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XB-Muse: Practical Multi-User Dynamic Searchable Symmetric Encryption for Adaptive Revocation

IEEE Internet of Things Journal 2025年

作者： Jiang, Qin Yang, Xu Chen, Sifan Qi, Saiyu Song, Fuyuan Fu, Zhangjie Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education 210044 China Xi’an Jiaotong University School of Computer Science and Technology 710049 China

Dynamic searchable symmetric encryption (DSSE) schemes support keyword search queries on encrypted dynamic datasets with add-or-delete operations stored on an untrusted remote server. Multi-user DSSE (MUDSSE) further considers multiple users to access the encrypted dataset. Most works of MUDSSE focus on how to promise forward and backward privacy of queries. However, the problem in which the data owner can not revocate the deleted encrypted data on the encrypted dataset adaptively and efficiently is not sufficiently considered. To solve this problem, we propose a new multi-user DSSE scheme named X-MUSE, extending cryptographic primitives Symmetric Revocable Encryption (SRE) to design a new searchable encryption with optimal search time for the deletion operation. Furthermore, to minimize communication size and counter new integrity threats raised by malicious clients, we extend X-MUSE to design a new MUDSSE named B-MUSE by integrating blockchain-based technology. Our evaluation confirms that our schemes have practical search performance and lower storage costs on both the server and the client side compared to the state-of-the-art. © 2014 IEEE.

关键词： Differential privacy

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Concept-Aware Entity Alignment Network for Industrial Knowledge Graph

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4316-4323页

作者： Wu, Shuai Tong, Wei Hou, Yuhong Li, Ping Yang, Weidong Wu, Edmond Q. Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China School of Mechanical Engineering Northwest University of Technology Xi'an710072 China Chinese Flight Test Establishment Technology and Engineering Center of Chinese Flight Test Establishment Xi'an710076 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China Fudan University Shanghai200433 China

The industrial knowledge graph (IKG) can improve the cognitive intelligence of the manufacturing system and is recognized as one of the cores of the next-generation industrial management information system. Due to the multisource heterogeneous nature of industrial data, aligning entities with the same semantics (entity alignment) is the core technology for building large-scale, high-coverage IKGs. Existing approaches show that embedded learning of IKGs performs well for this task. However, most advanced methods ignore concept information when learning topological information about IKGs. Inspired by the ontology matching theory, in this article, we realize the importance of entity concepts in alignment. The conceptual semantics of entities can usually be obtained through the is-a relation. However, the IKG is usually constructed by triples (entity, relation, entity) automatically extracted from a large text corpus. This will lead to entities in the IKG having problems such as lacking conceptual information, belonging to multiple concepts, or having different concept granularities. To solve the two problems of lacking conceptual information and different concept granularity, we propose the concept-aware entity alignment network (CAEA), aggregating bidirectional relations and attributes to get the entity concept semantics by a novel concept-aware graph attention mechanism. The excellent performance of the CAEA can better support the construction of large and complete IKGs and support downstream applications such as industrial knowledge recommendation and assisted decision-making. To verify the performance of the CAEA on the IKG, we construct a new entity alignment benchmark using industrial control network security data and verify the effectiveness of the CAEA on the new benchmark and several mainstream datasets. Experimental results show that our method outperforms other state-of-the-art (SOTA) methods and promotes the development of IKGs. © 2025 IEEE. All rights rese

关键词： Knowledge graph

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WebShield 5.0: Harnessing AI and NLP to combat web threats in Industry 5.0

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Alexandria engineering Journal 2025年 127卷 677-689页

作者： Verma, Priyanka O'Shea, Donna Newe, Thomas Vidyarthi, Ankit Gupta, Deepak Ali, Jabir Aldawsari, Hamad Breslin, John G. School of Computer Science University of Galway University road GalwayH91 TK33 Ireland Department of Electronics and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Digital Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Department of CSE&IT Jaypee Institute of Information Technology sector 62 Noida201309 India Maharaja Agrasen Institute of Technology Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida Delhi India Department of Computer Science Haql University College University of Tabuk Tabuk Saudi Arabia Data Science Institute University of Galway University road GalwayH91 TK33 Ireland

