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EAEEIE Annual Conference,

作者： Tatjana Welzer Vladka Kožuh Ledinek Faculty of Electrical Engineering and Computer Science University of Maribor Maribor

Numbers of students with disabilities are growing recently around the whole Europe. Young people that are confronted with those special challenges are more and more interested in education at Higher Education Institutions, even more they are more and more interesting also in mobility what is a new motivation and challenge for them, their families and institutions. Different Higher Education Institutions have different support systems, different organizational rules and approaches to the teaching in the case of students with disabilities, but some common points (models) exists. Mostly all this rules have a background in the European and national legislation, but practical solutions (details) are different from the institution to the institution. In our paper we will present procedures from the University of Maribor - faculty of electrical engineering and computer science, as well as some available data.

关键词： Educational institutions electrical engineering computer science Europe Law

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Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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CNN-Transformer Based Cooperative Spectrum Sensing in Cognitive Radio Networks

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IEEE Wireless Communications Letters 2025年第5期14卷 1576-1580页

作者： Fang, Xiangqian Jin, Ming Guo, Qinghua Jiang, Tao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Recently, deep learning (DL)-based data-driven spectrum sensing has attracted much attention due to its powerful capabilities to exploit underlying signal features. Existing DL-based cooperative spectrum sensing (CSS) detectors usually extract the signal features from each secondary user (SU) individually, followed by feature fusion at a central fusion center (FC). However, this cannot extract correlation features across SUs. To address this limitation, this work proposes a CNN-Transformer neural network to exploit the correlations among the received signals across and within SUs. Leveraging the CNN-Transformer, we propose a posteriori probability ratio detector, which is optimal for the binary hypothesis testing problem under the Neyman-Pearson criterion. Moreover, unlike most DL-based CSS detectors, the proposed detector considers imperfect and independent reporting channels between SUs and the FC, and exploits the temporal correlation of signals among neighboring samples. Numerical results are provided to demonstrate the superior performance of the proposed detector, showing an improvement of approximately 6 dB over traditional model-driven energy detectors. Moreover, the proposed detector improves the detection probability to 0.99 from 0.88 of data-driven detectors under Rician fading channels. © 2012 IEEE.

关键词： Fading channels

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JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1345-1356页

作者： Liu, Jiangang Luo, Hanjiang Tao, Hang Liu, Jiahong Zhou, Jiehan Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission. © 2004-2012 IEEE.

关键词： Adaptive algorithms

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JC-GN: multi-scene joint conditional network for remote sensing sequence imagery generation under spatio-temporal variation

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Journal of King Saud University - computer and Information sciences 2025年第3期37卷 1-13页

作者： Zhao, Zi-Yi Jin, Xing Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China

Remote sensing imagery plays an important role in the research and application of environmental change, such as phenology changes, land surface parameter relationships, land degradation, etc. However, remote sensing observation data are often missing due to sensor resolution limitations and environmental factors. The missing images may occur randomly or continuously, which is difficult to deal with this situation with conventional methods. To address this issue, we perform the multi-scene joint conditional generation network (JC-GN) based on radiation properties to learn the complex mapping between phase images. The proposed model introduces multi-scene joint conditional loss to constrain the spatio-temporal characteristics of the generated image. Our experiments, conducted utilizing unmanned aerial vehicle (UAV) datasets and the Landsat-8 datasets, which are located in the southeast of Gansu Province (34.73∘N, 105.50∘E), demonstrate that the proposed methodology achieves promising performance in terms of both precision (RMSE decreases by about 2.5 points, PSNR increases by about 10 points, and SSIM increases by about 0.03 points) and effectiveness (time cost savings of about 50 percent), and data-driven model exhibits a superior capacity to simulate intricate information with greater fidelity. © The Author(s) 2025.

关键词： Data driven model Image generation Landsat-8 Multi-scene joint conditional network Unmanned Aerial Vehicle (UAV)

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Photovoltaic power one-day and multistep-hourly AI predictions using node-by-node evolved binomial tree structures to form L-transformed PDE modules

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Soft Computing 2025年第5期29卷 2483-2495页

作者： Zjavka, Ladislav Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 17. Listopadu 15/2172 Ostrava Czech Republic

Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead

关键词： Laplace transforms

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Zero-dynamics attack detection based on data association in feedback pathway

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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A Review of Anonymization Algorithms and Methods in Big Data

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Annals of Data science 2025年第1期12卷 253-279页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

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Annals of Data science 2025年第1期12卷 223-252页

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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