With the rapid development of cloud computing, the issue of how to reduce energy consumption has attracted a great deal of attention. Especially for dynamic workflow scheduling, dependency constraints between tasks an...
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ISBN:
(数字)9789819755783
ISBN:
(纸本)9789819755776;9789819755783
With the rapid development of cloud computing, the issue of how to reduce energy consumption has attracted a great deal of attention. Especially for dynamic workflow scheduling, dependency constraints between tasks and high quality of service requirements, such as real-time requirements and deadline constraints, make it very challenging. This paper focuses on the energy-efficient scheduling problem, which jointly considers the impact of finer-grained tasks with CPU and memory configurations on energy consumption. A dynamic workflow scheduling simulator is developed to mimic the scheduling process in real-world scenarios. Then, we propose a Cooperative Coevolution Genetic programming to learn heuristics for both the task selection decision and the instance selection decision, using the simulator for heuristic evaluation. The scheduling heuristics obtained by Cooperative Coevolution Genetic programming evolution can then be used to make real-time decisions in dynamic environments. The simulation results show that the proposed method has managed to obtain better scheduling heuristics than the baseline methods in terms of energy consumption and resource utilization.
Various methods of algorithm evaluation generate feedback on students' progress. Feedback is an important aspect of effective teaching and learning, and therefore, adequate attention to generating it is essential....
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ISBN:
(纸本)9783031824777;9783031824784
Various methods of algorithm evaluation generate feedback on students' progress. Feedback is an important aspect of effective teaching and learning, and therefore, adequate attention to generating it is essential. Although various methods of algorithm evaluation are discussed in the literature, not much is available on algorithm evaluation methodologies that yield feedback in the form of learning transitions from one algorithm to another. Quantitative methods of evaluation, like the allocation of marks to the sections of the algorithms, are statistical and do not fully generate a clear view of learning transitions. In this paper, we present a SOLO-adapted evaluation methodology (based on the SOLO taxonomy) that can generate meaningful feedback on learning transitions (learning progression) between algorithms. The SOLO-adapted evaluation was tested on actual introductory programming students' algorithms at a university. In the experiment, the feedback (learning transitions) generated by the SOLO-adapted evaluation was used to inform pedagogical intervention. The SOLO-adapted evaluation can show learning transitions as either an improvement, intermediate improvement, stagnant learning, deteriorating learning, intermediate deterioration of learning or already-know.
作者：
Villadsen, JørgenWeile, JonasAlgorithms
Logic and Graphs Section Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 Kongens Lyngby2800 Denmark
We provide an overview of the GOAL-DTU system for the Multi-Agent programming Contest, including the overall strategy and how the system is designed to apply this strategy. Our agents are implemented using the GOAL pr...
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At the 2019 annual Members Meeting of UCAR, an informal survey was made during a well-attended breakout session organized by the UCAR COMET program. The following polling question was posed to an audience of departmen...
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At the 2019 annual Members Meeting of UCAR, an informal survey was made during a well-attended breakout session organized by the UCAR COMET program. The following polling question was posed to an audience of department chairs and educational leaders of atmospheric and oceanic science programs at universities in the United States and Canada, "What is the greatest challenge students have when entering an atmospheric science program?" The majority of participants in the breakout session answered the question, and the dominant responses of these leading atmospheric science educators can be summarized with a single short word: "math." These responses included the topics of relating math and physics as well as computerprogramming and coding skills. The subsequent discussion explored the participants' experiences in greater detail and the nuances of the obstacle that math presents for many students entering atmospheric science programs. The conclusion that can be drawn from this one poll of atmospheric science educators is unequivocal. Mathematics, according to this poll, is by far the greatest challenge faced by undergraduate university students when they enter an atmospheric science program.
This paper determines the location of emergency supply points by constructing a multi-stage stochastic programming model with the goal of minimizing rescue costs. and reserves, and considers the robust optimization mo...
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Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its im...
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Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal ***,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction *** study explored the application of advancedmachine learning(ML)algorithms and Gene Expression programming(GEP)techniques to predict CO_(2)-induced alterations in coal *** were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.
Quantum computing's potential for exponential speedups over classical computing has recently sparked considerable interest. However, quantum noise presents a significant obstacle to realizing this potential, compr...
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ISBN:
(纸本)9783031645723;9783031645730
Quantum computing's potential for exponential speedups over classical computing has recently sparked considerable interest. However, quantum noise presents a significant obstacle to realizing this potential, compromising computational reliability. Accurate estimation and mitigation of noise are crucial for achieving fault-tolerant quantum computation. While current efforts focus on developing noise models tailored to specific quantum computers, these models often fail to fully capture the complexity of real quantum noise. To this end, we propose an approach that uses genetic programming (GP) to develop expression-based noise models for quantum computers. We represent the quantum noise model as a computational expression, with each function corresponding to a specific aspect of the noise behavior. By function nesting, we create a chain of operations that collectively capture the intricate nature of quantum noise. Through GP, we explore the search space of possible noise model expressions, gradually improving the quality of the solution. We evaluated the approach on five artificial noise models of varying complexity and a real quantum computer. Results show that our approach achieved an error difference of less than 2% in approximating artificial noise models and 15% for a real quantum computer.
Aim This study explores whether personality-based role assignments (Pilot, Navigator, Solo) can raise intrinsic motivation in pair programming, focusing on designing a framework and process extension for the resource-...
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Aim This study explores whether personality-based role assignments (Pilot, Navigator, Solo) can raise intrinsic motivation in pair programming, focusing on designing a framework and process extension for the resource-constrained environment of very small entities (VSEs). Method We employed a mixed-methods design across three quasi-experimental datasets (n = 73 participants), applying linear mixed-effects (LME) modeling to assess motivational outcomes and thematically analyzing (n = 25) interviews for socio-psychological insights. Findings Openness strongly correlates with Pilot roles;Extraversion & Agreeableness favor Navigator roles;and Neuroticism aligns more comfortably with Solo roles-each yielding substantial boosts in intrinsic motivation (up to 60-65%). Twelve qualitative themes underscore the influence of mentorship, pairing constellations, and flow disruptions on developer experiences. Implications Building on these results, we propose the role-optimization motivation alignment (ROMA) framework, mapped to the ISO/IEC 29110 Software Basic Profile and Agile Guidelines, with practical tasks (T1-T7) to facilitate systematic role-trait alignments in small agile teams. Although our data primarily involve Gen-Z undergraduates, the recurring patterns suggest broader applicability, further supported by a separately published application for ongoing generalizability. Conclusion Personality-driven role optimization may significantly enhance collaboration and developer satisfaction in VSEs, though further studies in professional settings and investigations into AI-assisted or distributed pair programming are warranted.
In current machine learning research, deep learning methodologies have become the prevalent approach across various domains, including decision-making processes. However, the interpretability of solutions generated by...
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ISBN:
(纸本)9783031611391;9783031611407
In current machine learning research, deep learning methodologies have become the prevalent approach across various domains, including decision-making processes. However, the interpretability of solutions generated by these algorithms remains a significant challenge, as these models do not inherently prioritize explainability. This lack of interpretability hampers the analysis of decision-making rationales. One potential remedy to this issue is the employment of Genetic Network programming (GNP), a method within the evolutionary computing paradigm, known for its ability to generate more interpretable solutions. This study provides a concise overview of GNP, exploring its modifications and applications to demonstrate its utility in addressing the interpretability challenge in machine learning algorithms.
programming is a fundamental subject in the majority of technology-oriented degrees, particularly in computer Engineering programs. Mastering programming requires continuous practice to understand the syntax, semantic...
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