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International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024

作者： Al Machot, Fadi Horsch, Martin Thomas Ullah, Habib Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway

ISBN: (纸本)9783031892738

Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like healthcare and finance, drive the need for explainable and trustworthy systems. While Large Language Models (LLMs) perform exceptionally well in generating accurate outputs, their "black box" nature poses significant challenges to transparency and trust. To address this, the paper proposes the TranspNet pipeline, which integrates symbolic AI with LLMs. By leveraging domain expert knowledge, retrieval-augmented generation (RAG), and formal reasoning frameworks like Answer Set Programming (ASP), TranspNet enhances LLM outputs with structured reasoning and verification. This approach strives to help AI systems deliver results that are as accurate, explainable, and trustworthy as possible, aligning with regulatory expectations for transparency and accountability. TranspNet provides a solution for developing AI systems that are reliable and interpretable, making it suitable for real-world applications where trust is critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ontology

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Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

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Digital Communications and Networks 2024年第2期10卷 380-388页

作者： Renwan Bi Mingfeng Zhao Zuobin Ying Youliang Tian Jinbo Xiong Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Faculty of Data Science City University of Macao999078MacaoChina State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

关键词： Mobile edge crowdsensing Dynamic privacy measurement Personalized privacy threshold Privacy protection Reinforcement learning

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Bridging Logic and Learning: A Neural-Symbolic Approach for Enhanced Reasoning in Neural Models (ASPER)

Bridging Logic and Learning: A Neural-Symbolic Approach for...

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International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024

作者： Al Machot, Fadi Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway

ISBN: (纸本)9783031892738

Neural-symbolic learning, an intersection of neural networks and symbolic reasoning, aims to blend neural networks’ learning capabilities with symbolic AI’s interpretability and reasoning. This paper introduces an approach designed to improve the performance of neural models in learning reasoning tasks. It achieves this by integrating Answer Set Programming (ASP) solvers and domain-specific expertise, which is an approach that diverges from traditional complex neural-symbolic models. In this paper, a shallow artificial neural network (ANN) is specifically trained to solve Sudoku puzzles with minimal training data. The model has a unique loss function that integrates losses calculated using the ASP solver outputs, effectively enhancing its training efficiency. Most notably, the model shows a significant improvement in solving Sudoku puzzles using only 12 puzzles for training and testing without hyperparameter tuning. This advancement indicates that the model’s enhanced reasoning capabilities have practical applications, extending well beyond Sudoku puzzles to potentially include a variety of other domains. The code can be found on GitHub: https://***/fadi2200/aspen. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neural network models

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Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowled...

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International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024

作者： Al Machot, Fadi Horsch, Martin Thomas Ullah, Habib Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway

ISBN: (纸本)9783031892738

This paper presents a hybrid methodology that enhances the training process of deep learning (DL) models by embedding domain expert knowledge using ontologies and answer set programming (ASP). By integrating these symbolic AI methods, we encode domain-specific constraints, rules, and logical reasoning directly into the model’s learning process, thereby improving both performance and trustworthiness. The proposed approach is flexible and applicable to both regression and classification tasks, demonstrating generalizability across various fields such as healthcare, autonomous systems, engineering, and battery manufacturing applications. Unlike other state-of-the-art methods, the strength of our approach lies in its scalability across different domains. The design allows for the automation of the loss function by simply updating the ASP rules, making the system highly scalable and user-friendly. This facilitates seamless adaptation to new domains without significant redesign, offering a practical solution for integrating expert knowledge into DL models in industrial settings such as battery manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Numerical solution hybridized by machine- leaning-based algorithm to provide an efficient method for analyzing thermomechanical shock behavior of the circumferentially-graded graphene-plates reinforced composite sandwich panel (sept, 10.1080/15376494.2023.2195417, 2023)

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第29期31卷 12345-12348页

作者： Han, Shaoyong Ye, Qianqian Hakamy, A. Abd-Elnaiem, Alaa M. Changzhou Vocational Institute of Industry Technology Changzhou Jiangsu China Postdoctoral Scientific Research Workstation Bank of Zhengzhou Zhengzhou Henan China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou Zhejiang China Department of Physics Faculty of Applied Science Umm Al-Qura University Makkah Saudi Arabia Physics Department Faculty of Science Assiut University Assiut Egypt

The present research bridges the gap between a numerical solution with efficient machine-learning-based algorithms to provide a strong platform for predicting thermomechanical shock behavior of the sandwich cylindrical panels with polymer-made core and circumferentially-graded graphene-plates reinforced (CG-GPLR) nanocomposite face-sheets. The comprehensive declaration of the elasticity theory is considered to establish the governing equations of the system in three orthogonal directions. The well-known Lord-Shulman heat transfer theorem is implemented to present the time-variant nature of the system’s heat. The differential quadrature method (DQM) is used as a numerical approach to solve the spatial dependency of the governing differential equations. While the Laplace transform solves the time dependency of the governing differential equations. In order to decline the computational cost of predicting the time-variant analysis of the thermomechanical shock response of the system, an efficient machine-learning-based algorithm is employed in a way that it only requires the thermomechanical shock information of the selective set of points known as the training set. After completion of the training procedure, this machine-learning predictor can easily predict the thermoelastic response of all points included either as the train set or a new point. Validation of the applied solution is performed through a comparative process between the present results with those determined in the published literature. The results of this study are in excellent compatibility with those obtained in a solo numerical solution.

