Programming Experience Research Articles

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

Investigation into the Influence of Socio-Cultural Factors on Attitudes toward Artificial Intelligence

In this study, the influence of socio-cultural factors on attitudes toward artificial intelligence (AI) was investigated. In total, 1,677 Korean middle school students were selected to participate, and a test tool was used to measure the attitude toward AI. As a result, according to socio-cultural factors, middle school students’ attitudes toward AI were affected differently by gender- or AI-related experiences. In particular, students experiencing difficulties because of socio-cultural factors showed a more positive attitude toward AI if they had an AI education. On the other hand, interest toward AI and programming experience had a significant effect on attitudes toward AI and was not affected by socio-cultural factors. In particular, students with high interest toward AI or experience with block- and text-based programming languages showed significantly positive attitudes toward AI. Hence, the disparity in middle school students’ attitude toward AI according to socio-cultural factors was found.

NGHIÊN CỨU BÀI TOÁN LẬP LỊCH BIỂU THEO HƯỚNG TIẾP CẬN MỤC TIÊU, ÁP DỤNG CHO TRƯỜNG ĐẠI HỌC

This paper focuses on researching, analyzing, and evaluating universities scheduling problem which requires scheduling multiple classes, different teachers and classrooms, along with various constraints such as teacher's teaching hours, classroom capacity, and other resources. In order to solve this problem, Tabu search algorithm has been proposed, which based on searching for the best solutions for adaptive memory and reactive exploration. The constraints and limitations of the problem will be used to create a list of Tabu conditions to ensure that non-optimal solutions will not be repeated. After initializing and updating the adaptive memory, the Tabu search algorithm will search for the next solutions based on the Tabu conditions and adaptive memory. In this way, the Tabu search algorithm can find optimal solutions for this scheduling problem for universities. However, there are many challenges and limitations in the application of this algorithm, such as the large number of constraints to be follow, the determination of suitable parameters, and the dependence on the programmer's experience

Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

Particle simulations for inquiry-based teaching of polymer shape and entropic elasticity using computational thinking

An inquiry-based approach is proposed that allows students to develop and understand computer codes for simulations at the particle level. Computational thinking is employed as a low-threshold approach for students without programming experience. The resulting computer code cores are concise and traceable. Here, the topic of the simulations concerns polymer molecules. A special property of polymers, coiling, and the related entropic elasticity is suitable for the setup of a simulation in class. Students at the upper secondary level were introduced into the topic by an activity. This was a stochastic game serving as a basis to for the development of the corresponding algorithm. Once students are involved in the development of the code, they can meaningfully investigate the model by running the program, changing its parameters, or altering the code. With these simulations, students can prove wrong the early belief that polymer molecules exhibit a stretched chain structure. Furthermore, the confinement of the system leads to a decrease in entropy, which in turn results in a force acting on the walls that can be elaborated comprehensively by students through such simulations.

Older adults’ expectations, experiences, and preferences in programming physical robot assistance

Robots are expected to be key enablers in assisting older adults with aging in place by providing cognitive, social, and physical assistance. Because older adults vary greatly in terms of their needs for robot assistance, there may be potential advantages to enabling them to tailor robot assistance to work for their unique contexts through end-user robot programming. However, little is known about the feasibility and potential of engaging older adults in programming robot assistance. In this work, we explore the possibility of engaging older adults in programming physical robot assistance through field study sessions in older adults’ homes. Through interviews and observations of older adults’ programming experiences using a contemporary commercial robot programming method, we found that familiarity with other forms of automation and interactions, changes in abilities due to aging, multi-user and collaborative programming, cognitive exercise, and mental model formation can play an important role in shaping older adults’ expectations, experiences, and preferences in programming physical robot assistance. Based on these findings, we recommend guidelines to consider when designing future robot programming interactions for older adults.

The professional identities of prospective mathematics teachers who teach through programming

AbstractAs has been the case in many countries around the world, the new Norwegian curriculum from 2020 included programming as part of mathematics education. However, little is known about how prospective teachers perceive this addition in regard to their developing professional identities. When the results from an electronic survey of 394 prospective teachers showed unexpected findings, five of the subjects were asked to participate in a focus group interview in order to explore some of these results. The focus group interview was conducted to understand how prospective teachers considered the past, present and future aspects of their professional identities as teachers of mathematics through programming. The results reveal that, although the prospective teachers had little experience of programming, they were positive regarding its implementation in mathematics lessons because they identified themselves as digital natives; they therefore believed that learning to program would be easy. They aligned themselves with their students, as masters of technology, in contrast to their future colleagues, whom they implicitly described as digital immigrants. The findings of this study have implications for teacher education. Even if the prospective teachers have a positive attitude toward programming and consider themselves digitally competent, a limited understanding of how programming can be integrated into their mathematics teaching will affect the identities that they see for themselves as teachers who teach mathematics through programming.

