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A novel weighted approach for time series forecasting based on visibility graph

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arXiv 2021年

作者： Zhan, Tianxiang Xiao, Fuyuan School of Computer and Information Science Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

LSTM-Gate CNN network for Aspect Sentiment Analysis

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LSTM-Gate CNN network for Aspect Sentiment Analysis

Information science, Computer Technology and Transportation (ISCTT), International Conference on

作者： Shuhua Cao Pengxiang Gao School of Data Science and Software Engineering Qingdao University Qingdao China

ISBN: (数字)9781728185750

ISBN: (纸本)9781728185767

In aspect sentiment analysis tasks, when one or more aspect words appear in the text, different aspects correspond to different sentiment tendencies. Therefore, it is necessary to accurately identify the corresponding emotional tendencies of different aspects. Concerning the problem that the traditional neural network model cannot accurately construct the text aspect feature and sentiment feature information, an aspect sentiment analysis model based on the LSTM-GateCNN network is proposed. Textual contextual semantic information is modeled through the LSTM network fused with attention mechanism to obtain deep semantic information, combined with the gated convolutional neural network to simultaneously model the textual and emotional information, and finally, text sentiment classification is performed at the softmax function layer. Experiments on the AI Challenger Chinese dataset have verified the effectiveness of the model, which has a further improvement in accuracy compared with the previous method.

关键词： Sentiment analysis Analytical models Semantics Logic gates Feature extraction Convolutional neural networks Task analysis

Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

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Attention-Weighted Federated Deep Reinforcement Learning for...

作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

Conditional Generative Modeling for Amorphous Multi-Element Materials

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

Quantized Federated Learning under Transmission Delay and Outage Constraints

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arXiv 2021年

作者： Wang, Yanmeng Xu, Yanqing Shi, Qingjiang Chang, Tsung-Hui The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The Shenzhen Research Institute of Big Data Shenzhen518172 China The School of Software Engineering Tongji University Shanghai201804 China

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance of FedTOE for deep learning-based classification tasks with transmission latency constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： Outages

A call to action to address escalating global threats to academic research

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The Innovation 2025年第4期6卷 17-19页

作者： Gaelle Piret Fun Man Fung Josie Fullerton Giuseppe Fico Dmitriy Ponkratov Wenxin Chen Daniela Latorre Kirsty Y.Wan Nima Aghaeepour Jules Welgryn Adeel Razi Patricia Silveyra Ahmet Altun Renata Z.Jurkowska Alice C.Hughes Joy Wolfram INSERM U1205 UGA38400 Saint Martin d’HèresFrance School of Chemistry University College DublinD04 C1P1 BelfieldIreland UCD Geary Institute for Public Policy University College DublinD04 N9Y1 DublinIreland School of Cardiovascular&Metabolic Health University of GlasgowG128TA GlasgowUK European Alliance for Medical and Biological Engineering and Science 3001 LeuvenBelgium Life Supporting Technologies Universidad Politécnica de Madrid28040 MadridSpain Siemens Digital Industries Software B908BG SolihullUK Department of Philosophy of Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Microbiology ETH Zurich8093 ZurichSwitzerland Living Systems Institute&Department of Mathematics and Statistics University of ExeterEX44QD ExeterUK Department of Anesthesiology Painand Perioperative MedicineDepartment of PediatricsDepartment of Biomedical Data ScienceStanford University School of MedicineStanfordCA 94305USA UniversitéParis Nanterre 92000 NanterreFrance Turner Institute for Brain and Mental Health School of Psychological Sciencesand Monash Biomedical ImagingMonash UniversityClaytonVIC 3800Australia Wellcome Centre for Human Neuroimaging University College LondonWC1N 3AR LondonUK CIFAR Azrieli Global Scholars Program TorontoON M5G 1M1Canada Department of Environmental and Occupational Health Indiana University School of Public Health BloomingtonBloomingtonIN 47408USA Department of Pharmacology Sivas Cumhuriyet UniversitySivas 58140Turkey Division of Biomedicine School of BiosciencesCardiff UniversityCF103AX CardiffUK School of Biological Sciences University of Hong KongHong Kong SAR 999077China Australian Institute for Bioengineering and Nanotechnology The University of QueenslandBrisbaneQLD 4072Australia School of Chemical Engineering The University of QueenslandBrisbaneQLD 4072Australia

This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic research to end-user applications,a disconnect between academics and policymakers,emerging barriers to international collaboration,and a reliance on conventional metrics to evaluate academic *** article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers,academic institutions,and society.

