Programming Solution Research Articles

CodeContrast: A Contrastive Learning Approach for Generating Coherent Programming Exercises

Generating high-quality programming exercises with well-aligned problem descriptions, test cases, and code solutions is crucial for computer science education. However, current methods often lack coherence among these components, reducing their educational value. We present CodeContrast, a novel generative model that uses contrastive learning to map programming problems, test cases, and solutions into a shared feature space. By minimizing the distance between matched components and maximizing it for non-matched ones, CodeContrast learns the intricate relationships necessary to generate coherent programming exercises. Our model architecture includes three encoder networks for problem descriptions, test cases, and solutions. During training, CodeContrast processes positive triplets (matching problem, test case, solution) and negative triplets (non-matching combinations) and uses a contrastive loss to position positive triplets close in the feature space while separating negative ones. Comprehensive evaluations of CodeContrast—through automatic metrics, expert ratings, and student studies—demonstrate its effectiveness. Results show high code correctness (92.3% of test cases passed), strong problem–solution alignment (BLEU score up to 0.826), and robust test case coverage (85.7% statement coverage). Expert feedback and student performance further support the pedagogical value of these generated exercises, with students performing comparably to those using manually curated content. CodeContrast advances the automated generation of high-quality programming exercises, capturing relationships among programming components to enhance educational content and improve the learning experience for students and instructors.

Counter‐Example to Diaby’s et al. Linear Programming Solution to the Traveling Salesman Problem

Counter‐Example to Diaby’s et al. Linear Programming Solution to the Traveling Salesman Problem

Otimização do Carregamento de Veículos no Sistema de Distribuição de Materiais da Força Aérea Brasileira

In the planning of the vehicle loading in the material distribution system of the Brazilian Air Force, one may decide for designating vehicles to predefined routes and allocate cargoes to these vehicles, considering the parameters and restrictions of the cargoes and the vehicles, aiming at obtaining the maximum benefit to the transportation. In this context, this research has the objective to develop a method of solution, especially designed to optimize the transport and distribution operations of the materials in the interest of the Brazilian Air Force made from a distribution center, having developed a mathematical formulation and implemented a Linear Programming solution capable of offering an optimal planning of the allocations cargo-vehicle-route that maximizes the total benefit for the cargo.

Optimal Crop Planting Scheme Based on Mixed Integer Programming

In order to achieve sustainable development of rural economy, make full use of limited cultivated land resources, improve production efficiency, reduce planting risks, and consider the actual rural situation and crop yield per mu affected by climate, market and other factors. It is necessary to comprehensively consider the expected sales volume, yield per mu, uncertainty of planting cost and selling price of various crops and potential risks, and give the optimal planting plan of crops. First, the maximum expected benefit is taken as the objective function, the planting area of crops planted in each plot in each season of the year is taken as the decision variable, and the influence of uncertain factors such as climate and market conditions is comprehensively considered. The parameter change model of each situation in which the uncertain factors are represented is established, and the objective function, constraints and uncertainties are comprehensively considered. Using Monte Carlo simulation optimization algorithm, the Monte Carlo optimal benefit model is established. Through programming solution, we obtained that the planting plots of similar crops were concentrated, and most of them only planted one crop in one plot, or at most two crops in one plot. In this paper, the Monte Carlo simulation algorithm is innovatively adopted to fully consider the uncertainties of crop sales volume, price, per mu yield and planting cost, making the decision more scientific and reasonable.

A Survey on IoT Programming Platforms: A Business-Domain Experts Perspective

The vast growth and digitalization potential offered by the Internet of Things (IoT) is hindered by substantial barriers in accessibility, interoperability, and complexity, mainly affecting small organizations and non-technical entities. This survey article provides a detailed overview of the landscape of IoT programming platforms, focusing specifically on the development support they offer for varying end user profiles, ranging from developers with IoT expertise to business experts willing to take advantage of IoT solutions to automate their organization processes. The survey examines a range of IoT platforms, classified according to their programming approach between general-purpose programming solutions, model-driven programming, mashups, and end-user programming. Necessary IoT and programming backgrounds are described to empower non-technical readers with a comprehensive field summary. In addition, the article compares the features of the most representative platforms and provides decision insights and guidelines to support end users in selecting appropriate IoT platforms for their use cases. This work contributes to narrowing the knowledge gap between IoT specialists and end users, breaking accessibility barriers and further promoting the integration of IoT technologies in various domains. 1