Perceptions of community-based online exercise programming for persons with multiple sclerosis during COVID-19: A qualitative case study

ABSTRACT Exercise can preserve physical, cognitive, and physiological functioning in adults living with relapsing remitting multiple sclerosis (RRMS), making it critical to understand exercise programming options, such as online programming, that may reduce exercise related barriers in this population. Using an exploratory case study design, we qualitatively examined the perceptions and experiences of online exercise programming among persons with multiple sclerosis (PwMS). Seven individuals recruited from the Brock Functional Inclusive Training Centre (Bfit) completed semi-structured interviews which were audio recorded and transcribed verbatim. Data were analysed using thematic analysis. Three major themes were generated from the data: (1) Give and take: Accessibility to exercise, (2) It’s just not real: Experiences and perceptions of technology, and (3) It’s so much more than exercise: The importance of connecting with others. Participants described increased accessibility to exercise, however, barriers to accessibility were also articulated. Previous experiences and attitudes towards using online platforms strongly impacted perceptions of technology positively and negatively. Perceptions of community in the online exercise programming impacted participants’ experiences and motivation to engage in exercise. In comparison to facility-based programs, social experiences were hindered; however online exercise programming offered more social opportunities than having no online options. Our findings highlight the complex perceptions of online exercise programming for PwMS. Practitioners should attempt to address challenges of online exercise offerings to create a better experience for this population.

WebNORM: a web application for calculating normative mineralogy

Normative mineralogy is a valuable tool for interpreting the mineralogical composition of rocks based on their bulk rock geochemical data. Despite the development of various computational tools for normative mineralogy calculations, the accessibility and continued usage of many existing tools is limited by the use of older languages, licensing constraints, and restricted access to the source code. In this paper, we introduce a Python-based implementation of the normative mineralogy algorithm, which is accessible through a user-friendly web application webNORM, or as part of the pyrolite Python package. The algorithm uses major, minor, and trace element oxides as input and returns up to 31 normative minerals. It offers two methods for estimating the Fe2O3:FeO ratio. The web application offers a convenient interface for users without programming experience, while the pyrolite integration allows more advanced users to leverage the Python ecosystem for end-to-end geochemical data analysis. Comparison between our implementation of the normative calculation and previous implementations shows that our version is accurate and reliable. webNORM offers the geoscience community an accessible solution for calculating normative mineralogy. The algorithm and web application are open-source, and we encourage contributions and adaptations by the community for specific needs and improvements.

ChromatoShiny: an interactive R/Shiny App for plotting chromatography profiles.

Unicorn™ software on Äkta liquid chromatography instruments outputs chromatography profiles of purified biological macromolecules. While the plots generated by the instrument software are very helpful to inspect basic chromatogram properties, they lack a range of useful annotation, customization and export options. We use the R Shiny framework to build an interactive app that facilitates the interpretation of chromatograms and the generation of figures for publications. The app allows users to fit a baseline, to highlight selected fractions and elution volumes inside or under the plot (e.g. those used for downstream biochemical/biophysical/structural analysis) and to zoom into the plot. The app is freely available at https://ChromatoShiny.bio.ed.ac.uk. It requires no programming experience, so we anticipate that it will enable chromatography users to create informative, annotated chromatogram plots quickly and simply.

LeafAI: query generator for clinical cohort discovery rivaling a human programmer.

Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria. The task of query creation from eligibility criteria requires solving several text-processing problems, including named entity recognition and relation extraction, sequence-to-sequence transformation, normalization, and reasoning. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries. LeafAI matched a mean 43% of enrolled patients with 27 225 eligible across 8 clinical trials, compared to 27% matched and 14 587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI. Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base. We demonstrate that LeafAI can rival an experienced human programmer in finding patients eligible for clinical trials.

SENTIMEN ANALISIS CHATGPT DENGAN ALGORITMA NAÏVE BAYES DAN OPTIMASI PSO

Abstract— ChatGPT which is an OpenAI technology that responds to conversations between humans and machines. enabling users of all ages and backgrounds to communicate naturally in multiple languages ​​without having prior knowledge or experience in programming or the computer world. However, a technology will always be at odds and has flaws on the human side, various assumptions about chatGPT are formed from many sides, such as in the world of education, chatGPT creates parallels for teachers and lecturers. When giving assignments, students/students can use chatGPT as material in answering assignments from teachers/lecturers. And that results in students/students not carefully reading the answers to these assignments, if that continues to happen, students/students will find it too easy to get something and then will lose interest in solving problems with their own efforts. This article aims to analyze sentiment analysis whose data is taken from Twitter using the keyword "CahtGPT OpenAI". With 2,000 data calculated using the naive Bayes algorithm and optimized using PSO, it is found that sentiment analysis for chatGPT itself has an accuracy of 69.23% with a positive class of 0.503 and a negative of 0.497 and obtains an AUC curve value of 0.68 +/- 0.55..

