Despite the need to build a quantum workforce, current courses that introduce quantum programming are rooted in quantum notation that students may find intimidating. We propose Q-CS1, a quantum equivalent of CS1 that ...
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programming languages are essential tools for developers, and their evolution plays a crucial role in supporting the activities of developers. One instance of programming language evolution is the introduction of synt...
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Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilit...
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ISBN:
(纸本)9798400705311
Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computerscience and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research.
Novice learners of programming tend to neglect error messages, even though the messages have a lot of useful information for solving problems. While there exists research that aims to user-friendly error messages by c...
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ISBN:
(纸本)9798400701382
Novice learners of programming tend to neglect error messages, even though the messages have a lot of useful information for solving problems. While there exists research that aims to user-friendly error messages by changing the wording and by adding visual assistance, most of them do not focus on drawing learners' attention to error messages. We propose the enbugging quiz, a novel quiz format that requests the learner to craft a program that produces a specified error. This paper reports our design of enbugging quizzes and reports the results of our initial experiment, where we observed positive effects on the learners' attitudes towards error messages.
Eye movement data provides valuable insights that help test hypotheses about a software developer's comprehension process. The pupillary response is successfully used to assess mental processing effort and attenti...
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Although answer set programming has been a tried way of problem-solving for over thirty years, few tools and methodologies exist today to test it rigorously. Previous research suggests that mutation testing is able to...
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ISBN:
(纸本)9783031808883;9783031808890
Although answer set programming has been a tried way of problem-solving for over thirty years, few tools and methodologies exist today to test it rigorously. Previous research suggests that mutation testing is able to uncover flaws even with small inputs. In this paper, we introduce clingabomino, a mutant generator implemented in C++ that directly operates on the abstract syntax tree of the Clingo solver. Our tool consists of a command-line application that implements commonly useful mutation operators as well as a library to aid in the development of domain-specific mutation operators.
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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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:
(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.
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.
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.
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