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4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Lin, Zhenxian Feng, Feirong Yang, Xiaobao Ding, Chen School of Science Xi'an University of Posts and Telecommunications China School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Northwestern Polytechnical University China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Image captioning is usually based on the mainstream encoder-decoder framework, the accuracy of the encoders and the generation capability of the decoders directly affect the quality of image captions. But no matter how the encoders and the decoders are combined, the generated captions will only imitate the ground-truth captions, and its diversity is suppressed. To generate more accurate and diverse captions while exploring the impact of different encoder-decoder frameworks for the quality of the generated captions, we conducted an exploration of an efficient image captioning method based on GAN. Specifically, we use ResNet152, EfficientNet-B0, and EfficientNet-B2 as the encoders to extract image features, GRU or Transformer as the decoders to generate captions, respectively. Based on MS COCO2014 dataset, a large number of experiment results show that the EfficientNet-B2+GRU framework has best performance, training time is reduced, BLUE-4 score increases by 0.6, parameters reduce by 0.66 million, which can be used as an efficient encoder-decoder framework for generating diverse captions. © 2021 ACM.

关键词： Generative adversarial networks

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Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems

arXiv

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arXiv 2023年

作者： Zhu, W. Tuan, H.D. Dutkiewicz, E. Fang, Y. Poor, H.V. Hanzo, L. School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Electrical and Data Engineering University of Technology Sydney BroadwayNSW2007 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Temporal Threat Recognition in Supply Chains: Integrating Hidden Markov Models for Proactive Security with AI-Driven Automated Threat Hunting

Temporal Threat Recognition in Supply Chains: Integrating Hi...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Ketan Rathor Dhananjayamurthy BV Abdul Jaleel D Subharun Pal Bhavani P Neerav Nishant Gyan Sys Inc. Mumbai India Department of Mathematics NITTE Meenakshi Institute of Technology Bangalore India Department of Computer Science &Technology Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering Indian Institute of Technology Jammu Jammu & Kashmir (UT) India Department of Artificial Intelligence and Data Science K Ramakrishnan College of Engineering Trichy Tamilnadu India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

In order to tackle the pressing problem of “Temporal Threat Recognition in Supply Chains,” this study presents a novel integrated model that combines RNNs with Hidden Markov Models (HMMs). Proactive security measures and operational integrity rely on the prompt and correct identification of temporal risks within the dynamic environment of supply chains. This is the crux of the issue statement. By combining RNN for fine-grained identification with HMM for high-level threat classification, our proposed system provides a holistic approach that can detect both broad patterns and subtle details. Our integrated model outperforms previous work in terms of performance measures, as seen by its 0.92 precision, 0.88 recall, 0.90 F1-Score, and 0.91 total accuracy. The model's usefulness in improving temporal threat identification is confirmed by its outperformance in precision, recall, accuracy, and AUC-ROC, as demonstrated in a comparative comparison with state-of-the-art approaches. The computational efficiency of the model makes it a viable option for realistic supply chain applications in the real world. In addition to bringing a fresh perspective to the field of temporal threat modelling, our study has practical implications for supply chain security and offers researchers and practitioners in the industry with a powerful and effective tool.

关键词： Industries Threat modeling Accuracy Recurrent neural networks Computational modeling Supply chains Hidden Markov models

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RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians

arXiv

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arXiv 2025年

作者： Peng, Sen Xie, Weixing Wang, Zilong Guo, Xiaohu Chen, Zhonggui Yang, Baorong Dong, Xiao College of Computer Engineering Jimei University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Computer Science The University of Texas at Dallas Richardson United States School of Informatics Xiamen University Xiamen China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China

We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two main modules: Gaussian initialization module and Gaussian rectification module. We embed Gaussians into triangular faces and control their motion through the mesh, which ensures low-frequency motion and surface deformation of the avatar. Due to the limitations of LBS formula, the human skeleton is hard to control complex non-rigid transformations. We then design a pose-related Gaussian rectification module to learn fine-detailed non-rigid deformations, further improving the realism and expressiveness of the avatar. We conduct extensive experiments on public datasets, RMAvatar shows state-of-the-art performance on both rendering quality and quantitative evaluations. Please see our project page at https://***. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Transition between distribution patterns in human dynamics with high activity

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Physical Review Research 2023年第3期5卷 033179-033179页

作者： Ming Zhao Jiani Chen Jiasheng Lao Yanqing Hu Jiarong Xie College of Physical Science and Technology Guangxi Normal University Guilin 541004 People's Republic of China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen 518055 People's Republic of China School of Computer Science and Engineering Sun Yat-sen University Guangzhou 510006 People's Republic of China

Complex interactions among a large number of individuals lead to multiple patterns of collective human behavior. However, the theoretical prediction of pattern transitions has not been empirically confirmed. This is because in previous empirical studies, different patterns were observed in different systems, and the coexistence of multiple patterns in the same system was rarely found. By investigating nearly 10 million messages in 252 QQ groups, we find rich distribution patterns of interevent time for human collective behavior, including the transitions between bimodal distribution, double-power-law distribution, and single-power-law distribution. The model developed in this paper suggests that human physiological rhythms and the high collective activity play key roles in the presentation of the single-power-law distribution. These results enhance the empirical research in the field of human dynamics, and are helpful for understanding many complex socioeconomic phenomena.

关键词： Collective behavior in networks Stochastic processes Social dynamics Time series analysis

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Machine Learning-Based Crop Recommendation System

Machine Learning-Based Crop Recommendation System

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Ebenezer V Akash Nair Stewart Kirubakaran S Praising Linijah N L Jerlin Rajan Santhiya P Division of Data Sciences and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Karunya School of Management Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The time-honored way that farmers have been used to apply to the identification of the best crops for better yield is not exhaustive. Under this technique, the management process of powerful and productive resources, cannot be done properly. The crop recommendation system comprising a machine learningbased model can be deployed to solve the problem. While using techniques such as Logistic Regression, Decision Trees, Random Forests, XGB boost, SVM and Stacking Classifier, the machine learning model attempts to predict the most suitable crops by taking and observing soil contents and environmental parameters. That study is devoted to helping farmers have comprehensive prescriptions and appropriate crop selections to optimize productivity and guarantee sustainability.

关键词： Support vector machines Computational modeling Stacking Crops Soil Ubiquitous computing Sustainable development Recommender systems Regression tree analysis Random forests

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FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices

arXiv

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arXiv 2025年

作者： Yao, Dezhong Shi, Yuexin Liu, Tongtong Xu, Zhiqiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process in conventional FL introduces significant computation and communication overhead, which is unfriendly for resource-constrained edge devices. One-shot FL has emerged as a promising approach to mitigate communication overhead, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups. Our code is available at https://***/YXShi2000/FedMHO. © 2025, CC BY.

关键词： Federated learning

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data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

引用

data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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A multi-dimensional index for privacy-preserving queries in cloud computing

A multi-dimensional index for privacy-preserving queries in ...

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作者： Xu, Hui Ding, Xiaofeng Jin, Hai Yu, Qing National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles. © 2019 John Wiley & Sons, Ltd.

关键词： Sensitive data

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