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关键词： Data Center   Trend Of Energy   Energy Efficiency   Computer Science   Energy Requirements   Energy Use   Energy Intensity   Cryptocurrencies   Large Scale Calculations   Information Processing   Power Calculation   Energy Calculations   Electric Vehicles   Computer System   Energy Distribution   Power Grid   Energy Generation   Power Demand   Energy Usage   Resource Requirements   Digital Twin   Use Of Artificial Intelligence   Smart Grid   International Energy Agency   Grid System   Department Of Energy   Thermodynamic Limit   Digital Computer   Computational Model  

摘要： The unsustainable energy requirements of computing data centers are driven by the growth of artificial intelligence, crypto coin mining, and other emerging applications underscoring the need for energy efficiency across all layers of computing.

关键词： Contrastive Learning  

摘要： The educational application of Generative AI (GAI) has garnered significant interest, sparking discussions about the pedagogical value of GAI-generated content. This study investigates the perceived effectiveness of concept explanations produced by GAI compared to those created by human teachers, focusing on programming concepts of sequence, selection, and iteration. The research also explores teachers' and students' ability to discern the source of these explanations. Participants included 11 teachers and 70 sixth-grade students who were presented with concept explanations created or generated by teachers and ChatGPT. They were asked to evaluate the helpfulness of the explanations and identify their source. Results indicated that teachers found GAI-generated explanations more helpful for sequence and selection concepts, while preferring teacher-created explanations for iteration (χ2(2, N = 11) = 10.062, p = .007, ω = .595). In contrast, students showed varying abilities to distinguish between AI-generated and teacher-created explanations across concepts, with significant differences observed (χ2(2, N = 70) = 22.127, p © 2024 The Authors

关键词： Resource management   Learning automata   Computer science   Linear programming   Tuning   Noise measurement   Convergence   resource allocation   stochastic nonlinear fractional equality knapsack (NFEK)   two-timescale learning  

摘要： In this article, we introduce a novel learning automata (LA) solution to the nonlinear stochastic proportional polling (NSPP) problem. The only available solution to this problem in the literature is that given by Nicopolitidis et al. (2003), Obaidat et al. (2002), and Papadimitriou et al. (2002). It was shown to solve a large set of the adaptive resource allocation problems under noisy environments (Nicopolitidis et al., 2003;Obaidat et al., 2002;Papadimitriou and Pomportsis, 2000 and 1999;Nicopolitidis et al., 2004;Obaidat et al., 2001;and Papadimitriou and Pomportsis, 2000). We make a threefold contribution. First, we take a two-timescale approach to the field of LA by estimating the reward probabilities on a faster timescale than the timescale for updating the polling probabilities. Second, by making a not-obvious choice of the objective function, we show that the NSPP problem is indeed an instantiation of the stochastic nonlinear fractional equality knapsack (NFEK) problem, which is a substantial resource allocation problem based on the incomplete and noisy information (Granmo and Oommen, 2010). Third, in contrast to the legacy approach taken by Papadimitriou and Maritsas (1992 and 1996), we show through the extensive experimental results that our solution is remarkably robust to the choice of tuning parameters and that it outperforms the state of the art solution in terms of the Bayesian expected loss.

关键词： Partitioning algorithms   Skeleton   Transforms   Pattern analysis   Computer science   Testing   Task analysis   Conditional independence test   causal partitioning   causal discovery  

摘要： Causal partitioning is an effective approach for causal discovery based on the divide-and-conquer strategy. Up to now, various heuristic methods based on conditional independence (CI) tests have been proposed for causal partitioning. However, most of these methods fail to achieve satisfactory partitioning without violating d-separation, leading to poor inference performance. In this work, we transform causal partitioning into an alternative problem that can be more easily solved. Concretely, we first construct a superstructure G of the true causal graph G(T) by performing a set of low-order CI tests on the observed data D. Then, we leverage point-line duality to obtain a graph G(A) adjoint to G. We show that the solution of minimizing edge-cut ratio on G(A) can lead to a valid causal partitioning with smaller causal-cut ratio on G and without violating d-separation. We design an efficient algorithm to solve this problem. Extensive experiments show that the proposed method can achieve significantly better causal partitioning without violating d-separation than the existing methods.

关键词： Contrastive learning   Semantics   Noise   Computational modeling   Training   Computer science   Australia   Representation learning   Perturbation methods   Learning (artificial intelligence)   Graph representation learning   graph contrastive learning   knowledge distillation  

摘要： Graph Contrastive Learning (GCL) stands as a potent framework for unsupervised graph representation learning that has gained traction across numerous graph learning applications. The effectiveness of GCL relies on generating high-quality contrasting samples, enhancing the model's ability to discern graph semantics. However, the prevailing GCL methods face two key challenges: 1) introducing noise during graph augmentations and 2) requiring additional storage for generated samples, which degrade the model performance. In this paper, we propose novel approaches, GKCL (i.e., Graph Knowledge Contrastive Learning) and DGKCL (i.e., Distilled Graph Knowledge Contrastive Learning), that leverage multi-level graph knowledge to create noise-free contrasting pairs. This framework not only addresses the noise-related challenges but also circumvents excessive storage demands. Furthermore, our method incorporates a knowledge distillation component to optimize the trained embedding tables, reducing the model's scale while ensuring superior performance, particularly for the scenarios with smaller embedding sizes. Comprehensive experimental evaluations on three public benchmark datasets underscore the merits of our proposed method and elucidate its properties, which primarily reflect the performance of the proposed method equipped with different embedding sizes and how the distillation weight affects the overall performance.