Optimal coordination of directional overcurrent relays: A fast and precise quadratically constrained quadratic programming solution methodology

AbstractNowadays, power system protection is increasingly important because of the growing number of customers and the pressing need for timely fault resolution and relay operations. This paper addresses the non‐linear nature of the objective function in the optimal coordination of directional overcurrent relays (DOCRs) by employing a quadratic Taylor series expansion around an operating point, converting the problem into a quadratically constrained quadratic programming problem, ensuring a global optimal solution with increased computational efficiency. Additionally, the quadratic constraints are converted into second‐order cone constraints for compatibility with the CPLEX solver. Using the least square method, the operating point values are determined and further fine‐tuned using iterations with the DOCR operation times. The IEEE 3‐bus, IEEE 8‐bus, and IEEE 14‐bus test systems are used to test the method, which shows higher improvement rates in reducing DOCR operation times and enhancing cooperation than conventional and metaheuristic methods. The simulation results verify the numerical superiority of the method in optimizing the protection system's efficiency while obtaining rapid and accurate solutions. The proposed method was tested on IEEE 3‐bus, 8‐bus, and 14‐bus systems, optimizing relay operating times to 0.87, 2.96, and 7.05 s, respectively, demonstrating the method's efficiency over conventional approaches.

Women's Perspectives on the Unique Benefits and Challenges of Self-Injectable Contraception: A Four-Country In-Depth Interview Study in Sub-Saharan Africa.

Implementing self-injection (SI) of subcutaneous depot-medroxyprogesterone acetate (DMPA-SC) is a key self-care strategy for sexual and reproductive health, but SI uptake remains low, and assertions about the potential of SI to increase women's control over contraceptive use lack evidence. We sought to qualitatively explore how women with diverse contraceptive experiences-including those with and without experience using SI-view the benefits and challenges of SI as compared to other methods. We conducted 241 in-depth interviews with women across four sub-Saharan African countries and found alignment between the perceived and experienced benefits of SI across our diverse sample. Through the benefits of privacy, easier access, and self-management, we found SI can promote greater control over the contraceptive experience by facilitating a woman's ability to act on her preferences and control who is involved in or aware of her contraceptive use. Interviews revealed SI's potential is, however, constrained by inherent limitations in the method; for example, it is often not private or accessible enough and many fear injecting themselves. SI has the most potential when implemented with programmatic solutions that mitigate challenges women experience or anticipate and allow more women to benefit from the privacy, easier access, and self-management that SI offers.

Bridging the Gap: Remote Evaluation and Programming of Cardiac ImplantableDevices in Medically Underserved Areas.

Delivering care in medically underserved areas (MUA) is a public health challenge. The purpose of this study was to evaluate the advantages of a noncommercial multivendor solution for remote interrogation and programming of cardiac implantable electronic devices (CIEDs) among inhabitants of MUAs. This prospective comparative study evaluated benefits and safety of remote CIEDs evaluation in patients living >100km from the Bordeaux University Hospital. A conventional in-person evaluation at the Bordeaux hospital was compared with a remote evaluation performed 6months later at a nurse office close to the patient home. The technical solution consisted of remote controlling CIED programmers from the 5 main manufacturers allowing evaluation of all kinds of CIEDs. A majority of the 34 included patients (94%) were satisfied with the remote evaluation strategy. To attend the in-person evaluation at the Bordeaux Hospital, the patients covered a median distance of 253km (ICR: 221-258km) compared with 9km (ICR: 6-19km) (P< 0.001) to get to the nurse's office. The use of a medical vehicle was reduced by a factor 7 (44% vs 6%; P = 0.001) with the remote evaluation, which translated into a reduction in the total traveling cost per patient by a factor 32 (96 Euros [87-268 Euros] vs 3 Euros [2-8 Euros]; P< 0.001). The travel carbon footprint was 14 times lower (28 Kg CO2-eq [24-28 Kg CO2-eq] vs 2 Kg CO2-eq [1-5 Kg CO2-eq]; P< 0.001) with the remote evaluation strategy. Remote evaluation of CIEDS in patients living MUA is feasible and safe. This approach is associated with a high level of satisfaction, lower economic and ecological costs compared with the in-person assessment in this population. (Remote Programming of Cardiac Implantable Electronic Devices 2 [REACT 2]; NCT06272344).

