Program Generation Research Articles

Legendary Culinary Tourism in Jakarta, Indonesia

This research explores legendary Jakarta culinary delights reflecting local traditions, global influences, and the city's cultural identity. The study aims to identify and analyse iconic restaurants such as Ragusa Es Italia and Nasi Goreng Kebon Sirih. The methods include semi-structured interviews, participant observation, and document collection to understand culinary delights' experience, history, and social impact. The results identified eight legendary restaurants, each significantly contributing to Jakarta's culinary culture. The culinary experience at these places is not just about the food but also strengthens social interactions between diners, creating connected communities. Analysis shows that the restaurant is a custodian of cultural and historical values, connecting different generations. The conclusion is that the culinary legend not only serves delicious food but also builds the city's identity. Means for preserving culinary heritage include collaboration between government, communities, and entrepreneurs and training programs for the younger generation to maintain traditional cooking techniques. Hopefully, this research will become a reference in developing culinary studies and preserving cultural heritage in Jakarta. Keywords: legendary culinary delights, Jakarta, cultural identity, social interaction, heritage preservation

Testing Interventions for Mobility through Exercise (TIME): Study protocol for a randomized trial comparing a novel, brief home-based exercise program and a standard home-based group exercise for older adults with mobility disability

One in four older adults report difficulty walking, greatly increasing the risk of future disability and death. Though exercise improves mobility, too few older adults do it. While studies show that brief exercise sessions provide most of the benefit of longer sessions and that older adults note that “time” is a critical barrier to being active, what remains unknown is whether briefer RT sessions can improve mobility as well as, or better than, longer traditional sessions, possibly due to greater adherence. We present the design of a 12-month randomized controlled trial of 700 older adults with self-reported walking difficulty. Participants will be randomly assigned, in a 2 × 2 factorial design, to one of two home-based exercise programs: 1) Standard of Care: 45-min, three-times weekly sessions or 2) Experimental: 5-min daily sessions and to one of two doses of behavior change techniques (Standard or Enhanced) as part of their exercise program. The primary outcome measure is self-reported physical function. Secondary outcome measures include objectively measured lower extremity physical performance, walking endurance, balance, walking speed, strength and physical activity as well as self-reported falls, pain, fatigue and balance. This is one of the first studies to examine the clinical outcomes of brief exercise sessions, which may lead to a new generation of exercise programs that are optimized not only for impact, but for adherence as well.

HiPy: Extracting High-Level Semantics from Python Code for Data Processing

Data science workloads frequently include Python code, but Python's dynamic nature makes efficient execution hard. Traditional approaches either treat Python as a black box, missing out on optimization potential, or are limited to a narrow domain. However, a deep and efficient integration of user-defined Python code into data processing systems requires extracting the semantics of the entire Python code. In this paper, we propose a novel approach for extracting the high-level semantics by transforming general Python functions into program generators that generate a statically-typed IR when executed. The extracted IR then allows for high-level, domain-specific optimizations and the generation of efficient C++ code. With our prototype implementation, HiPy, we achieve single-threaded speedups of 2-20x for many workloads. Furthermore, HiPy is also capable of accelerating Python code in other domains like numerical data, where it can sometimes even outperform specialized compilers.

Rustlantis: Randomized Differential Testing of the Rust Compiler

Compilers are at the core of all computer architecture. Their middle-end and back-end are full of subtle code that is easy to get wrong. At the same time, the consequences of compiler bugs can be severe. Therefore, it is important that we develop techniques to increase our confidence in compiler correctness, and to help find the bugs that inevitably happen. One promising such technique that has successfully found many compiler bugs in the past is randomized differential testing , a fuzzing approach whereby the same program is executed with different compilers or different compiler settings to detect any unexpected differences in behavior. We present Rustlantis , the first fuzzer for the Rust programming language that is able to find new correctness bugs in the official Rust compiler. To avoid having to deal with Rust’s strict type and borrow checker, Rustlantis directly generates MIR, the central IR of the Rust compiler for optimizations. The program generation strategy of Rustlantis is a combination of statically tracking the state of the program, obscuring the program state for the compiler, and decoy blocks to lead optimizations astray. This has allowed us to identify 22 previously unknown bugs in the Rust compiler, most of which have been fixed.

