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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 95-105页

作者： Eliades, Demetrios G. Malialis, Kleanthis Vrachimis, Stelios Polycarpou, Marios M. University of Cyprus KIOS Research and Innovation Center of Excellence Nicosia1678 Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This position paper introduces a novel Cyber-Physical-Socio-Environmental Systems (CPSES) framework for Smart Water Networks (SWN). The proposed framework introduces environmental aspects as a key constituent, which is important for addressing the interconnected challenges of water systems, such as climate change impacts, contamination risk, and sustainability. The CPSES framework dynamically incorporates interactions between physical components (sensors, actuators), cyber systems (control, monitoring, digital twins), social factors (policy, demand management, crisis response), and environmental impacts (emissions, resource availability). The significance of this framework lies in its potential to enable more resilient, sustainable, and secure SWNs by incorporating feedback loops among the CPSES constituents. Key CPSES research challenges are outlined, such as modeling the impacts of climate change, dynamic risk estimation, and ethical and fairness aspects within SWN. A real-world use case on water contamination crisis management demonstrates the framework's practical application and relevance for researchers. © 2024 The Authors.

关键词： Water pollution control

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Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 898-915页

作者： Kangkang Zhang Christodoulos Keliris Thomas Parisini Bin Jiang Marios M.Polycarpou Department of Electrical and Electronic Engineering Imperial College LondonLondon SW72AZUKand also with the KIOS Research and Innovation Center of ExcellenceUniversity of CyprusNicosia 1678Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of CyprusNicosia 1678Cyprus Department of Electrical and Electronic Engineering Imperial College LondonLondon SW72AZUK Department of Engineering and Architecture University of TriesteTrieste 34127Italy KIOS Research and Innovation Center of Excellence University of CyprusNicosia 1678Cyprus College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyberphysical systems subject to process disturbances and measurement noise.A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack *** stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state *** order to help detect such attacks,a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change,due to the attack,at a time prior to the attack occurrence time.A key characteristic of this residual is that its magnitude increases every time a new attack *** estimate this unknown residual,an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting *** smoother design guarantees robustness with respect to process disturbances and measurement noise,and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting *** adaptive threshold is designed based on the estimated backward-in-time residual,and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed ***,a simulation example is used to demonstrate the effectiveness of the developed methodology.

关键词： Backward-in-time equivalent quantity fixed-point smoother intermittent integrity attacks

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Fault Accommodation for a Class of Nonlinear Uncertain Systems With Event-Triggered Input

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 235-245页

作者： Dong Zhao Marios M.Polycarpou IEEE KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer EngineeringUniversity of CyprusNicosia 1678Cyprus

The event-triggered fault accommodation problem for a class of nonlinear uncertain systems is considered in this *** control signal transmission from the controller to the system is determined by an event-triggering scheme with relative and constant triggering *** the event-induced control input error and system fault threat,a novel eventtriggered active fault accommodation scheme is designed,which consists of an event-triggered nominal controller for the time period before detecting the occurrence of faults and an adaptive approximation based event-triggered fault accommodation scheme for handling the unknown faults after detecting the occurrence of *** closed-loop stability and inter-event time of the proposed fault accommodation scheme are rigorously *** cases for the fault accommodation design under constant triggering threshold are also *** example is employed to illustrate the effectiveness of the proposed fault accommodation scheme.

关键词： Adaptive control event-triggered control fault accommodation neural networks nonlinear systems

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Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

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computers, Materials & Continua 2023年第5期75卷 2921-2938页

作者： Muhammad Asfand-e-Yar Qadeer Hashir Syed Hassan Tanvir Wajeeha Khalil Department of Computer Science Center of Excellence in Artificial Intelligence(CoE-AI)Bahria UniversityIslamabad44000Pakistan Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25000Pakistan