РОЗПІЗНАВАННЯ ОБЛИЧ В РЕАЛЬНОМУ ЧАСІ ЗА ДОПОМОГОЮ БІБЛІОТЕКИ OPENCV ТА МОВИ ПРОГРАМУВАННЯ PYTHON

The article discusses the process of face recognition, which is one of the key tasks in the field of computer vision. The main focus is on the use of the OpenCV library, which provides tools for efficient and fast face detection in images and real-time video. The paper describes the Haar cascade and the Viola-Jones algorithm, which form the basis of the face detection implemented in OpenCV. It also discusses the step-by-step stages of project implementation in the Python programming language, as well as image processing. The results of experiments demonstrating the effectiveness and accuracy of the proposed approach are presented. The article will be useful for developers, engineers, and researchers interested in implementing face recognition technologies in their projects using basic computer vision algorithms. In addition to the fundamental techniques, the article delves into the nuances of optimizing the face detection process. This includes adjusting parameters such as the scale factor and the minimum number of neighbors, which are crucial for balancing detection accuracy and performance. The importance of preprocessing steps like grayscale conversion and using the correct type's cascade file is also highlighted, as these steps significantly enhance the detection rate by improving image contrast. The theoretical implementation section provides a step-by-step conception of face detection and recognition using the Haar cascade and Viola-Jones method. Also, need to underline the importance of this open topic about the recognition of human emotions using the research by Robert Plutchyk. He determines the main human emotions that are the basis for combining the recognition of a person and his emotions. The practical implementation section provides a realization of face detection and recognition using OpenCV and Python: loading image data, applying the Haar cascade classifier, and testing the program solution. To validate the effectiveness of the approach, several experiments were conducted using diverse image datasets. The results, summarized in tabular format, indicate a high detection rate with minimal input data. The experiments also compare the performance with different counts of input data, highlighting the advantages and disadvantages of the Viola-Jones method.

Boltzmann sampling with quantum annealers via fast Stein correction

Despite the attempts to apply a quantum annealer to Boltzmann sampling, it is still impossible to perform accurate sampling at arbitrary temperatures. Conventional distribution correction methods such as importance sampling and resampling cannot be applied, because the analytical expression of sampling distribution is unknown for a quantum annealer. Stein correction [Q. Liu and J. Lee, in , Proceedings of Machine Learning Research (PMLR, 2017), Vol. 54, pp. 952–961] can correct the samples by weighting without the knowledge of the sampling distribution, but the naive implementation requires the solution of a large-scale quadratic program, hampering usage in practical problems. In this article, a fast and approximate method based on a random feature map and exponentiated gradient updates is developed to compute the sample weights and is used to correct the samples generated by D-Wave quantum annealers. In benchmarking problems, it is observed that the residual error of thermal average calculations is reduced significantly. If combined with our method, quantum annealers may emerge as a viable alternative to long-established Markov chain Monte Carlo methods. Published by the American Physical Society 2024

A user-centric model of connectivity in street networks

Modelling street network connectivity is a fundamental research problem in transportation science. Here, we argue that the connectivity of a street network cannot be defined independently from the users of that street network. That is, different users will experience different levels of connectivity depending on their corresponding travel behaviour. In this work, we propose a model of connectivity that is defined with respect to a user’s travel behaviour within a given street network. We demonstrate that many real-world problems can be posed as instances of an optimisation problem defined with respect to this model. This includes optimising a user’s home location with respect to their connectivity and optimising the location of a facility with respect to the connectivity of its users. We prove the above optimisation problem is NP-hard and present an integer programming solution that scales to large problem instances.

