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关键词： Industrial Internet of Things   Security   Informatics   Computer science   Privacy   Feature extraction   Image edge detection  

关键词： information technologies   computer science education   COVID-19   distance learning   evaluation   fuzzy logic  

摘要： In this paper, a fuzzy-based evaluation method is presented for the impact of e-learning on several aspects of the learning lives of academic students of Information and Communication Technologies (ICT) during the COVID-19 pandemic. Specifically, the academic year 2020-2021 was considered when a general lockdown was imposed in Greece and all courses were delivered exclusively through the web. The evaluation criteria are (i) e-learning acceptance, (ii) learning effectiveness, (iii) students' engagement and (iv) socializing and interpersonal relationships in the educational community. The evaluation was conducted through questionnaires. Ninety two (92) undergraduate and postgraduate students and Ten (10) computer science in-structors of the department of Informatics of the University of Piraeus, Greece, participated in this survey. The questionnaire answers were analyzed using a fuzzy-based mechanism. Particularly, five fuzzy sets were used to describe the 5-point Likert scale answers to the questionnaires with linguistic values, and four other fuzzy sets were used for the description of the evaluation results concerning the four defined evaluation criteria. Moreover, 209 fuzzy rules were created to analyze and calculate the evaluation results per criterion, considering both the students' answers (119 fuzzy rules) and the instructors' answers (90 fuzzy rules) to the questionnaires. The gain of this approach is that the analysis of evaluation data with fuzzy rules imitates the human way of thinking and, thus, makes the process more explainable. The evaluation results showed a positive impact of e-learning on student confidence, self-discipline and active participation and a negative impact on student interpersonal relationships. The impact of e-learning on other learning issues was neutral.

关键词： Neural networks   Degradation   Kernel   Computer science   Task analysis   Semantics   Principal component analysis   Discriminability   domain adaptation (DA)   inter-class distance   intra-class distance   maximum mean discrepancy (MMD)  

摘要： Existing domain adaptation approaches often try to reduce distribution difference between source and target domains and respect domain-specific discriminative structures by some distribution [e.g., maximum mean discrepancy (MMD)] and discriminative distances (e.g., intra-class and inter-class distances). However, they usually consider these losses together and trade off their relative importance by estimating parameters empirically. It is still under insufficient exploration so far to deeply study their relationships to each other so that we cannot manipulate them correctly and the model's performance degrades. To this end, this article theoretically proves two essential facts: 1) minimizing MMD equals to jointly minimizing their data variance with some implicit weights but, respectively, maximizing the source and target intra-class distances so that feature discriminability degrades and 2) the relationship between intra-class and inter-class distances is as one falls and another rises. Based on this, we propose a novel discriminative MMD with two parallel strategies to correctly restrain the degradation of feature discriminability or the expansion of intra-class distance;specifically: 1) we directly impose a tradeoff parameter on the intra-class distance that is implicit in the MMD according to 1) and 2) we reformulate the inter-class distance with special weights that are analogical to those implicit ones in the MMD and maximizing it can also lead to the intra-class distance falling according to 2). Notably, we do not consider the two strategies in one model due to 2). The experiments on several benchmark datasets not only prove the validity of our revealed theoretical results but also demonstrate that the proposed approach could perform better than some compared state-of-art methods substantially. Our preliminary MATLAB code will be available at https://***/WWLoveTransfer/.

摘要： In search of more efficient data sharing.

ISBN: (纸本)9787111716020

关键词： Computational thinking   Computer science integration   Enacted curriculum   K-12 teachers   TPACK-CT  

摘要： Integrating computational thinking (CT) and/or computer science (CS) into K-12 curricula has the potential to provide learners with early exposure to the possibilities of creation, engagement, and contributions within a technology-driven society. However, in order to weave CT and CS into their existing curricula and classrooms, K-12 teachers need training. While prior research has studied the design and immediate impact of computing-focused professional learning experiences for teachers (e.g., trainings, classes), there is little research on teachers’ translation of these experiences into their own classrooms. This comparative multi-case study addresses this gap and contributes to the fields of computer science education and teacher education as it explores three K-12 Minnesota educators’ enacted curriculum of integrating computing into their curricula and expands the research on CS/CT by illuminating teachers’ conceptualization(s) of computing as it relates to their classrooms. Utilizing the technological pedagogical content knowledge (TPACK) framework with the incorporation of computational thinking (TPACK-CT) as a lens and conceptual framework, this study highlights the pedagogical and technological strategies for how a K-5 STEM specialist, a fourth grade teacher, and a high school computer science teacher in Minnesota integrated CT/CS into content areas. To answer the research questions an inductive, constant comparative method and qualitative content analysis were used on the data (surveys, observations, interviews, and reflections) to construct three rich and detailed case portraits, followed by a cross-case analysis. Four themes (PD as a bridge for buy-in, integration, and permission; importance of conceptualizing CT for their classroom; (missing) connections in student-centered, collaborative, and balanced pedagogical strategies; and the (un)plugged fun and supportive role of technology) are discussed in an attempt to capture and make sense of the common thr

关键词： Computer Science   Engineering \u0026 Technology  

ISBN: (数字)9788770040204 ISBN: (纸本)9788770040211

摘要： This book demystifies how Kubernetes networking actually works by walking through a simple but practical simulation that mirrors reality as closely as possible, while skipping the gory details. Widespread adoption of container orchestration platforms like Kubernetes have spawned a whole field of industry products, startups and academic research in the field of container networking, typically termed as cloud-native networking. But, what is cloud-native networking? What are the various pieces and how do they fit together? Over recent years, most applications have been deployed on cloud infrastructure. Kubernetes has been the widely adopted orchestrator for these clouds. Application developers in most cases are unaware of the underlying plumbing in Kubernetes that holds their applications running as containers. Networking is an integral part of any Kubernetes environment and efficiently drives the various abstractions provided by it. Needless to say, it greatly effects the performance of applications, which in general have a humongous amount of inter-microservice communication. The impact is even more profound in multiple cloud environments.

关键词： Machine learning   3D computer vision   Agriculture   Meteorology  

摘要： Advancements in machine learning techniques and the availability of high-powered computing devices have led to an increase in the use of machine learning in various fields such as medical sciences, agriculture, and atmospheric sciences. However, with the growing volume of data, data preprocessing and analysis have become challenging and alterations to machine learning methods may be necessary to effectively analyze the data. This PhD dissertation explores machine learning and computer vision techniques that are adapted and customized to suit unique data in various fields such as plant sciences, meteorology, hydrology, and atmospheric sciences. The thesis is divided into three chapters, each focusing on a distinctive and innovative approach aimed at addressing the challenges encountered in three different projects. Two of the chapters revolve around the Phytooracle project that investigates the effects of water-stress treatments on phenotypic features of tens of thousands of plants with various genotypes, while the other project concerns meteorology, hydrology, and atmospheric sciences. In each chapter, specific challenges encountered are highlighted, and modifications made to the methods are discussed to fit the data and address the problems effectively. The dissertation describes the development of MegaStitch, a novel method for image stitching, geo-correction, and alignment, NowcastingNets, a set of neural network structures designed to reduce latency in precipitation prediction, and PlantSegNet, a new neural network model designed for instance segmentation of nearby plant organs in 3D point clouds. Through the domain problems, the thesis demonstrates how machine learning and computer vision techniques can be improved and modified to address challenges encountered in analyzing large-scale data using these techniques.