Reviewerly/exHarmony huggingface dataset:
Resource Usage and Optimization Opportunities in Workflows of GitHub Actions

Continuous integration and continuous delivery (CI/CD) has become a prevalent practice in software development. GitHub Actions is emerging as a popular platform for implementing CI/CD pipelines, called workflows, especially because the platform offers 2,000 minutes of computation for free to public repositories each month. To understand what these resources are used for and whether CI/CD could be more efficient, this paper presents the first comprehensive empirical study of resource usage and optimization opportunities of GitHub Action workflows. Our findings show that CI/CD imposes significant costs, e.g., $504 per year for an average paid-tier repository. The majority of the used resources is consumed by testing and building (91.2%), which is triggered by pull requests (50.7%), pushes (30.9%), and regularly scheduled workflows (15.5%). While existing optimizations, such as caching (adopted by 32.9% of paid-tier repositories), demonstrate a positive impact, they overall remain underutilized. This result underscores the need for enhanced documentation and tools to guide developers toward more resource-efficient workflows. Moreover, we show that relatively simple changes in the platform, such as deactivating scheduled workflows when repositories are inactive, could result in reductions of execution time between 1.1% and 31.6% over the impacted workflows. Overall, we envision our findings to help improve the resource efficiency of CI/CD pipelines.
Revealing Hidden Threats: An Empirical Study of Library Misuse in Smart Contracts

Smart contracts are Turing-complete programs that execute on the blockchain. Developers can implement complex contracts, such as auctions and lending, on Ethereum using the Solidity programming language. As an object-oriented language, Solidity provides libraries within its syntax to facilitate code reusability and reduce development complexity. Library misuse refers to the incorrect writing or usage of libraries, resulting in unexpected results, such as introducing vulnerabilities during library development or incorporating an unsafe library during contract development. Library misuse could lead to contract defects that cause financial losses. Currently, there is a lack of research on library misuse. To fill this gap, we collected more than 500 audit reports from the official websites of five audit companies and 223,336 real-world smart contracts from Etherscan to measure library popularity and library misuse. Then, we defined eight general patterns for library misuse; three of them occurring during library development and five during library utilization, which covers the entire library lifecycle. To validate the practicality of these patterns, we manually analyzed 1,018 real-world smart contracts and publicized our dataset. We identified 905 misuse cases across 456 contracts, indicating that library misuse is a widespread issue. Three patterns of misuse are found in more than 50 contracts, primarily due to developers lacking security awareness or underestimating negative impacts. Additionally, our research revealed that vulnerable libraries on Ethereum continue to be employed even after they have been deprecated or patched. Our findings can assist contract developers in preventing library misuse and ensuring the safe use of libraries.
Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors

As a dynamic programming language, Python has become increasingly popular in recent years. Although the dynamic type system of Python facilitates the developers in writing Python programs, it also brings type errors at run-time which are prevalent yet not easy to fix. There exist rule-based approaches for automatically repairing Python type errors. The approaches can generate accurate patches for the type errors covered by manually defined templates, but they require domain experts to design patch synthesis rules and suffer from low template coverage of real-world type errors. Learning-based approaches alleviate the manual efforts in designing patch synthesis rules and have become prevalent due to the recent advances in deep learning. Among the learning-based approaches, the prompt-based approach which leverages the knowledge base of code pre-trained models via pre-defined prompts, obtains state-of-the-art performance in general program repair tasks. However, such prompts are manually defined and do not involve any specific clues for repairing Python type errors, resulting in limited effectiveness. How to automatically improve prompts with the domain knowledge for type error repair is challenging yet under-explored.
Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems

Abstract This paper presents a novel metaheuristic algorithm inspired by the actions of stadium spectators affecting behavior of players during a match which will be called stadium spectators optimizer (SSO) algorithm. The mathematical model of the SSO algorithm is presented and the performance and efficiency of the presented method is tested on some of the well-known mathematical test functions and also CEC-BC-2017 functions. The SSO algorithm is a parameter-free optimization method since it doesn't require any additional parameter setup at any point throughout the optimization process. It seems urgently necessary to design a novel metaheuristic algorithm that is parameter-free and capable of solving any optimization problem without taking into account extra parameters, as the majority of metaheuristic algorithms rely on the configuration of extra parameters to solve different problems efficiently. A positive point for the SSO algorithm can be seen in the results of the suggested technique, which indicate a partial improvement in performance. The results are compared with those of golf optimization algorithm (GOA), Tiki taka optimization algorithm (TTA), Harris Hawks optimization algorithm (HHO), the arithmetic optimization algorithm (AOA), CMA-ES and EBOwithCMAR algorithms. The statistical tests are carried out for the obtained results and the tests reveal the capability of the presented method in solving different optimization problems with different dimensions. SSO algorithm performs comparably and robustly with the state-of-the-art optimization techniques in 14 of the mathematical test functions. For CEC-BC-2017 functions with ten dimensions, EBOwithCMAR performs better than the proposed method. However, for most functions of CEC-BC-2017 with ten dimensions, the SSO algorithm ranks second after EBOwithCMAR, which is an advantage of the SSO since the proposed method performs better than the well-known CMA-ES optimization algorithm. The overall performance of the SSO algorithm in CEC-BC-2017 functions with 10 dimensions was acceptable, in dimension of 30, 50 and 100, the performance of the proposed method in some functions decreased.
Current Trends and Beyond Conventional Approaches: Advancements in Breast Cancer Surgery through Three-Dimensional Imaging, Virtual Reality, Augmented Reality, and the Emerging Metaverse

Breast cancer stands as the most prevalent cancer globally, necessitating comprehensive care. A multidisciplinary approach proves crucial for precise diagnosis and treatment, ultimately leading to effective disease management. While surgical interventions continue to evolve and remain integral for curative treatment, imaging assumes a fundamental role in breast cancer detection. Advanced imaging techniques not only facilitate improved diagnosis but also contribute significantly to the overall enhancement of breast cancer management. This review article aims to provide an overview of innovative technologies such as virtual reality, augmented reality, and three-dimensional imaging, utilized in the medical field to elevate the diagnosis and treatment of breast cancer. Additionally, the article delves into an emerging technology known as the metaverse, still under development. Through the analysis of impactful research and comparison of their findings, this study offers valuable insights into the advantages of each innovative technique. The goal is to provide physicians, surgeons, and radiologists with information on how to enhance breast cancer management.
Challenges and Opportunities for Virtual Reality in Higher Education

Virtual reality (VR) has made a successful transition from the entertainment domain to the skills training domain.As expected, there is now considerable research being conducted on utilizing the affordances of VR in higher education.A properly designed VR-based learning environment can engage students not only cognitively but also affectively and behaviorally.This paper provides details of the process of developing VR lessons for introductory level undergraduate courses in STEM disciplines.The lessons were prepared using a web-based VR development environment.Some challenges in developing these lessons were identified, and are shared.The impact of the VR lessons on students' perceptions and attitudes was analyzed using self-reported data that were gathered through surveys.The results of the data analysis are included.
Promoting collaborative learning in architectural engineering design through multi-user augmented reality

As a highly interdisciplinary industry, architecture, engineering, and construction (AEC) require effective and seamless collaboration to deliver projects timely and with high quality.To better prepare students with the demanding cooperative skill, collaborative learning has always been an integral part of the education of undergraduate students.Augmented reality (AR) technology has matured rapidly in recent years and has been widely used in various fields such as medical, manufacturing, entertainment, and education.The seamless combination of the real world and virtual environment is one of the most important characteristics of AR technology [1].In this study, we utilized this characteristic to develop a multi-user design application in Microsoft HoloLens.The AR application enables students to participate in the collaborative architectural design by manipulating architectural objects in virtual environment as well as directly communicating with teammates in physical environment.The objective of the research is to enhance collaborative learning in architectural engineering design through multi-user AR.In order to test the feasibility of integrating the AR application into the traditional collaborative design process, we deployed an AR module based on the AR application into two architectural design studio courses including 19 participants as a preliminary case study.This paper reports the effectiveness of the AR module on students' active participation and engagement in collaboration, skills and knowledge gains, and the comfort level of collaborative design in AR.In addition, students' communication behaviors during collaborative design have been assessed by analyzing recorded videos of users' view in AR.
Lessons learned: faculty watch parties are a powerful approach to foster diversity and inclusivity discussions