The growth of the internet and technology has had a significant effect on social *** information has become an important research topic due to the massive amount of misinformed content on social *** is very easy for any user to spread misinformation through the ***,misinformation is a problem for professionals,organizers,and ***,it is essential to observe the credibility and validity of the News articles being shared on social *** core challenge is to distinguish the difference between accurate and false *** studies focus on News article content,such as News titles and descriptions,which has limited their ***,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user *** the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and *** calculate user credibility and combine it with article content with the user’s *** combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or ***,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required *** dataset contains 23000+articles with millions of user *** highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest *** discoveries confirmed that the proposed classifier would effectively classify misinformat

关键词： Misinformation user credibility fake news machine learning

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Federated Learning for Enhanced Intrusion Detection in Smart City Environments 18

Federated Learning for Enhanced Intrusion Detection in Smart...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798331508692

The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection Systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyco

关键词： Federated learning

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SeerahGPT: Retrieval Augmented Generation based Large Language Model 18

SeerahGPT: Retrieval Augmented Generation based Large Langua...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah Univesity for Women Karachi Pakistan

ISBN: (纸本)9798331508692

Large Language Models(LLMs) have become widely recognized in recent years for their exceptional performance in language generation capabilities. As a result, an unprecedented rise is seen in its use cases in various domains specifically involving Natural Language Processing(NLP). These models however perform suboptimally when exploited in the field of studies where authenticity of the generated content is a critical aspect. One such domain is usage of LLM for exploration of the life of Prophet Muhammad S.A.W(commonly referred to as Seerah). It is of utmost significance to ensure the authenticity and reliability in the sources used and reported by the LLM due to the sensitive nature of the domain. The contemporary LLMs, however, lack the explainability in their response due to their inherent black-box nature. In our study, we have presented a novel LLM named SeerahGPT that addresses this challenge with the help of retrieval-augmented generation (RAG). This technique enables the model to utilize both parametric and nonparametric memories for generating response of queries. Our model, built on the Llama-2-7b architecture, employs Sentence Transformer embedding to effectively retrieve relevant information. The model's capabilities are augmented by integrating it with a corpus having Islamic texts such as the Quranic translation and Hadith collections, and historical accounts. The model's performance is benchmarked against its base model using both quantitative and qualitative metrics. The comparative analysis with Llama-2-7b revealed that SeerahGPT incorporation with external knowledge sources, provided more authentic and verifiable responses, despite the others exhibiting greater fluency. Performance metrics such as BLEU, ROUGE, and METEOR indicated SeerahGPT's better accuracy and contextual handling. This study paves way for analysis of such sensitive domains in more efficient way that can be utilized in other complex domains such as Islamic theology and Fiqh or legal

关键词： Natural language processing systems

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Experimental Investigation of Inverter-Battery System Efficiency for Energy Scheduling

Experimental Investigation of Inverter-Battery System Effici...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Tziovani, Lysandros Hadjidemetriou, Lenos Timotheou, Stelios University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

Battery energy storage systems (BESSs) are widely used in energy management applications to maximize arbitrage profits. The consideration of the power losses from both battery and inverter components in optimization schemes is vital to ensure the economic BESSs scheduling. Towards this direction, this work develops two optimization models that consider the combined inverter-battery efficiency. The first model represents the non-linear efficiency curve of the inverter using a piecewise linear approximation, resulting in mixed-integer linear programming formulations. The second model approximates the nonlinear efficiency curve using a constant term, resulting in linear programming formulations which can be fast and reliably solved. Moreover, an experimental investigation is performed to derive the efficiency curve of a real inverter-battery system, where the parameters of the two models are identified. Simulation results highlight the necessity to consider the efficiencies from both battery and inverter components to reduce modelling inaccuracies and hence to maximize profits. The results also show the capability of the linear programming model to yield high-quality solutions with an approximation error of less than 1%. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Increasing the Fairness of Photovoltaic Curtailments for Voltage Management in Distribution Grids

Increasing the Fairness of Photovoltaic Curtailments for Vol...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kotsonias, Andreas Hadjidemetriou, Lenos Asprou, Markos Panayiotou, Christos G. University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

