This work introduces and evaluates ccheck, a lenient automatic grader and C style-checker, to guide students to improve their coding practices. Many computing classes rely heavily on autograders-software that automate...
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ISBN:
(纸本)9798400704239
This work introduces and evaluates ccheck, a lenient automatic grader and C style-checker, to guide students to improve their coding practices. Many computing classes rely heavily on autograders-software that automates grading and alleviates staff workload in classes with large enrollments. At best, autograders offer timely and consistent feedback to students. However, existing autograders primarily judge on functional correctness-they are generally strict and inflexible in marking beginner programming assignments. They tend not to provide feedback on programming style and structure, which instead requires delayed, tedious manual assessment. ccheck, the tool we introduce, aims to address this gap and provide more meaningful, real-time feedback with a pedagogical focus. We deploy ccheck in a class of 440 first-year computerscience students. Teaching assistants employ the system for marking assistance, while students use the same system for self-evaluation prior to finalizing their submissions. Feedback was solicited through a survey of 76 students and a focus group of the teaching team. 82% of the students surveyed said that the system helped them learn good coding practices, while 75% emphasized that the feedback received from the system is meaningful and helpful. The teaching team focus group related to how they valued the automation of menial marking tasks, which enabled them to direct their time toward other meaningful feedback. Overall, we find that teaching, learning and student experiences are improved through the deployment of ccheck.
Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constr...
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ISBN:
(纸本)9783031569562;9783031569579
Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called "informal CDGP," to software synthesis problems. Our results show that informal CDGP finds solutions faster (i.e. with fewer program executions) than standard GP. Additionally, we propose two new variants to informal CDGP, and find that one produces significantly more successful runs on about half of the tested problems. Finally, we study whether the addition of counterexample training cases to the training set is useful by comparing informal CDGP to using a static subsample of the training set, and find that the addition of counterexamples significantly improves performance.
"Set and Graph Theory" is the theoretical foundation for computerscience and technology. We construct joint experimental curriculums for set and graph theory and programming curriculums based on programming...
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The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex progr...
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The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex programming to discrete point-set functions, the discrete convex analysis and discrete steepest descent (DSD) algorithm are introduced. To overcome combinatorial complexity of the DSD algorithm, we employ the sequential approximate integer programming (SAIP) to explicitly and linearly approximate the implicit objective and constraint functions. Considering the multiple potential changed directions for multi-valued design variables, the random discrete steepest descent (RDSD) algorithm is proposed, where a random strategy is implemented to select a definitive direction of change. To analytically calculate multimaterial discrete variable sensitivities, topological derivatives with material contrast is applied. In all, the MVIP is finally transferred as the linear 0-1 programming that can be efficiently solved by the canonical relaxation algorithm (CRA). Explicit nonlinear examples demonstrate that the RDSD algorithm owns nearly three orders of magnitude improvement compared with the commercial software (GUROBI). The proposed approach, without using any continuous variable relaxation and interpolation penalization schemes, successfully solves the minimum compliance problem, strength-related problem, and frequency-related optimization problems. Given the algorithm efficiency, mathematical generality and merits over other algorithms, the proposed RDSD algorithm is meaningful for other structural and topology optimization problems involving multivalued discrete design variables.
This paper considers how a curricular design that integrated computerprogramming and creative movement shaped students' engagement with computing. We draw on data from a camp for middle schoolers, focusing on an ...
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This paper considers how a curricular design that integrated computerprogramming and creative movement shaped students' engagement with computing. We draw on data from a camp for middle schoolers, focusing on an activity in which students used the programming environment NetLogo to re-represent their physical choreography. We analyze the extent to which students noticed incompatibilities (mismatches between possibilities in dance and NetLogo), and how encountering them shaped their coding. Our findings suggest that as students attended to incompatibilities, they experienced struggle, but persisted and engaged in iterative cycles of design. Our work suggests that tensions between arts and programming may promote student engagement.
Minimum flow decomposition (MFD) is a common problem across various fields of computerscience, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA tra...
