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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Linlin Sun, Heli Gao, Qunshu Liu, Yuze He, Liang College of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China College of Software Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)157735897X

Multimodal Aspect-Based Sentiment Analysis (MABSA) plays a pivotal role in the advancement of sentiment analysis technology. Although current methods strive to integrate multimodal information to enhance the performance of sentiment analysis, they still face two critical challenges when dealing with multi-aspect and multi-sentiment data: i) the importance of aspect terms within multimodal data is often overlooked, and ii) models fail to accurately associate specific aspect terms with corresponding sentiment words in multi-aspect and multi-sentiment sentences. To tackle these problems, we propose a novel multimodal aspect-based sentiment analysis method that combines Aspect Enhancement and Text Simplification (AETS). Specifically, we develop an aspect enhancement module that boosts the ability of model to discern relevant aspect terms. Concurrently, we employ text simplification module to simplify and restructure multi-aspect and multi-sentiment texts, accurately capturing aspects and their corresponding sentiments while reducing irrelevant information. Leveraging this method, we perform three tasks including multimodal aspect term extraction, multimodal aspect sentiment classification, and joint multimodal aspect-based sentiment analysis. Experimental results indicate that our proposed AETS model achieved state-of-the-art performance on two benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Emotion Recognition

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CEMOP: Enhancing MOBA match outcome predictions by understanding teammates and opponents effects 3

CEMOP: Enhancing MOBA match outcome predictions by understan...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Zhao, Jiangfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518057 China

ISBN: (纸本)9781510681316

Multiplayer Online Battle Arena (MOBA) games currently dominate the esports landscape, offering a concrete and vivid embodiment for team comparisons, where accurately predicting the winning team is both important and challenging. Recent studies often rely on the static features of team members to evaluate a team’s capabilities and predict their likelihood of winning. However, such approaches fail to consider the critical game-contextual influences of interactions with teammates and opponents on individuals. During a match, an individual’s performance is consistently influenced by teammates and opponents, leading to variations in individual features across different games. Moreover, the measurement of team capabilities also needs improvement, as the single scalar rating employed is too simplistic for sophisticated MOBA games. To this end, we propose a novel Context-Enhanced Match Outcome Prediction model (CEMOP), which incorporates the effect of both teammates and opponents for a better measurement of team capabilities. Specifically, we designed a match context encoder for MOBA games to evaluates the impact of teammates and opponents on individuals separately and embeds the information into the individual representations. After that the single team member’s representations which contain the influence of teammates and opponents are aggregated based on their importance to create a comprehensive representation of the team capabilities, enables CEMOP to adaptively adjust team evaluations. Experimental results from three real-world MOBA games clearly verify the effectiveness of CEMOP compared with several state-of-the-art methods. © 2024 SPIE.

关键词： Data mining

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Multiple sclerosis lesions prediction and segmentation using an optimized deep network system

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Multimedia Tools and Applications 2025年 1-23页

作者： Goura, Kavita Harsoor, Anita Department of Computer Science and Engineering P.D.A College of Engineering Karnataka Gulbarga585102 India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India

Multiple Sclerosis (MS) is an immunological disorder that causes tumors in the central nervous system. Brain Magnetic Resonance Images (MRI) were considered for the visualization of MS. In the past, neural approaches were widely utilized to detect MS, but those models failed to attain the best segmentation outcome. Moreover, traditional Deep Learning (DL) methods require massive resource data to train the system. So, a novel hybrid approach named the Buffalo-based Convolutional Neural Lesion Prediction (BCNLP) model was presented in this article to predict and segment MS lesions. In the developed model, the pre-processing function is incorporated into the hidden layer of the BCNLP. In addition, the Buffalo fitness function provides the highest prediction rate by tracking and segmenting the diseased features. Considering the other optimization model, the buffalo optimal model has future prediction behavior, and this behavior helps to track and predict the disease with high accuracy. Finally, the outcomes are determined and compared against the existing MS lesion segmentation approaches. Moreover, the improvement score in segmentation quality over the competing methods is evaluated on various metrics, such as Dice and false positive ratio. The dataset considered for validation is brain lesson MRI data from Kaggle cite. The obtained data contains 6673 images, of which 3976 are normal images, and 2697 are MS lesion images. After splitting the 70% training and 30% testing ratio, there are 2783 normal and 1888 MS lesion images. In addition, there are 1193 normal images and 809 MS lesion images for testing. The highest accuracy reported by the presented novel BCNLP for segmenting the MS is 96.4%, which is relatively higher than the traditional approaches. Also, the False Positive Rate was minimized by 0.023%. This less FPR has revealed that the proposed method is more accurate in MS segmentation. © The Author(s), under exclusive licence to Springer science+Business Media,

