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HeX: Encrypted Rich Queries with Forward and Backward Privacy Using Trusted Hardware

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Haotian Peng, Zhe Xiao, Jiang Xue, Lei Lin, Chenhao Chung, Sai-Ho The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hung Hom Hong Kong The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong Huazhong University of Science and Technology School of Computer Science and Technology China Sun Yat-sen University School of Cyber Science and Technology China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China

Dynamic searchable symmetric encryption (DSSE) schemes empower data owners to outsource their encrypted data to clouds while retaining the ability to update or search on it. Despite a lot of efforts devoted in recent years, there are still several challenges that have not been well addressed. Firstly, the confidentiality of data might be compromised if forward privacy and backward privacy cannot be ensured. Secondly, only the traditional single keyword-file search has attracted tremendous attention, while other popular queries like Boolean queries and range queries are not fully investigated. Lastly, how to solve these problems on untrusted servers that may deviate from pre-defined protocols is also challenging. In this paper, aiming to tackle the above problems, we propose a novel DSSE scheme named HeX based on Trusted Execution Environment (TEE) that supports rich queries on untrusted servers while guaranteeing forward and backward privacy. We achieve strong forward and backward security by designing a deferred obfuscating read-write technique atop the bitmap index. We further extend the basic scheme to realize Boolean queries and range queries by reducing them to basic keyword queries. Strict theoretical analysis is conducted to prove the security of HeX, and extensive evaluations illustrate its efficiency and practicality. © 2004-2012 IEEE.

关键词： Differential privacy

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Subgrid informed neural networks for high-resolution flood mapping

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Journal of Hydrology 2025年 660卷

作者： Herath, Herath Mudiyanselage Viraj Vidura Marshall, Lucy Saha, Abhishek Rasnayaka, Sanka Seneviratne, Sachith Faculty of Science and Engineering Macquarie University SydneyNSW Australia Faculty of Engineering The University of Sydney SydneyNSW Australia Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Hydroinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore Transport Health and Urban Systems Research Lab Melbourne School of Design The University of Melbourne MelbourneVIC Australia Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC Australia

Physics-based hydrodynamic models are essential for accurate flood prediction but are computationally expensive, limiting their applicability for real-time forecasting and probabilistic analyses. Conversely, pure machine learning (ML) models offer both computational efficiency and accuracy but often lack interpretability. To address this gap, we propose SGUnet, a physics-informed ML model and a hybrid theory-guided data science approach, for rapid, high-resolution flood mapping. It utilizes a neural network with U-Net architecture and integrates subgrid-based coarse-grid hydrodynamic model predictions as initial estimates, upskilling them to achieve fine-grid model accuracy. Unlike traditional hydrodynamic models, the subgrid method embeds fine-scale topographic details within coarse-grid cells, enhancing both computational efficiency and predictive accuracy. SGUnet processes flood depth raster patches (512 × 512 pixels) and corresponding digital elevation models as inputs. It functions as a deep learning-based corrector, refining flood predictions from numerical simulators. Trained through supervised learning, SGUnet learns to correct deviations in coarse-grid predictions using fine-grid model outputs as target values. The model is evaluated across three large Australian watersheds—Wollombi, Chowilla, and Burnett River—using HEC-RAS flood simulations with subgrid formulation. SGUnet reduces root mean squared error by a factor of 4.5–5.3 compared to coarse-grid models, achieves a critical success index exceeding 0.9 for flood extent mapping, and delivers a 50x speed-up over fine-grid hydrodynamic models. Furthermore, SGUnet outperforms a state-of-the-art ML-based upskilling model in depth and extent predictions. By effectively correcting flood artifacts from coarse-grid models, SGUnet achieves near fine-grid accuracy with significantly reduced computational cost, demonstrating its potential for real-time flood risk assessment. © 2025 The Authors

关键词： Deep learning

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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach

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Scientific Reports 2025年第1期15卷 1-33页

作者： Hassan, Osama F. Ibrahim, Ahmed F. Gomaa, Ahmed Makhlouf, M.A. Hafiz, B. Information Systems Department Faculty of Computers and Informatics Suez Canal University Ismailia 41522 Egypt Artificial Intelligence Department Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai 46511 Egypt Computer Science and Engineering Department Egypt Japan University of Science and Technology (E-JUST) Alexandria 21934 Egypt Computer Science and Engineering Department National Research Institute of Astronomy and Geophysics (NRIAG) Helwan 11731 Egypt

Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging state-of-the-art transformer architectures and transfer learning models to achieve unprecedented accuracy and reliability. The proposed methodology addresses key challenges in drowsiness detection by integrating advanced data preprocessing techniques, including image normalization, augmentation, and region-of-interest selection using Haar Cascade classifiers. We employ the MRL Eye Dataset to classify eye states into “Open-Eyes” and “Close-Eyes,” evaluating a range of models, including Vision Transformer (ViT), Swin Transformer, and fine-tuned transfer learning models such as VGG19, DenseNet169, ResNet50V2, InceptionResNetV2, InceptionV3, and MobileNet. The ViT and Swin Transformer models achieved groundbreaking accuracy rates of 99.15% and 99.03%, respectively, outperforming all other models in precision, recall, and F1-score. To ensure the generalization and robustness of the proposed models, we also evaluate their performance on the NTHU-DDD and CEW datasets, which provide diverse real-world scenarios and challenging conditions. This represents a significant advancement over existing methods, demonstrating the effectiveness of transformer-based architectures in capturing complex spatial dependencies and extracting relevant features for drowsiness detection. The proposed system also incorporates a real-time drowsiness scoring mechanism, which triggers alarms when prolonged eye closure is detected, ensuring timely intervention to prevent accidents. A key novelty of this work lies in the integration of Class Activation Mapping (CAM) for enhanced model interpretability, allowing the system to focus on critical eye regions and improve decision-making transparency. The system was rigorously tested under varying lighting condit

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Domain adaptive FL for edge-enabled privacy-preserving MRI analysis

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Alexandria engineering Journal 2025年 128卷 324-339页

作者： Saeed Iqbal Imran Arshad Choudhry Inam Ullah Kuljeet Kaur Bong Jun Choi Mohammad Mehedi Hassan College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 China Department of Computer Science Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Department of Computer Engineering Gachon University Seongnam 13120 Republic of Korea Electrical Engineering Department École de technologie supérieure Montréal QC H3C1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India School of Computer Science and Engineering Soongsil University Seoul Republic of Korea Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Data heterogeneity, privacy leakage challenges, the ineffectiveness of conventional collaborative learning techniques, and unresolved managing non-IID data distributions are some of the major obstacles to implementing artificial intelligence (AI) in healthcare. Federated learning (FL) frameworks frequently have trouble distinguishing between privacy protection and model accuracy, especially when used for delicate medical imaging applications. This study presents a novel framework that synergizes federated learning (FL) with edge computing to address these issues while safeguarding patient privacy. Our proposed Domain Adaptive Federated (DAD) learning approach effectively mitigates both inter-client and intra-client data heterogeneity, enabling collaborative model training across diverse medical imaging modalities (MRI, CT, PET) through cross-domain adaptation. Experimental evaluations on MRI brain segmentation datasets demonstrate the superior performance of DAD compared to traditional FL methods, as evidenced by significant improvements in F1-score (96.3), sensitivity (96.0), specificity (97.1), and AUC (96.7). This enhanced accuracy and robustness in handling heterogeneous and privacy-sensitive data render DAD an ideal candidate for privacy-preserving AI in consumer healthcare. By pioneering innovative strategies for collaborative model training and data privacy, this research contributes to the emerging field of edge intelligence, paving the way for improved patient outcomes while adhering to stringent confidentiality and ethical mandates.

关键词： Multi-modal data Domain adaptive learning Federated learning Privacy preservation Domain-specific Data privacy

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A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade

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Social sciences and Humanities Open 2025年 11卷

作者： Kovari, Attila Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

This review examines the current state of integration and impact of AI-enhanced collaborative learning in the higher education sector. Given the rapid advances in technology, AI has enormous potential for application in educational settings, with benefits in terms of personalizing learning, better engaging learners and improving learning outcomes. Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. Furthermore, this review concludes that predictive analytics and multimodal approaches supported by artificial intelligence have been shown to enhance student engagement and motivation, while personalized learning systems and recommender algorithms ensure the effectiveness of collaborative learning environments. It also identifies two other critical issues: good task design and effective emotional engagement and social presence in AI-based environments. In addition, it highlights some of the problems and ethical considerations arising from the integration of AI like transparency, data protection, and a balance between full automation and human touch. This review aims to integrate the current state and future opportunities of AI-enhanced collaborative learning within a higher education context to inform educators, researchers, and policy makers in pursuit of improving teaching and learning practices. © 2025 The Author

关键词： AI-Powered collaborative learning Educational outcomes Higher education Personalized learning Social presence Student engagement

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Unmanned aerial vehicle based multi-person detection via deep neural network models

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Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

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Energy generation in a multi-sided photovoltaic system: Analysis of the prediction effectiveness

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Renewable Energy 2025年 251卷

作者： Zator, Slawomir Osuchowski, Jakub Kasana, Singara Singh Shelke, Nitin Arvind Tomaszewski, Michal Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh India School of Computer Science and Engineering Technology Bennett University UP Greater Noida India

This article investigates the role of multi-oriented photovoltaic (PV) systems in enhancing energy self-sufficiency and reducing greenhouse gas emissions. Focusing on the evaluation of energy production prediction performance, the study utilizes data from four multi-oriented PV installations from 2022, standardized to 1 kWp capacity for comparative analysis. A custom-built measurement system, incorporating single-phase and three-phase meters, and solar irradiance data from the Solcast API, facilitated the creation of a dataset for predictive modeling. The Random Forest machine learning algorithm was employed to develop predictive models, enabling a comprehensive analysis of different installation orientations, including feature importance, prediction errors, and self-consumption and self-sufficiency rates. The study introduces new indicators alongside typical metrics to assess the accuracy of energy production predictions, aiming to address the surplus or deficit in energy forecasting. The results offer valuable insights into optimizing energy generation and grid integration of PV systems, highlighting the potential for improved prediction reliability and precision in multi-oriented installations. © 2025

