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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yunseong Lee Donghyun Lee Junsuk Oh Chihyun Song Wonjong Noh Sungrae Cho School of Computer Science and Engineering Chung-Ang University Seoul Republic of Korea School of Software Hallym University Chuncheon Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper explores reinforcement learning (RL) based on resource allocation in cell-free networks, a promising alternative to traditional cellular architectures. Cell-free networks eliminate cell boundaries by using distributed access points (APs) to collaboratively serve users, improving spectral efficiency and service uniformity. However, managing power allocation, beamforming, and AP clustering in such decentralized environments presents new challenges. We present several RL-based algorithms aimed at optimizing key network functions. Specifically, the study explores dynamic power control, advanced beamforming techniques, and multi-agent RL frameworks for clustering access points. The proposed methods leverage the adaptability of RL to optimize network performance in real-time under varying conditions.

关键词： Wireless communication Array signal processing Spectral efficiency Microprocessors Refining Power control Reinforcement learning computer architecture Real-time systems Resource management

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KnowPO: Knowledge-Aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models 39

KnowPO: Knowledge-Aware Preference Optimization for Controll...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Ruizhe Xu, Yongxin Xiao, Yuzhen Zhu, Runchuan Jiang, Xinke Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China

ISBN: (纸本)157735897X

By integrating external knowledge, Retrieval-Augmented Generation (RAG) has become an effective strategy for mitigating the hallucination problems that large language models (LLMs) encounter when dealing with knowledge-intensive tasks. However, in the process of integrating external non-parametric supporting evidence with internal parametric knowledge, inevitable knowledge conflicts may arise, leading to confusion in the model’s responses. To enhance the knowledge selection of LLMs in various contexts, some research has focused on refining their behavior patterns through instruction-tuning. Nonetheless, due to the absence of explicit negative signals and comparative objectives, models fine-tuned in this manner may still exhibit undesirable behaviors such as contextual ignorance and contextual overinclusion. To this end, we propose a Knowledge-aware Preference Optimization strategy, dubbed KnowPO, aimed at achieving adaptive knowledge selection based on contextual relevance in real retrieval scenarios. Concretely, we proposed a general paradigm for constructing knowledge conflict datasets, which comprehensively cover various error types and learn how to avoid these negative signals through preference optimization methods. Simultaneously, we proposed a rewriting strategy and data ratio optimization strategy to address preference imbalances. Experimental results show that KnowPO outperforms previous methods for handling knowledge conflicts by over 37%, while also exhibiting robust generalization across various out-of-distribution datasets. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge acquisition

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A Multi-Metric Ranking with Label Correlations Approach for Library Migration Recommendations

A Multi-Metric Ranking with Label Correlations Approach for ...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Jiancheng Zhang Qin Luo Peng Wu School of Computer Science and Software Engineering SouthWest Petroleum University Chengdu China School of Information and Engineering Sichuan Tourism University Chengdu China

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

While third-party libraries provide benefit to software systems, they also bring unique challenges. It often happens that developers need to replace some already-used libraries with other functionality-equivalent libraries. However, it is not easy to find a relevant candidate from overwhelming libraries. Despite several approaches have been proposed to mine library migrations from historical data, the study of library recommendation from the perspective of both open-source projects and third-party libraries is lacking. Therefore, conducting such research may assist developer better select suitable third-party libraries. In this paper, we propose a multi-metric ranking with label correlations (MMRLC) algorithm, which can recommend libraries holistically from the both perspectives. Not only does it mine library migrations from existing software data, MMRLC further leverages label correlations of libraries in Maven Central Repository to make recommendations. To demonstrate the usefulness, three popular algorithms were conducted on a benchmark dataset for comparison. The results show that our approach can recommend libraries with precision @ 1 of 0.8454 and recall @20 of 0.9301. Moreover, to demonstrate the generality, we select 366 libraries and resort to TagWiki to generate the related library labels, and the results show that our approach still has comparable performance.

关键词： Electronic publishing Correlation software algorithms Buildings Benchmark testing software systems Libraries

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Decentralized Federated Graph Learning With Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku 102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users’ efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Spatiotemporal Feature Extraction and Fusion for Longitudinal Alzheimer’s Disease Diagnosis 17th

Spatiotemporal Feature Extraction and Fusion for Longitudina...

