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17th IEEE International Conference on computer Research and Development, ICCRD 2025

作者： Ye, Zhenyu Liu, Guangcong Chen, Zhendong Guangdong University of Technology School of Computer Science and Technology Guangzhou China

ISBN: (纸本)9798331531881

Recommender systems are information filtering tools designed to recommend items of potential interest to users based on their preferences. Graph Neural Network (GNN)-based recommender systems have demonstrated state-of-The-Art performance and introduced contrastive learning to improve robustness. However, as the number of GNN propagation layers increases, node features tend to become overly similar, making it difficult to capture user intentions and degrading recommendation performance. Furthermore, traditional data augmentation methods in contrastive learning exacerbate the data sparsity issue, making it problematic to generate high-quality item representations with limited interaction data. To address these challenges, we propose a Contrastive Learning Hypergraph Neural Network Based on Multimodality Replacing Data Augmentation (CHNM). The proposed method uses knowledge graphs to assist hypergraph structures in aggregating high-order information and capturing user-item relationships more precisely, thereby better identifying users' latent intentions. Additionally, multimodality replaces traditional data augmentation in contrastive learning, modeling the connection between multimodal features and items to optimize their representations. Experimental results demonstrate that the proposed model outperforms existing methods across multiple benchmark datasets, validating the critical role of hypergraph structures and multimodality in enhancing recommendation performance. © 2025 IEEE.

关键词： Recommender systems

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Inter-technology Backscatter Communication: A Bidirectional Zigbee-BLE System 18th

Inter-technology Backscatter Communication: A Bidirectional ...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Yan, Kailai Xu, Zhaoyuan Gong, Wei School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9783031714665

With the development of IoT (Internet of Things), the number of communication devices will grow rapidly, which leads to a higher requirement for devices’ power consumption. Thus, Zigbee backscatter is a promising research direction, as Zigbee devices are ultra-low power, and the power can be further reduced by utilizing backscatter technique. Considering the need for the research on Zigbee backscatter and widespread ambient BLE (Bluetooth Low Energy) signals, we proposed an inter-technology backscatter system in which the tag can create Zigbee-compatible signals over ambient BLE carriers. Previous similar backscatter systems used BLE single-tone carriers, which are generated by setting BLE packets to specific values. Compared with them, our backscatter tag can utilize widespread ambient BLE signals as carriers and is much easier to widely deploy. In addition, by using BLE extended advertising packets as carriers, we significantly improved the goodput. Besides these, we constructed a reverse data path from the Zigbee device to the tag by utilizing the BLE carrier provider as a relay, which enables the Zigbee receiver to communicate to the BLE excitation and the tag at the same time. We have done some experiments and have successfully verified the feasibility of the system. The experimental results show that our system has a comparable BER (bit error rate) to the systems that used single-tone carriers and can achieve a goodput of up to 23.2 kbps. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zigbee

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Ultrasonic Metal Recognition Based on a Time-Frequency Feature Multi-Scale Fusion Network 24

Ultrasonic Metal Recognition Based on a Time-Frequency Featu...

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14th International Conference on Communication and Network Security, ICCNS 2024

作者： Li, Aiguo Jia, Yanbo School of Computer Science and Technology Xi'an University of Science and Technology Shaanxi Xi'an China

ISBN: (纸本)9798400717994

When identifying metals with the same appearance and material non - destructively, the existing ultrasonic metal identification methods often overlook unknown - class identification, have poorly constructed data sets and limited practicality. This paper proposes an ultrasonic metal identification method based on the multi - scale fusion network of time - frequency features. It uses the multi - head multi - scale residual convolution module to extract and fuse time - frequency features, and combines the attention mechanism to process the fused features, which overcomes the over - fitting problem of a single time - domain feature and improves the generalization ability. An independent data set including training, validation and test sets is constructed. The training and validation sets each contain ultrasonic data of 12 test blocks, and the test set contains 13 classes (the 13th class is 5 test blocks of unknown categories). The test - set data are not used in model training to simulate actual scenarios. Experiments show that this method can efficiently identify metals in the self - built data set, and its effectiveness in classifying long sequences is verified on the UCR data set. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ultrasonic testing

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua science and technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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Distribution-Consistency-Guided Multi-modal Hashing 39

Distribution-Consistency-Guided Multi-modal Hashing

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Jin-Yu Mao, Xian-Ling Che, Tian-Yi Tu, Rong-Cheng School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)157735897X

