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International Conference on Mobile Data Management

作者： Hongjie Hao Xueqin Zhang Yuan Chi Rongxin Gao Anke Xie Mingliang Xu School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Yunnan Innovation Institute Beihang University Kunming China Yunnan Key Laboratory of Blockchain Application Technology Kunming China

Carrier Aircraft support operation is an important component of the carrier aviation support system, and its efficiency is closely related to the aircraft sortie rate. However, the carrier deck faces significant challenges such as limited space, scarce resources and constantly changing environmental conditions. These factors have a profound impact on the aircraft support operations, particularly in complex scenarios, requiring careful and rational scheduling. To solve above problems, we propose a collaborative fast scheduling framework for Multi-Agent support operations, which is based on a distributed partially observable Markov decision process (Dec-POMDP) model. Within this framework, we develop a dynamic scheduling algorithm based on centralized training with decentralized execution framework (CTDE). To evaluate the performance of our approach, we establish a simulation environment for aircraft scheduling and compare our algorithm’s performance with VDN and QMIX in various carrier aircraft scenarios. Experimental results demonstrate that our algorithm outperforms others in terms of both training efficiency and scheduling quality.

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An asymptotically consistent morphoelastic shell model for compressible biological structures with finite-strain deformations

arXiv

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arXiv 2024年

作者： Yu, Xiang Chen, Xiaoyi Department of Mathematics School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Department of Applied Mathematics BNU-HKBU United International College Zhuhai519087 China

We derive an asymptotically consistent morphoelastic shell model to describe the finite deformations of biological tissues using the variational asymptotical method. Biological materials may exhibit remarkable compressibility when under large deformations, and we take this factor into account for accurate predictions of their morphoelastic changes. The morphoelastic shell model combines the growth model of Rodriguez et al. and a novel shell model developed by us. We start from the three-dimensional (3D) morphoelastic model and construct the optimal shell energy based on a series expansion around the middle surface. A two-step variational method is applied that retains the leading-order expansion coefficient while eliminating the higher-order ones. The main outcome is a two-dimensional (2D) shell energy depending on the stretching and bending strains of the middle surface. The derived morphoelastic shell model is asymptotically consistent with three-dimensional morphoelasticity and can recover various shell models in literature. Several examples are shown for the verification and illustration. Copyright © 2024, The Authors. All rights reserved.

关键词： Shells (structures)

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PAGE: Equilibrate Personalization and Generalization in Federated Learning

arXiv

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arXiv 2023年

作者： Chen, Qian Wang, Zilong Hu, Jiaqi Yan, Haonan Zhou, Jianying Lin, Xiaodong State Key Laboratory of Integrated Service Networks Xidian University Xi’an China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Zhejiang China iTrust Singapore University of Technology and Design Singapore Singapore School of Computer Science University of Guelph Guelph Canada

Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server). Representing clients’ current demands and the server’s future demand, local model personalization and global model generalization are separately investigated, as the ill-effects of data heterogeneity enforce the community to focus on one over the other. However, these two seemingly competing goals are of equal importance rather than black and white issues, and should be achieved simultaneously. In this paper, we propose the first algorithm to balance personalization and generalization on top of game theory, dubbed PAGE, which reshapes FL as a co-opetition game between clients and the server. To explore the equilibrium, PAGE further formulates the game as Markov decision processes, and leverages the reinforcement learning algorithm, which simplifies the solving complexity. Extensive experiments on four widespread datasets show that PAGE outperforms state-of-the-art FL baselines in terms of global and local prediction accuracy simultaneously, and the accuracy can be improved by up to 35.20% and 39.91%, respectively. In addition, biased variants of PAGE imply promising adaptiveness to demand shifts in practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

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LeGalFormer: A Graph Representation Learning and Transformer-based Approach for Legal Similar Case Retrieval

