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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

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computers in Biology and Medicine 2025年 185卷 109593-109593页

作者： Khan, Zeshan Aslam Waqar, Muhammad Raja, Muhammad Junaid Ali Asif Chaudhary, Naveed Ishtiaq Khan, Abeer Tahir Mehmood Anwar Raja, Muhammad Asif Zahoor International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Rawal Institute of Health Sciences Islamabad Pakistan

Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria. The plasmodium falciparum infection, the disease origin causes the intestinal illness. Fortunately, advances in artificial intelligence techniques have made it possible to use visual data sets to quickly and effectively diagnose malaria which has also proven to be cost and time effective. In literature, several DL approaches have previously been used with good precision but suffer from computational inefficiency and interpretability. Therefore, this research proposes a generalized fractional order-based explainable lightweight convolutional neural network model to overcome these limitations. The fractional order optimization algorithms have proven worth in terms of estimation accuracy and convergence speed for different applications. The proposed fractional order optimizer-based model offers an improved solution to malaria disease diagnosis with a percentage accuracy of 95 % using the standard NIH dataset and outperforms the existing complex models concerning speed and effectiveness. The proposed fractionally optimized lightweight CNN model has shown substantial performance on the external MP-IDB dataset and M5 test set as well by achieving a generalized test accuracy of 92 % and 90.4 % which verifies the robustness and generalizability of the proposed solution under available circumstances. Moreover, the efficacy of the proposed lightweight architecture is endorsed through evaluation metrics of precision, recall, and F1-score. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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Nonsaturated large magnetoresistance and transport dynamics in a n-doped cryogenic WSe2 field-effect device

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Physical Review B 2025年第12期111卷 125307-125307页

作者： Wei-Chen Lin Ching-Chen Yeh Jia-Zhu Zou Nhat Anh Nguyen Phan Inayat Uddin Hai Yen Le Thi Kenji Watanabe Takashi Taniguchi Gil-Ho Kim Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Physics National Taiwan University Taipei 106 Taiwan Department of Electrical and Computer Engineering Sungkyunkwan University (SKKU) Suwon 164 Republic of Korea Sungkyunkwan Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan

Interest in the two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) continues to intensify, driven by their suitable band gaps to supplant silicon as next-generation semiconductor materials. Among various TMDs, tungsten diselenide (WSe2) is renowned for its superior electrical properties in carrier density and mobility under ambient conditions. Despite its notable attributes, the behavior of monolayer WSe2 in the electron-doped regime under cryogenic conditions remains largely uncharted, particularly concerning its magnetotransport properties. In this study, we reveal the transport mechanisms of monolayer WSe2 from high temperatures down to the cryogenic regime. As evident by Efros–Shklovskii variable-range hopping (E-S VRH) in the cryogenic regime, strong Coulomb interactions arise between electrons. Above 8 K, an uncommon nonsaturated quadratic large magnetoresistance (MR) can be explained by the wave-function shrinkage model, which is consistent with the E-S VRH transport mechanism. Notably, the nonsaturated quadratic large MR shows a magnitude up to 1740% at 13 T. These findings underscore the potential applications for monolayer WSe2 in cryogenic field-effect devices, magnetic sensors, and memory devices and mark a significant advance in magnetotransport research.

关键词： Monolayers

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Comparative Study on Electromagnetic Performance of Slotted and Slotless PM Synchronous Motors According to Aspect Ratios

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IEEE Transactions on Magnetics 2025年

作者： Hong, Hyeonbin Lee, Jae-Kwang Jung, Dong-Hoon Lee, Ju Kim, Hyunwoo Hanyang University Department of Electrical Engineering Seoul04763 Korea Republic of Korea Electronics Technology Institute Intelligent Mechatronics Research Center Bucheon14502 Korea Republic of Andong National University Department of Electrical and Materials Engineering Andong36729 Korea Republic of Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47907 United States

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high torque density. PMSM are generally classified into slotted and slotless types, each offering distinct advantages. A key design parameter of PMSM is the aspect ratio, defined as the ratio of the stator outer diameter to the stack length, which significantly affects electromagnetic performance and application suitability. In this paper, a comparative study of 1.35kW slotted and slotless PMSM is proposed based on aspect ratios under the same volume, using a finite element analysis (FEA). Although the volume of PMSM primarily determines electromagnetic performance, such as electromagnetic torque and loss, the aspect ratio is also crucial because of its impact on magnetic leakage flux. Constraints, including the volume of PMSM, magnet thickness, and current density, are selected to compare electromagnetic performances based on aspect ratio. Optimal models of slotted and slotless PMSM are designed, and their electromagnetic performances are compared. The optimal slotless PMSM achieves a torque ripple approximately 31.08%p lower than that of the optimal slotted PMSM. Furthermore, the total harmonic distortion (THD) of the induced voltage is reduced by approximately 20.16%p. To verify this study, the 1.35kW optimal slotless PMSM is manufactured and experimentally tested, showing errors of less than 5% compared to FEA results. © 1965-2012 IEEE.