Her research focuses on human-centered AI, developing systems that understand human behavior.She has worked on projects in human activity recognition to recognize activities of daily living (ADLs), intelligent tutoring systems to teach perspective sketching, and cognition-aware computing to measure people's mental workload on spatial visualization tasks.
Work In Progress: A Metro Map-Based Curriculum Visualization for Examining Interrelated Curricula

Prior research has indicated that academic curriculum can be difficult for students to understand: especially for new students or students considering multiple degree programs.Based on the existing curriculum visualization literature, we hypothesize that the visualization of multiple curricula in one diagram might aid students in understanding curricular requirements and analyzing the different course paths they could take to complete a desired degree program.This work in progress describes the design and implementation of a visualization of the degree programs within an engineering college at a large University, where the interrelated web of courses and majors is displayed in the form of a metro map.In this metro map, the course requirements of individual majors are represented by metro 'lines', with each represented by a 'station' on each line.If multiple majors require the same course to complete the degree, then the 'line' representing each major will connect to the 'station' for that course.Through this visualization, we aim to create a tool that students could use to better understand the path of their degree program, and the ways in which their program requirements interact with those of related programs.We describe our methods for collecting course data as well as the design and implementation of the visualization.We close with a proposed set of evaluations for determining the effectiveness of this tool, and describe the limitations involved in creating similar visualizations for additional universities.Figure 1: A section of the metro map representation of 15 engineering degree programs.Each chain of colored lines represents a degree, while each circular 'station' represents a single required course.
Digital transformation in engineering education: a gap between teaching and management

Techonology and System profesional, with extensive
Results of 2021 Energy Education Stakeholder Survey

This paper presents the results of a recent energy education stakeholder survey that was conducted to examine energy industry trends and educational programming needs for the next decade.The survey was sent to over 700 faculty ranging from middle school to university level along with 150 industry representatives.The survey generated a response rate just over 10% for both the educator and industry groups.The top three technologies identified as areas for growth in the next decade were solar photovoltaics, energy storage, and electric vehicles.The biggest obstacle faced by faculty were a lack of tools, materials, and supplies necessary to provide hands-on learning with energy technology.Educators expressed a strong preference for face-to-face instruction that included access to tools and equipment, and also indicated a need for electronic open educational resources that were compliant with website accessibility requirements.The results of the survey are presented along with analysis, conclusions, and recommendations for engineering educational programs that address energy technology.
A Holistic Implementation of Data Science in Clean Energy Engineering Education

Abstract A Holistic Implementation of Data Science in Clean Energy Engineering Education Advances in technology and economic expansion require increased use of energy. At the same time, technology has significantly improved energy efficiency and allows exploitation of a variety of diverse and plentiful sources of clean energy. This has led to a clean energy revolution, as stated by the Department of Energy [1]. We are witnessing expansion of economic activities driven by advances in clean energy technologies, which is in turn leading to new technological breakthroughs [2]. It is predicted that by 2040, artificial intelligence (AI) applications, in combination with other technologies, will become prevalent in almost every aspect of life, including but not limited to energy, healthcare, and transportation. Empowered by simultaneous increases in high-quality data, computing resources and communication infrastructure, AI will challenge societies to gain most of the benefits and at the same time reduce negative social effects [3]. This paper describes the development of a program that provides students with knowledge and skills in various aspects of energy, economics, and data science, calling attention to the areas in the program impacted by the newly developed courses, and expands on several activities designed to engage students through active learning and research experiences.
Using GitHub as a Teaching Tool for Programming Courses

