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MoreGCN: Distributed IoT Service Recommendation Considering Temporal User Interest Dynamics

IEEE Internet of Things Journal 2025年第10期12卷 13657-13670页

作者： Zhou, Yu Hu, Chunqiang Liu, Zewei Xing, Xiaoshuang Li, Xingwang Chongqing University School of Big Data and Software Engineering Chongqing401331 China China Southern Power Grid Joint Laboratory on Cyberspace Security Guangdong510663 China Changshu Institute of Technology School of Computer Science and Engineering Suzhou215556 China Henan Polytechnic University College of Physics and Electronic Information Engineering Jiaozuo454000 China

With the continuous development of Internet of Things (IoT), significant value has been generated, but numerous challenges remain. Recommender systems, as an effective tool to optimize IoT services, can significantly enhance user experience. However, the IoT’s demands for low latency and high-computational load make it difficult for traditional recommender systems to adapt. Moreover, traditional approaches often overlook the dynamic nature of user preferences, which are crucial for determining user satisfaction with IoT services. To address these issues, we propose a novel method called MoreGCN, which quantifies the temporal evolution of user preferences and integrates user interest modeling to accurately match similar users. This approach guides the learning of convolutional networks during the recommendation process. Deployed within a distributed computing framework and combined with meta computing, MoreGCN significantly improves computational efficiency and recommendation accuracy. Experimental results demonstrate that, across three benchmark datasets, MoreGCN consistently outperforms several existing state-of-the-art methods in terms of performance. © 2014 IEEE.

关键词： Internet of Things Recommender systems Computational modeling Accuracy Load modeling Edge computing Feature extraction Collaborative filtering Vectors Servers

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Lift: Exploiting Hybrid Stacked Memory for Energy-Efficient Processing of Graph Convolutional Networks 23

Lift: Exploiting Hybrid Stacked Memory for Energy-Efficient ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jiaxian Chen Zhaoyu Zhong Kaoyi Sun Chenlin Ma Rui Mao Yi Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350323481

Graph Convolutional Networks (GCNs) are powerful learning approaches for graph-structured data. GCNs are both computing- and memory-intensive. The emerging 3D-stacked computation-in-memory (CIM) architecture provides a promising solution to process GCNs efficiently. The CIM architecture can provide near-data computing, thereby reducing data movement between computing logic and memory. However, previous works do not fully exploit the CIM architecture in both dataflow and mapping, leading to significant energy *** paper presents Lift, an energy-efficient GCN accelerator based on 3D CIM architecture using software and hardware co-design. At the hardware level, Lift introduces a hybrid architecture to process vertices with different characteristics. Lift adopts near-bank processing units with a push-based dataflow to process vertices with strong re-usability. A dedicated unit is introduced to reduce massive data movement caused by high-degree vertices. At the software level, Lift adopts a hybrid mapping to further exploit data locality and fully utilize the hybrid computing resources. The experimental results show that the proposed scheme can significantly reduce data movement and energy consumption compared with representative schemes.

关键词： 3D-stacked memory

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Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing 34

Dealing with Noisy Data in Federated Learning: An Incentive ...

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34th ACM Web Conference, WWW 2025

作者： Wang, Hengzhi Chen, Haoran Ma, Minghe Cui, Laizhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798400712746

Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client’s data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism. © 2025 Copyright held by the owner/author(s).

关键词： Federated learning incentive mechanism noisy data

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Research on Chip Special Layout Recognition Technology 24

Research on Chip Special Layout Recognition Technology

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Jimao Jia Software Engineering in College of Computer Science College of Software Inner Mongolia University Hohhot Inner Mongolia China

ISBN: (纸本)9798400709753

Chip analysis is an important means of chip research, and with the progress of the chip process, the traditional means are subject to great challenges. For this reason, the research and application of chip automation and intelligent technology is an important topic. In this paper, for the chip analysis in the layout presents special process holes, missing effective elements, poor image quality and other characteristics, the existing recognition algorithms have been applied to the difficulties of bad results, proposed deep learning-based improvement algorithms, basically solves the layout of the large holes, hollow holes, dark holes, and other special elements of the problem, the recognition effect reaches the expected.

关键词： chip analysis

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Human Action Recognition in Multi-Level Convolutional Temporal Attention Network

Human Action Recognition in Multi-Level Convolutional Tempor...

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

作者： Qian Huang ZhongQi Chen Chang Li WeiWen Qian College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action recognition. Experiments on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches.

关键词： Human computer interaction Convolution Time series analysis Noise Speech recognition Medical services Human activity recognition Speech processing Older adults Faces

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Leveraging Chain of Thought for Automated Medical Coding of Lymphoma Cases 10th

Leveraging Chain of Thought for Automated Medical Coding o...