Industry 5.0 characterized by the integration of human intelligence and advanced technologies is inherently more connected and interdependent than previous industrial paradigms. This increased connectivity exposes to various web-based attacks and calls for strong security controls. To address the challenges and enhance attack detection, this paper introduces Ingress Manager (IM), a novel approach that amalgamates Natural Language Processing (NLP) with Machine Learning (ML) to mitigate web-based threats. By combining multimodal data and utilizing the Mayfly optimization algorithm for feature selection, IM carries out a thorough analysis for efficient web-based attack detection. Mayfly Optimization is considered to be a variation of Particle Swarm Optimization (PSO), combining the benefits of Firefly Algorithm, Genetic Algorithm (GA), and PSO. Experiments on the HTTP CSIC-2010 dataset show that this integration is effective, as evidenced by the notable gains in accuracy, precision, and F-score above baseline models. Notable performance metrics such as accuracy of 98.5753% along with thorough component analysis (ablation study) add deeper understanding to the proposed approach. The paper's contributions lie in its utilization of Industry 5.0 principles, the incorporation of Mayfly optimization for feature selection, and the innovative combination of NLP and ML for robust web-based attack detection. © 2025

关键词： Genetic algorithms

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An Enhanced Product Recommendation System Using Decision Tree Algorithm 7th

An Enhanced Product Recommendation System Using Decision Tre...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Akinyoola, Oluwatimilehin Moses Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany

ISBN: (纸本)9789819764648

Product recommendation systems are a critical element of e-commerce platforms, as they enable customers to identify the items that best meet their needs. Product recommendation systems are important tools used by organizations to increase customer engagement, satisfaction, and loyalty. Existing product recommendation systems have several weaknesses, such as an inability to incorporate features outside of customer preferences. Therefore, this paper proposes an enhanced product recommendation system using a decision tree algorithm. The main objectives of this system are to improve the accuracy and efficiency of the existing product recommendation systems. To accomplish this, a framework includes a data preprocessing phase, a feature selection phase, and a model training phase. The data preprocessing phase is used to clean the data and eliminate any noise. The feature selection phase is used to identify the most informative features, which are then used to train the decision tree model. The model is trained using CART, a supervised learning algorithm, and is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Finally, the model is tested on a test dataset to compare with existing solutions. The results show that the proposed system outperforms existing recommender systems in terms of all the evaluation metrics discussed. Furthermore, the improved system also provides useful insights into the product recommendation process for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Chat with MES: LLM-driven user interface for manipulating garment manufacturing system through natural language

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Journal of Manufacturing Systems 2025年 80卷 1093-1107页

作者： Yuan, Zhaolin Li, Ming Liu, Chang Han, Fangyuan Huang, Haolun Dai, Hong-Ning Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Guangdong China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong Research Centre for Digital Transformation of Tourism The Hong Kong Polytechnic University Hung Hom Hong Kong Centre for Transformative Garment Production Hong Kong Science Park Pak Shek Kok Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong School of Intelligent Systems Science and Engineering Jinan University Zhuhai China

This paper presents Chat with MES (CWM), an AI agent system, which integrates LLMs into the Manufacturing Execution System (MES), serving as the "ears, mouth, and the brain". This system promotes a paradigm shift in MES interactions from Graphical User Interface (GUI) to natural language interface", offering a more natural and efficient way for workers to manipulate the manufacturing system. Compared with the traditional GUI, both the maintenance costs for developers and the learning costs and the complexity of use for workers are significantly reduced. This paper also contributes two technical improvements to address the challenges of using LLM-Agent in serious manufacturing scenarios. The first one is Request Rewriting, designed to rephrase or automatically follow up on non-standardized and ambiguous requests from users. The second innovation is the Multi-Step Dynamic Operations Generation, which is a pre-execution planning technique similar to Chain-of-Thought (COT), used to enhance the success rate of handling complex tasks involving multiple operations. A case study conducted on a simulated garment MES with 55 manually designed requests demonstrates the high execution accuracy of CWM (80%) and the improvement achieved through query rewriting (9.1%) and Multi-Step Dynamic operations generation (18.2%). The source code of CWM, along with the simulated MES and benchmark requests, is publicly accessible. © 2025 The Authors