GitHub has become the most popular code management platform in the software development industry.It allows developers to manage their software development projects and collaborate with each other.Recently, educators also started using GitHub as a teaching tool for programming courses by hosting code samples and managing student assignments.In this study, we examine how GitHub is being used in academia, and we discuss the motivations and the benefits of using this platform.We also present authors' experience of using GitHub in programming courses of a software engineering program.We discuss the benefits and challenges of using GitHub and GitHub classroom in the classroom.
Memory-based Distribution Shift Detection for Learning Enabled Cyber-Physical Systems with Statistical Guarantees

Incorporating learning based components in the current state-of-the-art cyber physical systems (CPS) has been a challenge due to the brittleness of the underlying deep neural networks. On the bright side, if executed correctly with safety guarantees, this has the ability to revolutionize domains like autonomous systems, medicine and other safety critical domains. This is because, it would allow system designers to use high dimensional outputs from sensors like camera and LiDAR. The trepidation in deploying systems with vision and LiDAR components comes from incidents of catastrophic failures in the real world. Recent reports of self-driving cars running into difficult to handle scenarios is ingrained in the software components which handle such sensor inputs. The ability to handle such high dimensional signals is due to the explosion of algorithms which use deep neural networks. Sadly, the reason behind the safety issues is also due to deep neural networks themselves. The pitfalls occur due to possible over-fitting, and lack of awareness about the blind spots induced by the training distribution. Ideally, system designers would wish to cover as many scenarios during training as possible. But, achieving a meaningful coverage is impossible. This naturally leads to the following question: Is it feasible to flag out-of-distribution (OOD) samples without causing too many false alarms? Such an OOD detector should be executable in a fashion that is computationally efficient. This is because OOD detectors often are executed as frequently as the sensors are sampled. Our aim in this paper is to build an effective anomaly detector. To this end, we propose the idea of a memory bank to cache data samples which are representative enough to cover most of the in-distribution data. The similarity with respect to such samples can be a measure of familiarity of the test input. This is made possible by an appropriate choice of distance function tailored to the type of sensor we are interested in. Additionally, we adapt conformal anomaly detection framework to capture the distribution shifts with a guarantee of false alarm rate. We report the performance of our technique on two challenging scenarios: a self-driving car setting implemented inside the simulator CARLA with image inputs and autonomous racing car navigation setting with LiDAR inputs. From the experiments, it is clear that a deviation from in-distribution setting can potentially lead to unsafe behavior. Although it should be noted that not all OOD inputs lead to precarious situations in practice, but staying in-distribution is akin to staying within a safety bubble and predictable behavior. An added benefit of our memory based approach is that the OOD detector produces interpretable feedback for a human designer. This is of utmost importance since it recommends a potential fix for the situation as well. In other competing approaches such a feedback is difficult to obtain due to reliance on techniques which use variational autoencoders.
Supplementary Figure S1 from Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

&lt;p&gt;Supplementary Figure S1 shows that the best-fit piecewise linear model was found when the turning point was selected at the age of 46; the AIC was 2.00-8.81 units smaller than the AICs of the models when the turning point was selected at other ages. It also shows that the best-fit piecewise linear model was found when the turning point was selected at the age of 65; the AIC was 1.55-5.58 units smaller than the AICs of the models when the turning point was selected at other ages.&lt;/p&gt;
Supplementary Methods S1 from Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

&lt;p&gt;Supplementary Methods S1 describes the five models that were used for fitting the age-related trend of total variance and familial correlations in Cirrus&lt;/p&gt;
Data from Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

&lt;div&gt;AbstractBackground:&lt;p&gt;Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus.&lt;/p&gt;Methods:&lt;p&gt;We measured Cirrus for 3,195 breast cancer–free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants.&lt;/p&gt;Results:&lt;p&gt;There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%).&lt;/p&gt;Conclusion:&lt;p&gt;Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins.&lt;/p&gt;Impact:&lt;p&gt;Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens.&lt;/p&gt;&lt;/div&gt;