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27th RoboCup International Symposium, 2024

作者： Gabel, Thomas Godehardt, Eicke Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main60318 Germany

ISBN: (纸本)9783031858581

We present a continuous integration and deployment (CI/CD) framework for Soccer Simulation 2D. On the one hand, we aim to share that system publicly with the community and, therefore, describe its components and their composition. On the other hand, we relate statistics produced by that system to concepts from reliability theory, which facilitates the derivation of reliabilty metrics for assessing a team’s defensive performance. In line with that, we present the results of a comprehensive case study in which the CI framework was utilized to find critical game situations, uncover weaknesses in the playing behavior of a soccer simulation team, and implement and evaluate appropriate counter measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Emotion-based Dynamic Difficulty Adjustment in Video Games 10

Emotion-based Dynamic Difficulty Adjustment in Video Games

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Kutt, Krzysztof Sciga, Lukasz Nalepa, Grzegorz J. Jagiellonian University Faculty of Physics Astronomy and Applied Computer Science Kraków Poland

ISBN: (纸本)9798350345032

Current review papers in the area of Affective Computing and Affective Gaming point to a number of issues with using their methods in out-of-the-lab scenarios, making them virtually impossible to be deployed. On the contrary, we present a game that serves as a proof-of-concept designed to demonstrate that - being aware of all the limitations and addressing them accordingly - it is possible to create a product that works in-the-wild. A key contribution is the development of a dynamic game adaptation algorithm based on the real-time analysis of emotions from facial expressions. The obtained results are promising, indicating the success in delivering a good game experience. © 2023 IEEE.

关键词： Human computer interaction

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The Lawn Mowing Problem: From Algebra to Algorithms 31

The Lawn Mowing Problem: From Algebra to Algorithms

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Fekete, Sándor P. Krupke, Dominik Perk, Michael Rieck, Christian Scheffer, Christian Department of Computer Science TU Braunschweig Germany Faculty of Electrical Engineering and Computer Science Bochum University of Applied Sciences Bochum Germany

ISBN: (纸本)9783959772952

For a given polygonal region P, the Lawn Mowing Problem (LMP) asks for a shortest tour T that gets within Euclidean distance 1/2 of every point in P;this is equivalent to computing a shortest tour for a unit-diameter cutter C that covers all of P. As a generalization of the Traveling Salesman Problem, the LMP is NP-hard;unlike the discrete TSP, however, the LMP has defied efforts to achieve exact solutions, due to its combination of combinatorial complexity with continuous geometry. We provide a number of new contributions that provide insights into the involved difficulties, as well as positive results that enable both theoretical and practical progress. (1) We show that the LMP is algebraically hard: it is not solvable by radicals over the field of rationals, even for the simple case in which P is a 2 × 2 square. This implies that it is impossible to compute exact optimal solutions under models of computation that rely on elementary arithmetic operations and the extraction of kth roots, and explains the perceived practical difficulty. (2) We exploit this algebraic analysis for the natural class of polygons with axis-parallel edges and integer vertices (i.e., polyominoes), highlighting the relevance of turn-cost minimization for Lawn Mowing tours, and leading to a general construction method for feasible tours. (3) We show that this construction method achieves theoretical worst-case guarantees that improve previous approximation factors for polyominoes. (4) We demonstrate the practical usefulness beyond polyominoes by performing an extensive practical study on a spectrum of more general benchmark polygons: We obtain solutions that are better than the previous best values by Fekete et al., for instance sizes up to 20 times larger. © Sándor P. Fekete, Dominik Krupke, Michael Perk, Christian Rieck, and Christian Scheffer.

关键词： Approximation algorithms

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Yang-Lee zeros for real-space condensation

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Physical Review E 2025年第1期111卷 L012101-L012101页

作者： Zdzislaw Burda Desmond A. Johnston Mario Kieburg Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 30-059 Kraków Poland School of Mathematical and Computer Sciences Heriot-Watt University Riccarton Edinburgh EH14 4AS United Kingdom and Maxwell Institute of Mathematical Sciences Edinburgh United Kingdom School of Mathematics and Statistics University of Melbourne Parkville VIC 3010 Australia Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 30-059 Kraków Poland and Faculty of Physics Bielefeld University P.O. Box 100131 D-33501 Bielefeld Germany

Using the electrostatic analogy, we derive an exact formula for the limiting Yang-Lee zero distribution in the random allocation model of general weights. This exhibits a real-space condensation phase transition, which is induced by a pressure change. The exact solution allows one to read off the scaling of the density of zeros at the critical point and the angle at which the locus of zeros hits the critical point. Since the order of the phase transition and critical exponents can be tuned with a single parameter for several families of weights, the model provides a useful testing ground for verifying various relations between the distribution of zeros and the critical behavior, as well as for exploring the behavior of physical quantities in the mesoscopic regime, i.e., systems of large but finite size. The main result is that asymptotically the Yang-Lee zeros are images of a conformal mapping, given by the generating function for the weights, of uniformly distributed complex phases.