Dynamical Sphere Regrouping Particle Swarm Optimization Programming: An Automatic Programming Algorithm Avoiding Premature Convergence

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO.

Prevalence and Impacts of Body Shaming on Social Media among University Students in Bangladesh

Body shaming is a social disease that has spread to different segments of society. It has been constantly growing in the hands of the media, especially social media and the commercial world. Objective: The objectives of the study are to examine the prevalence and impacts of social media on body shaming faced by university students in Bangladesh. Methodology: This study followed the quantitative research methodology to conduct the research. It was from 08 June 2024 to 14 June 2024, when the researcher collected the data through an online survey consisting of 35 questions. A total of 477 students participated in the study; the target population was formed with part of the students at Begum Rokeya University, Rangpur. Results: The study found that 66.45% of the students were faced with body shaming, and 42.76% of these happened through social media. Female students reported a higher prevalence of body shaming (37.11%) than that of male students (29.14%), with an odds ratio of 1.493, indicating that females were almost 1.5 times more likely to experience body shaming. Most of the students were body shamed for their body shape and weight. Students perceive that bullying tendency, lack of understanding, jealousy, and low self-confidence are the major causes of body shaming. According to the research, the impacts of body shaming are multifaceted in most of the students; with issues such as emotional distress (47.59%), body anxiety or dissatisfaction (40.25%), depression and inferiority (39.62%) coming up as a high rate of result. Alarmingly, 10.27% of students reported noticing suicidal tendencies in themselves due to body shaming. Female students generally reported higher rates of negative impacts compared to male students across most categories. Conclusion: There is an urgent requirement for awareness campaigns and legal actions to be taken against body shaming on social media. Educational institutions should also implement awareness programs for the younger generation to make them aware of the negative consequences of body shaming.

ARC WELDING ROBOTIC FLEXIBLE CELL SIMULATION USING ROBOGUIDE SOFTWARE

The objective of the work is the design and simulation of a flexible manufacturing structure with two robots, integrated in a welding application, using the Roboguide software. FANUC ROBOGUIDE is a robot simulator that simulates both the movement of the robots and the commands from the designed application, significantly reducing the time needed to create new motion configurations.The possibility to import the CAD models of the components, of the fixing devices and of the final effectors of the robots integrated into the designed application, allows the creation and evaluation of various structures. Automatic program generation simplifies manual programming operations. ROBOGUIDE allows pre-programming of robots before they are installed in a cell, as well as visualization and confirmation of robot trajectories before downloading the programs to the real robot.

PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs

This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.

Design of a Robotic 3D Printing System

Abstract Additive manufacturing enables the production of complex shapes with a large degree of freedom compared to conventional manufacturing techniques such as injection molding, thermoforming or pressing. In particular, the creation of moving parts, complex structures and large-scale printing remains underexplored and research is constantly expanding. Due to design limitations related to the construction of currently available printers, 3D printing technology is increasingly being combined with robotics. Using a robot to 3D print products eliminates the need to design complex printer structures, which ultimately should simplify the process of manufacturing large-scale products and shorten the time required for preparation and production. The paper deals with the description of the designed 3D printing system and the procedure of the steps of creating a robotic simulation model with the generation of a control program for the FDM 3D printing process by an industrial robot.

Zootechnical parameters and hepatosomatic index of two genetic strains of Nile tilapia fed with β-Glucan + Mannan oligosaccharide (βG+MOS)

