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The development of a summer study abroad program for engineering and computer science students at Baylor University

The development of a summer study abroad program for enginee...

34th Annual Frontiers in Education: Expanding Educational Opportunities Through Partnerships and Distance Learning - Conference Proceedings, FIE

作者： Fry, Cynthia C. Maastricht Summer Study Abroad Program School of Engineering and Computer Science Baylor University One Bear Place #97356 Waco TX 76798

The Baylor In Maastricht (BIM) Summer study Abroad program is a 41-day study abroad program in the Netherlands. Baylor In Maastricht is a summer program that is sponsored by the School of Engineering and science of Baylor University in Waco, Texas, in conjunction with the Universiteit Maastricht, in the Netherlands. The program gives engineering and computer science students an opportunity to take two Baylor courses (six hours), while also providing an abroad experience in the heart of Europe. It also answers the charge given four years ago by the Dean of the School of Engineering and computer science to get our students out of the building! This paper will discuss the goals and objectives of such a program, and will chronicle the design, development, and execution of the resulting study abroad program. We will explore, in depth, the number and type of participants, the details of trip planning and organization, the decision on which courses to offer, planned weekend group trips, the facilities and support of the Universiteit Maastricht, and accommodations. This paper will also discuss the execution of the first two study abroad programs to Maastricht, the Netherlands - what worked, what didn't work, what was good, where we have improved. A summary of the student evaluations from the program is included. © 2004 IEEE.

关键词： Engineering education

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Simulation of Electromagnetic Wave Propagation in Graphene-Coated 1D Photonic Crystals Using the Finite Volume Method

Simulation of Electromagnetic Wave Propagation in Graphene-C...

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2024 International Conference on Interdisciplinary Physics, ICIPs 2024

作者： Negara, Teguh Puja Hardhienata, Hendradi Alatas, Husin Study Program of Computer Science Faculty of Mathematics and Natural Science Universitas Pakuan Bogor Indonesia Theoretical Physics Division Department of Physics Faculty of Mathematics and Natural Science IPB University Bogor Indonesia

A numerical study on the interaction of electromagnetic waves with photonic crystals with one-dimensional periodicity has been carried out using the Finite Volume method. The photonic crystals with one-dimensional periodicity consists of 7 alternating layers of and with thicknesses of and, respectively. Electromagnetic field components have been simulated to propagate through the photonic crystal structure, experiencing reflection and transmission at each layer of the structure. The simulation results show the occurrence of absorption precisely at the Bragg frequency by the photonic crystal, which is referred to as the photonic band gap (PBG). A comparison of the simulation results has been conducted using different fluxes, such as the Lax-Friedrichs and upwind schemes, under PBG conditions. The results show smoother absorption using the Lax-Friedrichs scheme. Additionally, the use of a single-layer graphene on the front side of the photonic crystal has shown more perfect absorption compared to without the single-layer graphene. Therefore, the integrated graphene-photonic crystal system can be used as a perfect reflector. The simulation runs well using effective absorbing boundary conditions (ABCs) to reduce wave reflections at the edges of the computational window. Based on the simulation results, the Finite Volume method has been proven to be a powerful tool in describing electromagnetic wave propagation through photonic crystal systems with or without graphene. These findings provide a foundational model for fabrication approaches and holds potential for future advancements in sensor technologies, moreover two-dimensional and three-dimensional structures. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Photonic band gap

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Interconnected Through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. This research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. This research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. The total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. The research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. The survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. The robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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Multi-Normalization Residual Graph Convolutional Network for 3D Human Pose Estimation

Multi-Normalization Residual Graph Convolutional Network for...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Pramono, Andy Chang, I-Cheng Puspasari, Betty Dewi Animation Study Program Universitas Negeri Malang Jawa Timur Malang Indonesia Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Informatics Engineering Institut Teknologi Nasional Malang Jawa Timur Malang Indonesia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

3D human pose estimation (HPE) has improved significantly through Graph Convolutional Networks (GCNs), which effectively model body part ***, GCNs have limitations, including uniform feature transformations across nodes and reliance on skeleton-based graphs that may miss complex motion *** address these issues, we introduce a Multi-Normalization Residual Graph Convolutional Network that fine-tunes the graph structure through affinity multi-normalization and activation, allowing the representation of additional connections beyond the *** extensive ablation study shows that this approach enhances performance with minimal overhead while maintaining the same model size, consistently outperforming state-of-the-art techniques on two benchmark datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Integrating Robot-Based Resources to Foster Computational Thinking and Communication Skills in EFL Education: A Path Towards Sustainable Development 2nd

Integrating Robot-Based Resources to Foster Computational Th...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Muthmainnah, Muthmainnah Al Yakin, Ahmad Seraj, Prodhan Mahbub Ibna Darmawati, Besse Elngar, Ahmed A. Obaid, Ahmed J. Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia American International University Dhaka Bangladesh Badan Riset Dan Inovasi Nasional Jakarta Indonesia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq

ISBN: (纸本)9783031717284

The current monolithic approach in teaching and assessing English as a foreign language (EFL) communication in Indonesia has resulted in a gap between students’ understanding of the nature of communication skills and their ability to use them correctly in their speaking, leading to anxiety, lack of self-confidence and lack of mastery vocabulary. Computational thinking (CT), a high-level approach to problem-solving and optimizing knowledge acquisition, was used to design solutions in this study using a Virtual Robot application to stimulate their speaking motivation. They are taught using a CT teaching model. To find out the impact of this method, Pre-test and post-tests were conducted. All undergraduate students showed considerable improvement in their use of vocabulary when speaking, a new level of agency and interest in learning English was evident and their dialogue confidence improved. In the field of English as a foreign language (EFL), CT is an important social mediation technique. This research is the best attempt to incorporate CT into the thinking process of students learning English with the virtual assistance of robot artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Deep Learning for Cyber Security: Deephoax Detection

Deep Learning for Cyber Security: Deephoax Detection

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： SY. Yuliani Yakub Rivo Juicer Wowor David Agustriawan Irmawati Dinar Ajeng Kristiyanti P.M. Winarno Rosilah Hassan Roshayu Mohamad Randy Zahran Razzaq Department of Informatics Universitas Multimedia Nusantara Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Center for Cyber Security (CYBER) Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Informatics UIN Sultan Maulana Hasanuddin Banten Indonesia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats.

关键词： Deep learning Measurement Training Surveys Adaptation models Technological innovation Accuracy computer security Fake news Videos

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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