关键词： Formal Techniques   Temporal Logic   Computer Science   Cloud Computing   Practical Examples   Microwave Oven   Software Engineering   Safety Critical   Model Checking   Theoretical Computer Science   Health Related Quality Of Life   System Dynamics   Temporal Dimension   Discrete Data   Student Understanding   Understanding Of Methods   First Order Logic   Modern Software  

摘要： Our hands-on course introduces model checking using Temporal Logic of Actions through practical examples like the control logic for a microwave oven. Due to highly positive initial feedback from our own students, we plan to broaden our reach by partnering with other institutions.

关键词： Adversarial machine learning  

摘要： Artificial intelligence (AI) has gained acceptance in the field of education. Nevertheless, existing research on AI in education, particularly in foreign language (FL) learning and teaching, is notably limited in scope and depth. In the present study, we addressed this research gap by investigating social and computer science students' perceptions of the integration and use of AI-based technologies in education, focusing specifically on foreign language teaching. Using an online questionnaire, we analysed factors such as students' field of study, gender differences, and the type of AI used. The questionnaire included statements categorised into thematic clusters, with responses measured on a five-point Likert scale. Statistical analysis, including chi-square tests and Cohen's d, revealed that individuals studying computer science, males, and supporters of generative AI are more likely to use AI tools for educational purposes. They perceive fewer barriers to the integration of AI into FL education. Social science students and women are less likely to use AI tools in FL education and express scepticism about their potential to improve academic outcomes. They tend to be more critical or cautious regarding the role of AI in FL education. They view AI as a valuable tool that enhances the learning experience but, at the same time, recognise the irreplaceable role of human teachers. The study highlights the need for targeted educational initiatives to address gender and disciplinary gaps in AI adoption, promote informed discussions on AI in education, and develop balanced AI integration strategies to improve FL learning. These findings suggest educators and policymakers should implement comprehensive AI training programs and ethical guidelines for responsible AI use in (FL) education. © 2024 The Authors

关键词： Bipartite graph   Computers   Scalability   Electronic commerce   Computer science   Sun   Data security   Graph computing   bipartite graph   maximal biclique enumeration  

摘要： Maximal biclique enumeration (MBE) in bipartite graphs is a fundamental problem in data mining with widespread applications. Many recent works solve this problem based on the set-enumeration (SE) tree, which sequentially traverses vertices to generate the enumeration tree nodes representing distinct bicliques, then checks whether these bicliques are maximal or not. However, existing MBE algorithms only expand bicliques with untraversed vertices to ensure distinction, which often necessitate extensive node checks to eliminate non-maximal bicliques, resulting in significant computational overhead during the enumeration process. To address this issue, we propose an aggressive set-enumeration (ASE) tree that aggressively expands all bicliques to their maximal form, thus avoiding costly node checks on non-maximal bicliques. This aggressive enumeration may produce multiple duplicate maximal bicliques, but we efficiently eliminate these duplicates by leveraging the connection between parent and child nodes and conducting low-cost node checking. Additionally, we introduce an aggressive merge-based pruning (AMP) approach that aggressively merges vertices sharing the same local neighbors. This helps prune numerous duplicate node generations caused by subsets of merged vertices. We integrate the AMP approach into the ASE tree, and present the Aggressive Maximal Biclique Enumeration Algorithm (AMBEA). Experimental results show that AMBEA is 1.15x to 5.32x faster than its closest competitor and exhibits better scalability and parallelization capabilities on larger bipartite graphs.

关键词： Games   Education   Collaboration   Heuristic algorithms   Data collection   Proposals   Particle measurements   Educational technology   Economics   Computer science   Gale-Shapley algorithm   stable matching   digital game-based learning   compatibility measures   competitive learning  

摘要： The Gale-Shapley algorithm solves the problem of stable pair formation across various fields including economics, labor markets, biology, computer science, and physics. This study modifies the algorithm to use a single list of participants and calculates compatibility scores using Jaccard similarity coefficients from students' proficiency tests and academic performance. We compared the effectiveness of this modified algorithm by evaluating two groups of students engaged in digital educational games: an experimental group matched by the modified algorithm and a randomly matched control group. The results show that the modified algorithm forms pairs with superior compatibility, consistent performance, and balanced competition. These findings suggest integrating the Gale-Shapley algorithm into educational technologies can enhance learning environments. The results significantly impact educational practices indicating that systematic peer training can improve collaboration, competition, and student engagement.

关键词： Students  

摘要： This study examines the adaptation of the problem-solving studio to computer science education by combining it with pair programming. Pair programming is a successful software engineering practice in industry, but has seen mixed results in the classroom. Recent research suggests that pair programming has promise and potential to be an effective pedagogical tool, however what constitutes good instructional design and implementation for pair programming in the classroom is not clear. We developed a framework for instructional design for pair programming by adapting the problem-solving studio (PSS), a pedagogy originally from biomedical engineering. PSS involves teams of students solving open-ended problems with real-time feedback given by the instructor. Notably, PSS uses problems of adjustable difficulty to keep students of all levels engaged and functioning within the zone of proximal development. The course structure has three stages, first starting with demonstration, followed by a PSS session, then finishing with a debrief. We studied the combination of PSS and pair programming in a CS1 class over three years. Surveys of the students report a high level of engagement, learning, and motivation. © 2024 American Society for Engineering Education. All rights reserved.