Intelligent Program Research Articles

Human Intelligence to Artificial Intelligence: Critical thinking and Emotional Intelligence

Artificial Intelligence may be it is branch of computing. The computer science engineering is creating intelligent machines and especially intelligent computer programs. AI could also be how of making a computer. A computer-controlled robot or software thinks intelligently. AI is accomplished by studying the way to think human brain. How humans learn, decide, and work while trying to unravel a haul. We also create intelligent machines. It is often developing human activities. Some human activities are reading, listening, each and are going to be covered. Machine learning, deep learning may be a part of this AI. The AI projects have a machine control and remote. Mostly AI is machine learning. Current status will be there from AI. Feature goals in AI. Computing system study that plan to model and applying human brain. It is a branch of computer simulating a intelligent behavior. The machine capacity is intimate human behavior. The intelligence has some types are going to be there, linguistic intelligence, musical intelligence, logical-mathematical intelligence, spatial intelligence, intra-personal intelligence, interpersonal intelligence. The intelligence composed by reasoning, learning, problem solving, perception, linguistic intelligence and so on. Ability to calculate a system, reasoning, perceives a relationships and analogs, learn from experience. Store and retrieve information from memory. Solving a problems and complex ideas to use tongue fluently classifying. Generalize and adapt new situation

A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into ‘electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. This study provides a coordinated smart building energy-sharing concept for smart neighborhood buildings integrated with renewable energy sources and energy storage devices within the building itself. This neighborhood energy management model's primary objective is to reduce the total power cost of all customers of smart buildings in the neighborhood by increasing the use of locally produced renewable energy. In the first stage, a group of optimum consumption schedules for each HEMS is calculated by an Improved Butterfly Optimization Algorithm (IBOA). A neighborhood energy management system (NEMS) is established in the second stage based on a consensus algorithm. A group of four smart buildings is used as a test system to evaluate the effectiveness of the suggested neighborhood smart building energy management model. These buildings have varying load profiles and levels of integration of renewable energy. In this paper, the proposed framework is evaluated by comparing it with the Grey Wolf optimization (GWO) algorithm and W/O scheduling cases. With applying GWO, the total electricity cost, peak load, PAR, and waiting time are improved with 3873.723 cents, 21.6005 (kW), 7.162225 (kW), and 87 s respectively for ToU pricing and 11217.57 (cents), 18.0425(kW), 5.984825 (kW), and 98 s respectively for CPP tariff. However, using the IBOA Improves the total electricity cost, peak load, PAR, and waiting time by 3850.61 (cents), 20.1245 (kW), 6.7922 (kW), and 53 s respectively, for ToU and 10595.8 (cents), 17.6765(kW), 5.83255(kW), and 74 s for CPP tariff. Also, it is noted that the run time is improved using GWO and IBOA by 13% and 47%, respectively, for ToU and 2% and 26% for CPP. However, the number of iterations required to obtain the optimal solution is reduced using the GWO and IBOA by 60% and 81% for ToU and 55% and 80% for CPP tariffs. The results show significant improvements obtained by applying just intelligent programming and management.

OPTIMASI NAIVE BAYES BERBASIS PSO UNTUK ANALISA SENTIMEN PERKEMBANGAN ARTIFICIAL INTELLIGENCE DI TWITTER

At present the development of Artificial Intelligence technology is progressing rapidly. There are many new artificial intelligence technologies available in various fields. Artificial Intelligence is an artificial intelligence program that can study data, perform processes of thinking and acting like humans. The presence of Artificial Intelligence technology has many positive impacts, especially in increasing work effectiveness and efficiency. However, AI is also a threat to human resources because slowly human work is being replaced by Artificial Intelligence. Various opinions about the development of Artificial Intelligence are widely discussed on social media such as Twitter. Sentiment analysis is a computational study to automatically categorize opinions into positive or negative categories. In this study, the Naive Bayes algorithm was used to analyze sentiment or public opinion regarding the development of Artificial Intelligence for Twitter users. The data collection method used is crawling data on Twitter. The results of the sentiment classification test for the development of Artificial Intelligence using Naive Bayes yield an accuracy value of 86.42%. Meanwhile, the results of the sentiment classification test using Naive Bayes based on Particle Swarm Optimization (PSO) increased with an accuracy value of 87.55%. Based on the results of this study, the use of PSO as an optimization technique for the Naive Bayes algorithm is proven to be the best algorithm model in sentiment analysis for the development of Artificial Intelligence for English text.