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CMTJ:Simulation package for analysis of multilayer spintronic devices

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npj Computational Materials 2023年第1期9卷 1804-1813页

作者： Jakub Mojsiejuk Sławomir Ziętek Krzysztof Grochot Witold Skowroński Tomasz Stobiecki Institute of Electronics AGH University of Science and TechnologyAl.Mickiewicza 30Kraków 30-059Poland Faculty of Physics and Applied Computer Science AGH University of Science and TechnologyAl.Mickiewicza 30Kraków 30-059Poland

We present CMTJ—a simulation package for large-scale macrospin analysis of multilayer spintronics *** from conventional simulations,such as magnetoresistance and magnetisation hysteresis loops,CMTJ implements a mathematical model of dynamic experimental techniques commonly used for spintronics devices characterisation,for instance:spin diode ferromagnetic resonance,pulse-induced microwave magnetometry,or harmonic Hall voltage *** find that macrospin simulations offer a satisfactory level of agreement,demonstrated by a variety of *** a unified simulation package,CMTJ aims to accelerate wide-range parameter search in the process of optimising spintronics devices.

关键词： multilayer analysis hysteresis

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Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

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computers, Materials & Continua 2024年第9期80卷 3785-3802页

作者： Ahmad Alzu’bi Amjad Albashayreh Abdelrahman Abuarqoub Mai A.M.Alfawair Department of Computer Science Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Science The University of JordanAmman11942Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversitySalt19117Jordan

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse *** the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain *** paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT *** effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time *** leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world ***,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack *** experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

关键词： DDoS attack classification deep learning explainable AI cybersecurity

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Multi-Task Learning Enhanced Physics-Informed Neural Network for Solving Fluid-Structure Interaction Equations 7

Multi-Task Learning Enhanced Physics-Informed Neural Network...

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Zhao, Yihe Yao, Jun Wang, Jian Xie, Xuetao Ablameyko, Sergey V. Qingdao China Qingdao China Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk220030 Belarus

ISBN: (纸本)9798350370782

Physics-informed neural networks (PINNs) incorporate physical constraints into their loss functions, allowing them to efficiently solve Partial Differential Equations (PDEs). In this work, we introduce an innovative network structure based on PINNs, specifically designed to address fluid-structure interaction (FSI) problems in porous media, referred to as M-PINN. Our approach is inspired by fully coupled traditional numerical methods, leveraging a multi-task learning (MTL) framework with soft parameter sharing to enhance the interactions across different physical fields. This alignment leads to an improved representation of multi-physics coupling phenomena. Experiments on the constant pressure production problem demonstrate that our method provides better performance than both traditional and sequential PINN approaches. © 2024 IEEE.

关键词： Liquefied gases

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An Intelligent System for Diagnosing Kidney Diseases Using Machine Learning 1

An Intelligent System for Diagnosing Kidney Diseases Using M...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hagree, Salah Alalayah, Khaled M. Alnahdi, Mohammed Alhomdy, Marwan Alawdi, Ala'a Alhamami, Khaled Alsaiari, Faraj Computer Science Department Faculty of Computer and Information Technology Sana’a University Yemen Computer Science Department Faculty of Sciences Ibb University Yemen Aljazeera University Yemen Applied College Imam Abdurahman Bin Faisal University Saudi Arabia Department of Computer Science College of Science and Arts Najran University Sharurah Saudi Arabia

ISBN: (纸本)9798331533557

Early diagnosis plays a critical role in preventing severe complications and fatalities associated with kidney disease, especially in regions like Saudi Arabia, where healthcare access may be limited. Prompt identification of the disease can significantly mitigate its severity and enhance patient outcomes. This study introduces an intelligent system for early kidney disease diagnosis utilizing machine learning (ML) models. We conducted a comparative analysis of various models, such as Random Forest (RF), Logistic Regression (LR), Support Vector Classifier (SVC), Gradient Boosting (GB), K-Nearest Neighbor (KNN), and Decision Tree (DT), with the SVC model achieving the highest accuracy of 72.00%. RF and KNN closely followed with 68.00% accuracy each, while the DT model, despite its interpretability benefits, achieved an accuracy of 56.00%. The interpretability of the DT model could be particularly advantageous in healthcare diagnostics where transparency is crucial. RF exhibited the highest precision at 0.71, proving effective in reducing false positives. The proposed system presents a promising solution for improving early kidney disease diagnosis and could be seamlessly integrated into mobile platforms, offering practical real-world applications for healthcare professionals and patients, especially in underserved areas. © 2024 IEEE.

关键词： Intelligent systems

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in Engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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