关键词： academic research calls action global threats public mistrust sciencechallenges translating academic research disconnect academics policymakers call action translation research

Water Quality Prediction Based on Multi-Indicator Relation Learning and Knowledge Transfer

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Water Quality Prediction Based on Multi-Indicator Relation L...

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Huan Wu Feng Jiang Zehua Zhao Liang Tao Lin Peng Wenxun Han Min Gao T.Y.Lin International Engineering Consulting (China) Co. Ltd. Chongqing China Tongji University Shanghai China College of Environmental Science and Engineering Chongqing Business Vocational College Chongqing China Faculty of Engineering the University of Sydney Sydney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

Water quality prediction is widely used in many aspects of water pollution. In recent years, researchers have begun using deep learning models for prediction, significantly improving prediction performance. However, these methods usually aim single-prediction task. They are not good at linking multiple prediction indicators of water quality, and are difficult to train at water quality monitoring stations with small samples. For this reason, we explore a model based on multi-indicators relationship learning and knowledge transfer for the prediction in a small sample scenario. Firstly, we propose a water quality multi-indicator gated implicit variable parameter sharing model MGH to extract the common characteristics, individual characteristics, and correlation of water quality multi-indicators at multi-sample sites. Then, we combine two migration methods to carry out the water quality prediction model in the small sample area to migrate the trained model parameters or sample distribution knowledge to the small sample area. The experimental results show that our multi-indicator relationship learning and knowledge transfer model can achieve better prediction accuracy. We also verified the role of the model in ensuring the prediction effect of small sample monitor stations. The proposed model provides data support for water quality managers to understand the changes in water quality indicators in advance and make corresponding water quality management decisions.

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An anomaly detection method to improve the intelligent level of smart articles based on multiple group correlation probability models

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International Journal of Crowd science 2019年第3期3卷 333-347页

作者： Lu, Xudong Wang, Shipeng Kang, Fengjian Liu, Shijun Li, Hui Xu, Xiangzhen Cui, Lizhen School of Software Shandong University Jinan China School of Software Research Center of Software and Data Engineering Shandong University Jinan China School of Computer Science and Technology Shandong University Jinan China Shandong University Jinan China

Purpose: The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge. Design/methodology/approach: In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized. Findings: The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences. Originality/value: The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network. © 2019, Xudong Lu, Shipeng Wang, Fengjian Kang, Shijun Liu, Hui Li, Xiangzhen Xu and Lizhen Cui.

关键词： Anomaly detection

Efficient Shapley Value-based Non-Uniform Pruning of Large Language Models

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arXiv 2025年

作者： Sun, Chuan Yu, Han Cui, Lizhen Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore School of Software Shandong University China Department of Electrical and Computer Engineering The University of British Columbia Canada

Pruning large language models (LLMs) is a promising solution for reducing model sizes and computational complexity while preserving performance. Traditional layer-wise pruning methods often adopt a uniform sparsity approach across all layers, which leads to suboptimal performance due to the varying significance of individual transformer layers within the model not being accounted for. To this end, we propose the Shapley Value-based Non-Uniform Pruning (SV-NUP) method for LLMs. This approach quantifies the contribution of each transformer layer to the overall model performance, enabling the assignment of tailored pruning budgets to different layers to retain critical parameters. To further improve efficiency, we design the Sliding Window-based Shapley Value approximation method. It substantially reduces computational overhead compared to exact SV calculation methods. Extensive experiments on various LLMs including LLaMA-v1, LLaMA-v2 and OPT demonstrate the effectiveness of the proposed approach. The results reveal that non-uniform pruning significantly enhances the performance of pruned models. Notably, SV-NUP achieves a reduction in perplexity (PPL) of 18.01% and 19.55% on LLaMA-7B and LLaMA-13B, respectively, compared to SparseGPT at 70% sparsity. © 2025, CC BY.

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