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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Gul, Aisha D. Turner, Liam Fuentes, Carolina School of Computer Science & Informatics Cardiff University Cardiff United Kingdom

ISBN: (纸本)9789819635245

With the global population aging, there is a growing need for innovative assistive technologies to support unpaid carers in maintaining older adults’ quality of life. Socially Assistive Robots (SARs) offer a potential solution by assisting with daily tasks, providing companionship, and easing the unpaid carers’ burden. For successful integration, SARs must deliver personalised interactions that implicitly promote supporting the needs of both carers and older adults. We conducted a qualitative study with 15 unpaid carers who interacted with a Pepper robot to understand the perception of unpaid carers towards using SARs as an assistive tool for providing care to older adults. Thematic analysis revealed concerns about the lack of human touch, the role of SARs as assistants rather than replacements, and the potential of robots for companionship. Carers also expressed distrust in technology, lack of confidence in machine capabilities, and safety concerns. From these findings, we propose that future research studies consider the collective set of dyadic interactions as a triad between unpaid carers, older adults, and SARs in order to further investigate and design personalised care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Assistive technology

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Accelerating Federated Codistillation via Adaptive Computation Amount at Network Edge

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5584-5597页

作者： Zeng, Zhihao Zhang, Xiaoning Zhao, Yangming Zoha, Ahmed Imran, Muhammad Ali Zhang, Yan University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China University of Science and Technology of China School of Computer Science and Technology Hefei China University of Glasgow School of Engineering United Kingdom University of Oslo Department of Informatics Norway

The advent of Federated Learning (FL) empowers IoT devices to collectively train a shared model without local data exposure. In order to address the issue of Non-IID that causes model performance degradation, the recently proposed federated codistillation framework has shown great potential. However, due to the system heterogeneity of devices, the federated codistillation framework still faces a synchronization barrier issue, resulting in a non-negligible waiting time with a fixed computation amount (epoch or batch size) assigned. In this paper, we propose Adaptive Computation Amount Allocation (ACAA) to accelerate federated codistillation. Specifically, we leverage a criterion, solution inexactness, to quantify the computation amount. We dynamically adjust the solution inexactness of devices based on their computing power and bandwidth to enable them nearly simultaneous completion of training, reducing synchronization waiting time without sacrificing the training performance. The minimum required computation amount is determined by the coefficient of the distillation term and the gradient dissimilarity bound of Non-IID. We theoretically analyze the convergence of ACAA. Extensive experiments show that, compared to benchmark algorithms, ACAA can accelerate training by up to 5×. © 2002-2012 IEEE.

关键词： Economic and social effects

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Intelligent Omni-Surface-Aided Integrated Sensing and Communications Based on Deep Reinforcement Learning with Knowledge Transfer

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IEEE Transactions on Wireless Communications 2025年第5期24卷 4344-4360页

作者： Ye, Xiaowen Mao, Yuyi Yu, Xianghao Fu, Liqun City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong Macau University of Science and Technology School of Computer Science and Engineering Taipa China Xiamen University School of Informatics Xiamen China

This paper investigates an intelligent omni-surface (IOS)-assisted integrated sensing and communication (ISAC) system, where a base station provides both target sensing and communication services with an IOS. The sensing signal-to-noise ratio (SNR) is maximized while satisfying the communication requirement by optimizing IOS configurations. Conventional approaches typically need real-time and accurate channel state information (CSI) and have high computational complexity, making them difficult to implement in realistic systems. To circumvent this problem, this paper puts forth a new framework based on deep reinforcement learning (DRL) with knowledge transfer. In particular, an online learning scheme called Deep reinforcement learning IOS-ISAC (DeepOSC), is first proposed to optimize the reflecting and refracting coefficients of the IOS. Thereafter, to enable powerful reasoning and fast decision-making, we incorporate an echo state network (ESN) with separate output into DeepOSC. To further accelerate convergence, two transfer learning approaches, namely staged policy reuse (SPR) and staged policy distillation (SPD), are developed to guide the learning process of a newly deployed agent by leveraging policies of pre-trained agents. Numerical results show that compared to various benchmarks, DeepOSC attains significant sensing and communication performance gains and is more robust against outdated CSI coefficients. In addition, in comparison to conventional neural networks, ESN shortens the run-time of DeepOSC by more than ten times and is more efficient for temporal inference. Besides, we demonstrate the capabilities of SPR and SPD in accelerating the convergence of DeepOSC. 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Towards Net Zero Resilience: A Futuristic Architectural Strategy for Cyber-Attack Defence in Industrial Control Systems (ICS) and Operational Technology (OT)

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computers, Materials & Continua 2025年第2期82卷 3619-3641页

作者： Hariharan Ramachandran Richard Smith Kenny Awuson David Tawfik Al-Hadhrami Parag Acharya The Office of Gas and Electricity Markets(Ofgem) LondonE144PUUK School of Computer Science and Informatics Cyber Technology Institute De Montfort UniversityLeicesterLE19BHUK Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK

This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.

