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Proceedings of the tenth SIGCSE technical symposium on computer science education

作者： A. J. Adams John Maniotes Information Systems and Computer Programming Department Purdue University Calumet Campus Hammond Indiana

ISBN: (纸本)9781450374316

It is the intent of this paper to describe an implementation of a curriculum compatable with the ACM “Curriculum Recommendations and Guide-lines for the Community and Junior College Career Program in computer programming” (also known as the “Little Report”). In order to facilitate comparisons among curricula, this paper follows the “format” developed by members of the ACM C3S-CAJC working group.

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Implementation of Safety-Related Intrusion Detection for an Industrial Robotic Arm

Implementation of Safety-Related Intrusion Detection for an ...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Haydar, Georgio Abou Telawi, Samer Borcsok, Josef Architecture and System Programming University of Kassel Department of Computer Kassel Germany

ISBN: (纸本)9798331529604

Ensuring the safety of human workers collaborating with industrial robots is paramount. This research work presents a novel approach by developing a safety-related intruder detection system for the operational zones of industrial robots, following the IEC 61508 standard and adopting the related redundant architecture. The hardware integrates light curtains and radar sensors in a dual-layer configuration, while the software utilizes a safety system-on-chip to implement real-time diagnostics and fault tolerance. Protecting employees and ensuring a smooth and safe work environment are significant concerns for all companies. As a result, numerous standards and regulations have been established to provide safe collaboration between humans and machines. However, despite these measures, accidents continue to occur, resulting in varying degrees of injuries. The proposed system is depicted through a comprehensive schematic, emphasizing the integration of safety measures within the robotic environment. The detailed prototype demonstrates practical application, show-casing the system's ability to detect intrusions, initiate emergency responses, and transition to fail-safe states during component failures. This research work advances safety protocols in human-robot collaboration within industrial settings, fostering a safe working environment through a well-defined safety framework. © 2025 IEEE.

关键词： Industrial robots

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An Improved Numerical Solution of Modified Regularized Long Wave Equation by Quartic Trigonometric B-Spline

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International Journal of Applied and Computational Mathematics 2025年第2期11卷 1-17页

作者： Kırlı, Emre Mersin, Mehmet Ali Department of Mathematics Aksaray University Aksaray 68000 Türkiye Computer Programming Aksaray University Aksaray 68000 Türkiye

This study presents the application of a numerical method specifically designed to solve the Modified Regularized Long Wave equation, a crucial model in the analysis of non-linear wave dynamics. The proposed method employs a Quartic Trigonometric B-spline approach for spatial discretization, which ensures smooth and accurate interpolation across the spatial domain, while temporal integration is conducted using the well-established fourth-order Runge–Kutta (RK4) scheme, known for its stability and precision. To evaluate the performance of the method, it is applied to three test problems: the propagation of a single solitary wave, the interaction between two and three solitary waves. The invariant quantities are computed for all test cases. To ensure the stability of the method, a stability analysis is performed through the computation of eigenvalues. The results demonstrate that the proposed method achieves a high degree of accuracy in preserving the invariant properties and produces minimal error, highlighting its efficiency and reliability. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Modified regularized long wave Quartic trigonometric B-spline Runge Kutta Solitary wave

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A fault-tolerant scheduling strategy through proactive and clustering techniques for scientific workflows in cloud computing

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Soft Computing 2025年第2期29卷 755-781页

作者： Farhood, Suha Mubdir Khorsand, Reihaneh Hussein, Nashwan Jasim Ramezanpour, Mohammadreza Branch Islamic Azad University Isfahan Iran Department of Programming Collage of Information Technology University of Babylon Hillah Iraq Department of Computer Engineering Mobarakeh Branch Islamic Azad University Mobarakeh Isfahan Iran

Cloud computing offers solutions for various scientific and business applications. Large-scale scientific applications, which are organized as scientific workflows, are carried out using cloud computing. However, the higher failure rates in cloud computing can be attributed to the numerous servers and components dealing with intense workloads. This study presents a fault-tolerant scheduling approach using proactive and clustering methods for scientific workflows in cloud computing. Initially, the task clustering issue is addressed by consolidating multiple short-duration tasks into a single job to improve the runtime performance of workflow executions. Subsequently, an automated workflow scheduling strategy is outlined with four key stages: monitoring, analysis, planning, and execution. During monitoring, clustered jobs and the capacities of available cloud resources are observed. In the analysis phase, the accuracy of failure prediction is enhanced by employing the Group Method of Data Handling (GMDH) neural network prior to any faults or failures. The planning stage introduces a novel hybrid multi-objective algorithm, MOPSO-aSA, based on MOPSO and adaptive simulated annealing (SA), to streamline workflow scheduling in error-prone execution environments. Moreover, the reliability of application execution is maintained through re-clustering and migration techniques following any faults or failures. Finally, based on the experimental findings, it is evident that the proposed strategy surpasses other methods in terms of makespan, total cost, energy consumption, and failure rate. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Simulated annealing

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Analyzing a Transportation Problem with Reliability Under the Shadowed Environment

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Operations Research Forum 2025年第2期6卷 1-38页

作者： Bera, Raj Kumar Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore India

