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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Baun, Christian Bouché, Johannes Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Nibelungenplatz 1 Frankfurt am Main60318 Germany

ISBN: (纸本)9783031680045

Desktop-as-a-Service (DaaS) is a cloud service category with tremendous potential for improving the everyday work of running and using applications. However, existing DaaS solutions have several drawbacks, which have limited the popularity of this service category in the past. Our novel DaaS solution, DESIGN, overcomes existing solutions limitations and offers significant benefits. It can potentially revolutionize how we use applications, supporting non-modified Linux and Windows applications with just a browser on the client side. This paper describes some of the knowledge we gained during the development because of the challenges we faced and the solutions we found. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data as a service (DaaS)

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Real-World Implementation of Offline Model-Free Reinforcement Learning for Thermostat Control

Real-World Implementation of Offline Model-Free Reinforcemen...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Ghane, Sara Elmaz, Furkan Jacobs, Stef Huybrechts, Thomas Verhaert, Ivan Mercelis, Siegfried Mannens, Erik University of Antwerp - imec IDLab - Faculty of Applied Engineering Antwerp Belgium University of Antwerp EMIB Faculty of Applied Engineering Antwerp Belgium University of Antwerp - imec IDLab Department of Computer Science Antwerp Belgium

ISBN: (纸本)9798331533892

This study introduces a model-free, offline Reinforcement Learning (RL) approach for optimizing the thermostat control in heating systems. Specifically, historical data from a real-world building was used to train the RL agent that is based on the Dueling Double Deep Q-Network (DQN) algorithm. The RL agent's objective is to balance thermal comfort with energy savings, by controlling indoor temperature set points. Traditional rule-based thermostat control methods often depend on fixed schedules and lack adaptability to varying conditions, resulting in energy inefficiencies. Training the RL model offline eliminates the requirement for live interaction, thereby preventing potential disruptions to occupant comfort. During evaluation, the RL model achieved an 18.66% reduction in energy use compared to a rule-based controller, while maintaining recommended indoor temperature levels. © 2025 IEEE.

关键词： Reinforcement learning

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Intrusion Detection for Internet of Things Integrating Blockchain Technologies: A Review Paper 7

Intrusion Detection for Internet of Things Integrating Block...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Bouhassane, Mohammed Kaabouch, Abderrahim Sadiq, Abdelalim Aziz, Oussama Oualla, Mohamed Ibn Tofail University LaRI Laboratory Faculty of Science Kenitra Morocco Ibn Tofail University National School of Applied Science Department of Computer Science Kenitra Morocco -Errachidia Department of Computer Science Errachidia Morocco

ISBN: (纸本)9798350367027

the growing emergence of the Internet of Things (IoT) has introduced numerous critical security issues. In IoT, the decentralized deployment of devices and each device possibly having one-of-many network configurations creates additional complexity that makes traditional intrusion detection systems (IDS) reach their limit due to being resource constrained. As is often the case, the decentralized, unalterable, and secure structure of blockchain technology has emerged as a potential solution to these challenges. This paper focuses on the challenges and scope of blockchain-integrated intrusion detection systems for IoT, while also examining the advantages of blockchain technology in creating a more secure ecosystem for IoT. Various blockchain-based architectures are also considered in the paper, along with several future research directions for highly scalable and efficient systems. © 2024 IEEE.

关键词： Network intrusion

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and Engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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Findings of the Third Shared Task on Multilingual Coreference Resolution 7

Findings of the Third Shared Task on Multilingual Coreferenc...

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7th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2024

作者： Novák, Michal Dohnalová, Barbora Konopík, Miloslav Nedoluzhko, Anna Popel, Martin Pražák, Ondřej Sido, Jakub Straka, Milan Žabokrtský, Zdeněk Zeman, Daniel Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798891761711

The paper presents an overview of the third edition of the shared task on multilingual coreference resolution, held as part of the CRAC 2024 workshop. Similarly to the previous two editions, the participants were challenged to develop systems capable of identifying mentions and clustering them based on identity coreference. This year’s edition took another step towards real-world application by not providing participants with gold slots for zero anaphora, increasing the task’s complexity and realism. In addition, the shared task was expanded to include a more diverse set of languages, with a particular focus on historical languages. The training and evaluation data were drawn from version 1.2 of the multilingual collection of harmonized coreference resources CorefUD, encompassing 21 datasets across 15 languages. 6 systems competed in this shared task. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Transformer-Based Question Answering Model for the Biomedical Domain 5

Transformer-Based Question Answering Model for the Biomedica...

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Haddouche, Ahcene Rabia, Ikram Aid, Aicha Bouira University Faculty of Sciences and Applied Sciences LIM Laboratory Computer Science Department Algeria

ISBN: (纸本)9798350381450

Motivation: Question Answering (QA) is a highly focused topic in the field of Natural Language Processing (NLP). Recent progress in neural network models and the availability of large datasets like SQuAD have played a significant role in improving performance in open domains. However, there remains a need to further effectively implement these systems in more specific domains, especially in the biomedical field, to help medical practitioners provide accurate solutions for inquiries related to medicine and healthcare, including specific subjects such as the COVID-19 disease. Fortunately, recent models, such as transformers, have opened up avenues and modern techniques for developing accurate ***: In this work, we aim to leverage transformer models and Transfer Learning to effectively train models in the biomedical domain. By taking a pre-Trained model for Question Answering tasks and further fine-Tuning it on specific domains, we enhance the system's performance in the biomedical domain. Our ultimate goal is to develop a QA model specifically tailored for COVID-19 ***: We have trained BERT and RoBERTa models on the COVID-QA dataset and achieved competitive results on COVID-19 QA. Our RoBERTa model achieved an Exact Match (EM)/F1 score of 0.38/0.64, respectively, on COVID-QA, indicating successful performance in COVID-19 QA. © 2023 IEEE.

关键词： COVID-19

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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

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computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods 28th

Evaluation and Comparison of Emotionally Evocative Image Aug...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Ignatowicz, Jan Kutt, Krzysztof Nalepa, Grzegorz J. Jagiellonian Human-Centered AI Lab Mark Kac Center for Complex Systems Research Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Lojasiewicza 11 Kraków30-348 Poland

Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain. © 2024 The Authors.

关键词： Generative adversarial networks

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