关键词： Random forests

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AI-based models to predict decompensation on traumatic brain injury patients

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computers in Biology and Medicine 2025年 186卷 109634-109634页

作者： Ribeiro, Ricardo Neves, Inês Oliveira, Hélder P. Pereira, Tania INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal FCUP - Faculty of Sciences of the University of Porto Porto Portugal ULS - Unidade Local de Saúde São José Lisbon Portugal FCTUC - Faculty of Sciences and Technology of the University of Coimbra Coimbra Portugal

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%–40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation. This study uses learning models based on sequential data from Electronic Health Records (EHR). Decompensation prediction was performed based on 24-h in-mortality prediction at each hour of the patient's stay in the Intensive Care Unit (ICU). A cohort of 2261 TBI patients was selected from the MIMIC-III dataset based on age and ICD-9 disease codes. Logistic Regressor (LR), Long-short term memory (LSTM), and Transformers architectures were used. Two sets of features were also explored combined with missing data strategies by imputing the normal value, data imbalance techniques with class weights, and oversampling. The best performance results were obtained using LSTMs with the original features with no unbalancing techniques and with the added features and class weight technique, with AUROC scores of 0.918 and 0.929, respectively. For this study, using EHR time series data with LSTM proved viable in predicting patient decompensation, providing a helpful indicator of the need for clinical interventions. © 2025 Elsevier Ltd

关键词： Electronic health record

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Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features

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computerized Medical Imaging and Graphics 2025年 123卷 102559-102559页

作者： Pannu, Rabia Zubair, Muhammad Owais, Muhammad Hassan, Shoaib Umair, Muhammad Usman, Syed Muhammad Albashrawi, Mousa Ahmed Hussain, Irfan Faculty of Information Technology & Computer Science University of Central Punjab Lahore Pakistan Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia and Department of Mechanical & Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad Pakistan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs like mild eye redness, slightly blurred vision, and eye pain may go unnoticed, earning it the nickname "silent thief of sight." Its prevalence is rising with an aging population, driven by increased life expectancy. Most computer-aided diagnosis (CAD) systems rely on the cup-to-disc ratio (CDR) for glaucoma diagnosis. This study introduces a novel approach by integrating CDR with the neuro-retinal rim ratio (NRR), which quantifies rim thickness within the optic disc (OD). NRR enhances diagnostic accuracy by capturing additional optic nerve head changes, such as rim thinning and tissue loss, which were overlooked using CDR alone. A modified ResUNet architecture for OD and optic cup (OC) segmentation, combining residual learning and U-Net to capture spatial context for semantic segmentation. For OC segmentation, the model achieved Dice Coefficient (DC) scores of 0.942 and 0.872 and Intersection over Union (IoU) values of 0.891 and 0.773 for DRISHTI-GS and RIM-ONE, respectively. For OD segmentation, the model achieved DC of 0.972 and 0.950 and IoU values of 0.945 and 0.940 for DRISHTI-GS and RIM-ONE, respectively. External evaluation on ORIGA and REFUGE confirmed the model's robustness and generalizability. CDR and NRR were calculated from segmentation masks and used to train an SVM with a radial basis function, classifying the eyes as healthy or glaucomatous. The model achieved accuracies of 0.969 on DRISHTI-GS and 0.977 on RIM-ONE. © 2025 The Authors

关键词： Semantic Segmentation

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Exploring the Potential of LLM-based Chatbots for Task Scheduling in Robot Operations

Exploring the Potential of LLM-based Chatbots for Task Sched...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Catarina Rema Armando Sousa Heber Sobreira Pedro Costa Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The rise of Industry 4.0 has revolutionized manufacturing by integrating real-time data analysis, artificial intelligence (AI), automation, and interconnected systems, enabling adaptive and resilient smart factories. Autonomous Mobile Robots (AMRs), with their advanced mobility and navigation capabilities, are a pillar of this transformation. However, their deployment in job shop environments adds complexity to the already challenging Job Shop Scheduling Problem (JSSP), expanding it to include task allocation, robot scheduling, and travel time optimization, creating a multi-faceted, non-deterministic polynomial-time hardness (NP-hard) problem. Traditional approaches such as heuristics, meta-heuristics, and mixed integer linear programming (MILP) are commonly used. Recent AI advancements, particularly large language models (LLM), have shown potential in addressing these scheduling challenges due to significant improvements in reasoning and decision-making from textual data. This paper examines the application of LLM to tackle scheduling complexities in smart job shops with mobile robots. Guided by tailored prompts inserted manually, LLM are employed to generate scheduling solutions, being these compared to an heuristic-based method. The results indicate that LLM currently have limitations in solving complex combinatorial problems, such as task scheduling with mobile robots. Due to issues with consistency and repeatability, they are not yet reliable enough for practical implementation in industrial environments. However, they offer a promising foundation for augmenting traditional approaches in the future.

关键词： Job shop scheduling Service robots Large language models Chatbots Real-time systems Mobile robots Resource management Reliability Autonomous robots Smart manufacturing

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