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关键词： Computer science education   Broadening participation   Equity   Gender   Stereotypes  

摘要： While a growing emphasis has been placed on broadening participation in computer science (CS) education, an enduring gender gap exists. One reason for this is gender-based CS stereotypes, which serve as gatekeepers and act in exclusionary ways. However, some high schools in the U.S. have still built gender-inclusive CS programs. We conducted a 3-month long case study at one such school (Forest View High School, FVHS, pseudonym) to better understand existing stereotypes. Data sources included student and alumnae interviews, student reflections, and teacher interviews. Constant comparative analysis was used to uncover emergent themes. Overall, gender-based stereotypes did not appear to be present, potentially as a result of mitigating strategies like access to women role models and recognition of the importance of a growth mindset. However, nerd-genius stereotypes were common. Despite a focus on nerd-genius stereotypes, students also held perceptions that computer scientists could be any type of person.

关键词： Image Net   Family Crisis   High School   Mentors   Computer Science   Private Firms   Private Interests   Sabbatical  

摘要： Fei-Fei Li is known for leading the development of ImageNet, which helped catalyze machine learning approaches to vision recognition, and for being an essential voice shaping the science behind artificial intelligence (AI) today. She offers an extended, thoughtful, and heartfelt memoir in this book.1 Beautifully written and grounded in many rich, thought-provoking observations, the book describes her journey from being a child immigrant from China to her present position as a Stanford professor.

关键词： Computational Thinking   Computer Science   Engineering Education   primary education   secondary education  

摘要： This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lacking digital skills. The study sought to answer several research questions, including the impact of online versus in-person teaching on preuniversity students and gender differences in Computer Science perception, and Computational Thinking skills performance. The study employed a four-phase methodology, consisting of pre- and posttraining measurements of Computer Science perception and Computational Thinking skills development through specific activities delivered in-person or online. The results indicate that in-person training is more effective for developing Computational Thinking skills, particularly at the secondary education level. Furthermore, there is a need to focus on maintaining girls' interest in Computer Science during primary school, as interest levels tend to decline significantly in secondary school. These findings have significant implications for Engineering Education in the context of digital transformation and the increasing importance of Computational Thinking skills in various fields of engineering. This study highlights the importance of developing Computational Thinking skills among preuniversity students and the need for effective training methods to achieve this goal and underscore the significance of investing in Engineering Education to prepare the next generation of engineers for the rapidly changing digital landscape.

关键词： Innovative technologies   Educational, training and supervision  

摘要： Outcomes1. Comprehension - Participants will demonstrate an understanding of how SOPHIE addresses the challenges of traditional communication workshops and its potential to enhance clinician-patient interactions. 2. Application - Participants will be able to assess the research design used in the study and evaluate its appropriateness for investigating the effectiveness of communication training tools. Key MessageSOPHIE is a web-based digital tool designed for healthcare professionals to practice and improve communication skills with realistic, diverse virtual patients. Our results show that SOPHIE training improved key communication skills. It is scalable and customizable, potentially improving availability of high-quality communication training worldwide. ImportanceEffective clinician-patient communication is vital for improving care for patients with serious illness, however, traditional training is often costly, challenging to scale, and logistically complex [1 2, 3, 4, 5, 6]. Objective(s)To evaluate whether a virtual, interactive, AI-augmented communication training program can provide an effective, easily accessible alternative for improving end-of-life communication [7, 8, 9, 10]. Scientific Methods UtilizedWe conducted a randomized, controlled pilot study to determine whether SOPHIE (Standardized Online Patient for Health Interaction Education), a digital tool using an on-screen “virtual patient" with automated, personalized feedback, can improve end-of-life communication skills. Participants (N=30) included 12 medical students, 9 nurses, 4 residents, 2 physician assistants, 2 psychologists, and 1 chaplain who were randomly assigned to Control (no training) or training using SOPHIE. Feedback criteria and outcome measures using human standardized patients included validated measures of end-of-life communication skills (e.g., Empathize, be Explicit, and Empower) based on the Medical Situation, Values, Plan (MVP) model [11]. ResultsSOPHIE participants outperfor

