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Game Development Education: Approaches for teaching Software engineering Students 27th

Game Development Education: Approaches for Teaching Software...

引用

27th international conference on Interactive Collaborative learning, ICL 2024

作者： Maksimenkova, Olga Lebedev, Sergey Pozdnyakov, Denis National Research University Higher School of Economics Moscow Russia

ISBN: (纸本)9783031835193

Game development education has become more popular all over the World. A review shows several topics, which deal with games in application to teaching and learning: gamification, designing separate courses, and designing full educational programs. Within this last topic presented article discusses the issues of training specialists in the field of computer games development. The authors analyze the world and landscape of this area and discuss the experience of building systematic training within the framework of the educational program of higher education in the field of "Software engineering". In addition, the authors consider the issues of scientific and applied projects related to the field of computer games development, as well as their mutual influence on each other and on the training of specialists. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Game design

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Boosting Software Quality: A Machine learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： Data reliability

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Continuous-Time Double Actors and Regularized Critics in Reinforcement learning 14th

Continuous-Time Double Actors and Regularized Critics in R...

引用

14th international conference on Frontier computing, FC 2024

作者： Li, Shuhao Zhang, Shengjie Zhang, Han Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9789819623945

Despite advances in continuous-time reinforcement learning, employing continuous-time actor-critic (CTAC) algorithm is not always the best choice due to the lack of value estimation method. This paper introduces a novel Continuous-time Double Actors and Regularized Critics method. We apply the underlying ideas behind the success of continuous control reinforcement learning to continuous-time tasks, aiming to achieve better value estimation and exploration compared to CTAC. Experimental results demonstrate that our method can reach the maximum reward in fewer training rounds, significantly outperforming CTAC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Crypto Trading Platform: MERN Stack with Real-Time Data and Social Features 2

Crypto Trading Platform: MERN Stack with Real-Time Data and ...

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2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Gurupriya, M. Mouhitha, A. Gujjula, Saranya Ankireddypalli, Okesh Reddy Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798331505745

The increasing complexity of cryptocurrency markets necessitates the development of efficient portfolio management tools that provide real-time tracking, price updates, and market awareness. This paper focuses on an advanced concept of a cryptocurrency wallet service provision that provides a summary of available coins, their prices, available quantities, and customers' transaction histories. Developed for the 'frontend' using *** and for the 'backend' using ***, which uses the CoinGecko API for receiving live market prices and collecting curated news blog articles from other APIs. The data related to the wallets are efficiently managed since the application is built based on the MongoDB schema, and in addition to this, the user authentication is also integrated. A responsive design of the user interface allows for showing both a portfolio summary and the latest news related to cryptocurrencies, thus suggesting an 'everything in one place' solution for users interested in cryptocurrencies. This system shows how real-time data integration could be implemented, how the system is friendly to users, and how the system manages the content. This system could be expanded in the future with predictive analytics and an AI recommendation system for the portfolio management and contents. © 2025 IEEE.

关键词： Financial markets

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Educational Computer Vision Materials for Classification and Tracking of Objects 25th

Educational Computer Vision Materials for Classification and...

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25th international conference on Intelligent Data engineering and Automated learning

作者： Emersic, Ziga Hrastnik, Gregor Peer, Natasa Meh Kirn, Vasja Lev Justin, Aljaz Videnovic, Jovana Markicevic, Luka Peer, Peter Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia Sch Ctr Velenje Velenje Slovenia

ISBN: (纸本)9783031777370;9783031777387

With the rise of artificial intelligence (AI), the need for AI curriculum rose as well. In order to tackle the problem, AIM@VET (Artificial Intelligence Modules for Vocational Education and Training) EU project, was initiated. This paper presents a comprehensive set of teaching materials designed to introduce pre-university students to the fundamental techniques and concepts in object tracking and classification within the field of computer vision. The materials cover key topics including the basics of object tracking, region of interest selection, single and multiple object tracking methods, evaluation metrics, motion prediction, classification, and advanced concepts such as deep neural networks. By mastering these skills, students are equipped to contribute to real-world technological advancements and are well-prepared for future academic and professional pursuits in computer vision and related disciplines.