Engineering Students’ Experiences of Assessment in Introductory Computer Science Courses

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This study evaluates the generalizability of previously identified perceptions among engineering students of assessments in introductory programming (CS1). The students’ perceptions of their instructors’ and teaching assistants’ (TAs) roles in these assessments are also studied, and differences based on prior programming experience, gender, and course explored. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> Basic programming skills are desirable also for students who are not majoring in computer science (CS). Students’ experience of assessments has not been fully explored. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Questions:</i> 1) How do engineering students experience the assessment (lab assignments, midterm exam, and project) in their CS1 courses? 2) What are the students’ perceptions of the TAs and instructors in relation to these assessment situations? 3) What significant differences can be found based on students’ prior experience in programming, gender, and course? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> Previously identified themes from an interview study worked as a framework for the formulation of 25 statements used in a survey among 137 students in six CS1 courses (second part of a mixed-method study). Descriptive statistics, Mann–Whitney U Test, and Kruskal–Wallis tests with Bonferroni corrections, were used to analyze the data. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> Laboratory assignments were experienced as an opportunity to learn while the exams were viewed as predictable. The projects were perceived as authentic, although varying in difficulty, and as a huge leap from the lab assignments. Students perceived the instructors to put their touch on the course but viewed their TAs as carrying out the assessments, and experienced variations between TAs. Female students experienced these variations to a larger extent and perceived received feedback as less useful.

Examining Factors Influencing Cognitive Load of Computer Programmers.

In this study, the factors influencing the cognitive load of computer programmers during the perception of different code tasks were investigated. The eye movement features of computer programmers were used to provide a significant relationship between the perceptual processes of the sample codes and cognitive load. Thanks to the relationship, the influence of various personal characteristics of programmers on cognitive load was examined. Various personal parameters such as programming experience, age, native language, and programming frequency were used in the study. The study was performed on the Eye Movements in Programming (EMIP) dataset containing 216 programmers with different characteristics. Eye movement information recorded during two different code comprehension tasks was decomposed into sub-information, such as pupil movement speed and diameter change. Rapid changes in eye movement signals were adaptively detected using the z-score peak detection algorithm. Regarding the cognitive load calculations, canonical correlation analysis was used to build a statistically significant and efficient mathematical model connecting the extracted eye movement features and the different parameters of the programmers, and the results were statistically significant. As a result of the analysis, the factors affecting the cognitive load of computer programmers for the related database were converted into percentages, and it was seen that linguistic distance is an essential factor in the cognitive load of programmers and the effect of gender on cognitive load is quite limited.

MIT Zero-G outreach initiative: Using experiment design and virtual reality to inspire the next generation of space scientists and engineers

MIT's Space Exploration Initiative offers a course on project development, prototyping, and deployment readiness for parabolic flights, culminating in an annually chartered research flight with Zero-G. MIT's Resource Exploration and Science of our Cosmic Environment (RESOURCE) team participated in the course in the fall of 2021 with a Zero-G flight in May of 2022 testing technology for a virtual reality platform to enable science on Lunar rover exploration missions. In parallel with the scientific effort, the team developed the MIT Zero-G Outreach Initiative (0G-OI), an outreach program to engage with the Cambridge Public School's (CPS) grade 7 classes to teach them about research in microgravity, parabolic flight, and experiment design. The goal of the outreach program is to inspire the next generation of space scientists and engineers using virtual reality.The CPS grade 7 curriculum covers ‘Mysteries of the Universe’ where students consider the role of gravity in the solar system. They also complete a unit on roller coasters in which they learn about key ideas of force, motion, and energy through the context of roller coasters. The 0G-OI Initiative ties these two units together through five videos covering: 1) an overview of the initiative, 2) the basics of parabolic flight and how we perceive gravity, 3) an overview of gravity and its function in the universe, 4) why and what we can study in microgravity and 5) how to design an experiment for microgravity. The students then design their own experiments for microgravity. These are down-selected to the top 5 experiments that are reviewed by an astronaut to select one experiment to fly on the MIT chartered Zero-G parabolic flight. The flight and the experiment are filmed in VR video by the MIT RESOURCE team. Each participating school is provided with an Oculus Quest 2 VR headset and a flight day is held at each school where the students experience the Zero-G flight and see the results of their experiment in an immersive VR environment. Using this initial setup, the 0G-OI can be run annually in parallel with MIT's SEI parabolic flight course.Some key development parameters included keeping the educational videos under 10 min, that having an astronaut select the winning experiment provided incentive and finally, using VR, as this technology is commonly associated with gaming and entertainment. Lessons learned focused on improvements to scheduling and coordination with Zero-G, in-person programming and classroom experience optimization. The 0G-OI aims to develop a lasting relationship with the CPSs and make the exciting experience of parabolic flight and space exploration an accessible experience.

