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7th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2023

作者： Benabbes, S. Hemam, S.M. ICOSI Laboratory Department of Mathematics and Computer Science Abbes Laghrour University Khenchela Algeria Department of Mathematics and Computer Science Abbes Laghrour University Khenchela Algeria

ISBN: (纸本)9783031606281

The Cloud IoT paradigm, designed to combine Cloud Computing (CC) and the Internet of Things (IoT) benefits, is increasingly utilized for extensive services and addressing users' connectivity, data processing, and storage needs. However, achieving load balancing in Cloud-IoT setups remains challenging due to dynamic and diverse connected objects and Cloud resources. This paper presents a novel approach that employs neural networks for task classification from connected objects, coupled with a task scheduler merging genetic and grasshopper optimization algorithms. This aims to dynamically balance loads in the Cloud-IoT environment. The proposed method is compared with recent alternatives, assessing metrics like makespan, throughput, resource use, energy consumption, and cost. Experimental results compellingly demonstrate the superiority of our approach across these factors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cloud computing

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Unsupervised Action Recognition using Spatiotemporal, Adaptive, and Attention-guided Refining-Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期6卷 1-12页

作者： Yin, Xinpeng Zhang, Cheng Huang, ZiXu He, Zhihai Cao, Wenming State Key Laboratory of Radio Frequency Heterogeneous IntegrationDepartment of Electronics and Information EngineeringShenzhen University Department of Computer Science and EngineeringSouthern University of Science and Technology Department of Electronic and Electrical EngineeringSouthern University of Science and Technology

Previous works on unsupervised skeleton-based action recognition primarily focused on strategies for utilizing features to drive model optimization through methods like contrastive learning and reconstruction. However, designing application-level strategies poses challenges. This paper shifts the focus to the generation-level modelings and introduces the Spatiotemporal Adaptively Attentions-guided Refining Network (AgRNet). AgRNet approaches the reduction of costs and enhancement of efficiency by constructing the Adaptive Activity- Guided Attention (AAGA) and Adaptive Dominant-Guided Attenuation (ADGA) modules. The AAGA leverages the sparsity of the correlation matrix in the attention mechanism to adaptively filter and retain the active components of the sequence during the modeling process. The ADGA embeds the local dominant features of the sequence, obtained through convolutional distillation, into the globally dominant features under the attention mechanism, guided by the defined attenuation factor. Additionally, the Progressive Feature Modeling (PFM) module is introduced to complement the progressive features in motion sequences that were overlooked by AAGA and ADGA. AgRNet shows efficiency on three public datasets, NTU-RGBD 60, NTU-RGBD 120, and UWA3D. IEEE

关键词： Time series analysis

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Optimizing Neighbor Collectives with Topology Objects

Optimizing Neighbor Collectives with Topology Objects

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Collom, Gerald Schafer, Derek Bienz, Amanda Bridges, Patrick Shipman, Galen University of New Mexico Department of Computer Science Albuquerque United States Los Alamos National Laboratory Department of Computer Science Los Alamos United States

ISBN: (纸本)9798350358711

Many HPC applications implement non-cartesian neighbor data exchanges using MPI point-to-point operations rather than utilizing native MPI neighbor collective methods. Each application must therefore implement their own commu-nication optimizations, rather than leveraging any optimizations that could be provided by MPI. While an interface for such optimizations is provided within MPI through neighborhood collectives, applications avoid these methods due to the lack of performance optimizations within them along with large costs associated with graph communicator formation. This paper presents a novel approach for creating local, non-cartesian topol-ogy objects that provides finer control over the aforementioned setup costs. Any additional setup costs, such as initializing per-iteration optimizations, can then be deferred until additional information is available, such as within persistent initialization calls. This paper describes our implementation within an MPI extension library and demonstrates the effectiveness of our approach in simple benchmarks and real-world applications. © 2024 IEEE.

关键词： Benchmarking

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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Optimizing Forecast Accuracy in Cryptocurrency Markets:Evaluating Feature Selection Techniques for Technical Indicators

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computers, Materials & Continua 2025年第5期83卷 3411-3433页

作者： Ahmed El Youssefi Abdelaaziz Hessane Imad Zeroual Yousef Farhaoui IMIA Laboratory Faculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia52003Morocco Department of Computer Science Faculty of Science of MeknesMoulay Ismail University of MeknesMeknes50000Morocco

This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical *** this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection ***,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing *** feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 *** evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading *** results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast ***,indicators related to volume and trend provide incremental benefits in select market ***,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator *** findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model *** research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction *** outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resi

关键词： Cryptocurrency forecasting technical indicator feature selection walk-forward volatility momentum trend

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FindLoc: A Sentiment Analysis-Based Recommender System 3

FindLoc: A Sentiment Analysis-Based Recommender System

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Boughareb, Djalila Boughareb, Rima Guerri, Nesserine Seridi, Hamid University of Guelma Labstic Laboratory Computer Science Department Guelma Algeria University of Badji Mokhtar Labged Laboratory Computer Science Department Annaba Algeria University of Guelma Computer Science Department Guelma Algeria

