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关键词： Education   Employment   Computer science   Accreditation   Technological innovation   Reviews   Ecosystems   Bridges   Vocabulary   Industries   Career paths   computing skills   employability   employment   innovation   interdisciplinary   lifelong learning   student assessment  

摘要： The purpose of this article is to share worked examples of skills profiling within the universal micro-credential framework, a novel approach designed to further the enablement of 21st century skills which are defined here as a generalized series of competencies gained by a learner that enable them to self-reflect, self-regulate and self-optimize their capabilities within highly emergent contexts. This approach improves interdisciplinary learning and assessment paths for computing education by providing personalized learning and assessment options and by enabling curricula to better accommodate both internal and external interdisciplinary content. Through these worked examples, this article addresses potential solutions to fundamental and persistent structural issues in today's learning-earning ecosystem. Most significant of these issues is what the authors refer to as a "capability-competency chasm," a term used to describe an enduring gap that creates problematic deficits for learners as they transition between education and employment. At the core of the universal micro-credential framework is skills profiling, which can leverage micro-credential pathways and badge catalogues, both within the context of computing education in particular and across higher education in general. Accounting for the complexity of implementing system-wide redesign in highly variable higher-education contexts, this article also briefly references the role of design patterns to enable such implementation. Collectively, these flexible credentialing innovations offer a globally innovative solution to systemic challenges, explaining both the importance of addressing such challenges and the opportunities arising from them.

关键词： Robustness   Training   Standards   Perturbation methods   Stars   Optimization   Computer science   Adversarial robustness   adversarial training   unrestricted adversarial examples   standard generalizability  

摘要： Adversarial training (AT) is widely considered as the most promising strategy to defend against adversarial attacks and has drawn increasing interest from researchers. However, the existing AT methods still suffer from two challenges. First, they are unable to handle unrestricted adversarial examples (UAEs), which are built from scratch, as opposed to restricted adversarial examples (RAEs), which are created by adding perturbations bound by an l(p) norm to observed examples. Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i.e., the accuracy on natural examples) because they make a tradeoff between them. To overcome these challenges, we propose a unique viewpoint that understands UAEs as imperceptibly perturbed unobserved examples. Also, we find that the tradeoff results from the separation of the distributions of adversarial examples and natural examples. Based on these ideas, we propose a novel AT approach called Provable Unrestricted Adversarial Training (PUAT), which can provide a target classifier with comprehensive adversarial robustness against both UAE and RAE, and simultaneously improve its standard generalizability. Particularly, PUAT utilizes partially labeled data to achieve effective UAE generation by accurately capturing the natural data distribution through a novel augmented triple-GAN. At the same time, PUAT extends the traditional AT by introducing the supervised loss of the target classifier into the adversarial loss and achieves the alignment between the UAE distribution, the natural data distribution, and the distribution learned by the classifier, with the collaboration of the augmented triple-GAN. Finally, the solid theoretical analysis and extensive experiments conducted on widely-used benchmarks demonstrate the superiority of PUAT.

关键词： Computer science education   Computational thinking   Elementary education   Integrated computing  

摘要： Objectives. Computer Science (CS) education has become increasingly prevalent in elementary schools because of multiple rationales, such as the importance of computational literacy and the growing demand for CS-related workforce preparation. As elementary CS standards continue to be adopted by many states, more examples and voices from the field may help educators understand what effective computational thinking (CT)/CS integration looks like in practice. Method. We employed a descriptive qualitative approach to study eight award-winning K-8 CS teachers and aimed to answer three research questions: (1) What were elementary CS teachers' rationales for CT/CS integration? (2) How do elementary CS teachers integrate CT/CS into their classroom practices? (3) What are the needs of CS teachers for meaningful CT/CS integration in elementary schools? Data were collected through interviews, a questionnaire, and artifacts and analyzed using thematic analysis. Findings. Our findings of elementary CS teachers' rationales for CT/CS integration encompassed its essential nature as a literacy skill, its potential to promote equity in education, and its alignment with standards across various disciplines. To support CT/CS integration at the elementary level, CS teachers described important aspects with examples from their instructional practices: (a) Real-world applications;(b) hands-on activities;(c) strategies to scaffold and guide student learning;(d) collaboration with classroom teachers;and (e) support from professional development (PD). Conclusion. The study results shared CS teachers' voices from practice and shed light on the urgent need for more support and PD opportunities for both homeroom teachers and CS teachers in elementary schools, as well as the crucial need for multiple levels of support within K-12 school systems. Continuous efforts in creating systematic and sustainable PD plans and supporting collaborative professional communities for teachers within the instruct

关键词： Quantum physics   Computer science   Information technology  

摘要： PsiQuantum has ambitious goals to build a useful quantum machine by late 2027 - and has raised more than US$1 billion to do it.