关键词： Image segmentation

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MVDC: A Multi-view Dental Completion Model Based on Contrastive Learning

MVDC: A Multi-view Dental Completion Model Based on Contrast...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Xunyu Deng, Qingxin Huang, Minghan Jiang, Landu Zhang, Dian College of Computer Science and Software Engineering Shenzhen University Guangdong China Guangdong China

ISBN: (纸本)9798350368741

Restoring the patient's occlusal function of broken teeth is a challenging task since tooth texture is very complex, a slight deviation may affect the patient's chewing function and temporomandibular joint function. Therefore, how to efficiently repair the complete shape and real surface of the crown is a critical problem. Traditional technologies are hard to restore complete shape of the dental crown or lack inlay surface details, due to dataset limitations and complexity of missing parts. In this paper, we propose a multi-view crown restoration framework MVDC based on contrastive learning. Specifically, MVDC contains: 1) a multi-view generator with a specially designed loss measurement by using contrastive learning;2) a multi-scale discriminator mechanism able to consider relation and consistency between teeth from different scales;3) an occlusal groove extraction network to extract the occlusal details. We conducted extensive experiments on existing public datasets. The results showcase the superior performance of MVDC. © 2025 IEEE.

关键词： Contrastive learning Multi-view Teeth completion

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Rainfall Process Similarity Algorithm Research Based on Deep Learning by Using Precipitation Images 7

Rainfall Process Similarity Algorithm Research Based on Deep...

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7th International Conference on computer Information science and Application technology, CISAT 2024

作者： Yu, Yufeng Wu, Panqi Cai, Difei Liu, Weifu Wang, Xuhang Hohai University College of Computer Science and Software Engineering Nanjing210098 China

ISBN: (纸本)9798350375107

Rainfall is the main cause of flood disasters, and analyzing its features plays a crucial role in preventing flood disasters. How to extract rainfall process features and conduct rainfall similarity analysis is a challenging work. Based on the similarity search method for daily precipitation images, this paper proposes a rainfall process similarity search method, which extracts rainfall duration, cumulative precipitation, temporal distribution, spatial distribution and center movement trajectory as the features of rainfall process, and defines a set-weighting method based on the Grey Wolf Optimization algorithm (IGWO) to weight and fuse the five extracted features as the similarity measure of rainfall process. The similarity search results of rainfall process images on the Jialing Basin illustrate that the method proposed in this paper can efficiently and accurately identify similar rainfall processes in historical scenes, thereby providing a new technical support for intelligent rainfall-flood forecasting. © 2024 IEEE.

关键词： Rain

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Improve Robustness of Safe Reinforcement Learning Against Adversarial Attacks 4

Improve Robustness of Safe Reinforcement Learning Against Ad...

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4th International Conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Xiaoyan Zhang, Yujuan Huang, Lan College of Computer Science and Technology Jilin University Changchun China Jilin University Software Engineering Changchun China

ISBN: (纸本)9798331531409

Models trained with adversarial attack can be significantly improved stability and performance when faced with new uncertain environment. In this paper, we propose the robust training framework based on Wasserstein SA-MDP for safe RL, which combines ideas from reinforcement learning, adversarial examples generation, Wasserstein metric and robust training. This framework can improve the robustness of safe RL models against adversarial attacks by considering the dynamics of the adversarial interaction as a sequential decision-making process. We made the experiment in safety gym based on the proposed framework and report the experimental results. © 2024 IEEE.

