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关键词： Computer Science   Interest   Robotics   Teaching   Preferences  

摘要： Digital literacy is considered to be crucial for social and professional participation. Hence, several projects have been launched in school, as well as extracurricular activities to promote digital literacy in middle school. They aim, among other things, to increase interest in the so-called STEM subjects (science, technology, engineering, and mathematics), which include computer science. Interest in a topic is described in the literature as a crucial factor for learning success and career choice. Still, there is a potential for current research on the expression and distribution of interest in computer science. Therefore, this study aims to address this research gap by examining interest in computer science. The exploration of interest is expanded to include gender aspects and pupils' dispositions. Given the significantly lower number of women studying in STEM fields, there is a particular focus on an improved understanding of the interests of female pupils as early as middle school. Moreover, the literature suggests that pupils' dispositions should also be considered in instructional design to achieve stable interest. In this context, the following two dispositions shine out in literature and are therefore focused on in this article: first, interest in contextual, people-related topics (mainly found among women), and second, interest in factual, object-related topics (mainly found among men). In summary, this study investigates how pupils' interest in computer science is expressed before and after a programming intervention and how it is distributed across gender and dispositions. Therefore, the study surveyed 8th-grade pupils (N = 114) about their preferences and assessed their interest in computer science before and after an intervention with unplugged materials and playful robots. Results show that all groups of pupils find computer science interesting, and the intervention slightly increased interest. Girls show a marginally lower interest in computer science

关键词： Affective regret   Cognitive regret   Pro-environmental behavior   Environmental destruction   Negative emotion  

摘要： Regret is experienced when one imagines "what might have been". Although a familiar emotion to many, it has seldom been examined in the context of environmental destruction. We conducted three studies to examine the affective (negative affect in response to destruction) and cognitive (counterfactual thoughts of how things could be different) experiences of regret, and their roles in promoting pro-environmental behavior. Study 1 (N = 235) was a cross-sectional study and it showed that participants reported higher cognitive regret than affective regret when reflecting upon human-caused environmental destruction. However, affective regret was the stronger predictor of pro-environmental behaviors. Study 2 (N = 257) was a between-subjects experiment where we manipulated information exposure by presenting information about the loss of mangroves due to human causes or natural causes or presenting neutral information on mangrove species. Participants showed higher levels of both cognitive and affective regret in response to mangrove destruction (vs. neutral mangrove information), particularly when it was attributed to human actions rather than forces of nature. Both types of regret were found to mediate the effects of condition (human-caused vs. control) on pro-environmental behaviors, but affective regret was the more consistent mediator. Study 3 (N = 393) was a between-subjects experiment which manipulated regret focus through a writing task to elicit affective, cognitive, or no regret. Results showed that proenvironmental behavioral tendencies were highest in the affective regret condition, followed by cognitive regret condition, and lowest in the control condition, although these differences were not statistically significant. In summary, this research found that although people tend to show stronger cognitive regret than affective regret about environmental destruction, it is affective regret that more strongly predicts pro-environmental behaviors. Our findings highlig

关键词： IEEE Computer Society   Machine Learning   Computer Science   Interoperability   Global Community   Computer Technology   Dependability   Young Professionals   Daily Job   Artificial Intelligence Learning   Synopsys   Young Women   Diversity And Inclusion   IEEE Standard  

摘要： As the 2024 President of the IEEE Computer Society, Jyotika Athavale's passion for diversity, equity, and inclusion and inspiration for students and young professionals is evident in the countless initiatives that she spearheads across the global engineering community.

关键词： Plasticity  

摘要： The study of how infants learn to speak and acquire languages is central to our understanding of human development as well as important to teachers and new parents. Janet Werker's research has been instrumental in demonstrating how infants come prepared to attend to the sounds that compose the languages of the world, and how exposure to the languages that surround them hones their perceptual abilities over the first years of life. This critical period of plasticity can shift, depending on biological and environmental circumstances. Dr. Werker has also shown how the ability to make sounds is tied to the ability to decode linguistically relevant sounds. Her research has wide implications for understanding language development, plasticity, and critical periods, as well as applied implications for bilingualism and a variety of clinical conditions.

关键词： Task analysis   Noise reduction   Diffusion models   Diffusion processes   Training   Computer science   Trajectory   Diffusion models   image translation   deep learning   generative models  

摘要： Applying diffusion models to image-to-image translation (I2I) has recently received increasing attention due to its practical applications. Previous attempts inject information from the source image into each denoising step for an iterative refinement, thus resulting in a time-consuming implementation. We propose an efficient method that equips a diffusion model with a lightweight translator, dubbed a Diffusion Model Translator (DMT), to accomplish I2I. Specifically, we first offer theoretical justification that in employing the pioneering DDPM work for the I2I task, it is both feasible and sufficient to transfer the distribution from one domain to another only at some intermediate step. We further observe that the translation performance highly depends on the chosen timestep for domain transfer, and therefore propose a practical strategy to automatically select an appropriate timestep for a given task. We evaluate our approach on a range of I2I applications, including image stylization, image colorization, segmentation to image, and sketch to image, to validate its efficacy and general utility. The comparisons show that our DMT surpasses existing methods in both quality and efficiency. Code is available at https://***/THU-LYJ-Lab/dmt.

关键词： Electronic mail   Time complexity   Reviews   Optimization   Labeling   Cyberspace   Computer science   Bitmap signature   bitwise operation   generic set intersection   set intersection  

摘要： Set intersection is a fundamental operation in many applications, such as common neighbor computation in graph-based algorithms, set similarity computation, item recommendation, etc. In the literature, many set intersection methods are proposed. We observe that the state-of-the-art algorithm RCode bears several limitations, such as high index time complexity, inefficient for large-sized sets, and not friendly to the generic set intersection. In this paper, we introduce the B ucket S ig nature for Set ( BigSet ), an efficient generic set intersection algorithm. BigSet consists of two phases, namely the preprocessing phase and the query phase. In the preprocessing phase, BigSet partitions the elements of a record into O(2(k)) buckets and uses a bitmap to indicate the status of the buckets where n is the record length and k is the number of bits in the signature. In the query phase, BigSet calculates the results using a candidate generating-and-verification framework. Specifically, a set of candidate elements is identified as those falling in the same buckets. Then, for each bucket, BigSet collects the common elements using a merge-based method. To improve the performance, we introduce two optimizations, including bucket sharing and size-aware signature construction techniques. We conduct experiments on 10 real graph datasets and 5 real generic set datasets to evaluate the performance of our proposals. The experiment results show that BigSet is 20x faster than the leading generic set intersection algorithms. Besides it outperforms the RCode with 5x speedup, and while uses up to 8x less memory.

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.