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Minimum flow decomposition (MFD) is a common problem across various fields of computerscience, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA transcript or quasi-species assembly, the flow is erroneous since it is obtained from noisy read coverages. Typical generalizations of the MFD problem to handle errors are based on least-squares formulations or modelling the erroneous flow values as ranges. All of these are thus focused on error handling at the level of individual edges. In this paper, we interpret the flow decomposition problem as a robust optimization problem and lift error-handling from individual edges to solution paths. As such, we introduce a new minimum path-error flow decomposition problem, for which we give an Integer Linear programming formulation. Our experimental results reveal that our formulation can account for errors significantly better, by lowering the inaccuracy rate by 30-50% compared to previous error-handling formulations, with computational requirements that remain practical.
The paper examines methods for teaching programming through integrated curricula that build on the underlying mathematics that the students are familiar with. We present two examples to illustrate the approach. First,...
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ISBN:
(纸本)9783031442360;9783031442377
The paper examines methods for teaching programming through integrated curricula that build on the underlying mathematics that the students are familiar with. We present two examples to illustrate the approach. First, at the most basic level, we describe a successful curriculum for introducing middle-school students to programming through the use of variables, linear equations, and basic algebraic expressions. We motivate students to create digital images using NumPy arrays by experimenting with number representations and coordinate systems. The students create digital videos by building their video characters and moving them around from frame to frame. Second, we present an advanced example for establishing the convergence of machine learning algorithms based on fundamental theorems from real analysis. In the second example, we explain how to select an optimal model through the convergence of the validation loss sequence. For the results, we present how the students perceived the integration of Mathematics with computerprogramming.
In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of *** HC might be utilized tow...
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In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of *** HC might be utilized toward determining gestational age and tracking fetal *** automated approach is particularly valuable in low-resource settings where access to trained sonographers is *** CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal *** identified the HC using dynamic programming,an elliptical fit,and a Hough *** computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test *** used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,*** regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of *** mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 *** outcomes reveal that the computer-aided detection(CAD)program outperforms an expert *** paired with the classifications reported in the literature,the provided system achieves results that are comparable or even *** have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.
Proficiency in programming is crucial for driving the Fourth Industrial Revolution. Therefore, interest in programming needs to be instilled in students starting from the school level. While the use of robotics can at...
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Proficiency in programming is crucial for driving the Fourth Industrial Revolution. Therefore, interest in programming needs to be instilled in students starting from the school level. While the use of robotics can attract students' interest in programming, there is still a lack of research modeling, the impact of robotic learning experiences on programming interest using a structural equation modeling (SEM) approach. This study aims to analyze the structural relationship between interest in programming and learning experiences using a specially developed robotics module based on Kolb's experiential learning model and the programming development phases. An experiment involving 76 primary and secondary school students was conducted using the robotics module. Data were collected through a questionnaire containing 12 questions for five constructs: engagement, interaction, challenge, competency, and interest. These constructs, which are latent variables, formed the model using the partial least squares-SEM technique through the SmartPLS 4.0 software. The evaluation of the structural model found that the variables of engagement and competency had a significant impact on interest in programming, while interaction and challenge received low values. The developed model has moderate predictive power, indicating that interest in programming can be moderately predicted based on students' experiences using robots.
Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus ***,active power plays a significant role in microgrid voltag...
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Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus ***,active power plays a significant role in microgrid voltage ***-based demand response(PBDR)can shift load demand via determining time-varying prices,which can be regarded as an effective means for active power ***,due to the different characteristics,PBDR and inverter-based VVC lack systematic ***,this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method,including day-ahead PBDR price scheduling,hour-ahead reactive power dispatch of PV inverters,and realtime local droop control of PV *** their mutual influence,a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two *** solve the bilinear constraints of droop control function,the problem is reformulated into a second-order cone programming relaxation ***,the concave constraints are convexified,forming a penalty convex-concave model for feasible solution ***,a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty *** proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other *** results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.
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