Identifying an optimal basis for a linear programming problem is a challenging learning task. Traditionally, an optimal basis is obtained via the iterative simplex method which improves from the current basic feasible...
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Identifying an optimal basis for a linear programming problem is a challenging learning task. Traditionally, an optimal basis is obtained via the iterative simplex method which improves from the current basic feasible solution to the adjacent one until it reaches optimal. The obtained result is the value of the optimal solution and the corresponding optimal basis. Even though learning the optimal value is hard but learning the optimal basis is possible via deep learning. This paper presents the primal-optimal-binding LPNet that learns from massive linear programming problems of various sizes casting as all-unit-row-except-first-unit-column matrices. During the training step, these matrices are fed to the special row-column convolutional layer followed by the state-of-the-art deep learning architecture and sent to two fully connected layers. The result is the probability vector of non-negativity constraints and the original linear programming constraints at the optimal basis. The experiment shows that this LPNet achieves 99% accuracy of predicting a single binding optimal constraint on unseen test problems and Netlib problems. It identifies correctly 80% LP problems having all optimal binding constraints and faster than cplex solution time.
Research so far has overlooked the contribution of students' noncognitive factors to their performance in introductory programming in the context of personalized learning support. This study uses learning analytic...
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Research so far has overlooked the contribution of students' noncognitive factors to their performance in introductory programming in the context of personalized learning support. This study uses learning analytics to design and implement a Dashboard to understand the contribution of introductory programming students' learning motivation, personality, and class participation factors to their programming performance and provide adaptive support to improve their motivation and persistence. Moreover, this study evaluates the effectiveness and usefulness of the dashboard in two phases: committee evaluation and laypersons evaluation. The evaluation results indicate dashboard's effectiveness in students' better-informed decision-making related to their learning approach. In both phases, the dashboard has been perceived as useful and easy-to-use tool. These findings indicate that the dashboard is perceived as a useful tool for providing timely feedback about non-cognitive factors contributing to students' programming performance. The study provides insights into the noncognitive side of novice programmers and encourages further exploration of other aspects influencing their performance.
High-quality programming projects for education are critically required in teaching. However, it is hard to develop those projects efficiently and artificially constrained by the lecturers' experience and backgrou...
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High-quality programming projects for education are critically required in teaching. However, it is hard to develop those projects efficiently and artificially constrained by the lecturers' experience and background. The recent popularity of large language models (LLMs) has led to a great number of applications in the field of education, but concerns persist that the output might be unreliable when dealing with intricate requirements. In this study, we design a customized role-based agent (CRBA), which can be configured for different roles specializing in specific areas of expertise, making the LLM yield content of higher specialization. An iterative architecture of multi-CRBAs is proposed to generate multistep projects, where CRBAs automatically criticize and optimize the LLM's intermediate outputs to enhance quality. We propose ten evaluation metrics across three aspects to assess project quality through expert grading. Further, we conduct an A/B test among 60 undergraduate students in a programming course and collect their feedback through a questionnaire. According to the students' rating results, the LLM-generated projects have comparable performance to man-made ones in terms of project description, learning step setting, assistance to students, and overall project quality. This study effectively integrates LLM into educational scenarios and enhances the efficiency of creating high-quality and practical programming exercises for lecturers.
Zernike circular polynomials (ZCP) play a significant role in optics engineering. The symbolic expressions for ZCP are valuable for theoretic analysis and engineering designs. However, there are still two problems whi...
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Zernike circular polynomials (ZCP) play a significant role in optics engineering. The symbolic expressions for ZCP are valuable for theoretic analysis and engineering designs. However, there are still two problems which remain open: firstly, there is a lack of sufficient mathematical formulas of the ZCP for optics designers;secondly the formulas for inter-conversion of Noll's single index and Born-Wolf's double indices of ZCP are neither uniquely determinate nor satisfactory. An automatic method for generating symbolic expressions for ZCP is proposed based on five essential factors: the new theorems for converting the single/double indices of the ZCP, the robust and effective numeric algorithms for computing key parameters of ZCP, the symbolic algorithms for generating mathematical expressions of ZCP, and meta-programming & LATEX programming for generating the table of ZCP. The theorems, method, algorithms and system architecture proposed are beneficial to both optics design process, optics software, computer-output typesetting in publishing industry as well as STEM education.
The segmentation of phased array apertures into distinct sets of tile modules, referred to as irregular tiled arrays, is highly appealing in scenarios requiring cost efficiency. However, the limited scanning abilities...
