method is effective and feasible in collaborative filtering applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Normal distribution

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DTSpMV: An Adaptive SpMV Framework for Graph Analysis on GPUs

DTSpMV: An Adaptive SpMV Framework for Graph Analysis on GPU...

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

作者： Guoqing Xiao Tao Zhou Yuedan Chen Yikun Hu Kenli Li College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China National Supercomputing Center in Changsha Changsha Hunan China Shenzhen Research Institute Hunan University Shenzhen China

Sparse Matrix and Vector multiplication (SpMV) is one of the core algorithms in various large-scale scientific computing and real-world applications. With the rapid development of AI and big data, the input vector in SpMV becomes more and more sparse in many application fields. Especially in some graph analysis calculations, the sparsity of the input vector will change with the running of the program. At this time, the optimal SpMV kernel may be different, and a single SpMV kernel can no longer meet the acceleration requirements. In this paper, we propose a decision tree-based adaptive SpMV framework, named DTSpMV, that can automatically select appropriate SpMV kernels based on different input data in iterations of graph computation. Based on the analysis of computational patterns and serial and parallel algorithms, we encapsulate six SpMV cores within the proposed framework. We explore machine learning-based kernel selectors in terms of both accuracy and runtime overhead. Experimental results on NVIDIA Tesla P100 GPU show that our adaptive framework achieves the arithmetic average performance improvement of 60% compared to the state-of-the-art SpMV kernel benchmark, and the average ratio of the runtime prediction overhead of the framework to the total computing overhead is 1.9%.

关键词： Runtime Machine learning algorithms Smart cities Scientific computing Graphics processing units Benchmark testing Decision trees

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TianheQueries: Ultra-Fast and Scalable Graph Queries on Tianhe Supercomputer

TianheQueries: Ultra-Fast and Scalable Graph Queries on Tian...

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

作者： Xinbiao Gan Guang Wu Cong Liu Jiaqi Si Xuguang Chen Bo Yang Tiejun Li National University of Defense technology Information center of Logistic Support Department CMC

As graph-based services have been widespreadly integrated into daily lives, extremely-fast and scalable graph queries are receiving increasing attention. Graph querying al-gorithms including BFS(Breadth First Search) and SSSP(Single Source Shortest Path) are promising engines for graph-based applications. Unfortunately, current graph queries often deliver low efficiency owing to sequential execution and poor scalability. this paper presents TianheQueries: an ultra-fast and scalable graph querying engine on Tianhe supercomputer. TianheQueries proposes an efficient hardware prefetching and caching mechanism that can pipeline vertices gather, traversal and vertices scatter so as to accelerate graph queries with the utilization of pipeline parallelism. and (ii) presents a topology-aware communication aggregation for inter-domain messages more than from the same source node by leveraging the network topology information to perform more aggressively messages aggregation. We use both benchmark and real-world graphs to demonstrate the effectiveness of TianheQueries. Specially, TianheQueries helps Tianhe Exa-node win the top spot of the latest Green-Graph500 lists both big data and small data categories. Further-more, TianheQueries-based SSSP tests on Tianhe supercomputer is superior to the fastest SSSP systems in the latest Graph500 lists.

关键词： Network topology Prefetching Pipelines parallel processing Big Data Benchmark testing Supercomputers

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Efficiently Executing Sparse Matrix-Matrix Multiplication on General Purpose Digital Single Processor

Efficiently Executing Sparse Matrix-Matrix Multiplication on...

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

作者： Haibo Xu Xiaoxiong Zhu Qinglin Wang Jie Liu College of Computer Science and Technology National University of Defense Technology ChangSha China

Sparse Matrix-Matrix Multiplication (SpMM) is a kernel for large-scale scientific computing and deep learning. Previous SpMM optimization research focused on CPU or GPU architecture, with little attention paid to the General Purpose Digital Single Processor (GPDSP). Earlier work shows that reducing irregular memory access and increasing data reuse can improve the performance of SpMM. In this paper, we propose an effective SpMM vectorization method for GPDSP Matrix2000b. According to the memory hierarchy of Matrix2000b, we designed an adaptive sparse matrix tilling scheme and combined it with row-major compute in order to improve data reuse. then we implemented a multi-level parallel algorithm that includes thread-level parallelism, data-level parallelism and instruction-level parallelism based on the multi-level computing components of Matrix2000b to vectorization and eliminate irregular memory access. We also designed a three-level double buffer workflow based on the memory hierarchy to overlap the computation and memory access. When memory theoretical bandwidth is 42.6 G B/s, experimental evaluation reveals that the average performance can reach 34.81 GFlops/s and the maximum performance can reach 65.47 GFlops/s.