关键词： Computer vision teaching Material Classification Object Tracking

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Mobile-Based Peer Assessment in English Language learning: Facilitating Collaborative Feedback in ESL Classrooms 3

Mobile-Based Peer Assessment in English Language Learning: F...

引用

3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Naga Ramesh, Janjhyam Venkata Alazzam, Malik Bader Mahdi, Haider Sharif Kamal Sulaiman, Sazan Sharif, Salah Farhan A Infant Raj, I. Graphic Era Hill University Department of CSE Dehradun India Graphic Era Deemed To Be University Department of CSE Uttarakhand Dehradun India Jadara University Faculty of Information Technology Irbid Jordan Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq K.Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331523893

This study explores the effect of mobile-based peer feedback on enhancing ESL learners' language proficiency, engagement, and collaboration. By leveraging mobile technology to facilitate peer assessments, the research aims to compare the effectiveness of mobile-based feedback against traditional peer feedback methods in ESL classrooms. The study involved thirty-two upper-intermediate to advanced-level university students who engaged in mobile-assisted peer assessments of oral presentations over a four-week period. Data collection included pre- and post-test assessments of language proficiency, as well as surveys to evaluate student perceptions of the mobile feedback system. Statistical analysis using ANOVA was employed to test the hypotheses, which focused on improvements in language proficiency, the engagement level of students, and the quality of feedback provided through mobile platforms. The findings indicated that mobile-based peer feedback significantly improved ESL students' language proficiency compared to traditional methods, with students reporting higher engagement and satisfaction due to the accessibility and real-time nature of mobile feedback. Additionally, the study found a positive association among the quality of feedback and improvements in learners' collaborative skills and language development. This research contributes to the growing body of knowledge on mobile learning and peer feedback, highlighting the potential of mobile platforms to foster more dynamic, interactive, and effective learning environments in ESL education. The results highlight that the technology can be used to improve the teaching and learning of language particularly the use of language. © 2025 IEEE.

关键词： Collaborative learning ESL education Language proficiency Mobile-based peer feedback Peer assessment Technology-enhanced learning

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The Impact of 4C-Based Experiential learning on the Mathematical Performance of First-Year University Students in engineering 27th

The Impact of 4C-Based Experiential Learning on the Mathe...

引用

27th international conference on Interactive Collaborative learning, ICL 2024

作者： Szilágyi, Szilvia Körei, Attila Török, Zsuzsanna Vaičiulytė, Ingrida Department of Analysis Institute of Mathematics University of Miskolc Miskolc Hungary Department of Applied Mathematics Institute of Mathematics University of Miskolc Miskolc Hungary Institute of Anthropological and Philosophical Studies University of Miskolc Miskolc Hungary Faculty of Business and Technologies Šiauliai State Higher Education Institution Šiauliai Lithuania

ISBN: (纸本)9783031835193

Considering the benefits of experience-based learning, we combined the use of dynamic geometric software with project-based learning to make the teaching of engineering mathematics more effective, using the 4C principle, which is well established in the world of educational robotics. In this article, we present a detailed description of a segmented methodological model that creates a new opportunity to modernize the teaching of mathematics in engineering education and increase student motivation. The effectiveness of the method was demonstrated by statistical analysis of data from experiments with first-year Hungarian and Lithuanian engineering students during a two-year test period. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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3rd international conference on Intelligent computing Techniques for Smart Energy Systems, ICTSES 2023

3rd International Conference on Intelligent Computing Techni...