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The segmentation of phased array apertures into distinct sets of tile modules, referred to as irregular tiled arrays, is highly appealing in scenarios requiring cost efficiency. However, the limited scanning abilities restrict broader applications. To mitigate this limitation, we propose a hybrid subarray architecture that enhances flexibility by utilizing the diverse scanning advantages offered by various subarray sizes and shapes. A mixed integer linear programming model is developed to optimize the tiling configuration to adapt the array design to meet different sidelobe level (SLL) and maximum scanning angle requirements. Benders decomposition is employed to efficiently decouple and solve both integer and continuous variables. The proposed array structure exhibits network complexity that falls between traditional irregular arrays and nonadjacent arrays. Compared to traditional arrays, it achieves more than 4 dB sidelobe suppression and a 1.1 dB gain improvement. Compared with nonadjacent arrays, the proposed array not only extends the scanning angle but also achieves significantly lower SLLs, offering superior performance for wide-angle scanning applications.
Chemical reaction networks (CRNs) are an important tool for molecular programming. This field is rapidly expanding our ability to deploy computer programs into biological systems for various applications. However, CRN...
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Chemical reaction networks (CRNs) are an important tool for molecular programming. This field is rapidly expanding our ability to deploy computer programs into biological systems for various applications. However, CRNs are also difficult to work with due to their massively parallel nature, leading to the need for higher-level languages that allow for more straightforward computation with CRNs. Recently, research has been conducted into various higher-level languages for deterministic CRNs but modeling CRN parallelism, managing error accumulation, and finding natural CRN representations are ongoing challenges. We introduce Reactamole, a higher-level language for deterministic CRNs that utilizes the functional reactive programming (FRP) paradigm to represent CRNs as a reactive dataflow network. Reactamole equates a CRN with a functional reactive program, implementing the key primitives of the FRP paradigm directly as CRNs. The functional nature of Reactamole makes reasoning about molecular programs easier, and its strong static typing allows us to ensure that a CRN is well-formed by virtue of being well-typed. In this paper, we describe the design of Reactamole and how we use CRNs to represent the common datatypes and operations found in FRP. We demonstrate the potential of this functional reactive approach to molecular programming by giving an extended example where a CRN is constructed using FRP to modulate and demodulate an amplitude-modulated signal. We also show how Reactamole can be used to specify abstract CRNs whose structure depends on the reactions and species of its input, allowing users to specify more general CRN behaviors.
Recent studies show that AI-driven code generation tools, such as Large Language Models, are able to solve most of the problems usually presented in introductory programming classes. However, it is still unknown how t...
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ISBN:
(纸本)9798400701382
Recent studies show that AI-driven code generation tools, such as Large Language Models, are able to solve most of the problems usually presented in introductory programming classes. However, it is still unknown how they cope with Object Oriented programming assignments, where the students are asked to design and implement several interrelated classes (either by composition or inheritance) that follow a set of best-practices. Since the majority of the exercises in these tools' training dataset are written in English, it is also unclear how well they function with exercises published in other languages. In this paper, we report our experience using GPT-3 to solve 6 real-world tasks used in an Object Oriented programming course at a Portuguese University and written in Portuguese. Our observations, based on an objective evaluation of the code, performed by an open-source Automatic Assessment Tool, show that GPT-3 is able to interpret and handle direct functional requirements, however it tends not to give the best solution in terms of object oriented design. We perform a qualitative analysis of GPT-3's output, and gather a set of recommendations for computerscience educators, since we expect students to use and abuse this tool in their academic work.
The rise of Large Language Models, particularly the Chat-GPT model, has transformed the field of natural language information processing and has led to widespread adoption in a diverse range of applications and across...
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ISBN:
(纸本)9783031808883;9783031808890
The rise of Large Language Models, particularly the Chat-GPT model, has transformed the field of natural language information processing and has led to widespread adoption in a diverse range of applications and across a multitude of industries. In this paper, we focus on assessing the quality of the responses generated by Chat-GPT for the code generation tasks using seven different programming languages. We selected the languages considering diversity in terms of the fields of application, philosophies, and popularity. We carried out an experimental evaluation utilizing different introductory coding examples for each of the programming languages using the pass@k metric for evaluation. The results indicate a correlation between the effectiveness of the model and the popularity of programming languages.
In this article, we present the first rigorous theoretical analysis of the generalisation performance of a Geometric Semantic Genetic programming (GSGP) system. More specifically, we consider a hill-climber using the ...
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Each "Communication Corner"essay is self-contained;however, they build on each other. For best results, before reading this essay and doing the exercise, go to the first essay "How an Ugly Duckling Beca...
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