关键词： Ensemble learning   Reliability   Clustering methods   Task analysis   Data privacy   Computer science   Urban areas   Clustering ensemble   incomplete data  

摘要： Clustering ensemble often provides robust and stable results without accessing original features of data, and thus has been widely studied. The conventional clustering ensemble methods often take the full multiple base partitions as inputs and provide a consensus clustering result. However, in many real-world applications, full base partitions are hard to obtain because some data may be missing in some base partitions. To tackle this problem, in this paper, we propose a novel partial clustering ensemble method, which takes the partial multiple base partitions as inputs. In this method, we simultaneously fill the missing values in the base partitions and ensemble them by fully considering the consensus and diversity. Moreover, to address the unreliability issue in the partial data scenario, we seamlessly plug it into a self-paced learning framework. The extensive experiments on benchmark data sets demonstrate the effectiveness and efficiency of the proposed method when handling incomplete data.

关键词： Prediction algorithms   Task analysis   Graph neural networks   Electronic mail   Message passing   Approximation algorithms   Computer science   Graph neural networks (GNNs)   hashing   message passing (MP)   network embedding  

摘要： Given a network, it is well recognized that attributed network embedding represents each node of the network in a low-dimensional space, and, thus, brings considerable benefits for numerous graph mining tasks. In practice, a diverse set of graph tasks can be processed efficiently via the compact representation that preserves content and structure information. The majority of attributed network embedding approaches, especially, the graph neural network (GNN) algorithms, are substantially costly in either time or space due to the expensive learning process, while the randomized hashing technique, locality-sensitive hashing (LSH), which does not need learning, can speedup the embedding process at the expense of losing some accuracy. In this article, we propose the MPSketch model, which bridges the performance gap between the GNN framework and the LSH framework by adopting the LSH technique to pass messages and capture high-order proximity in a larger aggregated information pool from the neighborhood. The extensive experimental results confirm that in node classification and link prediction, the proposed MPSketch algorithm enjoys performance comparable to the state-of-the-art learning-based algorithms and outperforms the existing LSH algorithms, while running faster than the GNN algorithms by 3-4 orders of magnitude. More precisely, MPSketch runs 2121, 1167, and 1155 times faster than GraphSAGE, GraphZoom, and FATNet on average, respectively.

关键词： Computer science   Internet   Artificial intelligence  

摘要： Governing artificial intelligence involves multidisciplinary work across different fields to ensure human control over technology. This article aims to show how computer science can contribute to governance of responsible artificial intelligence technologies and applications.

关键词： Computational modeling   Processor scheduling   Training   Electronic mail   Computer science   Bipartite graph   Analytical models   Multi-server job   online gradient ascent   online scheduling   regret analysis  

摘要： Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal communication overhead, apart from the resource contention between jobs. To study the computation-communication tradeoff, we model the computation gain as the speedup on the job completion time when it is executed in parallelism on multiple computing instances, and fit it with utilities of different concavities. Meanwhile, we take the dominant communication overhead as the penalty to be subtracted. To achieve a better gain-overhead tradeoff, we formulate an cumulative reward maximization program and design an online algorithm, named OgaSched, to schedule multi-server jobs. OgaSched allocates the multi-type resources to each arrived job in the ascending direction of the reward gradients. It has several parallel sub-procedures to accelerate its computation, which greatly reduces the complexity. We proved that it has a sublinear regret with general concave rewards. We also conduct extensive trace-driven simulations to validate the performance of OgaSched. The results demonstrate that OgaSched outperforms widely used heuristics by 11.33%, 7.75%, 13.89%, and 13.44%, respectively.

关键词： Application programs  

摘要： This paper first presents the NSFC applications and grants of the General Program, Young Scientists Fund and Fund for Less Developed Regions, as well as the Major Program, Excellent Young Scientists Fund, and Distinguished Young Scholars Fund in computer science (F02) in 2023. Then, we analyze the funding situations of universities in computer science in 2023. Finally, the evaluation principles and measures of fund project for computer science are introduced. Meanwhile, the development prospects of computer science in the future are also summarized. © 2024 Science Press. All rights reserved.