The increasing expansion and the rising demand for food require a greater intensification in the production of Nile tilapia (Oreochromis niloticus), and genetic improvement programs are essential to achieve this goal. In managing these programs, new genetic material is introduced after several generations of selections to reduce inbreeding and enhance genetic diversity. In this sense, analyzing the zootechnical parameters of these introductions, with an emphasis on the use of nutritional additives, is of great importance for understanding their impact on breeding programs. Thus, the aim of this study was to evaluate the zootechnical parameters and hepatosomatic index of two genetic strains of Nile tilapia from the genetic improvement program fed with β-Glucan + Mannan oligosaccharide (βG+MOS). This study utilized a 2 × 2 factorial design containing two genetic groups and two treatments with βG+MOS (Control, 0.2%). The groups were formed from the 12th generation of the Tilamax genetic improvement program (TILAMAX) and by crossing Tilamax with an introduced test strain (CBTILAMAX). In total, 96 tilapias (31.1 ± 5.73 g) were distributed into 16 aquariums, with 6 fish per aquarium, 24 per group, and four repetitions. The experimental period lasted for 41 days, and the water parameters were measured daily and maintained under ideal conditions. The condition factor and hepatosomatic index were analyzed. Final weight, total length, and standard length were measured, and other zootechnical parameters were calculated based on these data. The condition factor showed that the fish in both groups exhibited adequate health and growth during the experiment, with no differences in strain or use of additives. Similarly, the hepatosomatic index did not differ between the groups, indicating that liver size was proportional to fish body size. Similarly, no significant differences (P>0.05) were observed for the other zootechnical parameters evaluated (initial and final total and standard lengths, final weight, total weight gain, condition factor using total and standard lengths, and specific growth rate). Based on these results, it is concluded that there were no differences between TILAMAX and CBTILAMAX related to performance and health under the effect of feeding with βG+MOS. This result is positive regarding the introduction of the test strain into the Tilamax genetic improvement program and establishes a starting point for further research that may provide greater clarity on its future effects.

Strengthening Muamalah Maliyah's Literacy Understanding of Online Transactions in Indonesia

This research aims to test and analyze the understanding of students majoring in Commerce Management at the ALKS study program in implementing online buying and selling schemes from an Islamic perspective, namely by using the Muamalah Maaliyaah contract. The population in this study was 278 people from each generation of the ALKS study program, a sample of at least 10 people was taken and then the sample size became 100 people. This research uses a quantitative approach. The data collection technique used was by distributing questionnaires and interviews. The model used is a simple linear regression model with statistical analysis tests used are the classical assumption test, T-test, validity, and reliability tests. The research results show that from a simple regression test, the results of the t-test variable for the variable Understanding the Muamalah Maaliyaah Agreement show t_count of 15.024 > t_tabel of 1.98472 and a Sig value of 0.000 < 0.05. These results show that "There is a significant relationship between the influence of Understanding the Muamalah Maaliyah Agreement (X) on the decision to carry out online transactions (Y)". It can be concluded that there is a very large influence of ALKS study program students' muamalah literacy on the decision to carry out online transactions.

Risk based design optimization via scenario generation and genetic programming under hybrid uncertainties

Abstract The design of complex systems often requires the incorporation of uncertainty optimization strategies to mitigate system failures resulting from multiple uncertainties during actual operation. Risk-based design optimization, as an alternative methodology that accounts for the balance between design cost and performance, has garnered significant attention and recognition. This paper proposes a risk design optimization method for tackling hybrid uncertainties via scenario generation and genetic programming. The hybrid uncertainties are quantified through scenario generation method to obtain risk assessment indicators. The genetic programming method is used to simulate the real output of the objective or constraints. To drive the optimization process, the sample-based discrete gradient expression is constructed, and the optimal scheme aligning the risk requirements is obtained. Three calculation examples of varying computing complexity are presented to verify the efficacy and usability of the suggested approach.

Quantitative descriptors that contribute to variability in the F3 population of ornamental pepper

The diversity of peppers from the Capsicum genus has always enriched global cuisine and is currently driving the growth of the ornamental plants market. In this sense, breeding programs with ornamental peppers play a crucial role in the evolution of the ornamental market. Therefore, the objective of this study was to evaluate genetic variability in an F3 population of ornamental pepper through principal component analysis (PCA) based on quantitative descriptors of ornamental interest and identify correlations between them through Pearson's correlation coefficient analysis. In the F3 generation of a genetic improvement program with ornamental pepper plants at the State University of Montes Claros, 200 genotypes were conducted. In these, four quantitative descriptors were evaluated: plant height (PH), fruiting stages (FS), production (PD) and average fruit mass (AFM). The data were subjected to normality and homogeneity of variance tests (p<0.05) and, subsequently, to principal component analysis (PCA) to evaluate genetic diversity between genotypes. Analyzes were performed using PAST software. The PCA results demonstrated that the variables PD and PH were the ones that contributed most to PCA I, while for PCA II it was FS. In the Pearson correlation coefficient, it was observed that the most significant correlation occurred between the PH and PD variables. AFM and PD were negatively correlated, indicating that the greater the average fruit mass, the lower the number of fruits produced. The results of this study will open up opportunities for the development of new cultivars of ornamental peppers, which are cultivated mainly by small producers or family farmers.