The increasing penetration of photovoltaic systems (PV) in distribution grids can create significant over-voltage conditions due to intense reverse power flow. In addition to reactive power control, PV curtailments are often employed for voltage regulation to ensure compliance with the regulatory limits. However, reducing the output power of a PV system results to a revenue drop for the owners, raising concerns about fairness. Relevant works often address this issue through penalty terms in the objective function, but most works neglect the trade-off between fairness and total curtailments, i.e., increasing fairness leads to an increase of total curtailments. This paper proposes a novel methodology to enhance the fairness of PV curtailments for voltage regulation while ensuring that the increase in total curtailments is within a specified acceptable limit. In this direction, the Jain's fairness index is used to quantitatively measure fairness, which is incorporated in the optimization process as a second order cone program constraint. An iterative algorithm based on the bisection method is then developed to increase fairness until the allowable total curtailments are reached. Simulation results demonstrate the capability of the proposed method to improve the fairness of PV curtailments, considering different levels of allowable total curtailments. © 2024 IEEE.

关键词： DC distribution systems

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Efficient Global Neural Architecture Search

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SN computer Science 2025年第3期6卷 1-12页

作者： Siddiqui, Shahid Kyrkou, Christos Theocharides, Theocharis Electrical and Computer Engineering Department University of Cyprus Nicosia Cyprus KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Neural architecture search (NAS) has shown promise towards automating neural network design for a given task, but it is computationally demanding due to training costs associated with evaluating a large number of architectures to find the optimal one. To speed up NAS, recent works limit the search to network building blocks (modular search) instead of searching the entire architecture (global search), approximate candidates’ performance evaluation in lieu of complete training, and use gradient descent rather than naturally suitable discrete optimization approaches. However, modular search does not determine network’s macro architecture i.e. depth and width, demanding manual trial and error post-search, hence lacking automation. In this work, we revisit NAS and design a navigable, yet architecturally diverse, macro-micro search space. In addition, to determine relative rankings of candidates, existing methods employ consistent approximations across entire search spaces, whereas different networks may not be fairly comparable under one training protocol. Hence, we propose an architecture-aware approximation with variable training schemes for different networks. Moreover, we develop an efficient search strategy by disjoining macro-micro network design that yields competitive architectures in terms of both accuracy and size. Our proposed framework achieves a new state-of-the-art on EMNIST and KMNIST, while being highly competitive on the CIFAR-10, CIFAR-100, and FashionMNIST datasets and being 2-4 times faster than the fastest global search methods. Lastly, we demonstrate the transferability of our framework to real-world computer vision problems by discovering competitive architectures for face recognition applications. © The Author(s) 2025.

关键词： AutoML Convolutional neural networks Deep learning Image classification Neural architecture search

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A Holistic Assessment Framework for Evaluating Buildings' Green and Digital Readiness Level

A Holistic Assessment Framework for Evaluating Buildings' Gr...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Stylianides, Nearchos Hadjidemetriou, Lenos Charalambous, Charalambos Panayiotou, Christos University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

Smart buildings assessment methodologies are important tools utilized across all stages of transitioning towards a greener, smarter, and energy-efficient building sector. Existing assessment methods either focus only on specific building aspects, or consider buildings with particular technical characteristics, or require explicit technical knowledge by the users to evaluate building performance. Consequently, this paper proposes a holistic and flexible assessment framework that encompasses all facets of a building, including green factor, energy efficiency, occupants' comfort, and digitization levels. The proposed framework does not require strong technical knowhow to perform the assessment and can be generally applied for evaluating different buildings by providing basic information about their main characteristics. The assessment framework was applied to a real commercial building, demonstrating its effective use in evaluating overall building performance and analyzing the score in each individual impact category or domain. In addition, two potential action plans were formulated by utilizing the proposed framework to explore different pathways for enhancing the building's performance. © 2024 IEEE.

关键词： Smart homes

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