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17th International Conference on Brain Informatics, BI 2024

作者： Guan, Zhenghua Yang, Peng Lei, Haijun Yang, Bao Song, Xuegang Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. The brain structure of AD patients progressively atrophies over time. Longitudinal data can capture the changes in imaging over time, providing important information for early detection and diagnosis of the disease, which is not achievable with images from a single time point. We propose a novel longitudinal AD diagnosis model that leverages deformation field data obtained by registering images from adjacent time points. Each pixel in the deformation field represents the displacement vector of the corresponding pixel between two scans, reflecting information about brain tissue atrophy. By effectively extracting both temporal and spatial features from the longitudinal data, our model enhances early AD diagnosis. The input data for the model consists of processed sMRI images and deformation fields calculated between each pair of time points in the longitudinal sMRI data. The model is composed of three modules: Convolutional Long Short-Term Memory (CLSTM), Gate Fusion Long Short-Term Memory (GFLSTM), and an attention module. CLSTM extracts temporal features while preserving spatial features by maintaining the 3D shape of the features, GFLSTM achieves spatiotemporal feature fusion within a cascaded network, and the attention module further enhances feature representation. Our proposed method was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and experiments demonstrate that it outperforms several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Video Editing

High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Vid...

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IEEE International Conference on Data Mining (ICDM)

作者： Yan Luo Zhichao Zuo Zhao Zhang Zhongqiu Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Text-guided image generative diffusion models achieve fast development on the generation and editing of high-quality images. To extend such success to video editing, some efforts combining image generation with video editing have been made, which however only achieve inferior performance. We attribute it to two challenges: 1) different from the static image generation, it is tricky for dynamic video information to ensure the temporal fidelity of motion consistency across different frames; 2) the randomness of the frame generation process makes it hard to continuously retain the similar spatial fidelity for the original detailed features. In this paper, we propose a new high-fidelity diffusion model-based zero-shot text-guided video editing network, called HiFiVEditor, which aims to conduct effective video editing with high fidelity of the original video's detailed and dynamic information. Specifically, we propose a Spatial-Temporal Fidelity Block (STFB) that enables the model to restore the spatial features by enlarging the spatial perceptual field to avoid loss of important information, and capture more dynamic information between different frames by using all frames for preserving temporal consistency to achieve better temporal fidelity. In addition, we introduce Null-Text Embedding to create a soft text embedding to optimize the noise learning process, so that the latent noise can be aligned with the prompt. Furthermore, to tune the video style and render it more realistic, we employ a Prior-Guided Perceptual Loss to constrain the prediction results to avoid deviating from the original video style. Extensive experiments demonstrate the superior video editing capability compared to existing works.

关键词： Image synthesis Noise Dynamics Diffusion models Data models Image restoration Data mining

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Blended Learning Application in the Principle of computer Composition 2021

Blended Learning Application in the Principle of Computer Co...

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5th International Conference on E-Society, E-Education and E-Technology, ICSET 2021

作者： Peng, Shih Jung School of Computer Science and Software Engineering Zhaoqing University 86-*** China

ISBN: (纸本)9781450390156

Blended learning is always a significant method of learning and teaching in education. This method combines the advantages of online and face-to-face learning models to improve students' interest in learning and teaching results. Because the course ĝ€Principle of computer Composition' is extensive, which is not easy to understand and absorb in the process of learning, students need more basic knowledge in learning. Therefore, we establish a blended learning platform for the course, the factors such as learning courses, learning media, and learning environment would be optimized. Utilizing blended learning modules, for instance, ĝ€e-learning platform', ĝ€online class group', ĝ€face-to-face classes', ĝ€validation in experiments', ĝ€discussion of course content', and ĝ€learning examination', we can optimize the teaching process and effect, and improve students' interest in learning and learning impact. At the end of the blended learning module, we feedback the student's learning data to the teacher who obtains the distribution of students' academic achievement through statistical analysis as a reference for adjusting teaching content, progress, and methods. © 2021 ACM.