TR-Fuzz: A syntax valid tool for fuzzing C compilers

Compilers play a critical role in current software construction. However, the vulnerabilities or bugs within the compiler can pose significant challenges to ensuring the security of the resultant software. In recent years, many compilers have made use of testing techniques to address and mitigate such concerns. Fuzzing is widely used among these techniques to detect software bugs. However, when fuzzing compilers, there are still shortcomings in terms of the diversity and validity of test cases. This paper introduces TR-Fuzz, a fuzzing tool specifically designed for C compilers based on Transformer. Leveraging position embedding and multi-head attention mechanisms, TR-Fuzz establishes relationships among data, facilitating the generation of well-formed C programs for compiler testing. In addition, we use different generation strategies in the process of program generation to improve the performance of TR-Fuzz. We validate the effectiveness of TR-Fuzz through the comparison with existing fuzzing tools for C compilers. The experimental results show that TR-Fuzz increases the pass rate of the generated C programs by an average of about 12% and improves the coverage of programs under test compared with the existing tools. Benefiting from the improved pass rate and coverage, we found five bugs in GCC-9.

On the Reliability and Explainability of Language Models for Program Generation

Recent studies have adopted pre-trained language models, such as CodeT5 and CodeGPT, for automated program generation tasks like code generation, repair, and translation. Numerous language model based approaches have been proposed and evaluated on various benchmark datasets, demonstrating promising performance. However, there is still uncertainty about the reliability of these models, particularly their realistic ability to consistently transform code sequences. This raises a question: are these techniques sufficiently trustworthy for automated program generation? Consequently, further research is needed to understand model logic and assess reliability and explainability. To bridge these research gaps, we conduct a thorough empirical study of eight popular language models on five representative datasets to determine the capabilities and limitations of automated program generation approaches. We further employ advanced explainable AI approaches to highlight the tokens that significantly contribute to the code transformation. We discover that state-of-the-art approaches suffer from inappropriate performance evaluation stemming from severe data duplication, causing overoptimistic results. Our explainability analysis reveals that, in various experimental scenarios, language models can recognize code grammar and structural information, but they exhibit limited robustness to changes in input sequences. Overall, more rigorous evaluation approaches and benchmarks are critical to enhance the reliability and explainability of automated program generation moving forward. Our findings provide important guidelines for this goal.

Research on Machine Learning Program Generation Algorithm Based on AORBCO

Abstract The design and development of machine learning programs require selecting appropriate data and algorithms, and coding and debugging based on specific task requirements and the programming experience of developers. However, the current knowledge structure in the field of machine learning is relatively complex, lacking systematic organization, and developers often face the problem of lack of experience when choosing algorithms and designing programs, resulting in a long development cycle and easy errors in machine learning programs. In response to the above issues, this article proposes and designs a machine learning program generation algorithm based on the AORBCO model. The program generation ability includes two sub abilities: algorithm decision-making ability and code generation ability. AD-EKG has been designed for algorithmic decision-making ability, allowing Ego to select appropriate machine learning algorithms based on datasets in massive data. This algorithm combines the characteristics of the AORBCO model's domain knowledge base, knowledge graph based recommendation algorithm, and collaborative filtering algorithm. By calculating the descriptive and structural information between the dataset and algorithm, the interaction probability between the dataset and algorithm is obtained, allowing Ego to make algorithmic decisions interaction probability based. Results of the experiment have shown that the AD-EKG algorithm can fully utilize structural and descriptive information to improve the accuracy of Ego algorithm decision-making. CodeT5-EKG has been designed for code generation capability, allowing Ego to automatically generate machine learning program code. This algorithm combines the CodeT5 generative model with the domain knowledge base of the AORBCO model, by adding auxiliary information extracted using DPR technology to the code generation task, and performing diversified fusion operations to improve code generation quality. The CodeT5-EKG algorithm combines the creativity and efficiency of generative models and DPR technology, and is an algorithm that can improve the quality of generated code while also having the advantages of generative models. The experiments have proved that the code generated by this algorithm has better quality compared to other generative models with the same number of parameters.