关键词： ICS/OT cyber Programmable Logic Controllers(PLC)security detection safety reliability proof testing gas compressor station ICS resilience security architecture ICS

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Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements 24

Uncertainty-Aware Reservoir Permeability Prediction using Ga...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Lawal, Ahmad Yang, Yingjie Baisa, Nathanael L. He, Hongmei School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Science Engineering and Environment University of Salford Manchester United Kingdom

ISBN: (纸本)9798400718014

This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Sea Volve field. The study uses a probabilistic machine learning method called Gaussian Process Regression (GPR) with different kernels, such as Matern52, Matern32, and Radial Basis Function (RBF). LSboost, K-nearest neighbour (KNN), and XGBoost are some of the existing models that are used for comparison. Performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R2) are utilized for assessment. Additionally, the uncertainty associated with different GPR kernels is analyzed, and confidence intervals are generated to provide insights into model behaviour. The inclusion of confidence intervals enhances interpretability by quantifying the range within which the true permeability value is likely to fall with a specified level of confidence, offering valuable information for decision-making processes in reservoir engineering applications. Findings demonstrate the effectiveness of GPR with Matern52 and Matern32 kernels in permeability prediction, offering competitive performance and robust uncertainty quantification. This research contributes to advancing reservoir engineering by providing a comprehensive and uncertainty-aware approach to permeability prediction. © 2024 Copyright held by the owner/author(s).

关键词： Radial basis function networks

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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FarExStance: Explainable Stance Detection for Farsi 31

FarExStance: Explainable Stance Detection for Farsi

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zarharan, Majid Hashemi, Maryam Behroozrazegh, Malika Eetemadi, Sauleh Pilehvar, Mohammad Taher Foster, Jennifer School of Computing Dublin City University Ireland Iran University of Science and Technology Iran Dadmatech Tehran Iran School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (纸本)9798891761964

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large language models in zero-shot, few-shot, and parameter-efficient fine-tuned settings on our new dataset. On stance detection, the most accurate models are the fine-tuned RoBERTa model, the LLM Aya-23-8B which has been fine-tuned using parameter-efficient fine-tuning, and few-shot Claude-3.5-Sonnet. Regarding the quality of the explanations, our automatic evaluation metrics indicate that few-shot GPT-4o generates the most coherent explanations, while our human evaluation reveals that the best Overall Explanation Score (OES) belongs to few-shot Claude-3.5-Sonnet. The fine-tuned Aya-32-8B model produced explanations most closely aligned with the reference explanations. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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(r, k, Ε)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, k-Anonymity, and Ε-Differential Privacy

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IEEE Access 2025年 13卷 70422-70435页

作者： Kara, Burak Cem Eyupoglu, Can Karakus, Oktay National Defence University Turkish Air Force Academy Department of Computer Engineering İstanbul34149 Turkey Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom

In recent years, there has been a tremendous rise in both the volume and variety of big data, providing enormous potential benefits to businesses that seek to utilize consumer experiences for research or commercial purposes. The general data protection regulation (GDPR) implementation, on the other hand, has introduced extensive control over the use of individuals’ personal information and placed many limits. Data anonymization technologies have become an important solution for businesses trying to generate value from data while adhering to GDPR limitations. To address these challenges, researchers have developed various methods, including k-anonymity and Ε -differential privacy, offering solutions for both industry and academia. However, protecting individuals’ privacy against diverse attack attempts presents significant challenges for anonymization models that rely solely on a single technique, highlighting the need for more adaptable and hybrid approaches. In this study, a new hybrid anonymization algorithm called (r, k, Ε )-anonymization has been proposed, which combines k-anonymity and Ε-differential privacy models in a consistent framework and provides stronger privacy guarantees compared to existing privacy-preserving models. The proposed algorithm is capable of overcoming well-known shortcomings of the k-anonymity and Ε -differential privacy models, and it has been confirmed by extensive tests on real-world datasets. The proposed (r, k, Ε )-anonymization algorithm outperforms k-anonymity and Ε -differential privacy in terms of the average error rate measure, achieving data utility increases of 31.74% and 26.99%, respectively. © 2013 IEEE.

关键词： Anonymity

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Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation 31

Automatic Extraction of Metaphoric Analogies from Literary T...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Boisson, Joanne Siddique, Zara Borkakoty, Hsuvas Antypas, Dimosthenis Anke, Luis Espinosa Camacho-Collados, Jose Cardiff NLP School of Computer Science and Informatics Cardiff University United Kingdom Amplyfi Cardiff United Kingdom

ISBN: (纸本)9798891761964

Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain experts. We compare the out-of-the-box ability of recent large language models (LLMs) to structure metaphoric mappings from fragments of texts containing proportional analogies. The models are further evaluated on the generation of implicit elements of the analogy, which are indirectly suggested in the texts and inferred by human readers. The competitive results obtained by LLMs in our experiments are encouraging and open up new avenues such as automatically extracting analogies and metaphors from text instead of investing resources in domain experts to manually label data. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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