Retracted: University Education Management Model Based on Artificial Intelligence Programming and Analysis Technology Foundation for Building Models and Applications

Retracted: University Education Management Model Based on Artificial Intelligence Programming and Analysis Technology Foundation for Building Models and Applications

Heuristic techniques for maximum likelihood localization of radioactive sources via a sensor network

Maximum likelihood estimation (MLE) is an effective method for localizing radioactive sources in a given area. However, it requires an exhaustive search for parameter estimation, which is time-consuming. In this study, heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors. Hence, the time consumption of MLE would be effectively reduced. First, the radiation source was detected using the k-sigma method. Subsequently, the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source. A comparative study was performed in which the estimation accuracy and time consumption of the MLE were evaluated for traditional methods and heuristic techniques. The traditional MLE was performed via a grid search method using fixed and multiple resolutions. Additionally, four commonly used heuristic algorithms were applied: the firefly algorithm (FFA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC). The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program. The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE. The time consumption for the heuristic-based MLE was 0.75, 0.03, 0.02, and 0.059 s for FFA, PSO, ACO, and ABC, respectively. The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE. Hence, heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.

Weizenbaum’s nightmare: The decay of language in AI-generated communication

Half a century on from the ELIZA program, with the rapid and widespread emergence of generative artificial intelligence (AI) programs, we are now on the cusp of realizing Joseph Weizenbaum’s nightmare: an absurd world that is not only populated by machines that can convincingly simulate human communication in its various forms (e.g. writing, visual art, performance and music) but in which these machines are readily accepted as authentic replacements. Drawing on the existentialist language philosophy of Vilém Flusser, this article argues that to defer cultural communication to generative AI programs is to step outside the ‘great conversation’ of human culture and to be condemned to an unutterable nothingness. To demonstrate this, I analyse several illustrative examples including Google’s Language Model for Dialogue Applications (LaMDA) language model, the text-based roleplaying game AI Dungeon (Latitude 2019), and the art installation UUmwelt (Huyghe 2018). I argue that generative AI programs only appear to ‘speak’ in a language we understand while continuing to ‘think’ in the formal language of mathematics and that their communications are merely transliterations of numbers. As such, these programs are not bound by the same moral and syntactical rules that we observe and abide by in cultural communication, and in using generative AI programs, we may very well bypass these rules to express raw intention. Though mechanized in the form of technical images, such a mode of expression would be akin to the nonsensical cries of animals in that it fulfils a fundamental desire but reveals nothing of a consciousness within. By deferring the labour of communication to an AI program, the human retreats inward and disappears from culture as they ‘speak’ in one language but ‘think’ in another. Thus, the nightmarish future that Weizenbaum envisioned is one filled with illusions of artistic expression, projected by both machines and humans, and yet there is no evidence of humanity in such a culture.

Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution

Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio-informatics, architecture design, financial modelling, music, software testing, game artificial intelligence and parallel programming. Multi-output problems deal with predicting numerous output variables simultaneously, a notoriously difficult problem. We present a Multi-Genome Grammatical Evolution better suited for tackling multi-output problems, specifically digital circuits. The Multi-Genome consists of multiple genomes, each evolving a solution to a single unique output variable. Each genome is mapped to create its executable object. The mapping mechanism, genetic, selection, and replacement operators have been adapted to make them well-suited for the Multi-Genome representation and the implementation of a new wrapping operator. Additionally, custom grammar syntax rules and a cyclic dependency-checking algorithm have been presented to facilitate the evolution of inter-output dependencies which may exist in multi-output problems. Multi-Genome Grammatical Evolution is tested on combinational digital circuit benchmark problems. Results show Multi-Genome Grammatical Evolution performs significantly better than standard Grammatical Evolution on these benchmark problems.

Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.

Application and development of artificial intelligence in environmental health

In recent years, major industries have been hit hard by the COVID-19 pandemic, and people have become more aware of issues such as environmental health. The development of computer programs, artificial intelligence (AI), has gradually entered the vision of researchers. The product of this technology can help people better manage and improve the current environmental conditions. This paper, through a method of literature review, aims to explain the development history of AI and its application in different fields such as the environment. Through the research, the paper concludes that artificial intelligence can observe in real time for long periods of time without getting tired like humans do. Based on the application of big data analysis and intelligent programs, artificial intelligence can make reasonable plans in a much shorter time than human beings. However, the solution is only based on the operation of the system. In the actual situation, there will be many obstacles. Therefore, it is still necessary for humans to make judgments based on the results of an operation rather than relying entirely on artificial intelligence. Complicated weather conditions can also make the results of the calculation inaccurate. So there will still be a need to evolve computer programs or for artificial intelligence to be able to calibrate the data it is monitoring from the past.

AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank.

By providing predicted protein structures from nearly all known protein sequences, the artificial intelligence program AlphaFold (AF) is having a major impact on structural biology. While a stunning accuracy has been achieved for many folding units, predicted unstructured regions and the arrangement of potentially flexible linkers connecting structured domains present challenges. Focusing on single-chain structures without prosthetic groups, an earlier comparison of features derived from small-angle X-ray scattering (SAXS) data taken from the Small-Angle Scattering Biological Data Bank (SASBDB) is extended to those calculated using the corresponding AF-predicted structures. Selected SASBDB entries were carefully examined to ensure that they represented data from monodisperse protein solutions and had sufficient statistical precision and q resolution for reliable structural evaluation. Three examples were identified where there is clear evidence that the single AF-predicted structure cannot account for the experimental SAXS data. Instead, excellent agreement is found with ensemble models generated by allowing for flexible linkers between high-confidence predicted structured domains. A pool of representative structures was generated using a Monte Carlo method that adjusts backbone dihedral allowed angles along potentially flexible regions. A fast ensemble modelling method was employed that optimizes the fit of pair distance distribution functions [P(r) versus r] and intensity profiles [I(q) versus q] computed from the pool to their experimental counterparts. These results highlight the complementarity between AF prediction, solution SAXS and molecular dynamics/conformational sampling for structural modelling of proteins having both structured and flexible regions.

The Validity and Reliability of Automatic Tooth Segmentation Generated Using Artificial Intelligence.

This study aimed at evaluating the precision of the segmented tooth model (STM) that was produced by the artificial intelligence (AI) program (CephX®) with an intraoral scan (IOS) and insignia outcomes. Methods. 10 patients with Cl I malocclusion (mild-to-moderate crowding) who underwent nonextraction orthodontic therapy with the Insignia™ system had IOS and CBCT scans taken before treatment. AI was used to produce a total of 280 STMs; each tooth will be measured from three aspects (apexo-occlusal, mesiodistal, and labiolingual) for DICOM and STL formats. Also, root volume measurements for each tooth generated by using the CephX® software and Insignia™ system were compared. The software used for these measurements was the OnDemand3D program used for the multiplanar reconstruction for DICOM format and Geomagic® Control X™ used for STL format. Statistics. An intraclass correlation (ICC) analysis was used to check the agreement between the volume measurement of the segmented teeth generated by using the CephX® and Insignia™ system. Also, it was used to check the agreement between the STL (IOS), STL (CephX®), and DICOM tooth models. In addition, it was used to determine the intraexaminer repeatability by remeasuring five randomly selected individuals two weeks after the initial measurement. After confirmation of the data normality using the Shapiro-Wilk test, the right and left tooth models and the differences between the DICOM, CephX® (STL), and IOS (STL) tooth models were compared using a paired t-test. The STL (IOS), STL (CephX®), and DICOM tooth models were compared utilizing the ANOVA test. p < 0.05 was set as the statistical significance level. Result. Overall data showed good agreement with ICC. The measurements of the various tooth types on the right and left sides did not differ significantly. Also, there was no significant difference between the three groups. Conclusions. The automatic AI approach (CephX®) may be advised in the clinical practice for patients with mild crowding and no teeth restorations due to its speed and effectiveness.

Artificial Intelligence and Article Writing

Dear Editors, I was very interested in your editorial [1]. Technological advancements are so rapid that humans are unable to keep up. And we eagerly await the outcomes of technological advancement. The study of artificial intelligence is one of the topics that has recently gained popularity. If someone had predicted a hundred years ago, "A technology will come that will detect the commands you receive, act accordingly, and write scientific articles," we would laugh at her. Many artificial intelligence dreams that appear weird to us now, I believe, will come true shortly. The Generative Pre-Trained Transformer 3 (GPT3) was developed a few years ago, in line with improvements in artificial intelligence. With this model, artificial intelligence was used to generate content that resembled human-written documents. A more advanced version of ChatGPT was produced a few years later. In March 2023, the most recent GPT4 version was launched. Errors in article writing have been reduced using this and comparable applications such as artificial intelligence. In fact, it has become nearly impossible to detect the difference between publications written by scientists and articles written by artificial intelligence. Some journals have begun to accept artificial intelligence apps as co-authors [2]. Article authoring is made considerably easier by artificial intelligence. In terms of time savings, using artificial intelligence products such as ChatGPT and GPT 4 may make sense. However, there are some risks associated with this circumstance. For example, because disseminating personal data is a felony, authors may feel compelled to conceal some information in order to preserve personal data. Artificial intelligence programs can readily perpetrate crimes involving personal data by releasing secret facts. This situation presents numerous legal issues. Furthermore, the language to be used in article writing may alter depending on the topic of study. In this instance, it is vital to go check the words chosen by artificial intelligence programs in the article. As a result, my recommendation is to use tools such as ChatGPT, GPT 4, or similar basic jobs once the user has written the article themselves. Leaving all of the work to these apps may cause more harm than good. Kind Regards

Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over?