关键词： Adversarial machine learning

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Runtime Verification of Neural-Symbolic Systems 10th

Runtime Verification of Neural-Symbolic Systems

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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Deng, Shaojun Liu, Wanwei Zhang, Miaomiao College of Computer Science and Technology National University of Defense Technology Changsha China School of Software Engineering Tongji Uniersity Shanghai China

ISBN: (纸本)9789819606016

Neural-symbolic systems (NSSs), which are typically cyber-physical systems integrated with artificial intelligence modules, have received much attention in both academic and industrial fields. However, thorough verification (such as model checking) of such systems in general leads to prohibitively high costs due to the scale of the state space, and this makes it barely accomplishable within desired time-span. Consequently, light-weight verification techniques are introduced to deal with such cases, e.g., runtime verification (RV, for short). In this paper, we investigate an RV framework for NSSs against signal temporal logic (STL) properties. To guarantee that the on-line monitoring could be accomplished in time, we utilize Euler-prediction to sample the system under scrutiny at discrete time-steps. Our framework supports both qualitative and quantitative on-line monitoring of STL specifications, and thus it terminates whenever a property is satisfied and/or violated by a finite prefix of the trajectory, and it can also provide the bounds of the quantitative satisfaction/violation w.r.t. the remaining execution. Notably, for the so-called piece-wise linear NSSs against affine STL formulas, our approach is exact. On top of it, a prototype tool is implemented, and is experimentally evaluated. The results demonstrate the feasibility of the presented runtime verification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model checking

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A Fair and Trustworthy Hierarchical Federated Learning Scheme for Digital Twins in the Internet of Vehicles

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IEEE Internet of Things Journal 2024年第13期12卷 22709-22724页

作者： Fan, Qi Xin, Yang Jia, Bin Zhang, Xinze Beijing University of Posts and Telecommunications College of Cyberspace Security Beijing100876 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Digital Twins (DTs) support real time analysis and provide a reliable simulation platform for the Internet of Vehicles (IoV). DT modeling relies on a large amount of data, based on their own safety considerations, most clients are not willing to provide relevant data. Due to the characteristics of distributed collaboration and privacy protection, Federated Learning (FL) is a promising technique for DT modeling. The combination of the above two technologies can greatly accelerate the development of the IoV. However, an important challenge is the unbalanced distribution of local data across clients, which leads to client drift and affects the performance of the global model. Malicious clients uploading false parameters or low-quality models will also cause unsatisfactory model accuracy. In addition, trust between clients is not established in advance, and hidden safety issues arise when clients perform cooperative training with each other. Therefore, in this paper, we propose a Hierarchical FL (HFL) scheme for performing DT modeling tasks. To solve the problems of performance degradation and the divergence of FL models caused by Non-Independent and Identical Distribution (Non-IID) data, we design a regularization algorithm and a data enhancement algorithm from the client-side and edge server-side respectively. In addition, we design an auditing method to defend against attacks from malicious clients. To build trust between clients, we present a reputation management model that can effectively prevent swing attacks. The numerical results show that our proposed scheme not only outperforms the baseline methods in terms of model accuracy and attack resistance, but also filters out malicious clients to perform fair and efficient reputation computations. © 2014 IEEE.

关键词： Federated learning

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ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

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computer Modeling in engineering & sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

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Algorithm for Identifying Critical Nodes in Networks Based on Spectral Clustering to Construct Hypergraphs 16

Algorithm for Identifying Critical Nodes in Networks Based o...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Xujie Shao, Fei Li, Haotian College of Computer Science and Software Engineering Hohai University Nanjing China College of Information Science and Engineering Hohai University Nanjing China

ISBN: (纸本)9798350390643

Currently, the basis for critical nodes definition and identification lies in the representation learning of the network and the extraction of local and global features of the nodes. The effectiveness of the algorithm is closely related to network modeling and network structural characteristics. However, with the increasing complexity of real-world systems, utilizing the continuity and integrity of traditional bipartite graph representation faces significant challenges. Different from traditional graph modeling, the hyperedges of a hypergraph can connect multiple nodes simultaneously, thus forming richer structural features and interaction information between nodes for the analysis and judgment of critical nodes. To more accurately identify critical nodes in complex networks, we propose an algorithm that establishes a hypergraph representation for traditional graph data to extract higher-order features of nodes for neural network training, in order to identify critical nodes in complex networks. The effectiveness of this method is proven by comparing it with various critical node identification algorithms under different infection rates. © 2024 IEEE.

关键词： Complex networks

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