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摘要： The recent pandemic attack COVID-19 has caused a great health issue for all people, mainly the workers in the industries. However, health monitoring systems are exceedingly perceptive and it becomes a prompting to recognize the health of each employee. In the current system, it takes a long time to recognize the health of an employee, which causes some security issues for the worker’s database. To overcome this, we have implemented an IoT-based health monitoring system using machine learning. It mainly accelerates and monitors the workers’ health based on four different parameters using a predictive algorithm. The database of workers’ health is stored in the Thingspeak cloud. Even if one of the four parameters exceeds the average value of normal health, a prescription is sent directly to the workers through e-mail. Thus, our work is highly efficient in terms of security and sending prescriptions compared to the existing system.

关键词： Software engineering  

摘要： HighlightsWe provide a literature review to analyze some of the main causes of the lack of women professionals in software engineering, some of which are underlooked in state-of-the-art studies on the gender gap in ICT;We build on previous projects to combat the gender gap in ICT - which often include mentoring programs, the use of role models, and gaming activities - by proposing a teaching methodology aimed both at highlighting the role of ICT technologies in addressing societal issues, and at strengthening soft *** present a strategy, that we call "greed(y)" because it has been conceived as a grid of vertical (hard) and horizontal (soft) skills, intertwining topics to which girls are traditionally sensitive, such as environmental sustainability, health, etc., with digital skills and soft skills that the education system more rarely considers - such as team-working, public speaking, social networking, and competition - with visible consequences more for girls than for boys. © 2023, The Authors. All rights reserved.

关键词： Application programs  

摘要： The field of computer science (CS) is facing a crucial challenge in broadening participation and embracing diversity, especially among underrepresented gender groups. The presented interdisciplinary educational program is an efficient response to this challenge, designed to catalyze diversity in CS through engagement with complex, interest-driven problems. This paper outlines the program's structure, elucidates the pedagogical underpinnings, and reflects on the emergent challenges and opportunities. We delve into how the fusion of CS with other academic disciplines can allure a more varied demographic, emphasizing the engagement of female high school students — a demographic pivotally positioned yet significantly untapped in CS. Through a systematic survey analysis, we measure the program's efficacy in increasing interest in CS and in cultivating an appreciation for its application in addressing real-world, cross-disciplinary challenges. Our findings affirm the program’s success in bridging the engagement gap by leveraging students’ intrinsic interests, thus charting alternative pathways into the CS field. These insights underscore the critical role of interdisciplinary approaches, establishing a new standard for transformative CS educational methods. © 2023, The Authors. All rights reserved.

关键词： Motivational climate   Engagement   Effort   Computer science   MUSIC model of motivation  

摘要： BackgroundThe motivational climate within a course has been shown to be an important predictor of students' engagement and course ratings. Because little is known about how students' perceptions of the motivational climate in a computer science (CS) course vary by sex, race/ethnicity, and academic major, we investigated these questions: (1) To what extent do students' achievement and perceptions of motivational climate, cost, ease, and effort vary by sex, race/ethnicity, or major? and (2) To what extent do the relationships between students' achievement and perceptions of motivational climate, cost, and effort vary by sex, race/ethnicity, and major? Participants were enrolled in a large CS course at a large public university in the southeastern U.S. A survey was administered to 981 students in the course over three years. Path analyses and one-way MANOVAs and ANOVAs were conducted to examine differences between ***' perceptions of empowerment, usefulness, interest, and caring were similar across sexes and races/ethnicities. However, women and Asian students reported lower success expectancies. Students in the same academic major as the course topic (i.e., CS) generally reported higher perceptions of the motivational climate than students who did not major or minor in the course topic. Final grades in the course did not vary by sex or race/ethnicity, except that the White and Asian students obtained higher grades than the Black students. Across sex, race/ethnicity, and major, students' perceptions of the motivational climate were positively related to effort, which was positively related to *** implication is that females, Asian students, and non-CS students may need more support, or different types of support, to help them believe that they can succeed in computer science courses. On average, these students were less confident in their abilities to succeed in the course and were more likely to report that they did not have t

摘要： This article deals with the problem of the functioning of existing training programs for students mastering natural science programs for applied training areas, which take into account the dominant part of the load on the problem-experimental essence of the knowledge acquisition process. It consists in solving specific problems with a different nature of phenomena, which are easier to consider in the context of simulation tools in a virtual environment. The nature of cognition is determined, affecting both the form of imparting properties to the studied phenomenon or process, and the specific characteristics of the assimilation of knowledge, mediated with work in special-purpose software environments. The authors also indicate the aspectology of the process of cognitive activity in a virtual environment exclusively - they emphasize the role of learning to work with a specific type of software, which often have a complicated and single-tasking graphical interface. The result of software development is demonstrated. It provides an adaptive design of teaching in accordance with the type of problem to be solved on the part of the student, the optimization role of the complex of topics under consideration is indicated.

摘要： Inequality prevails in science. Individual inequality means that most perish quickly and only a few are successful, while gender inequality implies that there are differences in achievements for women and men. Using large-scale bibliographic data and following a computational approach, we study the evolution of individual and gender inequality for cohorts from 1970 to 2000 in the whole field of computer science as it grows and becomes a team-based science. We find that individual inequality in productivity (publications) increases over a scholar’s career but is historically invariant, while individual inequality in impact (citations), albeit larger, is stable across cohorts and careers. Gender inequality prevails regarding productivity, but there is no evidence for differences in impact. The Matthew Effect is shown to accumulate advantages to early achievements and to become stronger over the decades, indicating the rise of a "publish or perish" imperative. Only some authors manage to reap the benefits that publishing in teams promises. The Matthew Effect then amplifies initial differences and propagates the gender gap. Women continue to fall behind because they continue to be at a higher risk of dropping out for reasons that have nothing to do with early-career achievements or social support. Our findings suggest that mentoring programs for women to improve their social-networking skills can help to reduce gender inequality. © 2023, CC BY.

关键词： Prediction models  

摘要： After completing their undergraduate studies, many computer science (CS) students apply for competitive graduate programs in North America. Their long-term goal is often to be hired by one of the big five tech companies or to become a faculty member. Therefore, being aware of the role of admission criteria may help them choose the best path towards their goals. In this paper, we analyze the influence of students’ previous universities on their chances of being accepted to prestigious North American universities and returning to academia as professors in the future. Our findings demonstrate that the ranking of their prior universities is a significant factor in achieving their goals. We then illustrate that there is a bias in the undergraduate institutions of students admitted to the top 25 computer science programs. Finally, we employ machine learning models to forecast the success of professors at these universities. We achieved an RMSE of 7.85 for this prediction task. © 2023, CC BY.

关键词： Computer science   Machine learning   Technology  

摘要： Naturestaff take on the big topics that matter in science.