引用

3rd international conference on Intelligent computing Techniques for Smart Energy Systems, ICTSES 2023

ISBN: (纸本)9789819784639

The proceedings contain 50 papers. The special focus in this conference is on Intelligent computing Techniques For Smart Energy Systems. The topics include: Analysis of Fault and Current-Limiting Methods in the Power System;UDS for Data Communication in Temperature Monitoring;approximation in the Weighted Lipschitz Class by (C,α,θ)(E,q) Mean of Its Fourier Series;demand Response and Load Profile Analysis in Modern Electricity Systems;First-Principles Study of the Structural, Optical and Electronic Properties of Zinc Blende GaN Through PBEsol and TB-mBJ Functionals;optimization-Based Cost Analysis for Energy Management System in Microgrids;classification and Prediction of Knee Osteoarthritis by Deep learning Approach;Predicting Stock Price and Market Direction Using Statistical and LSTM Models;Comprehensive Evaluation of Time Management with Balancing Resistors in Passive Cell Balancing Using the 1RC Model;PAWnnect: Pioneering IoT-ML Driven Pet Monitoring;transactive Energy Management for Microgrids: Peer-To-Peer Trading Using Stackelberg Game Approach;Unravelling the Mind's Emotions: A Review of EEG-Based Emotion Detection Using Machine learning and Deep learning;unlocking IoT Security: Enabling the Future with Lightweight Cryptographic Ciphers;Optimal Design of Nano Scale Voltage Amplifier Using Evolutionary Techniques for DG-MOSFET;flower Patterned with Elliptical Petals Multiband Microstrip Fractal Patch Antenna for Wireless Applications;Optimal Scheduling of Solar PV Generators and Network Assets in Distribution Network;revolutionizing Biomedical engineering with Microcontroller Applications: A Comprehensive Review;investigation of Battery Temperature Impact on High Voltage Cable of Electric Vehicles;deep learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality;Rectangular Shape Patch Antenna with Defected Ground Plane for 5G Sub 6 GHz Wireless Communication Systems.

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Intelligent scheduling and resource prediction framework for multidimensional data streams in cloud computing environments 2

Intelligent scheduling and resource prediction framework for...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Deng, Wei Qiu, Ziliang Liu, Weifeng Huang, Juzheng Ren, Binhua Qin, Siyuan Shenzhen Power Supply Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9781510689053

Cloud computing has become a foundational technology for handling the growing demands of data-intensive applications. Efficient resource allocation and scheduling are critical challenges, particularly when managing multidimensional data streams in dynamic environments. This paper introduces an intelligent framework for scheduling and resource prediction tailored to cloud computing systems. The proposed method integrates advanced machine learning techniques to predict resource demands accurately, enabling dynamic scheduling that optimizes resource utilization while reducing delays. The framework also includes an energy-aware optimization module that minimizes power consumption without compromising performance. The methodology leverages historical and real-time data to ensure adaptability and scalability across diverse workloads. Experimental results demonstrate the framework's high prediction accuracy, with minimal mean absolute error and root mean square error, its ability to maintain over 91% resource utilization under varying workload intensities. Moreover, the energy efficiency improvements of up to 15.4% highlight the system's sustainability. This study contributes a robust and scalable solution to modern cloud resource management, addressing both operational and sustainability goals. © 2025 SPIE.

关键词： Clouds Cloud computing Energy efficiency Data modeling Sustainability Modeling Machine learning Mathematical optimization Performance modeling Evolutionary algorithms

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Optimised Classification of Android Malware using ML Classifiers and Stacking Ensemble Algorithm 6

Optimised Classification of Android Malware using ML Classif...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Singh, Preet Hasija, Taniya Ramkumar, K.R. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331522667

One of the biggest threats that malicious software, or malware, poses to cybersecurity is exploiting vulnerabilities to infect computer systems. This research is based on malware classification using ensemble-based and kernel-specific machine-learning classification techniques. Advanced cyber threat scenarios have a critical need for solutions that balance accuracy, precision, recall, and F1 score while providing successful malware identification. The diverse base models integrated into the proposed methodology consisted of SVM with linear, polynomial, and RBF kernels, and a stacking classifier that utilizes the base model's strengths. The results showed that, compared with various single models, a stacking classifier achieved quite a high accuracy of 99.14% and maintained high precision and recall percentages for all evaluation metrics. It draws attention to hybrid and ensemble approaches in cybersecurity and the chances to raise detection rates at the cost of reduced false classifications. Advanced ensemble strategies, new deep learning models, as well as XAI will be used for the best enhancement of performance, adaptability, and transparency against evolving malware threats. © 2025 IEEE.

关键词： Android malware

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