关键词： Deep learning Scientific computing Instruction sets Graphics processing units Computer architecture Bandwidth Sparse matrices

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Exploratory Analysis of the News in Easy Language (NiEL) Corpus to Identify Characteristic Patterns for Natural Language processing 25th

Exploratory Analysis of the News in Easy Language (NiEL) Cor...

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25th international conference on Human-Computer Interaction, HCII 2023

作者： Hösel, Claudia Baumgart, Matthias Platte, Benny Roschke, Christian Ritter, Marc Hochschule Mittweida - University of Applied Sciences Mittweida09648 Germany

ISBN: (纸本)9783031360039

While comprehensive corpora are available for resource-rich languages such as English in different domains, which can be made usable for natural language processing (NLP) applications, this is not the case for resource-poor languages. parallel or monolingual corpora must first be created and adequately processed in order to make them usable for later NLP applications. In the past, selected variants of a standard language were increasingly identified as resource-poor languages and corresponding resources were created. the German Easy Language, as a highly simplified variant of the Standard German language, can be defined as a resource-poor language, since here, too, hardly any NLP-suitable corpora are available. In this paper, we present the News in Easy Language (NiEL) corpus, a monolingual text resource for German Easy Language. By means of exploratory analysis using selected NLP tools, characteristic patterns for Easy Language can be derived at both word and sentence level. the identified patterns of Easy Language can be compared in perspective with patterns from standard language texts. Our results show that multiple tools from the NLP domain are suitable for German Easy Language as well as for German Standard Language. Features like word variance, sentence depth but also average word and sentence length can be distinguished. the features extracted in this way are suitable for the development of models, whereby initial implications for the natural language processing of Easy Language can be derived. the results form an important basis for further research in the domain of Easy Language. As a low-resource language that has been primarily analyzed intellectually, another added value of our work also lies in the implications for natural processing of plain language derived from the exploratory analysis of the corpus. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pattern recognition

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NH-CIL: A Nested Hierarchy Algorithm for Class Incremental Learning 24

NH-CIL: A Nested Hierarchy Algorithm for Class Incremental L...

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24th IEEE international conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Liu, Mingyu Wang, Yijie Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781728165974

Class incremental learning is widely applied in the classification scenarios as the number of classes is usually dynamically changing. However, the existing algorithms increase computational cost to implement class incremental learning in order to increase classification quality. In this paper, we propose a nested hierarchy algorithm based on OCSVM for class incremental learning, called NH-CIL. We reuse support vectors to eliminate redundant instances and catch the key ones to replace the whole model because of the generalization ability of OCSVM. When a new class arrives, NH-CIL adopts OCSVM on the new class and the old classes respectively to get the corresponding sketching supports vectors. then NH-CIL reuses these two kinds of sketching support vectors to build a binary sub-classifier. these two steps are repeatedly nested to form a hierarchy classification model in a bottom-up manner while the number of classes increases. On the contrary, the testing phase is in a top-down manner. NH-CIL can be used as a flexible approach in the classification scenarios during the collaborative information processing. We conduct the experiments on 8 real-world benchmark datasets to compare NH-CIL with some other class incremental learning algorithms, e.g. SD-CIL, HS-CIL and OP-CIL. the experiment results show that NH-CIL averagely achieves more than 5.1%, 8.6% and 11.6% accuracy improvement and 39.8%, 24.7% and 12.6% efficiency improvement over SD-CIL, HS-CIL and OP-CIL, respectively.

关键词： Machine Learning Class Incremental Learning Multi-classification Collaborative Information processing

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Cloud-Edge Microservice Architecture for DNN-based Distributed Multimedia Event processing 8th

Cloud-Edge Microservice Architecture for DNN-based Distribut...

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on Engineering Service-Oriented Applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD Symposium, ESOCC 2020 EU Projects Track which took place at 8th European conference on Service-Oriented and Cloud Computing, ESOCC 2020

作者： Pontes, Felipe Arruda Curry, Edward Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland

ISBN: (纸本)9783030719050

the rise of Big Data, Internet of Multimedia things (IoMT), and Deep Neural Network (DNN) enabled the growth of DNN-based Computer Vision solutions to Multimedia Event processing (MEP) applications. When these are applied to a real-world scenario we notice the importance of having a system with a satisfactory speed that can fit in the limited resources of most IoMT devices. However, most solutions for distributed MEP are dependent on a Cloud architecture, which makes these applications migration to the Edge more challenging. As a response to this, we present a microservice architecture for DNN-based distributed MEP over heterogeneous Cloud-Edge environments. We describe our solution that allows for an easier deployment both on the Edge and on the Cloud. We show that choosing the proper tools for an Edge-Friendly solution can lead to 100 times less resource utilisation. Our preliminary investigation shows promising results, with a reduction in energy consumption by 8% with a minor drawback of 15% in throughput in the Edge and a negligible increase in energy consumption on the Cloud. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Efficient Erasure-Coded Data Update and Recovery Based on Machine Learning and I/O Mitigation

Efficient Erasure-Coded Data Update and Recovery Based on Ma...

