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Fine-Grained Access Control with Privacy-Preserving Data Retrieval for Cloud-Assisted IoV

IEEE Transactions on Vehicular technology 2025年

作者： Li, Wenchao Xia, Chunhe Yang, Simiao Wang, Kangming Huang, Guotao Huang, Luqi Guo, Fuchun Susilo, Willy Wang, Tianbo Beihang University School of Cyber Science and Technology Beijing100191 China University of Wollongong School of Computing and Information Technology Institute of Cybersecurity and Cryptology WollongongNSW2522 Australia Beihang University Beijing Key Laboratory of Network Technology Beijing100191 China Guangxi Normal University Guangxi Collab. Innovation Center of Multi-source Information Integration and Intelligent Processing Guilin541004 China Beihang University Sino-French Engineer School Beijing100191 China Beihang University Computer Science and Engineering School Beijing100191 China

In addition to the autonomous driving technology of single vehicles, the inter-group control algorithm serving the data sharing of multi-vehicle cooperative driving has also attracted widespread attention. To ensure secure communication, many encryption schemes have been proposed to protect the interaction data between vehicles. Nevertheless, traditional public key encryption schemes hinder the sharing of encrypted data. Based on the premise of ensuring the confidentiality of encrypted information, in order to facilitate efficient data sharing, conduct data searches across extensive cloud-based datasets, and authorize access under specified conditions, we introduce Fine-Grained Access Control with Privacy-Preserving Data Retrieval (FGAC-PPDR) for the Internet of Vehicles. This scheme offers a secure, flexible, and privacy-centric approach to data sharing for groups of vehicles in the IoV. Our proposed scheme enables encrypted data to be retrieved at the group level by the cloud server, and prevents vehicles outside the group from performing equality tests on the ciphertext. Furthermore, the data owner can create an authorization token with defined conditions to specify how the data is shared. During the process of data search and sharing, intensive computing tasks are undertaken by cloud servers with abundant computational resources. We also demonstrate that our scheme is secure against chosen ciphertext attacks (CCA). Finally, we provide security and performance analyses that verify the feasibility and effectiveness of our proposal. © 2025 IEEE.

关键词： Differential privacy

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An Advanced Cognitive Approach for Personalized Drug Formulation in Type 2 Diabetes Using Deep Neural Networks and Feature-based Fusion

An Advanced Cognitive Approach for Personalized Drug Formula...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Brijendra Pratap Singh Jagendra Singh Madhuri Suryavanshi Jailani S A. beer A. Amer Rakesh Kumar School of Computer Science Engineering & Technology Bennett University Greater Noida India Pimpri Chinchwad College of Engineering Pune India R&D Formulation Dev. Alpha Pharma KAEC Rabigh Saudi Arabia Sadat academy for management and Sciences Alexandria Egypt Department of Computer Science and I.T. Magadh University Bodh Gaya India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Personalized medicine is an innovative approach toward achieving improved therapeutic outcomes, notably in chronic diseases like Type 2 Diabetes (T2D), where individualised drug formulations represent the crux of treatment. Conventional methods of drug prescription tend to focus on generic approaches without considering the specific genetic, physiological, and lifestyle characteristics of individuals. This research fills this gap by developing an advanced deep learning-based method for personalised drug formulation in T2D. It integrates diverse patient data, including demographics, medical history, clinical measurements, genomic information, pharmacokinetic information, real-time monitoring data, and responses to previous treatments. For optimal drug formulation, it makes use of a combination of deep learning models, such as Recurrent Neural Network (RNN) for time-series prediction, Convolutional Neural Network (CNN) for genomic analysis, GAN for producing synthetic data, and auto-encoder for feature extraction. The dataset consists of 2700 patient records collected over one year, wherein the pre-processing phase includes handling missing values, normalising continuous variables, and encoding categorical features. Trained models were assessed based on precision, recall, F1-score, and AUC ROC levels; the best performance came from the RNN model with a maximum accuracy of 97.35%. CNN, GAN, and Autoencoder also obtained similar competitive results. However, the RNN-based model received the highest score in recommending the appropriate drugs. The application of this research can contribute to the broader discipline of precision medicine.

关键词： Drugs Recurrent neural networks Accuracy Autoencoders Genomics Generative adversarial networks Real-time systems Diabetes Convolutional neural networks Bioinformatics

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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors

Enhancing Performances of Deep Neural Networks with Ensemble...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing.