Persepsi Remaja Generasi Z Tentang Kesiapan Menikah Dan Keselarasan Kebijakan Pernikahan Authors

Generation Z teenagers are those born from 1995 to 2010 in the era of modern digitalization. Meanwhile, policy makers and implementers for generation Z teenagers in the pre-marital youth program are generations X and Y, those who live in the era of the digital revolution. These generations have different characters. The aim of this research is to determine the perspective of generation Z teenagers regarding marriage readiness with the alignment of marriage policies. This research method is a cross-sectional study with the subjects of generation Z teenagers, namely 360 students from the Faculty of Medicine, Brawijaya University, officers from the Office of Religious Affairs (KUA), health workers and sub-district officers. The research was carried out in the Lowokwaru area of Malang City. This research uses Google forms, interviews and observations. The results of this research showed that 50.00% of teenagers were not ready to marry. Pre-marital youth programs at the Department of Religion and Community Health Centers already exist but are not implemented well. The educational method used is still conventional, not using easily accessible social media. It is recommended that pre-marriage programs for generation Z teenagers can be synergistic between the Department of Religion and Community Health Centers. For further research, we can initiate website-based consultation and education applications for teenagers.

Design and implementation of an interactive networked condition monitoring strategy for plant-wide production equipment toward Industry 4.0

Equipment condition monitoring holds immense significance in modern industrial factories, facilitating sustained operation with heightened efficiency, safety, and cost-effectiveness. This paper presents an interactive networked condition monitoring strategy to enhance plant-wide production equipment condition monitoring, leveraging networked control and edge computing approaches. This strategy offers a rapid and efficient solution that comprises four key components: drag-and-drop monitoring algorithm design, executable program generation, networked algorithm implementation, and interactive monitoring process. Initially, algorithms for equipment condition monitoring are designed using pre-defined components and then seamlessly compiled into executable programs with a single click. Subsequently, these algorithms are implemented within an edge computing-based monitoring system using the networked control approach, where the generated programs are remotely deployed into edge nodes positioned adjacent to the equipment. Moreover, the monitoring process features visual displays of parameters and results using configurable widgets, enabling interactive adjustments. Real-world application cases from an aluminum manufacturing factory showcase the strategy’s effectiveness in comprehensive condition monitoring of hundreds of equipment across nine workshops, significantly reducing the equipment inspection time to tens of minutes compared to manual inspection. With extensive implementation in various industrial factories, this strategy emerges as a promising solution for equipment condition monitoring in smart factories.

Multi‐objective optimization‐based and fault localization‐oriented test case generation for novice programs

SummaryOnline judgment (OJ) systems are capable of evaluating program results by automatically executing test cases, significantly improving the efficiency of traditional guidance approaches. Moreover, existing studies attempt to assist novices through automated fault localization techniques to provide feedback to novices, which can help them quickly find the location of faulty statements. Among them, spectrum‐based fault localization (SBFL) techniques have been widely used for their lightweight and efficiency, which only requires coverage information and test results of test cases to conduct fault localization. However, manually constructing high‐quality test cases for a large number of OJ questions is tough work to complete. To solve this problem, we propose the novice program‐oriented Multi‐Objective Optimization‐Based Fault Localization‐Oriented Test Case Generation (MFTCG) for automatically generating test inputs. Specifically, we use multi‐objective optimization algorithms to evolve the test case in terms of both fault localization and faulty code detection capability. We conduct experiments with 8911 programs from the well‐known public OJ platform AtCoder. The results show that our proposed approach MFTCG can achieve the best fault localization performance compared with existing automated test case generation approaches in most cases and can achieve the similar faulty code detection capability compared to manually designed test cases.

Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding

ABSTRACT Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.