关键词： Coupled thermoelasticity analysis sandwich cylindrical shell optimized machine-learning regressor spatial and temporal stress distribution thermomechanical shock response

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An Efficient Pairing-Free Certificateless Signcryption Scheme under the Standard Model for VANET

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IEEE Internet of Things Journal 2025年第12期12卷 22142-22154页

作者： Liu, Xianxiang Zhou, Yanwei Yang, Bo Zhu, Tianqing Zhang, Mingwu Shaanxi Normal University School of Computer Science Xi'an710119 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China City University of Macau Faculty of Data Science Macau China Hubei University of Technology School of Computer Wuhan430068 China

With the rapid advancement of the Internet of Things (IoT), its applications are becoming increasingly essential in actual application. Specifically, the recent surge in electric vehicles has spurred significant advancements in vehicle ad hoc networks (VANET). Therefore, efficient data transmission is a critical challenge in IoT, especially VANET. The certificateless signcryption (CLS) scheme has high authentication efficiency, which has become increasingly important in IoT. However, most existing schemes rely on bilinear pairing, resulting in high calculation costs or failure to achieve their claimed security. Furthermore, the security of some CLS schemes is proved in the random oracle, which limits the usefulness of the CLS scheme. To further address these issues, we propose an efficient and secure CLS scheme in the standard model for VANET, which enhances its practical applicability in VANET. Our construction employs formal security proofs and cost simulations to ensure the scheme's security and low calculation costs, making it suitable for VANET. Finally, the security and efficiency analyses prove that our scheme offers enhanced security features and superior performance compared to existing schemes. © 2025 IEEE.

关键词： Cryptography

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Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

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Computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： Information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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A Review of IoT-Driven Innovations in Supply Chain Management 3

A Review of IoT-Driven Innovations in Supply Chain Managemen...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chandankhede, Arpit Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Meghe Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Supply Chain Management (SCM) worldwide makes major advancements through the Internet of Things (IoT) by enabling real-time monitoring and prediction analytics and automatic decision capabilities in worldwide deliver chains. Through IoT technology integration with Artificial Intelligence (AI) and blockchain and cloud computing the supply chain solves major operational problems including monitoring difficulties and waste reduction and regulatory compliance requirements and operation leadership deficits. The implementation of IoT for supply chain management encounters major adoption barriers from high installation costs combined with security risks and operability complications as well as limited scalability capacity that affects especially small to medium enterprises (SMEs) and developing markets. The evaluation paper examines the shifting impact of IoT in supply chain management which enhances performance metrics and brings greater clarity and strengthens supply chain resilience. A scientist studied existing papers and case studies to reveal several essential advantages which include better inventory management, predictive analysis systems and more effective supply-chain collaborations. The analysis emphasizes effective solutions for security privacy and moral and environmental issues to guarantee sustainable expanded IoT adoption. This paper delivers practical knowledge alongside best practices to help organizations adopt IoT systems which create adaptable efficient deliver chains with sustainability at their core thus achieving long-term market benefits in today's complex global marketplace. © 2025 IEEE.

关键词： Inventory control

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Hybrid Information Retrieval with Masked and Permuted Language Modeling (MPNet) and BM25L for Indonesian Drug data Retrieval 16

Hybrid Information Retrieval with Masked and Permuted Langua...

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16th International Conference on Knowledge and Smart technology, KST 2024

作者： Maryamah, Maryamah Wilsen, Geraldus Suhalim, Christeigen Theodore Septiana, Rafik Fajar, Aziz Solihin, Mahmud Iwan Universitas Airlangga Data Science Technology Faculty of Advanced Technology and Multidisipline Surabaya Indonesia UCSI University Mechanical Engineering Faculty of Engineering Technology and Built Environment Kuala Lumpur Malaysia

ISBN: (纸本)9798350370737

Lexical or statistical information retrieval confronts challenges such as the semantic gap and vocabulary mismatch. In the context of medical data, these difficulties are compounded by users' diverse backgrounds, resulting in disparities in perspective and vocabulary. The intricacies of medical language, including spelling variations, frequent acronyms, and ambiguous concepts, further amplify the semantic gap in medical texts. However, adopting a semantic approach can address these issues, albeit introducing a new challenge in the form of a soft matching nature leading to lower recall. In response, we propose a hybrid information retrieval method that combines semantic and lexical approaches. In contrast to recent experiments with the emphasis on utilizing semantic methods as re-ranker, our current approach diverges by incorporating semantic techniques as a fundamental part of the retrieval model. This shift aims to explore the efficacy of semantic methodologies in the initial retrieval stage rather than exclusively relying on them for post-retrieval refinement. The results obtained from both the semantic and lexical retrieval approaches are subsequently subjected to reranking through Reciprocal Rank Fusion. The proposed method outperforms lexical methods such as BM25L, Jaccard Similarity, and Query Likelihood Model, along with semantic methods, including doc2vec, multilingual BERT, IndoBERT, and MiniLM. It has additionally been demonstrated to be more effective than other hybrid models, PLM-based dense retrieval. This technique has successfully capitalized on the strengths of both semantic and lexical methods, resulting in enhanced overall performance in retrieving relevant documents. © 2024 IEEE.

关键词： Information retrieval

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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