LeGalFormer: A Graph Representation Learning and Transformer...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shang Gao Yanling Li Fengpei Ge Min Lin Haiqing Yu Sukun Wang Zhongyi Miao College of Computer Science and Technology Key Laboratory of Infinite-Dimensional Hamiltonian System and Its Algorithm Application Ministry of Education Inner Mongolia Normal University Hohhot China Library Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Legal Similar Case Retrieval (LSCR) is a critical application in legal Artificial Intelligence (AI). It involves retrieving the most relevant cases from legal case databases through query cases. Legal cases are semi-structured documents characterized by long text sequences and high specialization. Existing approaches rely on pre-trained language models for retrieval. However, these methods are constrained by the length of text input, preventing them from fully comprehending cases, which results in poor retrieval performance. Knowledge Graph (KG) are a type of graph structure data with dense knowledge and clear logic, which can represent the criminal processes and relationships among characters within legal cases. Currently, the mainstream approach for handling KG remains Graph Neural Networks (GNNs). However, these methods are limited by message passing and are prone to over-smoothing problems in the process of aggregating node features. To address these issues, we propose a Legal similar case retrieval model that combines Graph representation learning with the Transformer, called LeGalFormer. Three encoding methods are introduced to incorporate the structural information of the graph into the Transformer architecture. We evaluate the model on a real legal dataset, and the experimental results show that LeGalFormer significantly enhances the model's understanding capacity and achieves state-of-the-art performance.

关键词： Representation learning Training Law Message passing Transformers Encoding Graph neural networks

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More observation leads to more clarity: Multi-view collaboration network for camouflaged object detection

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Neurocomputing 2025年 645卷

作者： Wang, Fangyan Jiao, Ge Yue, Guowen College of Computer Science and Technology Hengyang Normal University Hengyang Hunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang Hunan421002 China Hunan Engineering Research Center of Cyberspace Security Technology and Applications Hengyang Normal University Hengyang Hunan421002 China

Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the multi-view collaboration network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head attention mechanisms for global feature embedding. In parallel, the local perception module processes the tilted, projected, and color-jittered views, extracting fine-grained local features through multi-branch deep convolutions and dilated convolutions. To facilitate deep interaction between global and local features, we introduce the hybrid interactive module, which explores the correlation of multi-view feature information and adaptively fuses features. For feature decoding, the dynamic pyramid shrinkage module integrates dynamic gated convolutions with a pyramid shrinkage mechanism, progressively aggregating semantic features through a hierarchical shrinking strategy and group fusion strategy. Experimental results on popular COD benchmark datasets show that MCNet outperforms 18 state-of-the-art methods. © 2025

关键词： Object detection

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Reversible Data Hiding Using Convolutional Neural Network and Digital Signal Processing Techniques 12

Reversible Data Hiding Using Convolutional Neural Network an...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Qingmei, Peng Shaozi, Li Yaojin, Lin Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. Based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development. © 2022 IEEE.

关键词： Authentication

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Specific Design and Implementation of the Characteristic Species Database in Wuling Mountain Area

Specific Design and Implementation of the Characteristic Spe...

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Information technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Shaowei Deng Yujun Yang Yimei Yang College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

This article aims to design and implement a database system for characteristic species in the Wuling Mountain area. The system will collect, organize, store, and analyze information on characteristic species in the Wuling Mountain area, providing data support for ecological research, biodiversity conservation, agricultural production, and other fields. This article will provide a detailed introduction to database requirements analysis, data model design, database structure implementation, data security management, and system testing and optimization.

关键词： System testing Data security Ecosystems Production Database systems Data models Biodiversity Information technology Optimization

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Legal Judgment Prediction via Fine-Grained Element Graphs and External Knowledge

