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关键词： Students  

摘要： Debugging-finding and fixing bugs in code-is a heterogeneous process that shapes novice learners' self-beliefs and motivation in computing. Our Debugging by Design intervention (DbD) provocatively puts students in control over bugs by having them collaborate on designing creative buggy projects during an electronic textiles unit in an introductory computing course. We implemented DbD virtually in eight classrooms with two teachers in public schools with historically marginalized populations, using a quasi-experimental design. Data from this study included post-activity results from a validated survey instrument (N=144). For all students, project completion correlated with increased computer science creative expression and e-textiles coding self-efficacy. In the comparison classes, project completion correlated with reduced programming anxiety, problem-solving competency beliefs, and programming self-concept. In DbD classes, project completion is uniquely correlated with increased fascination with design and programming growth mindset. In the discussion, we consider the relative benefits of DbD versus other open-ended projects. © 2023, CC BY-NC-ND.

关键词： Computer science   Software   Optoelectronic devices and components  

摘要： This research proposes a deep-learning paradigm, termed functional learning (FL), to physically train a loose neuron array, a group of non-handcrafted, non-differentiable, and loosely connected physical neurons whose connections and gradients are beyond explicit expression. The paradigm targets training non-differentiable hardware, and therefore solves many interdisciplinary challenges at once: the precise modeling and control of high-dimensional systems, the on-site calibration of multimodal hardware imperfectness, and the end-to-end training of non-differentiable and modeless physical neurons through implicit gradient propagation. It offers a methodology to build hardware without handcrafted design, strict fabrication, and precise assembling, thus forging paths for hardware design, chip manufacturing, physical neuron training, and system control. In addition, the functional learning paradigm is numerically and physically verified with an original light field neural network (LFNN). It realizes a programmable incoherent optical neural network, a well-known challenge that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space. As a promising supplement to existing power- and bandwidth-constrained digital neural networks, light field neural network has various potential applications: brain-inspired optical computation, high-bandwidth power-efficient neural network inference, and light-speed programmable lens/displays/detectors that operate in visible light.

关键词： Brain   Careers   Computer science   Machine learning   Neuroscience  

关键词： 循环水养殖   大口黑鲈   循环水池   养殖周期   养殖试验   工厂化   养殖效益   纯氧  

摘要： 当前工厂化循环水养殖大口黑鲈存在循环水池流速慢、养殖中后期溶氧低、病害多等问题,为解决以上问题,笔者探索了一种工厂化纯氧循环水养殖大口黑鲈新模式,并开展了养殖试验。试验表明,在合适的流速和稳定的高溶氧环境内,大口黑鲈养殖周期缩短,全程没有病害发生,养殖效益显著。

关键词： 成果导向   高职院校   计算机基础课程   教育教学  

摘要： 伴随着经济的快速发展和社会的优化转型,计算机已经应用到各行各业,成为了生产与生活不可缺少的工具,这对各高校计算机知识的教育提出了更高的要求。但高职院校的学生由于知识储备薄弱、学习情况差异大和自主学习能力不强等问题,使高职计算机基础课程的教育教学面临挑战。培养实用型的计算机人才一直是高职院校的重要目标,针对当前高职院校计算机基础课程教学面临的困境,可以采用成果导向教学方法,围绕高职学生预期学习成果来制定教学方案,通过设计翻转课堂提升学生的课堂参与度,开发微课丰富教学资源,搭建软硬件平台实现分层次教学,最后通过多维度的评量来检验教学效果,使高职计算机基础课程教学质量得以提升,有效增强学生的学习效果。

关键词： 文明的观点   哲学思维方式   内在性平面   多元哲学   生活世界  

摘要： 哲学是文明的活的灵魂。从文明的观点看,某种形态的文明是特定哲学的本体和前提,而某种哲学观点的思维方式是特定文明形态的理论自觉和贯通文明全体的灵魂。如果把基于文明多样性的多元观点的哲学思维方式看作人类最精致、最珍贵的文明成果,那么多元观点的哲学就都具有永恒的价值和意义。以多元观点的哲学思维方式作为多样文明的理论自觉,可以使我们在多样文明及其不同生活世界的交流、交往中把握哲学对话的界限和意义。因此,以构建人类命运共同体和人类文明新形态为现实的生活世界基础,以马克思主义哲学实践观点、文明观点的思维方式作为主要的内在性平面,在真诚的哲学对话中,用新的概念范畴、新的思维方式切入生活世界,就会产生新的文明观念和新的文明行为方式,从而带来世界的改变。

关键词： 计算机科学   创新策略   网络环境   信息安全  

摘要： 阐述计算机科学与技术在现代化应用中的作用、存在的问题、创新策略,包括构建完善的计算机科学与技术体系、重视工程技术人员综合能力和实践经验的提升、加强计算机网络环境和信息安全的保障措施。

关键词： Convolution   Weight measurement   Social networking (online)   Laplace equations   Indexes   Data mining   Computer science   Graph convolutional networks   vertex centrality   network analysis   graph learning   representation learning  

摘要： Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines.

关键词： Probabilistic logic   Hash functions   Data models   Computer science   Big Data   Art   Approximation algorithms   Data stream   sliding window   sketch   approximate query  

摘要： Data stream processing has become a hot issue in recent years. There are three fundamental stream processing tasks: membership query, frequency query, and Top-K query. While most existing solutions address these queries in fixed windows, this paper focuses on a more challenging task: answering these queries in sliding windows. While most existing solutions address different kinds of queries by using different algorithms, this paper focuses on a generic framework. In this paper, we propose a generic framework, namely the Sliding sketch, which can be applied to many existing solutions for the above three queries, and enable them to support queries in sliding windows. We apply our framework to five state-of-the-art sketches for the above three kinds of queries. Theoretical analysis and extensive experimental results show that the accuracy of existing sketches that do not support sliding windows becomes much higher than the corresponding prior art after using our framework. We released all the source code at Github.