关键词： Students

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CAPAST: Content Affinity Preserved Arbitrary Style Transfer

CAPAST: Content Affinity Preserved Arbitrary Style Transfer

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinyuan Zheng Xiaojie Li Canghong Shi Jia He Zhan Ao Huang Xian Zhang Imran Mumtaz School of Computer Science Chengdu University of Information Technology China School of Computer and Software Engineering Xihua University China Faculty of Engineering and Mathematical Sciences The University of Western Australia Australia Department of Computer Science University of Agriculture Faisalabad Pakistan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Balancing the consistency of style and the integrity of content is the main challenge in arbitrary style transfer domain. Currently, local style details can be effectively captured by attention mechanism but easily produce distorted style patterns and inconsistent content structure. In this paper, we propose a Content Affinity Preserving Arbitrary Style Transfer (CAPAST) framework to ensure style features can be stably integrated into the content structure. Considering the local feature learning ability of CNN and the global feature representation advantage of transformer, a dual encoder is proposed to capture local and global features of images with the combination between transformer and CNN. In addition, a channel and spatially aligned attention (CSAA) is introduced to generate high-quality results by stably fusing style features and content features. In experiments, we demonstrated the superior performance of our method in preventing content structure distortion and maintaining consistency between style and content. Codes are available at https://***/miaopashi-zxy/CAPAST.

关键词： Representation learning Acoustic distortion Codes Attention mechanisms Fuses Transformers Acoustics Speech processing

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MSAmba: Exploring Multimodal Sentiment Analysis with State Space Models 39

MSAmba: Exploring Multimodal Sentiment Analysis with State S...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Xilin Liang, Haijian Peng, Boyi Xie, Weicheng Khan, Muhammad Haris Song, Siyang Yu, Zitong Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

Multimodal sentiment analysis, which learns a model to process multiple modalities simultaneously and predict a sentiment value, is an important area of affective computing. Modeling sequential intra-modal information and enhancing cross-modal interactions are crucial to multimodal sentiment analysis. In this paper, we propose MSAmba, a novel hybrid Mamba-based architecture for multimodal sentiment analysis, consisting of two core blocks: Intra-Modal Sequential Mamba (ISM) block and Cross-Modal Hybrid Mamba (CHM) block, to comprehensively address the above-mentioned challenges with hybrid state space models. Firstly, the ISM block models the sequential information within each modality in a bi-directional manner with the assistance of global information. Subsequently, the CHM blocks explicitly model centralized cross-modal interaction with a hybrid combination of Mamba and attention mechanism to facilitate information fusion across modalities. Finally, joint learning of the intra-modal tokens and cross-modal tokens is utilized to predict the sentiment values. This paper serves as one of the pioneering works to unravel the outstanding performances and great research potential of Mamba-based methods in the task of multimodal sentiment analysis. Experiments on CMU-MOSI, CMU-MOSEI and CH-SIMS demonstrate the superior performance of the proposed MSAmba over prior Transformer-based and CNN-based methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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VEGAS: Towards Visually Explainable and Grounded Artificial Social Intelligence

arXiv

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arXiv 2025年

作者： Li, Hao Fei, Hao Hu, Zechao Yang, Zhengwei Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computing National University Singapore

Social Intelligence Queries (Social-IQ) serve as the primary multimodal benchmark for evaluating a model’s social intelligence level. While impressive multiple-choice question (MCQ) accuracy is achieved by current solutions, increasing evidence shows that they are largely, and in some cases entirely, dependent on language modality, overlooking visual context. Additionally, the closed-set nature further prevents the exploration of whether and to what extent the reasoning path behind selection is correct. To address these limitations, we propose the Visually Explainable and Grounded Artificial Social Intelligence (VEGAS) model. As a generative multimodal model, VEGAS leverages open-ended answering to provide explainable responses, which enhances the clarity and evaluation of reasoning paths. To enable visually grounded answering, we propose a novel sampling strategy to provide the model with more relevant visual frames. We then enhance the model’s interpretation of these frames through Generalist Instruction Fine-Tuning (GIFT), which aims to: i) learn multimodal-language transformations for fundamental emotional social traits, and ii) establish multimodal joint reasoning capabilities. Extensive experiments, comprising modality ablation, open-ended assessments, and supervised MCQ evaluations, consistently show that VEGAS effectively utilizes visual information in reasoning to produce correct and also credible answers. We expect this work to offer a new perspective on Social-IQ and advance the development of human-like social AI. © 2025, CC BY.

关键词： Emotional intelligence

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