关键词： Graphical user interfaces

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Quality of Control Based Resource Dimensioning for Collaborative Edge Robotics

Quality of Control Based Resource Dimensioning for Collabora...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Neelabhro Roy Mani H. Dhullipalla Gourav Prateek Sharma Dimos V. Dimarogonas James Gross School of Electrical Engineering and Computer Science & Digital Futures Research Center KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

关键词： Wireless communication Automation Service robots Robot kinematics System performance Simulation Delays Reliability Robots Multi-agent systems

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Breakthrough in breast tumor detection and diagnosis: a noise-resilient, rotation-invariant framework

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Multimedia Tools and Applications 2025年 1-27页

作者： Nosheen, Fariha Khan, Salabat Sharif, Muhammad Kim, Do Hyeun Alkanhel, Reem AbdelSamee, Nagwan Department of Computer Science COMSATS University Islamabad Attock Campus Attock43600 Pakistan Department of Computer Engineering Jeju National University Jeju Special Self-Governing Province Jeju-Si63243 Korea Republic of Big Data Research Center Jeju National University Jeju63243 Korea Republic of Department of Information Technology College of Computer and Information Sciences Princess Nourahbint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The fact that early-stage breast cancer typically presents no signs, poses a global risk to the lives of women. digital mammography is just one method among many that can detect breast cancer in its early stages. Despite extensive research, most methods for detecting breast cancers still produce a large number of false positives. The difficulty in improving detection accuracy is in reducing false positives by differentiating masses from normal tissues. Using the textural properties of the masses, this study aims to develop a computer-aided diagnosis system that reduces the number of false positive and negative mammography results. The suggested method initially partitions regions of interest (ROI) into small patches to extract an abnormality region and micro-pattern, which permits the extraction of specific information about the image's content in targeted areas. Reducing the computational complexity of an image analysis operation is accomplished by partitioning a ROI into smaller patches rather than processing the complete image at once. Due to the significant impact the noise has on detection accuracy in mammograms, a textural descriptor that is insensitive to noise is introduced to describe image features such as lines, spots, flat areas, and edges. By expanding the rotation-invariant and noise-tolerant descriptor histograms, we generate an improved histogram that preserves the original regional patterns and spatial relationships between masses. To distinguish between "normal" and "mass" and "benign" and "malignant", Support Vector Machines (SVM) is employed with grid search based hyperparameter optimization. Our proposed approach was evaluated using the digital Database for Screening Mammography (DDSM), which contains over 1024 ROI cases. This database is a well-established benchmark for testing new mammography analysis methods. Each case in the DDSM database has been analyzed and interpreted by qualified radiologists, with comprehensive information provided thr

关键词： Diseases

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Quality of Control Based Resource Dimensioning for Collaborative Edge Robotics 22

Quality of Control Based Resource Dimensioning for Collabora...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Roy, Neelabhro Dhullipalla, Mani H. Sharma, Gourav Prateek Dimarogonas, Dimos V. Gross, James School of Electrical Engineering and Computer Science Digital Futures Research Center Kth Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798331508050

关键词： Industrial robots

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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General and Offset-Resistant Physical-Layer Acknowledgement Approach for Cross-technology Communication

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IEEE Transactions on Communications 2025年

作者： Yao, Shumin Zhao, Qinglin Zhou, MengChu Feng, Li Zhang, Peiyun Albeshri, Aiiad Macau University of Science and Technology School of Computer Science and Engineering Avenida Wei Long Taipa China Pengcheng Laboratory Department of Broadband Communication Shenzhen518066 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China King Abdulaziz University Department of Computer Science Jeddah21481 Saudi Arabia

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies (e.g., Bluetooth, WiFi, ZigBee, etc.), thereby reducing the cost and complexity of their interconnections. However, CTC is essentially unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and are vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable feedback with practical considerations. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on these guidelines, we implement two design cases: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct real-world experiments and extensive simulations to verify its feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly. © 1972-2012 IEEE.

关键词： Firmware

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