The concept of reliability is crucial in assessing the ability of a transportation network to provide the continuity in operation. Because of this, in the recent transportation business, sources are being pressurized to deliver items reliably and cost-effectively. In such a direction, a classical transportation problem has been modified to a reliable transportation problem by incorporating the transportation reliability along with cost. Also, the main objective of this study is to minimize the cost and maximize the reliability of the proposed problem simultaneously. Here, the transportation reliability has been considered based on the deterioration of items and failure of the transportation mode. It is noted that both the cost and reliability of each path are assigned through negotiation at the beginning of the business; therefore, both data are uncertain. Also, to properly cover the uncertainty, the model has been formulated under the shadowed environment. To solve the proposed model, various methodologies are developed such as a weighted-based de-shadowed procedure to convert the uncertain model into an equivalent deterministic model, a weighted sum method to convert the multi-objective model into a single objective model, the lingo solver to find the optimal result, and a method to find the most efficient pareto optimal result. To justify the impact and significance of the proposed model in the real world, some numerical studies have been done. The variation of results due to uncertainty has been presented. In addition, the effect of negotiation on the uncertainty has been discussed.

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Implementation of Safety-Related Intrusion Detection for an Industrial Robotic Arm

Implementation of Safety-Related Intrusion Detection for an ...

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Control, Automation, and Instrumentation (IC2AI), International Conference on

作者： Georgio Abou Haydar Samer Telawi Josef Börcsök Department of Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (数字)9798331529604

ISBN: (纸本)9798331529611

关键词： Service robots Intrusion detection Prototypes Human-robot interaction Industrial robots Manipulators Safety System-on-chip IEC Standards Standards

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Academic Fraud Detection in Online Exams with DNN's Multilayer Model

Academic Fraud Detection in Online Exams with DNN's Multilay...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Bahaddin Erdem Murat Karabatak Department of Computer Programming Bitlis Eren University Bitlis Turkey Department of Software Engineering Firat University Elaziğ Turkey

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who “cheated” from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty.

关键词： Deep learning Analytical models Digital forensics Predictive models Nonhomogeneous media Data models Educational courses Fraud Security

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Educational Research Trends of the Use of Gaze Learning Data Through Topic Modeling and Scientometric Analysis 1st

Educational Research Trends of the Use of Gaze Learning D...

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1st International workshop on AI for Research, AI4Research 2024 and 4th International workshop on Democracy and AI, DemocrAI 2024, held in conjunction with 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Lee, Jungmin Shin, Huigon Yoo, YoungJoon Choi, Jongwon Kim, Mi Song Department of Advanced Imaging GSAIM Chung-Ang University Seoul Korea Republic of Computer Programming and Analysis School of Information Technology Fanshawe College London Canada Department of Artificial Intelligence Chung-Ang University Seoul Korea Republic of Curriculum Studies Western University London Canada

ISBN: (纸本)9789819795352

Multimodal learning analytics have become increasingly important in enabling a deeper understanding of teaching and learning in educational research. However, in comparison to other multimodal learning data, there is a limited understanding of the trends within gaze learning data. To address this challenge, this study aims to identify latent topics in gaze learning data through topic modeling and scientometric analysis. We analyzed the abstracts of 573 peer-reviewed and conference proceeding papers that used gaze learning data, written in English, and published between 2008 and February 2024. The findings are as follows. First, three main topics were identified through topic modeling analysis: (learning analytics, multimodal learning, and inclusive learning). Second, the scientometric analysis revealed the structure in which diverse clusters in cited references and institutions are connected around the emerging topics. Based on these findings, the study would provide insights into research directions in both educational research and applications using gaze learning data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Research and Development of Kubernetes Operator for Machine Learning Pipelines

Research and Development of Kubernetes Operator for Machine ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Andrey Trapezin Ivan Filianin Faculty of Programming and Infocommunications ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes.

关键词： Production systems Error analysis Machine learning Manuals Tagging Data models Reliability Research and development Load modeling Resilience

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Machine Learning in Defectoscopy: DBSCAN for Localization, Agglomerative Clustering for Typification

Machine Learning in Defectoscopy: DBSCAN for Localization, A...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ruslan Akhmadeev Yurii Litvinov Anastasiya Grigorieva Department of Computer Science Saint Petersburg State University Saint Petersburg Russia Department of System Programming Saint Petersburg State University Saint Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Acoustic emission (AE) is one of the most relevant and cost-effective non-destructive testing methods for identifying defects in industrial objects. This paper addresses two primary tasks in defectoscopy: locating hazardous AE zones and classifying signals according to the presumed defect type. Through our research, we have determined and experimentally confirmed that the most suitable machine learning method for localization is a modified DBSCAN, while Agglomerative Clustering is optimal for typification. This paper details the specific tuning and modifications applied to these methods for AE. The resulting models were successfully tested on real-world data. The accuracy of the localization results was assessed using statistical analysis and consultations with AE experts, while typification was validated probabilistically and tested on sample data.

关键词： Location awareness Accuracy Recurrent neural networks Statistical analysis Acoustic emission Probabilistic logic Real-time systems Reliability Tuning Testing

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