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A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2346-2348页

作者： Yi Liu Xiang Wu Yuming Bo Jiacun Wang Lifeng Ma the School of Automation Nanjing University of Science and Technology the Department of Computer Science and Software Engineering Monmouth University

Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1... 详细信息

关键词： Deep agent Framework

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Building LOD representation for 3D urban scenes

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 16-32页

作者： Pan, Shanshan Zhang, Runze Liu, Yilin Gong, Minglun Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Guelph Guelph Canada

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analyzing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations. © 2025

关键词： Three dimensional computer graphics

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A Generative Model-Based Network Framework for Ecological Data Reconstruction

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computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

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computers, Materials & Continua 2024年第2期78卷 1977-1993页

作者： Zhiqing Dong Zhao Zhang Hongyan Zhou Xuebo Chen School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of *** study presents a new approach to the encryption and compression of color *** is predicated on 2D compressed sensing(CS)and the hyperchaotic ***,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong ***,the processed images are con-currently encrypted and compressed using 2D *** them,chaotic sequences replace traditional random measurement matrices to increase the system’s ***,the processed images are re-encrypted using a combination of permutation and diffusion *** addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct *** with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational ***,it has better *** experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

关键词： Image encryption image compression hyperchaotic system compressed sensing

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A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of computer science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Multi-Task Chinese Speech Recognition Method Based on the Squeezeformer Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 23-31页

作者： Guo, Ying Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Technology Liaoning Anshan114051 China

End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose deployment challenges. To address these issues, we propose a multi-task Chinese speech recognition method based on the Squeezeformer model. We replace the FMCF structure in Conformer with an MF/CF structure, leveraging the convolutional module as a local Multi- Head Attention (MHA) module to enhance efficiency. Multilevel down-sampling and up-sampling using a time-series U-Net further reduce computational costs. By eliminating redundant LayerNorm layers and employing depthwise separable convolutions, we streamline the model, reduce parameters, and lower deployment costs. An Adaptor Layer is integrated into the MHSA module to mitigate the vanishing gradient problem, and a ScaleVar Layer is added to enhance flexibility. Additionally, the RealFormer module is introduced on the decoding side to improve context understanding. Combining Connectionist Temporal Classification (CTC) with attention-based encoding and decoding models for multi-task learning improves performance and accuracy. Experimental results show that the proposed method reduces the parameters on AISHELL-1 dataset by 16% and reduces the character error rate to 5.50%. At the same time, it also shows good performance on AISHELL-2 dataset. © (2025), (International Association of Engineers). All rights reserved.

关键词： Speech recognition

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

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China Communications 2024年第5期21卷 261-279页

作者： Sun Yi Wang Zhouyang Beijing University of Posts and Telecommunications School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications International School

The distribution of data has a significant impact on the results of *** the distribution of one class is insignificant compared to the distribution of another class,data imbalance *** will result in rising outlier values and ***,the speed and performance of classification could be greatly *** the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification *** with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone *** we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector *** introduce the cost control to solve the problem of sample ***,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is *** can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

关键词： fuzzy ordered regression(OR) relaxing variables twin support vector machine

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