Multi-modal hashing methods have gained popularity due to their fast speed and low storage requirements. Among them, the supervised methods demonstrate better performance by utilizing labels as supervisory signals compared with unsupervised methods. Currently, for almost all supervised multimodal hashing methods, there is a hidden assumption that training sets have no noisy labels. However, labels are often annotated incorrectly due to manual labeling in real-world scenarios, which will greatly harm the retrieval performance. To address this issue, we first discover a significant distribution consistency pattern through experiments, i.e., the 1-0 distribution of the presence or absence of each category in the label is consistent with the high-low distribution of similarity scores of the hash codes relative to category centers. Then, inspired by this pattern, we propose a novel Distribution-Consistency-Guided Multi-modal Hashing (DCGMH), which aims to filter and reconstruct noisy labels to enhance retrieval performance. Specifically, the proposed method first randomly initializes several category centers, each representing the region's centroid of its respective category, which are used to compute the high-low distribution of similarity scores;Noisy and clean labels are then separately filtered out via the discovered distribution consistency pattern to mitigate the impact of noisy labels;Subsequently, a correction strategy, which is indirectly designed via the distribution consistency pattern, is applied to the filtered noisy labels, correcting high-confidence ones while treating low-confidence ones as unlabeled for unsupervised learning, thereby further enhancing the model's performance. Extensive experiments on three widely used datasets demonstrate the superiority of the proposed method compared to state-of-the-art baselines in multi-modal retrieval tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reser

关键词： Unsupervised learning

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Entity Recognition with Large Language Models 31

EvoPrompt: Evolving Prompts for Enhanced Zero-Shot Named Ent...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Tong, Zeliang Ding, Zhuojun Wei, Wei Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China China

ISBN: (纸本)9798891761964

Large language models (LLMs) possess extensive prior knowledge and powerful in-context learning (ICL) capabilities, presenting significant opportunities for low-resource tasks. Though effective, several key issues still have not been well-addressed when focusing on zero-shot named entity recognition (NER), including the misalignment between model and human definitions of entity types, and confusion of similar types. This paper proposes an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. Specifically, we leverage the model to summarize the definition of each entity type and the distinctions between similar types (i.e., entity type guidelines). An iterative process is introduced to continually adjust and improve these guidelines. Additionally, since high-quality demonstrations are crucial for effective learning yet challenging to obtain in zero-shot scenarios, we design a strategy motivated by self-consistency and prototype learning to extract reliable and diverse pseudo samples from the model's predictions. Experiments on four benchmarks demonstrate the effectiveness of our framework, showing consistent performance improvements. Code is available at https://***/tongzeliang/ EvoPrompt. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Media Power Measuring via Emotional Contagion

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Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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FusionFlow: Neural Fusion and Compression for Communication-Efficient Edge-Cloud Collaborative Computing 18th

FusionFlow: Neural Fusion and Compression for Communication-...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Ningkang Wu, Guangyu Gu, Chao Yuan, Mu Li, Xiang-Yang School of Computer Science and Technology University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783031714665

With the recent advancement of the Internet of Things (IoT) and edge computing, there’s a surge in demand for effective intelligent data analytics, especially vision analysis. Neural network (NN) partitioning, an edge-cloud collaborative computing technique that divides NN models into distributable segments, offers a promising approach to deploy efficient AI-based intelligent system systems. However, to complete tasks such as after-the-fact video query and manual labeling in the cloud, the servers need to receive both intermediate features and original images, leading to significant communication redundancy. In this paper, we propose an approach to transmit a single data flow to support both image restoration and NN inference tasks. We design a bottleneck unit for fusing and compressing intermediate features and an image restoration module with self-attention blocks. Our proposed framework can be easily adapted to existing NN partitioning systems without modifying the structure of the model. With comprehensive evaluations, our system can reduce the amount of network transmission data by 57.3% within 1% accuracy loss, while introducing negligible overhead on local devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Communication Efficiency Edge Intelligence Model partitioning

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Meta-reinforcement learning-based task offloading method for UAV-enabled mobile edge computing

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Journal of Supercomputing 2025年第8期81卷 1-29页

作者： Liu, Yanpei He, Yanqiang Zhao, Haoyang Zhu, Liang Li, Zhigang Han, Chuang School of computer science and technology Zhengzhou University of Light Industry 136 Science Avenue Henan Zhengzhou450002 China

Existing task offloading models struggle to rapidly adapt to dynamic environmental changes in emergency scenarios, such as natural disasters or unforeseen events. To address this challenge, a meta-reinforcement learning-based task offloading method (MRLTO) for unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is proposed. First, an objective function for optimizing task offloading is formulated based on communication, local computing, and UAV computing models. Subsequently, the task offloading problem is reformulated as a Markov decision process (MDP). By incorporating a meta-learning framework, our approach consolidates the learning experiences of various mobile devices across diverse environments, allowing the offloading model to rapidly adapt to dynamic environmental changes. Furthermore, a dueling deep Q-network (D3QN) is employed to decouple state values and action advantage values, thereby improving task offloading decision mapping and dynamically generating more efficient offloading decisions. Finally, an experimental scenario is set up to compare MRLTO with RNOF, AGGO, and DTTO. The results show that MRLTO reduces the average latency by 8.14%, lowers the average energy consumption by 6.89%, and decreases the task drop rate by 12.89%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Drones

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