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A Near-Optimal Category information Sampling in RFID Systems

IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading 30th

Efficient Compute Resource Sharing of RISC-V Packed-SIMD Us...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Takata, Shogo Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan

ISBN: (纸本)9783031856372

AI tasks are gaining popularity in the area of IoT and edge devices. To run such tasks on devices, QNNs are used because of their reduced size and ability to be computed with simple integer arithmetic. There have been many implementations to support such a network format. However, when considering thread-level parallelism to speedup the program, many often implement a multi-core architecture or clusters which needs to copy all resources for each core. In this paper, we introduce a new RISC-V Out-of-Order Simultaneous Multi-Threading core "B4SMT" with RISC-V Packed-SIMD extension for evaluation. We also show that even a single executor could increase the performance of a 1D median filter by over 100×, and a matrix multiplication by over 30 on more than 16 threads efficiently. Furthermore, we suggest that other infrequently used executors may be placed as a shared resource efficiently in an SMT core. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Median filters

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

arXiv

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arXiv 2025年

作者： Cao, Yang Yang, Sikun Li, Chen Xiang, Haolong Qi, Lianyong Liu, Bo Li, Rongsheng Liu, Ming School of Computing and Information Technology Great Bay University China Great Bay Institute for Advanced Study Great Bay University China Graduate School of Informatics Nagoya University Japan School of Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum [East China China College of Cyberspace Security Zhengzhou University China School of Computer Harbin Engineering University China School of IT Deakin University Australia

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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Blockchain-based Edge Computing Service with Dynamic Entry and Exit Mechanism

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IEEE Transactions on Mobile Computing 2025年

作者： He, Qiang Feng, Zheng Fang, Hui Wang, Xingwei Zhao, Liang Yu, Keping Choo, Kim-Kwang Raymond Northeastern University College of Medicine and Biological Information Engineering China Shanghai University of Finance and Economics Research Institute for Interdisciplinary Science School of Information Management and Engineering China Northeastern University College of Computer Science and Engineering Shenyang110169 China Shenyang Aerospace University College of Computer Science and Engineering Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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A Hybrid Deep Learning Method for Forecasting Reservoir Water Level from Sentinel-2 Satellite Images

引用

computers, Materials and Continua 2025年第3期83卷 4915-4937页

作者： Hoang Thi Minh Chau Tran Thi Ngan Nguyen Long Giang Tran Manh Tuan Tran Kim Chau Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi 100000 Vietnam Faculty of Information Technology University of Economics Technology for Industries Hanoi 100000 Vietnam Artificial Intelligence Research Center VNU Information Technology Institute Vietnam National University Hanoi 100000 Vietnam International School Vietnam National University Hanoi 100000 Vietnam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi 100000 Vietnam Faculty of Computer Science and Engineering Thuyloi University Hanoi 100000 Vietnam Faculty of Water Resources Engineering Thuyloi University Hanoi 100000 Vietnam

Global climate change, along with the rapid increase of the population, has put significant pressure on water security. A water reservoir is an effective solution for adjusting and ensuring water supply. In particular, the reservoir water level is an essential physical indicator for the reservoirs. Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies. In recent years, deep learning models have been widely applied to solve forecasting problems. In this study, we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9, ConvLSTM, and linear interpolation to predict reservoir water levels. It utilizes data from Sentinel-2 satellite images, generated from visible spectrum bands (Red-Blue-Green) to reconstruct true-color reservoir images. Adam is used as the optimization algorithm with the loss function being MSE (Mean Squared Error) to evaluate the model’s error during training. We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam. To assess its performance, we also conducted comparative experiments with other related models, including SegNet_ConvLSTM and UNet_ConvLSTM, on the same dataset. The model performances were validated using k-fold cross-validation and ANOVA analysis. The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models. It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.

关键词： YOLOv9 ConvLSTM reservoir water level forecasting satellite images

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions 25

In-Group Love, Out-Group Hate: A Framework to Measure Affect...

引用

34th ACM Web Conference, WWW 2025

作者： Nettasinghe, Buddhika Rao, Ashwin Jiang, Bohan Percus, Allon G. Lerman, Kristina University of Iowa Tippie College of Business Iowa CityIA United States USC Information Sciences Institute Department of Computer Science Marina del ReyCA United States Arizona State University School of Engineering TempeAZ United States Claremont Graduate University Institute of Mathematical Sciences ClaremontCA United States

ISBN: (纸本)9798400712746

Affective polarization, the emotional divide between ideological groups marked by in-group love and out-group hate, has intensified in the United States, driving contentious issues like masking and lockdowns during the COVID-19 pandemic. Despite its societal impact, existing models of opinion change fail to account for emotional dynamics nor offer methods to quantify affective polarization robustly and in real-time. In this paper, we introduce a discrete choice model that captures decision-making within affectively polarized social networks and propose a statistical inference method estimate key parameters—in-group love and out-group hate—from social media data. Through empirical validation from online discussions about the COVID-19 pandemic, we demonstrate that our approach accurately captures real-world polarization dynamics and explains the rapid emergence of a partisan gap in attitudes towards masking and lockdowns. This framework allows for tracking affective polarization across contentious issues has broad implications for fostering constructive online dialogues in digital spaces. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

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Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer

arXiv

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arXiv 2025年

作者： Chen, Ziyang Zhang, Yongjun Li, Wenting Wang, Bingshu Wu, Yabo Zhao, Yong Chen, C.L. Philip College of Computer Science The State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Information Engineering Guizhou University of Commerce Guiyang550021 China School of Software Northwestern Polytechnical University Xi’an710129 China Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University Shenzhen518055 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

In light of the advancements in transformer technology, extant research posits the construction of stereo transformers as a potential solution to the binocular stereo matching challenge. However, constrained by the low-rank bottleneck and quadratic complexity of attention mechanisms, stereo transformers still fail to demonstrate sufficient nonlinear expressiveness within a reasonable inference time. The lack of focus on key homonymous points renders the representations of such methods vulnerable to challenging conditions, including reflections and weak textures. Furthermore, a slow computing speed is not conducive to the application. To overcome these difficulties, we present the Hadamard Attention Recurrent Stereo Transformer (HART) that incorporates the following components: 1) For faster inference, we present a Hadamard product paradigm for the attention mechanism, achieving linear computational complexity. 2) We designed a Dense Attention Kernel (DAK) to amplify the differences between relevant and irrelevant feature responses. This allows HART to focus on important details. DAK also converts zero elements to non-zero elements to mitigate the reduced expressiveness caused by the low-rank bottleneck. 3) To compensate for the spatial and channel interaction missing in the Hadamard product, we propose MKOI to capture both global and local information through the interleaving of large and small kernel convolutions. Experimental results demonstrate the effectiveness of our HART. In reflective area, HART ranked 1st on the KITTI 2012 benchmark among all published methods at the time of submission. Code is available at https://***/ ZYangChen/HART. Copyright © 2025, The Authors. All rights reserved.

关键词： Hadamard transforms

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