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关键词： Computer science   Optics and photonics  

摘要： An optical system that induces random fluctuations in a laser beam could be faster and more efficient at generating images than conventional computers are.

关键词： Tricalcium phosphate   Artificial neural network   Computer science   Mechanical properties prediction  

摘要： Tricalcium phosphate (TCP) bioceramics have emerged as a promising option to meet the specific requirements associated with athlete bone fractures. Advanced computational modeling techniques, such as artificial neural networks (ANNs), have been leveraged to optimize the formulation, structural properties, and implantation strategies of TCP-based biomaterials. This personalized approach enables the tailoring of TCP implants and scaffolds to match the specific biomechanical and biological requirements of individual athletes, maximizing the potential for successful bone regeneration and a timely return to athletic competition. The strategic application of TCP-based biomaterials, combined with personalized computational modeling, holds great promise in revolutionizing the management of athlete bone fractures. This article investigates the use of an ANN to understand the complex relationships between various parameters, such as porosity and bone growth, and their effects on the biodegradation rate, compressive strength, and hardness of TCP-based bioceramics. The accuracy of the neural network's predictions was validated using linear regression analysis, confirming its applicability in guiding the design and optimization of these biomaterials for sports-related bone injury applications. The study developed an artificial neural network (ANN) model to accurately predict the biodegradation rate, compressive strength, and hardness of tricalcium phosphate (TCP) bioceramics as a function of porosity and bone growth. The ANN demonstrated high accuracy in forecasting these key bioceramic properties.

关键词： Education   Accreditation   Artificial intelligence   Learning systems   Information and communication technology   Focusing   Evaluation models   Data mining   Computer science   Training   Course learning outcome (CLO)   fuzzy evaluation system   outcome-based education (OBE)   program learning outcome (PLO)   student performance assessment  

摘要： Contribution: This research designs a student evaluation framework integrating the fuzzy-logic system that assesses the student's performances in the soft boundary system for outcome-based education (OBE), measuring the course learning outcome (CLO) and program learning outcome (PLO). The framework fills the gap between conventional grading methods and offers insights into learning for course assessment and continuous development. Background: A well-established evaluation technique is a requirement to deliver a productive, skilled, worthy, and compatible student and faculty. Moreover, OBE, with a documented and structured academic curriculum, has to ensure the accreditation of an academic program. Research Questions: What are the drawbacks of traditional student evaluation techniques? Does the proposed system work as a better, more reliable, and meaningful student evaluation method? Methodology: To assess, it considers the final examination paper containing several questions and continuous assessment comprising a few items like class tests, quizzes, viva voce, homework, etc., where the course teachers and moderators assign marks on these questions and items considering the CLOs, learning methods, and Bloom's taxonomy. Then, the framework records and tracks the ratio of earned marks to assigned marks for the fuzzification, while the defuzzification computes the values indicating the CLOs and PLOs earned by a student. Findings: The results study cases for 40 courses of a particular student and analyze statistics for 100 students from the consecutive eight semesters. This fuzzy-logic-based evaluation technique is fairer, reliable, and unbiased to the learners and greatly helps to get accreditation and recognition for the degree worldwide.

关键词： Computer Science   STEM education   Intellectual potential   Cognition   Affect   Creativity   Intelligence   Mathematical competencies   Motivation  

摘要： Computer Science (CS), despite being an integral part of STEM education, remains less accessible to school students. This study examines the Challenge program, which enables high-achieving high school students to earn a ***. degree in CS. Over two years, from 458 applicants, 160 passed the admission tests, but only 34 completed their degree. The admission process evaluated cognitive and affective characteristics based on intellectual potential framework, using SAT-M for mathematical competencies and Raven Progressive Matrices Test (RPMT) for general intelligence as program enrolment criteria. Students who passed the admission tests demonstrated higher general and mathematical creativity, enhanced working memory, and stronger motivation in areas of future learning goals, home-school dissonance, and parent mastery goal. Program completers showed significantly higher general and mathematical creativity and auditory working memory compared to non-completers. While SAT-M scores correlated significantly with performance in core mathematical courses (Linear Algebra, Discrete Mathematics, Calculus, and Probabilistic Methods), general creativity correlated with success in Calculus, Introduction to Hardware, and Probabilistic Methods. Notably, RPMT scores showed no correlation with academic performance. Our key finding reveals that general creativity significantly impacts students' ability to complete CS studies, while mathematical competencies correlate only with specific courses. These results suggest that incorporating general creativity assessment alongside mathematical competencies could improve the admission process and potentially reduce dropout rates more effectively than the current RPMT requirement.

