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关键词： COVID-19  

摘要： Prior to the COVID-19 pandemic, the adoption of live polling and real-time feedback tools gained traction in higher education to enhance student engagement and learning outcomes. Integrating live polling activities has been shown to boost attention, participation, and understanding of course materials. However, recent changes in learning behaviours due to the pandemic necessitate a reevaluation of these active learning technologies. In this context, our study focuses on the Computer Science (CS) domain, investigating the impact of Live Polling Quizzes (LPQs) in undergraduate CS lectures. These quizzes comprise fact-based, formally defined questions with clear answers, aiming to enhance engagement, learning outcomes, and overall perceptions of the course module. A survey was conducted among 70 undergraduate CS students, attending CS modules with and without LPQs. The results revealed that while LPQs contributed to higher attendance, other factors likely influenced attendance rates more significantly. LPQs were generally viewed positively, aiding comprehension and maintaining student attention and motivation. However, careful management of LPQ frequency is crucial to prevent overuse for some students and potential reduced motivation. Clear instructions for using the polling software were also highlighted as essential. © 2023, CC BY.

关键词： Health sciences   Computer science   Human-centered computing  

摘要： This study examined the influence of set-interval and repetition-interval rest time of virtual reality (VR) boxing game in supine-lying posture. Fifty healthy middle-aged adults were randomly assigned into VR and non-VR groups to perform six different exercise protocols with varying set-interval and repetition-interval rest times (S0R0, S0R1/3, S0R2/3, S40R0, S40R1/3, and S40R2/3). Analysis on the non-VR group showed significant differences between exercise protocols for average heart rate (p < 0.001), maximum ventilation volume (p < 0.001), respiratory quo-tient (p < 0.001), oxygen pulse (p < 0.001), and excess post-exercise oxygen con-sumption (EPOC) (p = 0.003). VR appeared to have no further improvement on physical training effects in middle-aged adults, while the participants reported negative experience that might be associated with the over-exertion. Future study might need to explore game design elements that can accommodate high-exertion exercises.

关键词： Mathematics and computing   Astronomy and astrophysics   Computer science  

摘要： From 1956, Granville wrote software for IBM, moving to its New York City office to undertake numerical analysis at the Data Processing Center of the Service Bureau Corporation (an IBM subsidiary). In 1960, she married and moved to Los Angeles, California, working on orbit calculations at Space Technology Laboratories, before joining North American Aviation in 1962 to provide technical support, numerical analysis and computing expertise to a team working on designs for the Apollo programme. In 1967, she accepted a position at California State University (Cal State), Los Angeles, teaching programming at a time when computer science was in its infancy and few Black people were in positions of authority in the field.

关键词： Computer science   Developing world   Energy access   Energy grids and networks   Geography  

摘要： Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R-2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.

关键词： Computer science   Network models  

摘要： The current gap between computing algorithms and neuromorphic hardware to emulate brains is an outstanding bottleneck in developing neural computing technologies. Aimone and Parekh discuss the possibility of bridging this gap using theoretical computing frameworks from a neuroscience perspective. Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain's functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the development of neural computing technologies such as neuromorphic hardware and artificial intelligence. Furthermore, we discuss how this balanced perspective may be necessary to incorporate the neurobiological details that are critical for describing the neural computational disruptions within mental health and neurological disorders.

关键词： Computational methods   Computer science   Dynamical systems   Electrical and electronic engineering   Electronic devices  

摘要： Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to engineer unconventional computing systems in a systematic way, we need guidance from a formal theory that is different from the classical symbolic-algorithmic Turing machine theory. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call fluent computing. In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in a physical computing system. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures. Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.

关键词： Curricula  

摘要： All prior curricular guidelines for computer science have used a knowledge model, which consists of knowledge areas, knowledge units within the knowledge areas, and learning outcomes for the topics within those knowledge units. More recently, competency models have been explored for curricular guidelines. A competency model consists of competency specifications that list the knowledge, skills and dispositions needed to complete tasks. Both knowledge models and competency models have their benefits and shortcomings. We propose a model for computer science curricular guidelines that synergistically combines knowledge and competency models, in particular, the knowledge model last proposed in CS2013 [1] and the CoLeaf competency model last proposed in an ITiCSE working group report [8,11], both modified to facilitate integration. The combined model called CKC emphasizes both ends of the learning continuum and facilitates teaching as well as evaluation. It provides both an epistemological and teleological perspective of computer science content. We provide instructions for designing computer science curricula using the CKC model. © 2023 Association for Computing Machinery. All rights reserved.

关键词： Commerce  

摘要： the rapid growth of technology and computer science, which has led to a surge in demand for skilled professionals in this field. The skill set required for computer science jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the market. In this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computer science job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computer science job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computer science job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices. © 2023, CC BY.

关键词： Computer science   Diagnostic markers   Stroke  

摘要： Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (= 87%) and negative predictive value (= 93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p = 0.003 each). We provide an imaging platform () for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.

关键词： Computer science   Human behaviour   Visual system  

摘要： Artificial neural networks (ANNs) are vulnerable to subtle adversarial perturbations that yield misclassification errors. Here, behavioral studies demonstrate that adversarial perturbations that fool ANNs similarly bias human choice. Although artificial neural networks (ANNs) were inspired by the brain, ANNs exhibit a brittleness not generally observed in human perception. One shortcoming of ANNs is their susceptibility to adversarial perturbations-subtle modulations of natural images that result in changes to classification decisions, such as confidently mislabelling an image of an elephant, initially classified correctly, as a clock. In contrast, a human observer might well dismiss the perturbations as an innocuous imaging artifact. This phenomenon may point to a fundamental difference between human and machine perception, but it drives one to ask whether human sensitivity to adversarial perturbations might be revealed with appropriate behavioral measures. Here, we find that adversarial perturbations that fool ANNs similarly bias human choice. We further show that the effect is more likely driven by higher-order statistics of natural images to which both humans and ANNs are sensitive, rather than by the detailed architecture of the ANN.