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10th China Health Information Processing Conference, CHIP 2024

作者： Song, Wenlong Hu, Jiaxin Li, Zixuan Yuan, Chi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9789819642977

Objective: This study aims to explore the application of Chain of Thought (CoT) reasoning in automating ICD coding, specifically focusing on lymphoma cases. By leveraging large language models (LLMs) and CoT prompting, the research aims to improve the accuracy and interpretability of automated ICD coding in healthcare. Methods: We optimized CoT prompting for Qwen 2.5:14b to guide the model in reasoning through the diagnostic process in a step-by-step manner. There are three main components for our pipeline. This framework was then evaluated on multiple LLMs, including Qwen 2.5, Llama 3.1, and Gemma 2, using a clinical lymphoma dataset for automated ICD-10 and ICD-O coding tasks. The models were assessed on their ability to extract relevant diagnoses from clinical text and map them to appropriate ICD codes. Results: The results indicated that CoT reasoning improves the interpretability and accuracy of ICD coding. However, the generalizability of CoT-based prompt design is not guaranteed for all the LLMs. Prompts optimized for one model do not consistently transfer to others, highlighting the importance of customizing prompts for each specific model. Conclusion: Our findings suggest that CoT reasoning can enhance automated ICD coding when tailored to the characteristics of each model. Future work should focus on optimizing CoT prompting techniques for different models to enhance generalizability and improve the scalability of automated coding systems in healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Oncology

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Adversarial Knowledge Transfer for Black-Box Model Inversion Attack

Adversarial Knowledge Transfer for Black-Box Model Inversion...

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

作者： Xinhao Liu Zetao Lin Yingzhao Jiang Qiao Yan College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in model inversion attacks have raised privacy concerns, exploiting access to models to reconstruct private training data from inputs and outputs. These attacks are categorized as white-box, black-box, or label-only based on access level. We propose a new black-box model inversion attack, Label-Controlled Adversarial Knowledge Transfer (L-AdKT). L-AdKT leverages adversarial training with a Generative Adversarial Network (GAN) and a substitute model to extract knowledge from the target model. The substitute model minimizes discrepancies with the target model while guiding the generator to produce realistic samples. This approach enables white-box techniques to be applied in black-box settings. Experiments show that L-AdKT outperforms state-of-the-art black-box attacks by over 20% across benchmarks and remains robust against various defense mechanisms.

关键词： Training Closed box Training data Signal processing Generative adversarial networks Data models Speech processing Knowledge transfer Glass box Resilience

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Spatially Compact Dense Block Mining in Spatial Tensors 25

Spatially Compact Dense Block Mining in Spatial Tensors

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Weike Tang Dingming Wu Tsz Nam Chan Kezhong Lu College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400712456

Spatial tensors have been extensively used in a wide range of applications, including remote sensing, geospatial information systems, conservation planning, and urban planning. We study the problem of Spatially Compact Dense (SCD) block mining in a spatial tensor, which targets for discovering dense blocks that cover small spatial regions. However, most of existing dense block mining (DBM) algorithms cannot solve the SCD-block mining problem since they only focus on maximizing the density of candidate blocks, so that the discovered blocks are spatially loose, i.e., covering large spatial regions. Therefore, we first formulate the problem of mining top-k Spatially Compact Dense blocks (SCD-blocks) in spatial tensors, which ranks SCD-blocks based on a new scoring function that takes both the density value and the spatial coverage into account. Then, we adopt a filter-refinement framework that first generates candidate SCD-blocks with good scores in the filtering phase and then uses the traditional DBM algorithm to further maximize the density values of the candidates in the refinement phase. Due to the NP-hardness of the problem, we develop two types of solutions in the filtering phase, namely the top-down solution and the bottom-up solution, which can find good candidate SCD-blocks by approximately solving the new scoring function. The evaluations on four real datasets verify that compared with the dense blocks returned by existing DBM algorithms, the proposed solutions are able to find SCD-blocks with comparable density values and significantly smaller spatial coverage.

关键词： dense block

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Generative Diffusion Model-Based Deep Learning Framework for Remaining Useful Life Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 18431-18434页

作者： Ha, Sangjun Sung, Mingyu Saeed, Faisal Yun, Sangseok Kim, Il-Min Kang, Jae-Mo Kyungpook National University Department of Artificial Intelligence Daegu41566 Korea Republic of Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Pukyong National University Department of Information and Communications Engineering Busan48513 Korea Republic of Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada

In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: (i) a feature extractor corresponding to the encoder part of our customized U-Net and (ii) a RUL predictor constructed by a multi-layer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pre-trained for high-quality feature learning, and then, is re-trained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme. © 2014 IEEE.

关键词： Prediction models

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A Paradigm Shift to Causal Model-Driven Decision-Making With Generative AI 13th

A Paradigm Shift to Causal Model-Driven Decision-Making Wit...

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13th International Conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： He, Sheng Ning, Yishuang Zhang, Liang-Jie Lei, Kai National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen518057 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Peking University Shenzhen518055 China

ISBN: (纸本)9783031776809

In recent years, the rise of big data has popularized data-driven decision-making. However, the interpretability shortcomings of artificial intelligence (AI) models limit their reliability for critical decisions. This paper proposes a paradigm shift from conventional data-driven to causal model-driven decision-making, leveraging advancements in causal inference and large language models (LLMs). By applying the capabilities of generative AI, this paradigm shift enables the fusion of extensive domain knowledge, facilitating the development of causal models that capture the complexity of real-world systems and problems. The model-driven approach provides a better understanding of the causal mechanisms, relationships, and dynamics compared to correlational data-driven methods. Moreover, we introduce the concept of a composable business model based on modular causal components. We present a methodology for constructing robust causal model-driven decision frameworks, emphasizing the comprehensive utilization of generative AI to incorporate domain knowledge with causal inferences. Through an in-depth analysis of case studies across multiple domains, this model-driven approach empirically showcases its potential to improve decision quality, optimize resource allocation, and enhance process efficiency. Additionally, we critically discuss future research directions and challenges in the evolutionary trajectory of causal model-driven decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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