关键词： Computer science   Information entropy   Prediction   Inverse distance transform   String transformations  

摘要： Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework Phi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi$$\end{document} is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within Phi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi$$\end{document}, including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods. Thirty-two different greyscale raster images with varying resolutions and contents were used in the experiments. It was shown that Phi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi$$\end{document} reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in Phi\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Phi$$\end{document

摘要： The education system plays a crucial role in providing equal and unrestricted access to education for individuals throughout their lives, considering their interests, abilities, and needs. In today’s society, education is seen as a form of communication that involves a shared objective and guidance from teachers to help learners reach their goals. Cloud technologies are explored in this article as valuable tools in enhancing computer science education, along with examples of contemporary cloud-based services for educational purposes.

摘要： Development and scientific justification of the teaching methodology with the help of "perception map" - innovative pedagogical technology in the teaching of "Informatics" science, formation of skills and qualifications for application in educational practice. The use of "Mind maps" in the teaching of "informatics" serves to increase the interest of students and develop their creative thinking. This system, which has been developed and tested in practice, serves to improve the performance of students in the process of teaching "Informatics". The main proposals and recommendations presented in the thesis are aimed at improving the teaching of "Informatics" in secondary vocational education institutions, increasing the interest of students in the subject and their chosen profession, ultimately increasing the effectiveness of education, deeply contributing to society. Knowledgeable individuals and skilled professionals play an important role in delivery.

关键词： Materials properties  

摘要： Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be quantitatively characterized at the atomic scale. This greatly deepens our understanding of Nature and provides incredible insights supplementing experimental observations. Hitherto, a plethora of literature discusses the computational discoveries in studying glasses in which positional disorder is inherent in their configurations. Motivated by active research and knowledge sharing, we developed a data analysis library in Python for computational materials science research. We hope to help promote scientific progress and narrow some technical gaps for the wide communities. The toolkit mainly focuses on physical analyses of glassy properties from the open-source simulator LAMMPS. Nevertheless, the code design renders high flexibility, with functionalities extendable to other computational tools. The library provides data-driven insights for different subjects and can be incorporated into advanced machine-learning workflows. The scope of the data analysis methodologies applies not only to materials science but also to physics, chemistry, and beyond. Copyright © 2024, The Authors. All rights reserved.

关键词： Deterioration  

摘要： The geopolitical tensions between China and the US have dramatically reshaped the American scientific workforce's landscape. To gain a deeper understanding of this circumstance, this study selects the discipline of computer science as a representative case for empirical investigations, aiming to explore the current situation of US-based Chinese-descent computer science professors. One thousand and seventy-eight tenured or tenure-track professors of Chinese descent from the computer science departments of 108 prestigious US universities are profiled, in order to quantify their structure primarily along gender, schooling, and expertise lines. The findings presented in this paper suggest that China-US tensions have made it more difficult for the US higher education system to retain valuable computer science professors of Chinese descent, particularly those in their mid-to late career stages, and that nearly 50% of the existing professors have less than seven years of faculty experience. In addition, the deterioration in faculty retention varies across fields of research, education backgrounds, and gender groups. Specifically, among the professors we are concerned about, those who do not work on AI or Systems, those who lack study experience at US universities, and those who are women, are underrepresented, albeit in different forms and to varying degrees. In a nutshell, the focal professoriate has not only shrunk in size, as has been widely reported, but also lost some of its diversity in structure. This paper has policy implications for the mobility of scientific talent, especially in an era of geopolitical challenges. Copyright © 2024, The Authors. All rights reserved.

关键词： Crowdsourcing   Social networking (online)   Mobile computing   Recruitment   Computer science   Spatiotemporal phenomena   Predictive models   Faces   Sensors   Prediction algorithms   Mobile crowdsourcing   cold start problem   social networks   user retention  

摘要： Mobile crowdsourcing (MCS) has attracted widespread attention by recruiting users with mobile devices to collect crowdsourcing data. Existing research on MCS assumes that the platform has sufficient users. However, platforms in their early stages of development face the cold start problem, which can lead to their inability to grow or even result in bankruptcy. While some studies try to solve it by recruiting users through social networks to participate in crowdsourcing tasks, they only focus on how to recruit more users without addressing the issue of user retention. This can lead to an increasing proportion of users losing interest in the platform and dropping out and thus it fails to solve the cold start problem truly. In light of this, we present a task recommendation-based method to recruit new users via the social network and keep registered users active on the platform. Specifically, we first use an extended independent cascade model to describe the recruitment of users through social networks. Secondly, we use a task acceptance model to describe user decisions. Finally, we utilize a fuzzy control system that incorporates spatiotemporal crowdsourcing information to predict user behaviour and recommend tasks to users most likely to complete them. Extensive experiments on large-scale real datasets were conducted to evaluate the proposed solution. The results indicate that compared to existing methods such as SocialRecruiter, our solution reduces the 30-day average user churn rate by 23.90% while significantly boosting user retention and task completion rates by up to 23.73% and 48.7%, respectively.