Legal Judgment Prediction via Fine-Grained Element Graphs an...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yao Guo Yanling Li Fengpei Ge Haiqing Yu Sukun Wang Zhongyi Miao College of Computer Science and Technology Key Laboratory of Infinite-Dimensional Hamiltonian System and Its Algorithm Application (Ministry of Education) Inner Mongolia Normal University Hohhot China Library Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Legal Judgment Prediction (LJP), a paramount application of artificial intelligence in law, aims to infer outcomes from fact descriptions. These outcomes mainly involve determining applicable law articles, charges, and the term of penalty. Existing approaches typically treat this task as a text classification problem, utilizing end-to-end black box models for automatic prediction. However, these approaches overlook the fine-grained elements within cases and limit the model’s interpretability. In light of these limitations, we propose a novel approach for LJP. Our method employs Fine-Grained Element Graphs (FEGs) to represent fact descriptions and applies Graph Attention Network (GAT) to learn graph representation for subsequent prediction tasks. FEG is characterized by capturing the relevant adjudicative elements of each fact description and preserving important relations among them, such as causality and temporal sequences while filtering out irrelevant content. It enhances LJP task performance and provides interpretable references for legal professionals. Since increased or decreased penalties corresponding to different elements cannot be obtained from fact descriptions, we introduce Sentencing Guidance (SG) and combine it with FEGs as a supplementary feature. Experiments confirm the effectiveness of our method. Specifically, compared to the best baseline model, it improves macro-F1 by 3.09%, 0.57%, and 1.78% for the subtasks of law article prediction, charge prediction, and term of penalty prediction.

关键词： Automation Law Filtering Text categorization Semantics Neural networks Cause effect analysis

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Two-timescale Distributed Approach to Nash Equilibrium Seeking of Euler-Lagrange Systems in Multi-cluster Nonconvex Games

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IEEE Transactions on Automatic Control 2025年

作者： Huang, Banghua Liu, Yang Lu, Jianquan Jiang, Yunliang Gui, Weihua Zhejiang Normal University School of Mathematical Sciences Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application School of Mathematical Sciences Jinhua321004 China Zhejiang Normal University Hangzhou School of Automation Hangzhou311231 China Southeast University School of Mathematics Nanjing210096 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application School of Computer Science and Technology Jinhua321004 China Huzhou University School of Information Engineering Huzhou313000 China Central South University School of Automation Changsha410083 China

In this paper, we investigate a distributed generalized Nash equilibrium (GNE) seeking problem for multi-cluster games, where each cluster consists of multiple players. Different from most of the existing multi-cluster game studies, the multi-cluster games considered nonconvex cost functions, nonconvex (coupled) inequality constraints, and nonaffine (coupled) equality constraints. Each player in a cluster communicates and cooperates with other players based on a directed connected network to make optimal actions that minimize the cost function of their cluster. We propose a two-timescale multi-agent system (MAS) for multi-cluster nonconvex games. We further propose a two-timescale MAS in which players are formulated by Euler-Lagrange (EL) dynamics. We prove the convergence of the MAS with derived conditions to one of the local GNEs. We elaborate on a numerical example, a price-bidding problem in an electricity market, and a Nash-Cournot game to illustrate the characteristics of the proposed approach. © 1963-2012 IEEE.

关键词： Cost functions

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Re-introduction to Tibetan Case Structure and Its Grammatical Functions 8th

Re-introduction to Tibetan Case Structure and Its Grammatica...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Cai, Hua Guan, Bai Li, Kai Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu University Xining810007 China Computer College of Qinghai Normal University Xining810007 China Graduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology 1-1 Asahidai Ishikawa Nomi923-1292 Japan

ISBN: (纸本)9783031067600

Tibetan linguistics has a long history and has formed a relatively complete traditional grammar system, but there is no recognized and relatively complete modern formal grammar framework of Tibetan. In order to inherit, expand, extend and deepen the traditional Tibetan grammar and its formal requirements, this paper makes comparative study on Fillmore case grammar with traditional Tibetan grammar, and introduces a new Tibetan grammatical unit called case structure, and demonstrates that the case structure plays an important role in Tibetan syntactic and semantic analysis. Different case structures of the same verb form Tibetan sentences, and one Tibetan sentence can be uniquely decomposed into one or more case structures. Case structure not only carries the syntactic structure of the sentence, but also reflects the semantic components of the sentence. Therefore, under this grammatical framework, Tibetan syntactic and semantic analysis can be studied in an integrated way. This opinion has some certain theoretical significance for Tibetan grammar research and Tibetan natural language processing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Syntactics

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