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A Verifiable Trust-Based CP-ABE Access control Scheme for Cloud-Assisted Renewable Energy Systems

computers, Materials & Continua 2025年第1期82卷 1211-1232页

作者： Jiyu Zhang Kehe Wu Ruomeng Yan Zheng Tian Yizhen Sun Yuxi Wu Yaogong Guo Department of Control and Computer Engineering North China Electric Power UniversityBeijing102206China State Grid Information&Communication Company of Hunan Electric Power Corporation Changsha410118China State Grid Hunan Electric Power Corporation Chenzhou Power Supply Branch Chenzhou423000China

Renewable Energy Systems(RES)provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers(ES).However,securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research ***,managing attributes,including addition,deletion,and modification,is a crucial issue in the access control scheme for *** address these security concerns,a trust-based ciphertext-policy attribute-based encryption(CP-ABE)device access control scheme is proposed for RES(TB-CP-ABE).This scheme effectivelymanages the distribution and control of encrypted data on the cloud through robust attribute key *** introducing trust management mechanisms and outsourced decryption technology,the ES system can effectively assess and manage the trust worthiness of terminal devices,ensuring that only trusted devices can participate in data exchange and access sensitive ***,the ES system dynamically evaluates trust scores to set decryption trust thresholds,thereby regulating device data access permissions and enhancing the system’s *** validate the security of the proposed TB-CP-ABE against chosen plaintext attacks,a comprehensive formal security analysis is conducted using the widely accepted random oraclemodel under the decisional q-Bilinear Diffie-Hellman Exponent(q-BDHE)***,comparative analysis with other schemes demonstrates that the TB-CP-ABE scheme cuts energy/communication costs by 43%,and scaleswell with rising terminals,maintaining average latency below 50ms,ensuring real-time service *** proposed scheme not only provides newinsights for the secure management of RES but also lays a foundation for future secure energy solutions.

关键词： Access control renewable energy systems(RES) ciphertext-policy attribute-based encryption(CP-ABE) security

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Quantized Auto Encoder-based Anomaly Detection for Multivariate Time Series Data in 5G Networks

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IEEE Access 2025年 13卷 82668-82679页

作者： Trappolini, Giovanni Purificato, Antonio Siciliano, Federico D'Addona, Luigi Spagnolo, Anna Maria Dato, Domenico Silvestri, Fabrizio Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy istella SpA Pisa Italy

With the arrival of 5G technology, networks face critical challenges in detecting anomalies that can significantly impact performance and reliability. This paper introduces QAED (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: (1) a vector quantization mechanism that effectively captures discrete network states, (2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and (3) an integrated architecture that processes multivariate time series data in a unified framework. We provide a detailed evaluation of our model across 5G data scenarios, demonstrating its enhanced accuracy and efficiency in anomaly detection compared to existing state-of-the-art methods, with gains of up to 8%. © 2013 IEEE.

关键词： Network coding

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Large Language Models for topic analysis: insights from robotic accident narratives

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Procedia computer Science 2025年 253卷 1462-1472页

作者： Francesco Costantino Nicolò Sabetta Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 00185 Rome Italy

The integration of robotics in manufacturing has significantly enhanced productivity and safety by performing hazardous tasks. However, it also introduces new risks and accident profiles that require thorough analysis. This study explores the application of Large Language Models (LLMs) for topic modeling on accident narratives from the Occupational Safety and Health Administration (OSHA) database, focusing on incidents involving robots. Advanced Natural Language Processing (NLP) techniques, specifically LLMs, were employed to uncover prevalent themes, common risk factors, and patterns in these narratives. The methodology includes data collection, text processing, statistical analysis, and various topic modeling techniques. The analysis identifies six topics, and key themes including maintenance-related accidents, types of machinery involved, and specific body parts frequently affected. The findings highlight critical areas for improving safety measures, such as rigorous lockout/tagout procedures and enhanced training for maintenance personnel. This study provides a novel approach to analyze accident narratives.

关键词： Robot collaborative robot health safety NLP LLM topic modelling

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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IMPO: Interpretable Memory-based Prototypical Pooling 25

IMPO: Interpretable Memory-based Prototypical Pooling

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Ragno, Alessio Capobianco, Roberto Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy INSA Lyon CNRS LIRIS UMR 5205 Villeurbanne69621 France Sony AI Zurich Switzerland

ISBN: (纸本)9798400713293

Graph Neural Networks (GNNs) have proven their effectiveness in various graph-structured data applications. However, one of the significant challenges in the realm of GNNs is representation learning, a critical concept that bridges graph pooling, aimed at creating compressed graph representations, and explainable artificial intelligence, which focuses on building models with transparent reasoning mechanisms. This research paper introduces a novel approach called Interpretable Memory-based Prototypical Pooling (IMPO) to address this challenge. IMPO is a graph pooling layer designed to enhance the interpretability of GNNs while maintaining high performance in graph classification tasks. It builds upon the MemPool algorithm and incorporates prototypical components to cluster nodes around class-aware centroids. This approach allows IMPO to selectively aggregate relevant substructures, paving the way for generating more interpretable graph representations. The experimental results in our study underscore the potential of pooling architectures in constructing inherently explainable GNNs. Notably, IMPO achieves state-of-the-art results in both classification and explanatory capacities across a diverse set of graph classification datasets. © 2025 Copyright held by the owner/author(s).