关键词： Aspect ratio

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging

arXiv

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arXiv 2025年

作者： Wang, Junjia Xiong, Bo Zhou, You Cao, Xun Ma, Zhan School of Electronic Science and Engineering Nanjing University Nanjing210023 China National Engineering Research Center of Visual Technology Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China Medical School Nanjing University Nanjing210093 China

The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Silicon carbide-graphene nanocomposite electrodes for enhanced desalination via capacitive deionization

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Ceramics International 2025年

作者： Yousef, Ayman Hameed, R.M. Abdel Maafa, Ibrahim M. Abutaleb, Ahmed El-Halwany, M.M. Department of Chemical Engineering College of Engineering and Computer Sciences Jazan University Jazan45142 Saudi Arabia Chemistry Department Faculty of Science Cairo University Giza Egypt Engineering and Technology Research Center Jazan University P.O.Box 114 Jazan82817 Saudi Arabia Department of Mathematics and Physics Engineering Faculty of Engineering Mansoura University El-Mansoura Egypt Faculty of Engineering Mansoura National University Egypt

Graphene nanoflakes [GNFs] were ultrasonically modified with silicon carbide nanopowder with subsequent hydrothermal heating at 120 °C. A number of these SC-GNFs nanocomposites was fabricated based on altering their metallic carbide content [5, 10 and 15 wt%] in relation to that of carbonaceous substrate. The chemical composition, morphology and interaction with water droplets of SC-GNFs nanostructures were examined using suitable physical characterization tools. The ability of these carbide containing graphene nanomaterials to desalinate water samples was evaluated through varied electrochemical measurements. Outstanding specific capacitances could be measured at different SC-GNFs nanocomposites when related to those of bare components of SiC and GNFs. Incorporating 10 wt% SiC between graphene nanoflakes could significantly promote their capacitance by about nine folds. Galvanostatic charge-discharge experiments of SC-GNFs nanocomposite revealed good stability attitude within longer periods. Increased salt removal efficiencies were also demonstrated at all SC-GNFs nanopowders up to 83.33 % when contrasted to those of SiC [10.42 %] and GNFs [12.50 %]. These findings at graphene nanoflakes surfaces after their treatment with SiC nanoparticles might suggest further studies for relevant metallic carbides onto convenient carbonaceous based supports for valuable CDI systems. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Deionized water

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4D printing of dual-stage curing ink for graded functional materials and direct, robust, integrated shape morphing

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engineering Structures 2025年 336卷

作者： Wang, Yafei Liang, Weiqian Cai, Wei Zhang, Jingyi Guo, Weijia Qi, Biyun Zhang, He Lu, Haibao Ding, Zhen State Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of Technology Harbin150080 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China College of Computer Science and Cyber Security Chengdu University of Technology Chengdu610059 China Department of Mechanical Engineering Beijing Institute of Technology Beijing100081 China National Engineering Research Center of Novel Equipment for Polymer Processing South China University of Technology Guangzhou510641 China

4D printing marks a significant leap beyond traditional 3D printing, enabling structures to morph and adapt dynamically to changing environmental conditions. research efforts are increasingly being focused on this promising technology. Existing 4D printing methods, however, typically rely on shape-changing mechanisms that operate after material solidification in the 3D printing process, necessitating additional energy inputs or even manual mechanical interventions. In this study, we introduce a novel 4D printing approach that employs a specially designed dual-stage curing ink optimized for grayscale Digital Light Processing (g-DLP) 3D printing. This novel approach not only grants precise, on-demand control over the material properties of the printed structures but also seamlessly integrates shape transformation into the thermal curing stage of the 3D printing process, eliminating the need for extra energy or manual steps. Furthermore, these transformed shapes demonstrate exceptional robustness and permanence, as evidenced by our experimental results. We conduct a comprehensive investigation into the grayscale-dependent mechanical properties of this ink and highlight its potential applications through auxetic metamaterial lattice structures. The underlying mechanism behind this direct shape morphing during thermal curing is explored in depth, and effectively demonstrated in achieving desired shape transformations across various printed structures. The good agreement between our experimental results and simulations provides robust validation of our methodology. Our study provides a reliable framework for designing and optimizing 4D-printed structures with tailored properties and deformation behaviors, paving the way for future applications in various fields. © 2025 Elsevier Ltd

关键词： Thermal printing

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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