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

作者： Xiaosong Su Bing Wei Yalan Wu Jigang Wu School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of cryptography Hainan University Hainan China

Erasure coding is widely used in storage systems, because it can offer higher reliability at lower degree of data redundancy than data replication. However, erasure-coded systems have to update the parity chunk to maintain consistency, when data is updated by applications. this leads to a large amount of I/O overhead, thereby increasing the update latency. Moreover, after updating, the storage systems have to read the latest data and parity deltas randomly scattered in the log, resulting in long recovery time. this paper proposes an efficient erasure-coded data update and recovery approach named MPLR for storage systems. Regarding updates, MPLR initially classifies files into non-read-only and read-only using machine learning prediction, then adopts the suitable schemes to perform updates for a given class of files. For non-read-only files, MPLR performs in-place data updates on data chunk, performs log-based parity updates on parity chunks, and reserves disk space next to the parity chunk. this aims to balance the costs of updates and recovery, and mitigate disk seeks introduced by updates. For read-only files, MPLR also takes hybrid writes for data and parity updates, but it does not reserve the extra disk space for parity chunks. Regarding recovery, parity delta reads are performed on the reserved disk space for non-read-only files, and no parity delta read is involved for read-only files. Based on the shareability of parity delta, MPLR minimizes the amount of the parity deltas that are read in parallel during recovery to reduce the execution time. Extensive experimental results on real-world traces show that, MPLR can successfully improve the update and recovery performance.

关键词： Costs Smart cities File systems Redundancy Prototypes Machine learning parallel processing

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parallel Programming of Churn Prediction Using Gaussian Naïve Bayes 8

Parallel Programming of Churn Prediction Using Gaussian Naï...

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8th international conference on Information and Communication Technology, ICoICT 2020

作者： Barus, Dandi T. Elfarizy, R. Masri, F. Gunawan, P.H. Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781728161426

this paper presents churn predictions with the Gaussian Naïve Bayes method. Churn prediction is a forecasting method to predict customer decisions in a company's service or product (churn). With high public enthusiasm and an increasing number of customers in the Big Data era, a fast computing process is needed to predict churn as quickly as possible. In this paper, computing is accelerated by the OpenMP platform parallel algorithm. Churn prediction experiments are performed with different amounts of test data, ranging from 100, 300, 500, 700, to 900 data. the results obtained show that implementing OpenMP in predicting churn is faster than serial processing. the obtained speedup and efficiency reached more than 1.49 and 37%, even for test data of 300 and 500, based on the tests, the speedup and efficiency reached 1.99 and 50%. © 2020 IEEE.

关键词： Efficiency

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applied CNN for Automatic Diabetic Retinopathy Assessment Using Fundus Images 6th

Applied CNN for Automatic Diabetic Retinopathy Assessment Us...

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6th international conference on Business Intelligence, CBI 2021

作者： El Hossi, Amine Skouta, Ayoub Elmoufidi, Abdelali Nachaoui, Mourad Mathematics Applications Laboratory Faculty of Sciences and Technics Sultan Moulay Slimane University Beni Mellal Morocco Computer Science Modeling Systems and Decision Support Laboratory Faculty of Science Aïn Chock Hassan II University Casablanca Morocco Information Processing and Decision Support Laboratory Faculty of Sciences and Technics Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783030765071

In the area of ophthalmology, diabetic retinopathy affects an increasing number of people. Early detection avoids severe diabetic proliferative retinopathy complications. In this paper, we propose a method for binary classification of retinal images using convolutional neural networks architecture. this method is formed to recognize and classify a retinal image as normal or abnormal retina. the paper setup is, first of all a preprocessing step is applied, next by data augmentation, and then a CNN formed, and applied. To train, validate and test the proposed model, we have used a public dataset "Resized version of the Diabetic Retinopathy Kaggle competition dataset" from Kaggle web site. Proposed model has trained using 4000 images of the normal retina and 4000 images of abnormal diabetic retina, and 500 images of the normal retina and 500 images of abnormal diabetic retina for testing. the accuracy Achieves 89% in 100 images of single prediction words. © 2021, Springer Nature Switzerland AG.

关键词： Ophthalmology

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