教学课程资源库 更多>>

关键词： Physical layer   Zigbee   Wireless fidelity   Adaptation models   Mobile computing   Wireless communication   Computer science   Deep learning   Internet of Vehicles   LoRa   physical layer key generation  

摘要： In recent years, the Internet of Vehicles (IoV) has experienced significant growth, but the lack of effective secret key establishment remains a security concern due to the dynamic and ad-hoc nature of IoV communications. Physical layer key generation has emerged as a promising solution for establishing a pair of cryptographic keys in a lightweight and information-theoretic secure manner. However, previous works have primarily focused on legacy communication technologies, such as Wi-Fi, ZigBee, and 5 G, which are limited to short-range IoV communications. With the emergence of Long-range (LoRa) communication technology, which features long-range, low power, and extremely low data rates, new challenges arise for key generation in long-range IoV scenarios. This paper presents Vehicle-Key, a secret key generation system designed to secure LoRa-enabled IoV communications. Vehicle-Key presents an innovative scenario adaptive deep learning model that performs channel prediction and quantization concurrently while reducing the training cost through a data augmentation pipeline and enhancing the model's generalization using a domain-adaption method. Additionally, we propose a bloom filter-assisted autoencoder-based reconciliation method to significantly improve the key agreement rate. Comprehensive real-world experiments show that Vehicle-Key surpasses the State-of-the-Art, achieving a 15.26%-50.35% improvement in key agreement rate and a 9-15x increase in key generation rate. Moreover, the proposed method attains a 4.37-9.33% improvement when adapted to new scenarios with limited data sizes. A security analysis demonstrates that Vehicle-Key is resilient against several common attacks. Furthermore, we implement Vehicle-Key on a Raspberry Pi and demonstrate its ability to execute within 3.5 ms.

关键词： belonging   CSCL   gender   gender identity   hackathon   self-esteem  

摘要： Background: Gender stereotypes about women and men are prevalent in computer science (CS). The study's goal was to investigate the role of gender bias in computer-supported collaborative learning (CSCL) in a CS context by elaborating on gendered experiences in the perception of individual and team performance in mixed-gender teams in a ***: The dataset of this study was collected at a 3-day CSCL hackathon aimed at gaining knowledge on designing educational games. We assigned the 28 participants of the hackathon to mixed-gender groups and asked them to fill out a questionnaire, including collective self-esteem scales, before the start. During the hackathon, we again asked the participants to complete team progress evaluation surveys individually after each workday. Lastly, we interviewed 11 participants to elaborate on the quantitative findings with qualitative ***: We applied an exploratory mixed-method approach using quantitative survey data at several time points during the hackathon, which was analysed with clustering and descriptive statistics and complemented with qualitative coding of interviews with ***: The results demonstrate that social and psychological aspects of gender are important for understanding the outcomes and perceptions of gender in a CS hackathon. The analysis further suggests that collective self-esteem can be used as a key variable to assess gender differences in CSCL studies, providing explanatory benefits. More broadly, results gave reason to believe that CSCL in the CS domain currently severely fails to account for gender representation. Interviewed participants raised substantial concerns about the underlying gender stereotypes prevalent in communication, team roles, and work division. We provide recommendations for practitioners seeking to create gender-inclusive and counter-stereotypical CSCL and wider, critical proposals for how we, as researchers, can assess gender with appropriate methodol

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： Conceptions   Internet   Teachers   Children   Primary   Lower secondary   K-9   Teacher education   Conceptual change  

摘要： Objectives: Limited research exists on how new computer science (CS) teachers understand fundamental Internet principles. This knowledge gap hinders the development of effective upskilling courses, especially as new CS curricula are being introduced worldwide. This study investigates new CS teachers' conceptions of fundamental Internet principles, compares them with children's understanding, and examines the relationship between teachers' Internet knowledge and their knowledge of basic digital trace topics. Study Methods: A convergent mixed-methods approach was used, involving semi-structured interviews and drawing tasks with 50 new CS teachers. Data from a separate sample of 165 children (Grades 4, 6, 8) were included for comparison. A thematic analysis and frequency analysis of interview transcripts were conducted. Findings: Most new CS teachers recognized the Internet as a global network but had varied and often unstable conceptions about data storage and transfer. Compared to children, teachers demonstrated a more advanced understanding but still exhibited substantial knowledge gaps. There was no significant correlation between understanding Internet principles and basic digital trace topics, though a moderate relationship was found regarding news feed personalization. Conclusions: The study highlights the need for targeted interventions to improve new CS teachers' understanding of Internet principles, especially the invisible aspects, such as data storage and transfer mechanisms. By providing new data on a rarely studied group, this study contributes to both CS education and broader fields like developmental psychology and conceptual change, offering insights into the developmental trajectories of Internet comprehension and the integration of everyday and scientific knowledge.

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.