To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data. This retrospective quantitative cross-sectional analytical study utilized a data set consisting of 409 cephalograms obtained from a South African population. 19 landmarks were identified in each of the 409 cephalograms by the primary researcher using the two programs [(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances between corresponding pairs of observations was calculated. Precision was determined by using the standard deviation and standard error of the mean. The primary researcher acted as the gold-standard and was calibrated prior to data collection. The inter- and intrareliability tests yielded acceptable results. Variations were present in several landmarks between the two approaches; however, they were statistically insignificant. The computer-assisted examination software was very sensitive to several variables. Several incidental findings were also discovered. Attempts were made to draw valid comparisons and conclusions. There was no significant difference between the two programs regarding the precision of landmark detection. The present study provides a basis to: (1) support the use of automatic landmark detection to be within the range of computer-assisted examination software and (2) determine the learning data required to develop AI systems within an African context.

Battle of the (Chat)Bots: Comparing Large Language Models to Practice Guidelines for Transfusion-Associated Graft-Versus-Host Disease Prevention.

Published guidelines and clinical practices vary when defining indications for irradiation of blood components for the prevention of transfusion-associated graft-versus-host disease (TA-GVHD). This study assessed irradiation indication lists generated by multiple artificial intelligence (AI) programs, or chatbots, and compared them to 2020 British Society for Haematology (BSH) practice guidelines. Four chatbots (ChatGPT-3.5, ChatGPT-4, Bard, and Bing Chat) were prompted to list the indications for irradiation to prevent TA-GVHD. Responses were graded for concordance with BSH guidelines. Chatbot response length, discrepancies, and omissions were noted. Chatbot responses differed, but all were relevant, short in length, generally more concordant than discordant with BSH guidelines, and roughly complete. They lacked several indications listed in BSH guidelines and notably differed in their irradiation eligibility criteria for fetuses and neonates. The chatbots variably listed erroneous indications for TA-GVHD prevention, such as patients receiving blood from a donor who is of a different race or ethnicity. This study demonstrates the potential use of generative AI for transfusion medicine and hematology topics but underscores the risk of chatbot medical misinformation. Further study of risk factors for TA-GVHD, as well as the applications of chatbots in transfusion medicine and hematology, is warranted.

Exploring Artificial Intelligence Programs' Understanding of Lens, Cataract, and Refractive Surgery Information.

We aimed to evaluate the success of Chat Generative Pre-trained Transformer (ChatGPT), Bing, and Bard artificial intelligence programs, which were released free of charge by three different manufacturers, in correctly answering questions about lenses, cataract, and refractive surgery, and to investigate whether they are superior to each other. Seventy questions related to lens, cataract, and refractive surgery were obtained from the study questions section of the American Academy of Ophthalmology 2022 to 2023 Basic and Clinical Science Course Lens and Cataract and Refractive Surgery Books. The questions were asked separately for the ChatGPT, Bing, and Bard artificial intelligence programs. The answers were compared with answer keys and grouped as correct or incorrect. The accuracy rates of artificial intelligence programs were compared statistically. ChatGPT, Bing, and Bard chatbots gave correct answers to 35 (50%), 43 (61.4%), and 50 (71.4%) questions asked, respectively. The rate of correct answers to the questions of the Bard artificial intelligence program was significantly higher than that of ChatGPT (P = 0.009, Pearson's Chi-square test). Although the currently released artificial intelligence chatbots can be used to access accurate information about lenses, cataracts, and refractive surgery, one should always be careful about the accuracy of the answers given.

Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 2: Research and implementation

Information technologies, particularly artificial intelligence methods, involve more and more deeply into all spheres of human activity: science, technology, art, and education. Ukraine also has sufficient potential and needs to develop educational support, which is the subject of this paper. The work aims to demonstrate the components of information technology for the creation of Intelligent Tutoring Systems (ITS), which are involved in studying various engineering disciplines. The work includes methods of system analysis, mathematical and simulation modeling, technical diagnostics, and artificial intelligence. The proposed models and methods are implemented in ITS prototypes for teaching mathematics, programming, and the automatic control theory. The Intelligent Tutoring Systems were implemented in the educational process of KhAI University and other institutions in Ukraine, Great Britain, Austria, and China. Experimental studies have shown increased student learning success rates using ITS compared to traditional methods. Improved and adapted for computer training methods of technical diagnostics, Bayesian networks, and developed models of algorithmic tasks, the learning process and the learner are valuable from a scientific point of view. In a practical sense, the obtained results can be used to create new specialized ITSs and build an expandable common learning platform integrating the basic disciplines of a specific educational field.

Прогнозированиe трудоустройства студентов педагогического вуза на основе использования алгоритмов машинного обучения

One of the solutions to the problem, when not the best graduates enter the pedagogical profiles and after graduation are employed in the education system, is the prediction of professional orientation even at the stage of the student choosing their further professional trajectory. To solve this problem, the purpose of the study is to develop and experimentally prove the effectiveness of using a program for predicting the employment of students of a pedagogical university based on the introduction of various machine learning algorithms. Using a random selection of students, the collection and processing of their questionnaires (n=205) in 2011-2016 were carried out. Various machine learning algorithms were used to create the program: decision trees, logistic regression, and catboost. In the course of the experiment, the data of the questionnaires were loaded into the program for its training according to various algorithms, in order to ultimately obtain a finished intellectual product with the ability to predict the employment of graduates. In the final comparison, the program developed on the “decision trees” algorithm made only 2 out 19 questionnaires and 7 out 61, which was the best result - 89%. The implementation of this algorithm makes it possible to most accurately, with the least percentage of errors, identify students who will not be employed in the future according to their profile of study or not employed at all. Thus, the study developed an intelligent program that allows one to instantly process data and get an accurate forecast of employment with only a small probability of error.

An Automation Script Generation Technique for the Smart Home

A home automation system means monitoring and controlling various kinds of devices in the home remotely using the Internet of things (IoT). Technologies such as natural language processing techniques, user-friendly visual programming, and machine intelligence programming are already available for home automation. For such systems, the increase in the number of devices often makes users focused on the system’s ability to perform complex or composing tasks. However, some existing natural language processing systems can only perform simple tasks and cannot meet users’ needs. Thus, it is difficult for users to develop the home automation systems they need using visual programming systems because of the large amount of programming knowledge required. Meanwhile, automatic programming without user action can only write a few lines of code and implement little functionality. There are relatively few tools available for generating home automation scripting languages. To address this problem, we propose a practical method for generating executable home automation scripts using Chinese texts. Our method includes the following steps: it extracts critical information from the command sentences in Chinese; it uses first-order logic to check the validity of the extracted information; based on the validation, the correct sentences are mapped into the intermediate language scripts, which can interface with different home platforms. We conducted experiments on Home Assistant, converted intermediate scripts to Home Assistant, and collected 600 scenario descriptions. The experimental results show that the method can automatically generate executable scripts for the Home Assistant platform, and the correct rate was 93.66%.

Artificial intelligence software can generate residency application personal statements that program directors find acceptable and difficult to distinguish from applicant compositions

Study objectiveCreate personal statements using an artificial intelligence program for anesthesiology residency applications that residency program directors rate as acceptable. Study designGenerate two personal statements and survey program directors. SettingAnesthesiology residency training programs. InterventionsWe instructed ChatGPT, a new artificial-intelligence software program, to generate two 400-word personal statements using the common applicant experiences of involvement in athletics or gourmet cooking. MethodsWe sent the generated personal statements to anesthesia program directors and asked them if the statements were acceptable for application to their individual programs, to rate them as poor, good, or excellent, and determine if they could detect anything in the statements that indicated they were not written by an applicant. MeasurementsNinety-four program directors received and opened the survey, and 31 responded. Twenty-eight (90%) responding directors found the personal statement with athletic experience acceptable, with 22 (74%) rating it as good or excellent. Nineteen (61%) program directors did not detect anything in the statement to distinguish it from an applicant-written composition. Twenty-nine (97%) program directors found the personal statement with cooking experience acceptable, with 19 (63%) finding it good or excellent. Twenty-four (80%) directors did not detect anything in the statement to distinguish it from an applicant-written composition. ConclusionsChatGPT can create personal statements for residency applications that program directors find acceptable and difficult to differentiate from personally crafted statements. Applicants may stop using expensive contractor application services and start using artificial intelligence software to create their personal statements because of its quickness, low cost, and high quality.

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students.