关键词： Artificial intelligence   Education   Philosophical considerations   Ethics   Computer science   Engineering education   Transforms   Technological innovation   Surveys   Optimization   Artificial intelligence (AI)   education   engineering   ethics   philosophy  

摘要： This article explores the intersecting domains of artificial intelligence (AI), education, engineering, and the associated risks they entail. It highlights the transformative potential of AI in education, enabling personalized learning and innovative teaching methodologies. In the field of engineering, AI offers opportunities for process optimization, design innovation, and virtual laboratories, that can enhance both the practice and the teaching of engineering. To better comprehend how AI is used and perceived, a survey concerning AI and research in academic databases are also examined to better understand how the public is perceiving this new technology and what are the trends in publishing within these fields. This article highlights the problems we may face with the implementation of AI in our society and provides insights into AI's potential, risks, and the need for responsible implementation in education, engineering, and society.

关键词： Management Board   IEEE Computer Society   Vice President   Technical Actions   Computer Science   International Conference   Computer Technology   High Performance Computing   Electrical Engineering   Quantum Computing   Steering Committee   Edge Computing   Global Leadership   Industrial Organization   Senior Members   Artificial Intelligence Systems   Executive Committee   Young Professionals   Diversity And Inclusion   Artificial Intelligence Research   Program Committee   Leadership Experience   Cybersecurity   IT Professionals   IEEE Transactions   Hall Of Fame   Years Of Experience   Software Architecture   Homeland Security  

摘要： Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

关键词： Social networking (online)   Computer science   Electronic mail   Transformers   Social groups   Reviews   Training   Technological innovation   Semantics   Privacy   Deep learning   online social networks (OSNs)   structural hole (SH) spanner identification   transformer  

摘要： Influential users in online social networks have been given a lot of attention, which could maximize information diffusion in a centralized dissemination process. However, there exist users that can significantly promote diversified information communication among users, which is a pluralistic interaction process. Structural hole (SH) spanners serve as a bridge between social groups, promoting interactions between users and allowing ordinary users to receive diverse information. Filling structural holes, SH spanners have more opportunities to obtain innovative content. Identifying SH spanners is challenging due to their inconspicuous characteristics and privacy policy restrictions. In this article, we are the first to study the SH spanner identification problem using the behavior data generated by users, releasing the dependency on the entire graph structure to find SH spanners. We propose DeepHole to design both the semantic and the sequential behavior analysis modules on users' generated content, utilizing the TextCNN and Transformer models, respectively. The semantic and sequential embeddings are effective in distinguishing the SH spanners from ordinary users. We conduct comprehensive evaluations using the Yelp and Foursquare datasets simultaneously. Results show that DeepHole can achieve a satisfying performance in detecting SH spanners labeled by both constraint and effective size metrics, with AUC values of 0.923 and 0.933 for Yelp and 0.828 and 0.815 for Foursquare.

关键词： Brain   Computer science   Machine learning  

摘要： Two distinct computational approaches provide opportunities for bringing together different theories of cognition.

摘要： Gamification is a process of applying game-design elements (e.g., puzzle, storytelling, point scoring) into non-game areas such as education and health care, and several research projects utilized it to engage K-12 students in computing education where they can have playful experience while learning concepts regarding computer science. However, there was no report yet that summarized previous research projects to see what areas of computer science have been taught for K-12 students specifically via gamification, which can provide clear insight into what topics were less focused so far and need to be considered in the future. Therefore, in this paper, we review 23 published research articles and show which concepts and topics have been covered and how successfully they were taught through gamification. This report will help to see the trends of research in gamification for K-12 computing education and the possible future directions that can be pursued in this research field.

摘要： Teaching large classes, particularly in computer science (CS), presents notable challenges that can hinder student learning. Common issues include low student participation, often due to students feeling hesitant or uncomfortable speaking up in large groups, and difficulties in providing timely feedback. To address these challenges, this study introduces a straightforward and practical solution: a dual-component quiz system integrated into laboratory sessions. In this approach, students first complete a quiz individually, followed by a collaborative phase where they retake the same quiz in small groups. This structure promotes peer feedback and reinforces understanding. The grading scheme allocates 60% of the total score to individual performance and 40% to group performance, encouraging both personal accountability and collaborative engagement. To assess the effectiveness of this method, a comparative analysis was performed using course evaluations from five sections of CS 132 at Boston University. Two sections, serving as the baseline, did not include the quiz component, while three sections implemented the new system. The results of the Welch Statistical Test (WST) revealed that the collaborative quiz system significantly improved student participation, promptness of feedback, and quality of feedback to students.