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arXiv 2025年

作者： Zhang, Haijun Ni, Jiaxin Wu, Zijun Liu, Xiangnan Leung, V.C.M. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing10083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden College of Computer Science and Software Engineering Shenzhen University Shenzhen51806 China VancouverBCV6T 1Z4 Canada

Large model has emerged as a key enabler for the popularity of future networked intelligent applications. However, the surge of data traffic brought by intelligent applications puts pressure on the resource utilization and energy consumption of the future networks. With efficient content understanding capabilities, semantic communication holds significant potential for reducing data transmission in intelligent applications. In this article, resource allocation driven by large models in semantic-aware networks is investigated. Specifically, a semantic-aware communication network architecture based on scene graph models and multimodal pre-trained models is designed to achieve efficient data transmission. On the basis of the proposed network architecture, an intelligent resource allocation scheme in semantic-aware network is proposed to further enhance resource utilization efficiency. In the resource allocation scheme, the semantic transmission quality is adopted as an evaluation metric and the impact of wireless channel fading on semantic transmission is analyzed. To maximize the semantic transmission quality for multiple users, a diffusion model-based decision-making scheme is designed to address the power allocation problem in semantic-aware networks. Simulation results demonstrate that the proposed large-model-driven network architecture and resource allocation scheme achieve high-quality semantic transmission. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Web

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H∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

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IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Marine Engineering Keelung202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Membership functions

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Prophet: SSD Failure Analysis and Prediction Guided by Flash Reliability Characteristics in Data Centers

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IEEE Transactions on computers 2025年

作者： Song, Yunpeng Liang, Yujiong Liu, Jialin Shi, Liang East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University School of Computer Science and Technology Shanghai China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing. In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different "States". We define the "State" of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of

关键词： Failure rate

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Eco-Friendly Laser Deterrent System for Woodpeckers Utilizing YOLOv8

Eco-Friendly Laser Deterrent System for Woodpeckers Utilizin...

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IEEE International Conference on Robotic Computing (IRC)

作者： Wonah Kim Seung-Hun Han Hyojin Kim Jaden Soroka Justin Allange Anthony Smith Artificial Intelligence Dong-A University Busan Republic of Korea Software and Computer Engineering Ajou University Suwon Gyeonggi-do Republic of Korea Computer and Information Technology Purdue University West Lafayette Indiana United States

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

This research introduces a novel approach for deterring woodpeckers to safeguard wooden structures in an environmentally-friendly way. Woodpecker drilling and drumming poses a significant threat to wooden structures, causing extensive physical and financial damage. These issues range from property damage to noise, underscoring the urgent need for an effective deterrent method. Our study proposes a laser-based deterrent mechanism, designed to be eco-friendly by minimizing effects on other wildlife and humans. This environmentally conscious approach offers a humane solution to woodpecker control, ensuring compliance with the Migratory Bird Treaty Act (MBTA). Additionally, our approach utilizes deep learning technology to chase away specific targets. Our system integrates YOLOv8 for accurate and low-power identification, creating an efficient operating solution with independent internet connectivity. This enhancement enables the system to adapt across various environments. Testing in a controlled environment has verified the system's effectiveness in deterring woodpeckers in an eco-friendly way. This approach ensures minimal impact on other wildlife and humans, adhering to legal and environmental constraints.

关键词： Deep learning Accuracy Wildlife Lasers Transfer learning Noise Real-time systems Robots Testing Overfitting

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Fashion Recommendation System for E-Commerce using Deep Learning Algorithms

Fashion Recommendation System for E-Commerce using Deep Lear...

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International Conference on Computing and Networking technology (ICCNT)

作者： M S Arunkumar R Gopinath M Chandru R Suguna S Deepa V Omprasath Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchgode Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation System (FRS) leveraging deep learning techniques to offer personalized fashion suggestions to users. The system utilizes a combination of convolutional neural networks (CNNs) for image feature extraction. Through a comprehensive analysis of user interactions and preferences, the FRS generates tailored recommendations that align with individual style preferences and current trends. The work proposes a novel approach that utilizes the ResNet50 algorithm to analyze images of fashion items, extracting meaningful features that capture the essence of style, color, and texture. By training the ResNet50 model on vast datasets of fashion images, thereby enhancing its ability to make accurate recommendations. Through the integration of user preferences and historical data, this system can generate personalized fashion recommendations tailored to each user’s unique style and preferences, thus enhancing user satisfaction and engagement. By harnessing the power of deep learning and the ResNet50 algorithm, fashion retailers can offer personalized shopping experiences, increase customer retention, and drive sales.