Combining Precision Boosting with LP Iterative Refinement for Exact Linear Optimization

This article studies a combination of the two state-of-the-art algorithms for the exact solution of linear programs (LPs) over the rational numbers in practice, that is, without any roundoff errors or numerical tolerances. By integrating the method of precision boosting inside an LP iterative refinement loop, the combined algorithm is able to leverage the strengths of both methods: the speed of LP iterative refinement, in particular, in the majority of cases when a double-precision floating-point solver is able to compute approximate solutions with small errors, and the robustness of precision boosting whenever extended levels of precision become necessary. We compare the practical performance of the resulting algorithm with both pure methods on a large set of LPs and mixed-integer programs (MIPs). The results show that the combined algorithm solves more instances than a pure LP iterative refinement approach while being faster than pure precision boosting. When embedded in an exact branch-and-cut framework for MIPs, the combined algorithm is able to reduce the number of failed calls to the exact LP solver to zero while maintaining the speed of the pure LP iterative refinement approach. History: Accepted by Antonio Frangioni, Area Editor for Design and Analysis of Algorithms: Continuous. Funding: The work for this article has been conducted within the Research Campus Modal funded by the German Federal Ministry of Education and Research (BMBF) [Grants 05M14ZAM and 05M20ZBM].

Multistage stochastic programming for integrated network optimization in hurricane relief logistics and evacuation planning

AbstractIn this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters.

Representative applications of dynamic-programming in different fields: Analysis on problems of shortest-path, 0/1 knapsack and longest common sub-sequence

This article mainly focuses on exploring the current application status, concrete things implemented by dynamic programming and advantages of dynamic programming in problems (the concept and operation of dynamic programming and the role it plays in programs) in different fields, future development trends and areas for improvement. It also introduces the gaps and problems currently encountered by dynamic programming, then provides corresponding solutions to make it more helpful and efficient in the future. In this regard, by consulting different references and online resources written by previous scientists, it finds out that they all have different points of view on dynamic programming and different solutions to its shortcomings. In the current research, dynamic programming is the most widely used algorithm with few limitations that can achieve the global optimal solution at any time; however, it does not mean that it can achieve the local optimal solution at any time, which also indirectly results in the memory space it requires being larger than other optimization algorithms. After analyzing the literature and researching and testing multiple applications, it is obvious that dynamic programming can make the code run easier and faster than traditional methods, even though it will take up more space.

Imperative Genetic Programming

Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.

Navigating Implementation Challenges of Stunting Solutions in Indonesia's Health Programs

General Background: Indonesia faces a significant public health issue with fifth-highest stunting prevalence among toddlers, especially in East Java, and local efforts are being implemented to reduce rates. Specific Background: Klurak village government's stunting prevention programs, involving Posyandu cadres and midwives, face challenges due to communication, resources, disposition, and bureaucratic structure, as per Edward III's implementation theory. Knowledge Gap: While stunting programs are in place, the extent to which these programs effectively address local needs and barriers, particularly through an administrative lens, remains underexplored. Aims: This study aims to analyze the implementation of stunting prevention programs in Klurak Village using Edward III’s four-indicator framework and identify the key sectors hindering or supporting effective implementation. Results: Findings suggest that the stunting prevention program is moderately effective. Communication among village stakeholders is integrated but limited by budget constraints, while resources, particularly in human capacity and infrastructure, are inadequate. Despite positive dispositions among health workers, bureaucratic inefficiencies hinder maximum implementation. Novelty: The study provides a rare, localized analysis of stunting prevention through a public administration framework, offering practical insights into grassroots-level health governance in Indonesia. Implications: The research highlights the need for better resource allocation, continuous socialization efforts, and enhanced inter-agency collaboration to improve stunting prevention outcomes in rural settings. Highlights: Communication Gaps: Enhance stakeholder interaction to improve stunting awareness. Resource Needs: Adequate resources are essential for program effectiveness. Bureaucratic Issues: Structural inefficiencies hinder health program implementation. Keywords: Stunting, Klurak Village, Public Health, Program Implementation, Edward III Theory

A New Integer Model for Selecting Students at Higher Education Institutions: Preparatory Classes of Engineers as Case Study

This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the fair selection of students while respecting criteria such as academic qualifications, required skills, and student preferences. The goal is to develop a decision support system that efficiently selects qualified students at higher education institutions within a reasonable time. The model was tested using real data from Moroccan preparatory classes, achieving important assignment rates across all student categories. Results demonstrate significance in execution time, fulfillment of student choices, and prioritization of outstanding students. This approach offers a flexible, efficient solution for managing academic merit-based selections, optimizing resource utilization, and enhancing fairness in the selection process.