关键词： explainable AI

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A comparative analysis for automated information extraction from OSHA Lockout/Tagout accident narratives with Large Language Model 6

A comparative analysis for automated information extraction ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Sabetta, Nicolò Costantino, Francesco Stabile, Sara Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL Piazzale Giulio Pastore 6 Rome00144 Italy

Analysing workplace accidents is crucial for improving occupational safety by understanding causes and preventing recurrence. However, the primary challenge in analysing accident narratives lies in the unstructured nature of the text data. This study examines the effectiveness of Large Language Models (LLMs), specifically GPT-4 Turbo, in extracting information from lockout/tagout (LOTO) accident narratives in the Occupational Safety and Health Administration (OSHA) database. It compares the extracted features, namely the degree of fatality, nature of injury, and employee's occupation, with those recorded by OSHA supervisors. Despite occasional misclassifications and hallucinations, GPT-4 Turbo shows significant potential in automating critical information extraction, reducing reliance on human interpretation. Moreover, the model achieved high accuracy rates for each feature. These findings suggest that LLMs can enhance occupational safety data analysis, though improvements in prompt design and verification are recommended for further accuracy. © 2024 The Authors.

关键词： Generative AI Health Administration (OSHA) Industrial Safety LLM LOTO Occupational Safety

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Distributed Event-Orchestrated Digital Twin Architecture for Optimizing Energy-Intensive Industries 10

A Distributed Event-Orchestrated Digital Twin Architecture f...

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10th International Conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Bertozzi, Nicolò Geraci, Anna Bergamasco, Letizia Ferrera, Enrico Pristeri, Edoardo Pastrone, Claudio Fondazione Links Via Pier Carlo Boggio 61 Turin10138 Italy Department of Control and Computer Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy

ISBN: (纸本)9789897587504

This paper presents a novel distributed architecture designed to spawn digital twin solutions to improve energy efficiency in energy-intensive industrial scenarios. By executing user-defined workflows, our platform enables the implementation of real-time monitoring, forecasting, and simulation microservices to enhance decision-making strategies for optimizing industrial processes. Leveraging a stateless centralized orchestration mechanism built around an Apache Kafka-based backbone, the platform ensures scalability, fault tolerance, and efficient handling of heterogeneous data. Key features include intuitive workflow configuration, asynchronous communication for streamlined workflow execution, and API-driven scheduling for dynamic, event-based task management. This platform will be deployed and validated in several energy-intensive industrial scenarios, supporting the management of energy systems of different plants, to prove its effectiveness across a wide range of energy management challenges. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Foundries

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Pervasive Indoor User Identification Leveraging Mobile Single Station Localization

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IEEE Internet of Things Journal 2025年第11期12卷 15224-15237页

作者： Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Gui, Guan Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China Institute of Industrial Internet Chongqing401120 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using Information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single station localization (MSL) and MAC de-randomization based on users' spatio-temporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%. © 2014 IEEE.

关键词： Medium access control

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Multi-criteria Optimization Scheduling of Surgical Units: A Case Study at Policlinico Umberto I of Rome

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Operations Research Forum 2025年第2期6卷 1-26页

作者： Croella, Anna Livia Tronci, Edoardo Maria Nastasi, Alberto Palagi, Laura Petitti, Federico Romano, Ferdinando Department of Computer Control and Management Engineering Sapienza University of Rome Via Aroisto Rome 00185 Italy Accenture S.p.A. Rome Italy CNR-IASI Rome Italy AOU-Policlinico Umberto I Rome Italy

Nowadays, managing Operating Rooms (ORs) in healthcare systems presents a significant challenge, with underutilization of surgical facilities being a key concern. Several decision factors must be considered, including the number of ORs, operating hours, target utilization rates, and the possibility of patient transfers between surgical units (SUs). This study presents a multi-criteria mixed-integer linear optimization model designed for the specific context of Policlinico Umberto I in Rome. The model uses an open scheduling approach to determine the optimal day, time, and OR allocation for weekly surgeries. Using real data from two SUs and four ORs over twelve weeks, we assess the impact of different operating time policies on hospital Key Performance Indicators. Additionally, we introduce randomly generated data to simulate surgery priorities and further validate our model. Results demonstrate the effectiveness of mathematical optimization in improving OR utilization and overall healthcare efficiency. © The Author(s) 2025.

关键词： Health care management Mixed integer linear optimization Multi-criteria optimization Operating room scheduling

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