关键词： Deep learning Performance evaluation Accuracy Time series analysis Bidirectional long short term memory Performance gain Human activity recognition Ensemble learning Older adults Wearable sensors

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Local-photon model of the momentum of light

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Physical Review A 2025年第2期111卷 023703-023703页

作者： Gabriel Waite Daniel Hodgson Ben Lang Varghese Alapatt Almut Beige The School of Physics and Astronomy University of Leeds Leeds LS2 9JT United Kingdom Centre for Quantum Computation and Communication Technology and Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology Sydney NSW 2007 Australia The School of Physics and Astronomy University of Nottingham Nottingham NG7 2RD United Kingdom Instituto de Ciencia Molecular (ICMol) Universidad de Valencia E-46980 Paterna Valencia Spain

Recently we introduced a local photon approach for modeling the quantized electromagnetic field in position space. Using this approach, we define the momentum of light in this paper as in quantum mechanics as the generator for spatial translation. Afterward, we analyze the momentum dynamics of photonic wave packets which transition from air into a denser dielectric medium. Our analysis shines new light onto the Abraham-Minkowski controversy, which highlights the intricacies involved in the characterization of the momentum of the electromagnetic field. Although our results align with Minkowski's theory and with the definition of the canonical momentum of light in quantum electrodynamics, there are also some crucial differences.

关键词： Momentum

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Building Automated Computational Models for Predicting Energy Consumption in High-Performance Concrete Production

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Computational Economics 2025年 1-34页

作者： Yadav, Anupam Dasarathy, A. K. Thakur, Rishabh Abdulla, Mohammed Rauf Al-hedrewy, Marwea Padmapriya, R. Kedia, Navin Das, Priyadarshi Singh, Kamred Udham Department of Computer Engineering and Application GLA University Mathura India Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to Be University) Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Department of Construction and Project Management College of Engineering Alnoor University Mosul Iraq College of Technical Engineering the Islamic University Najaf Iraq College of Technical Engineering the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Civil Engineering Sathyabama Institute of Science and Technology Chennai India NIMS School of Civil Engineering NIMS University Rajasthan Jaipur India Department of Civil Engineering Siksha ‘O’ Anusandhan (Deemed to Be University) Bhubaneswar India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Dehradun India

This contribution provides a new energy consumption predictive model for producing high-performance concrete (HPC) using state-of-the-art machine learning techniques. Three of the most popular state-of-the-art regression models, Light Gradient Boosting Regression (LGBR), Extreme Gradient Boosting Regression (XGBR), and CatBoost Regression (CATR), are used in modeling energy demand. A novel hyperparameter optimization method called the Drawer Algorithm (DA) is introduced and used for improved model accuracy and generality in the training process. Beyond prediction, complete sensitivity analysis is performed to uncover and quantify the influence of the most vital features on energy consumption during different HPC production phases. The combination of robust ML models, an efficient optimization method, and complete and dense data on all of the main production phases of HPC enables the introduced framework to have robust accuracy, with the XGBR model optimized using DA (XGDA) yielding an R2 of 0.9875 and an RMSE of 53.6594. This study combines sophisticated machine learning and optimization strategies with exhaustive data from every phase of high-performance concrete production to produce a strong model for forecasting and optimizing energy consumption, minimizing costs, and encouraging building sustainability.

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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science China(Information sciences) 2025年第5期 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

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Integrated Communication and Navigation Enabled Low Earth Orbit Satellite Systems

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IEEE Network 2025年

作者： Liu, Binghong Peng, Mugen Li, Jiajing Yang, XuXu Liu, Yuanwei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Relying on the powerful communication capabilities, rapidly changing geometric configuration, and strong power, Low Earth Orbit (LEO) satellites own the potential to offer the integrated communication and navigation (ICAN) services with broader coverage, higher precision, and stronger reliability. Against this backdrop, LEO-ICAN has been attracting wide research attention. However, despite the above promising potential, many challenges are still unresolved in terms of architecture, key technologies and unified performance characterization. Therefore, in this article, we first elaborate three evolutionary stages of LEO-ICAN, where a fusional LEO-ICAN architecture is innovatively proposed to better meet the high-speed communication and high-precision navigation requirements of the emerging applications. Moreover, key technologies are discussed, and a use case referring to the joint optimization in fusional LEO-ICAN systems is studied, where we jointly optimize the satellite selection and beamforming design to simultaneously balancing the communication and navigation performance. Effectiveness of our proposed scheme is proved through extensive simulations. In addition, a performance evaluation index for LEO-ICAN systems is established based on the minimum variance unbiased estimate (MVUE), providing theoretical guidance and support for the improvement of both communication and navigation performance. Finally, open issues and future research directions are discussed. © 2024 IEEE.