Quadratic p-Median Problem: A Bender’s Decomposition and a Meta-Heuristic Local-Based Approach

In this paper, the quadratic p-median optimization problem is discussed, where the goal is to connect users to a selected group of facilities (emergency services, telecommunications servers, healthcare facilities) at the lowest possible cost. The problem is aimed at minimizing the cost of connecting these selected facilities. The costs are symmetric, meaning connecting two different points is the same in both directions. This problem extends the traditional p-median problem, a combinatorial problem used in various fields like facility location, network design, transportation, supply chain networks, emergency services, healthcare, and education planning. Surprisingly, the quadratic version has not been thoroughly considered in the literature. The paper highlights the formulation of two mixed-integer quadratic programming models to find optimal solutions to this problem. One model is a classic formulation, and the other is based on set cover theory. Linear versions and Bender’s decomposition formulations for each model are also derived. A Bender’s decomposition is solved using an algorithm that adds constraints during each iteration to improve the solution. Lazy constraints in the Gurobi solver’s branch and cut algorithm are dynamically added whenever a mixed-integer programming solution is found. Additionally, an efficient local search meta-heuristic is proposed that usually finds optimal solutions for tested instances. Challenging instances with up to 60 facilities and 2000 users are successfully solved. Our results show that Bender’s models with lazy constraints are the most effective for Euclidean and random test cases, achieving optimal solutions in less CPU time. The meta-heuristic also finds near-optimal solutions rapidly for these cases.

Advancements in Multi-Objective Mathematical Programming Models and Solution Approaches for Sustainable Waste Management Practices in Vehicle Routing Problem: A Systematic Literature Review

In recent years, the integration of sustainable principles into the Vehicle Routing Problem (VRP) for efficient waste management has gained significant attention. This paper provides an overview of advancements in multi-objective mathematical programming (MP) models and the optimization techniques for sustainable waste management practices within the VRP framework. Sustainable waste management practices in the context of the VRP involve optimizing routes and schedules aim to reduce the environmental impact of waste collection. It investigates the integration of multi-objectives with sustainability criteria, including social, economic, and environmental considerations. Furthermore, it surveys various optimization methodologies applied in waste management to enhance efficiency and sustainability practices. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was employed as a formal systematic review guideline for data gathering. Data was obtained from studies conducted between 2017 and 2023. The analysis included a total of 18 papers from online sources and databases. Based on the literature review, it has been observed that the majority of the research focused on employing Mixed-Integer Linear Programming (MILP) models to tackle the optimization problem for waste management. The literature survey demonstrated significant contributions in computational performance, cost-effectiveness, operational efficiency, and utilization of real-world data by improving the solution approaches, aiming to prioritize economic, social, and environmentally friendly solutions. The paper aims to serve as a convenient reference for researchers and practitioners seeking information on using the MP model and solution approaches to optimize VRP for waste management.

Evolving multispectral sensor configurations using genetic programming for estuary health monitoring

ABSTRACT Assessing ecosystem health on a large scale is crucial for a wide range of management and regulatory decisions. Technologies such as hyperspectral imaging allow noninvasive and rapid estimation of key attributes based on observed reflectance. However, these images are high-dimensional and real-world applications require models based on fewer wavelengths. This paper proposes a new wavelength selection and feature extraction method for hyperspectral image analysis based on genetic programming to automatically select key wavelength regions and informative image features. A dataset of hyperspectral images of sediment in the field was collected and paired with ground-truth measurements of the sediment porosity and organic matter content. Two new program structures were proposed to construct feature extraction trees from either the mean reflectance spectra (spectra-based) or full hyperspectral images (image-based). SVR models were constructed to predict attributes based on the extracted features. Various regression models were used to predict the porosity and organic matter content. Full-wavelength models were constructed to reliably predict the organic matter content. The proposed spectra-based genetic programming solutions show competitive results compared to common wavelength selection methods, such as SPA, CARS, and RC. Finally, the best-evolved solution was applied to predict sediment organic matter content across all collected images.