关键词： Deep learning Training Image color analysis Feature extraction Market research User experience Electronic commerce Convolutional neural networks Recommender systems Residual neural networks

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Learning Multitask Commonness and Uniqueness for Multimodal Sarcasm Detection and Sentiment Analysis in Conversation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1349-1361页

作者： Zhang, Yazhou Yu, Yang Zhao, Dongming Li, Zuhe Wang, Bo Hou, Yuexian Tiwari, Prayag Qin, Jing Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Hong Kong Artificial Intelligence Laboratory China Mobile Communication Group Tianjin Company Ltd. Tianjin300020 China College of Intelligence and Computing Tianjin University Tianjin300072 China Zhengzhou University of Light Industry Zhengzhou450002 China Software Engineering College Zhengzhou University of Light Industry Zhengzhou450002 China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China School of Information Technology Halmstad University Halmstad301 18 Sweden

Sarcasm is a form of figurative language device to express human inner feelings, where the author writes the positive sentence on surface form, while he/she actually expresses negative sentiment, vice versa. Sentiment, thus, comes into sight, and is closely related with sarcasm, leading to the recent popularity of multimodal sarcasm and sentiment joint detection in conversation (dialogue). The key challenges involve multimodal fusion and multitask interaction. Most of the existing studies have focused on building multimodal fused representation, while the commonness and uniqueness across related tasks has not received attention. To fill this gap, we propose a multimodal multitask interaction learning framework, termed MIL, for joint detection of sarcasm and sentiment. Specifically, a cross-modal target attention mechanism is proposed to automatically learn the alignment between texts and images/speeches. In addition, a multimodal interaction learning paradigm consisting of a dual-gating network, three separate fully connected layers that simultaneously capture the commonness and uniqueness. Comprehensive experiments on two benchmarking datasets (i.e., Memotion and MUStARD) show the effectiveness of the proposedmodel over state-of-the-art baselines with a significant improvement of 1.9%, 2.4% in terms of F1. Impact Statement-Human language is multi-affective in nature, where sentiment and sarcasm are often coupled with each *** propose a state-of-the-art multi-task learning (MTL) paradigm to jointly study them, which is of great significance for deepening the understanding of multi-affective analysis and emotional intelligence. The recent works still adopted the hard parameter sharing based MTL paradigm, which mainly focused on the commonness across tasks. Our work is quite different from them, where we make the first attempt to design a soft parameter sharing MTL paradigm, aiming to take the uniqueness of individual task into *** argue that both th

关键词： Sentiment analysis

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Energy-Efficient Tactile-Driven Rule Configuration and Anomaly Detection in Industrial IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Tan, Lizhuang Singh, Amritpal Zhang, Wei Pei, Hongjuan Zhang, Peiying Chahal, Prabhjot Kaur Singh, Maninderpal Ji'nan250014 China Pohang University of Science and Technology Department of Computer Science and Engineering Pohang37673 Korea Republic of Northumbria University Department of Computer and Information Sciences United Kingdom University of Chinese Academy of Sciences School of Engineering Science Beijing100049 China China University of Petroleum Qingdao Institute of Software College of Computer Science and Technology East China Qingdao266580 China Chandigarh Group of Colleges CEC Punjab Mohali India Southern Methodist University Electrical and Computer Engineering Department Dallas United States

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data is subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the Self-Organized Maps approach to distinguish between industrial traffic requiring tactile latencies and non-tactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic. © 2014 IEEE.

关键词： Table lookup

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Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Cui, Hui Sun, Changming Song, Yang Zheng, Jiangbin Cao, Leilei Wei, Leyi Su, Ran School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Software Shandong University Shandong China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. The two-stage structure complements with the uncertainty regularization strategy to avoid introducing extra modules in solving uncertainties and the aggregation mechanisms enable multi-scale and multi-stage feature integration. Comprehensive experimental results over the MoNuSeg and CRAG datasets show that our PG-FANet outperforms other state-of-the-art methods and our semi-supervised learning framework yields competitive performance with a limited amount of labeled data. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Optical encryption of gray image based on CS and CGH with double chaotic system

Optical encryption of gray image based on CS and CGH with do...

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World Conference on Mechanical engineering and Intelligent Manufacturing (WCMEIM)

作者： Xueyan Zhao Zhao Zhang Shuqi Niu Xintong Tian School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781665473705

In this paper, a novel image encryption algorithm is proposed by combining Logistic chaotic map, 4D NHS hyperchaotic system, compressed sensing (CS), computer generated hologram (CGH) and two-dimensional discrete cosine transform (DCT). Firstly, the gray image is sparse represented by two-dimensional discrete cosine transform (DCT). The sparse matrix obtained by index sort scrambling is scrambled. For further encryption, the pixel scrambling uses the Logistic chaotic system to generate the scrambling vector. Subsequently, CGH encryption was performed and the Logistic chaotic map and 4D NHS hyperchaotic system were used as double keys to obtain the final encrypted picture.

关键词： Optical imaging Encryption Manufacturing Discrete cosine transforms Sparse matrices Optical sensors Indexes

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TD3-Based Collaborative Computation Offloading and Charging Scheduling in Multi-UAV-Assisted MEC Networks

TD3-Based Collaborative Computation Offloading and Charging ...

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IEEE Conference on Wireless Communications and Networking

作者： Liang Zhao Yujun Yao Huan Zhou Hao Wang Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users (MUs)' tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks increase, a single UAV is difficult to meet all computational demands, result in the decline of QoS. To address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without charging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the highest overall system utility under different scenarios.

关键词： Processor scheduling Computational modeling Inductive charging Collaboration Charging stations Scheduling Resource management

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