关键词： Satellite communication systems

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Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Guo, Tong Mei, Yi Du, Wenbo Lv, Yisheng Li, Yumeng Song, Tao Beihang University State Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beijing100191 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Wellington New Zealand Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Jinan250353 China University of the Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Beijing Institute of Technology School of Aerospace Engineering Beijing China

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations. © 2020 IEEE.

关键词： Reinforcement learning

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Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-computer Interfaces

arXiv

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arXiv 2025年

作者： Jiang, Xiaowei Chen, Yanan Pal, Nikhil Ranjan Chang, Yu-Cheng Yang, Yunkai Do, Thomas Lin, Chin-Teng GrapheneX-UTS Human-centric AI Centre Australian AI Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Psychology and Behavior Henan University China Indian Statistical Institute Kolkata India

Fuzzy logic provides a robust framework for enhancing explainability, particularly in domains requiring the interpretation of complex and ambiguous signals, such as brain-computer interface (BCI) systems. Despite significant advances in deep learning, interpreting human emotions remains a formidable challenge. In this work, we present iFuzzyAffectDuo, a novel computational model that integrates a dual-filter fuzzy neural network architecture for improved detection and interpretation of emotional states from neuroimaging data. The model introduces a new membership function (MF) based on the Laplace distribution, achieving superior accuracy and interpretability compared to traditional approaches. By refining the extraction of neural signals associated with specific emotions, iFuzzyAffectDuo offers a human-understandable framework that unravels the underlying decision-making processes. We validate our approach across three neuroimaging datasets using functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG), demonstrating its potential to advance affective computing. These findings open new pathways for understanding the neural basis of emotions and their application in enhancing human-computer interaction. © 2025, CC BY.

关键词： Near infrared spectroscopy

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EASING SEASICKNESS THROUGH ATTENTION REDIRECTION WITH A MINDFULNESS-BASED BRAIN-computer INTERFACE

arXiv

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arXiv 2025年

作者： Bao, Xiaoyu Xu, Kailin Zhu, Jiawei Huang, Haiyun Li, Kangning Huang, Qiyun Li, Yuanqing School of Automation Science and Engineering South China University of Technology Guangzhou China Research Center for Brain-Computer Interface Pazhou Laboratory Guangzhou China School of Artificial Intelligence South China Normal University Foshan China School of Psychology South China Normal University Guangzhou China South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

Seasickness is a prevalent issue that adversely impacts both passenger experiences and the operational efficiency of maritime crews. While techniques that redirect attention have proven effective in alleviating motion sickness symptoms in terrestrial environments, applying similar strategies to manage seasickness poses unique challenges due to the prolonged and intense motion environment associated with maritime travel. In this study, we propose a mindfulness brain-computer interface (BCI), specifically designed to redirect attention with the aim of mitigating seasickness symptoms in real-world settings. Our system utilizes a single-channel headband to capture prefrontal electroencephalography (EEG) signals, which are then wirelessly transmitted to computing devices for the assessment of mindfulness states. The results are transferred into real-time feedback as mindfulness scores and audiovisual stimuli, facilitating a shift in attentional focus from physiological discomfort to mindfulness practices. A total of 43 individuals participated in a real-world maritime experiment consisted of three sessions: a real-feedback mindfulness session, a resting session, and a pseudofeedback mindfulness session. Notably, 81.39% of participants reported that the mindfulness BCI intervention was effective, and there was a significant reduction in the severity of seasickness, as measured by the Misery Scale (MISC). Furthermore, EEG analysis revealed a decrease in the theta/beta ratio, corresponding with the alleviation of seasickness symptoms. A decrease in overall EEG band power during the real-feedback mindfulness session suggests that the mindfulness BCI fosters a more tranquil and downregulated state of brain activity. Together, this study presents a novel nonpharmacological, portable, and effective approach for seasickness intervention, with the potential to enhance the cruising experience for both passengers and crews. © 2025, CC BY-NC-ND.

关键词： Electroencephalography

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