ISBN: (纸本)9798350321487

A recommendation system (RS) is a tool for filtering information that attempts to recommend interesting items. Globally, RS is based on analyzing users' preferences and their implicit or explicit evaluations of items. Searching for a good doctor, restaurant, school, dentist, cafeteria, etc., in real-time from the user's current location remains challenging in Algeria. In order to facilitate the finding of interesting locations for Algerian users, we propose in this work a collaborative recommendation system called FindLoc that learns to classify the different places according to their distances and previous users' evaluations. First, we gathered users' evaluations (ratings and comments) on various locations in Guelma city. Then, a sentiment analysis method is applied to analyze the dataset and distinguish positive from negative opinions. Then, a recommendation algorithm is proposed to recommend interesting locations based on four parameters: the location (longitude and latitude), previous item ratings, users' comments, and users' ages. Real users evaluated the system, and we obtained very encouraging results in terms of precision, which was 0.91. © 2023 IEEE.

关键词： Sentiment analysis

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OSFS-Vague: Online streaming feature selection algorithm based on vague set

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

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Estimation of the Impact of Dimensionality Reduction of the Feature Space on the Efficiency of Movements Classification Based on Surface Electromyography 5

Estimation of the Impact of Dimensionality Reduction of the ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Kuznetsov, Ivan V. Ponomarchuk, Yulia V. Far Eastern State Transport University Department of Computer Science and Computer Graphics Khabarovsk Russia Far Eastern State Transport University Faculty of Control Automation and Telecommunications Khabarovsk Russia

ISBN: (纸本)9798350363739

One of the actual tasks of movement classification based on electromyography is the choice of the most informative set of features characterizing the movements. Increasing the number of features may decrease the learning performance of classifiers and reduce the efficiency of gesture classification. This paper is devoted to study the possibilities of improving the movement's classification efficiency by using the feature space dimensionality reduction methods. Classifiers based on Support Vector Machines (SVM)., Decision Trees (DT)., Random Forest (RF) and K-nearest neighbors (KNN) were considered., and Principal Component Analysis method (PCA) was used to dimensionality reduction. 3% improvement in classification performance was achieved using PCA and KNN. © 2024 IEEE.

关键词： Support vector machines

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Learning to Combine Answer Boundary Detection and Answer Re-ranking for Phrase-Indexed Question Answering

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Chinese Journal of Electronics 2022年第5期31卷 938-948页

作者： WEN Liang SHI Haibo ZHANG Xiaodong SUN Xin WEI Xiaochi WANG Junfeng CHENG Zhicong YIN Dawei WANG Xiaolin LUO Yingwei WANG Houfeng Department of Computer Science and Technology Peking University Peng Cheng Laboratory Baidu Inc.

Phrase-indexed question answering(PIQA) seeks to improve the inference speed of question answering(QA) models by enforcing complete independence of the document encoder from the question encoder,and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy. In this paper, we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models. We propose a novel framework —Ans DR,which consists of an answer boundary detector(Ans D)and an answer candidate ranker(Ans R). More specifically, Ans D is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries; and Ans R is a lightweight ranking model to finely rerank the potential candidates without losing the efficiency. We perform the extensive experiments on public datasets. The experimental results show that the proposed method achieves the state of the art on the PIQA task.

关键词： text analysis unconstrained QA models Answer Re-ranking rough answer boundaries phrase-indexed question answering indexing lightweight ranking model information retrieval combine answer boundary detection question encoder natural language processing answer boundary detector QA model learning (artificial intelligence) encoding question answering (information retrieval) question answering models constrained model answer candidate ranker

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Empowering Biomedical Named Entity Recognition Through Multi-Tagger Collaboration 27

Empowering Biomedical Named Entity Recognition Through Multi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Jin Xie, Jian Zhu, Tinghui Guo, Qian Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781643685489

Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in BioNER. However, existing paradigms in the region-based methods suffer from inherent limitations, including the generation of negative samples, and the ignorance of token dependencies. To overcome these limitations, we propose a new paradigm, implemented as Token Cascade Tagger (TCT), which combines span identification and category classification. The TCT utilizes category information to enhance the correlation between the heads and tails of entities, effectively reducing the generation of negative samples. Additionally, we introduce a Token Dependency Tagger (TDT) that captures token dependencies within entity spans by identifying the longest span in a sentence. The TDT filters out incorrect spans and further improves the accuracy of span detection obtained from the TCT. Furthermore, we employ a multi-task learning framework to optimize both the TCT and TDT, leading to superior performance in BioNER. Extensive experiments on publicly available biomedical datasets demonstrate our method outperforms the previous state-of-the-art methods, achieving 92.44%, 92.54%, and 81.26% on NCBI-Disease, BC5CDR, and GENIA, respectively, in terms of F1 score. © 2024 The Authors.

关键词： Multi-task learning

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