Automatic detection of spatio-temporal signaling patterns in cell collectives.

Increasing experimental evidence points to the physiological importance of space-time correlations in signaling of cell collectives. From wound healing to epithelial homeostasis to morphogenesis, coordinated activation of biomolecules between cells allows the collectives to perform more complex tasks and to better tackle environmental challenges. To capture this information exchange and to advance new theories of emergent phenomena, we created ARCOS, a computational method to detect and quantify collective signaling. We demonstrate ARCOS on cell and organism collectives with space-time correlations on different scales in 2D and 3D. We made a new observation that oncogenic mutations in the MAPK/ERK and PIK3CA/Akt pathways of MCF10A epithelial cells hyperstimulate intercellular ERK activity waves that are largely dependent on matrix metalloproteinase intercellular signaling. ARCOS is open-source and available as R and Python packages. It also includes a plugin for the napari image viewer to interactively quantify collective phenomena without prior programming experience.

AlphaMat: a material informatics hub connecting data, features, models and applications

The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).

Leisure-time during COVID-19 in the Western Cape, South Africa: experiences of people with disabilities

ABSTRACT COVID-19 brought a significant shift in people’s daily routine, which became challenging for individuals to cope with. Leisure-time physical activities can significantly increase social effectiveness, a sense of control in the lives of people with disabilities, and social integration. The aim of this study was to explore the experiences of leisure-time physical activity participation levels and programming needs for people with disabilities in the Western Cape, South Africa, amidst COVID-19. This study used a descriptive-qualitative approach to collect data from 12 participants who were recruited in the City of Cape Town municipality. Purposive sampling method was used to select eight participants, while the four participants were selected using the snowball sampling method. The results of the study reported two themes: LTPA during COVID-19 and LTPA challenges pre- and during COVID-19. Prominent findings from this study emphasize that LTPA is perceived positively and remained relevant during COVID-19. Although people’s movements were limited due to the lockdown regulations in South Africa, participants in this study did not lose sense of what LTPA is or the value it brings to their lives.

Human growth data analysis and statistics – the 5th Gülpe International Student Summer School

The Summer School in Gülpe (Ecological Station of the University of Potsdam) offers an exceptional learning opportunity for students to apply their knowledge and skills to real-world problems. With the guidance of experienced human biologists, statisticians, and programmers, students have the unique chance to analyze their own data and gain valuable insights. This interdisciplinary setting not only bridges different research areas but also leads to highly valuable outputs. The progress of students within just a few days is truly remarkable, especially when they are motivated and receive immediate feedback on their questions, problems, and results. The Summer School covers a wide range of topics, with this year’s focus mainly on two areas: understanding the impact of socioeconomic and physiological factors on human development and mastering statistical techniques for analyzing data such as changepoint analysis and the St. Nicolas House Analysis (SNHA) to visualize interacting variables. The latter technique, born out of the Summer School’s emphasis on gaining comprehensive data insights and understanding major relationships, has proven to be a valuable tool for researchers in the field. The articles in this special issue demonstrate that the Summer School in Gülpe stands as a testament to the power of practical learning and collaboration. Students who attend not only gain hands-on experience but also benefit from the expertise of professionals and the opportunity to engage with peers from diverse disciplines.

What strategies do students use when they are programming a robot to follow a curved line?

During a relatively short period of time, programming has been implemented in the national curriculum of the compulsory school in Sweden. Since 2018, programming is a new content in the technology subject and the research field has discussed some of the challenges teachers and students, who generally have little experiences of programming, face when programming is introduced in teaching. In this study, we have explored what strategies lower secondary school students (ages 13–15) use when they are programming a robot to follow a curved line in technology education class. Data consists of screen recorded films when students are pair programming a robot. Student talks were transcribed verbatim and analysed using Practical Epistemological Analysis. The analysis revealed three different strategies that the students used when programming the robot: (1) sensor—follow the line, searching for a code that automatically would make the robot to follow the route, (2) sensor—wheels, using codes to create a feedback system between sensor and wheels, and (3) rotations—degrees–wheels, using the position of the robot to stepwise fine tune the movement of the wheels. In line with previous research, the students in our study spent much time discussing, testing, and debugging their code, and our findings contribute by showing how these discussions were aligned with the strategy used. Depending on the strategy, students actively looked for and tested codes affecting different aspects of the sensor-wheel system, such as for example sensor input, power, rotations or turning. Implications for teaching is discussed.