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摘要： In search of more efficient data sharing.

ISBN: (纸本)9787111716020

关键词： 互动仪式链   情感能量   纪录片   《人生第一次》  

摘要： 随着新媒介的发展,过去以长篇大论为主的电视纪录片已经逐渐变成以快节奏为主的纪录片,并且在拍摄题材上的选择上也越来越注重与时事热点相结合,且“弹幕”技术正深刻地影响着电视节目与受众之间的互动关系。故本文以纪录片《人生第一次》为主要研究对象,借助柯林斯的互动仪式理论,对纪录片弹幕进行分析,探求互动方式对纪录片的影响和对观众的吸引力。本文主要从下面四个方面展开。首先依据互动仪式链理论,分析纪录片《人生第一次》中所形成互动仪式链的基础要素,包括B站平台实现双方虚拟在场、限制局外人参与、情感连接产生关注焦点和多样互动方式助推情感共享四个方面。再者,对纪录片《人生第一次》互动过程进行探讨,阐述该纪录片中受众如何参与仪式,从弹幕词频及字幕词云角度对受众共同关注焦点分析情感能量如何产生并积蓄,并促成身份符号认同。最后,对每集弹幕词频统计分析,从互动结果出发,先对纪录片《人生第一次》的互动传播特性进行分析,包括传播形式、发展态势、内容要求、表现特征以及反馈路径五个方面,再对从群体符号的生成、情感能量的获得、人生价值观的传递三个方面进行分析纪录片的互动仪式结果。笔者认为观众通过B站获得准入机制,限制局外人参与进来,通过发送弹幕进入到互动仪式中,在纪录片视听语言和叙事方式的推动下,受众在互动中产生情感波动,并通过和其他参与互动者进行话语交流,以及有节奏的情感连带,找寻情感同频的共享者,在这个过程中共享情感积蓄能量并完成互动仪式。这是一次成功的互动仪式,且通过研究发现互动对纪录片的传播发展有极大影响力,故在新媒体环境下关于纪录片的创作更要紧跟时代步伐,更新新媒体时代纪录片的内容选择、创新传播路径,借助多种互动方式促进纪录片的传播发展。

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.

关键词： Students  

摘要： In 2001, the Wyoming Supreme Court ruled in State v. Campbell County that every Wyoming K-12 student has constitutional protection for a fair, complete, and equal education appropriate for the times. This protection results in the universal availability of a "basket of goods," or a set of educational deliverables guaranteed to each student independent of the resources of individual school districts. In 2019, an executive action by Governor Matt Mead added to the deliverables "the use and understanding of computer science." His executive action initiated a system in which universal computer science became available to every Wyoming K-12 student by the 2022–2023 school year. The purpose of this paper is to use archival research methods through the lens of systems theory to explore the process of conceiving and implementing universal K-12 computer science education in Wyoming by considering the research question: How does systems theory provide a model for understanding Wyoming’s universal K-12 computer science education delivery? Systems theory has been used to study organizational components and to analyze engineering systems since the 1950s, and a review of the literature indicates that many social systems can be understood through systems theory. However, using systems theory to study educational systems has not been studied in a comprehensive manner, resulting in a literature gap. This study analyzes how Wyoming’s universal K-12 computer science delivery acts as a system. This paper provides recommendations from existing conditions to full availability, based on autopoiesis (the ability of a system to maintain itself) and summum bonum, or the ultimate good. © 2023 American Society for Engineering Education. All rights reserved.

关键词： Protocols   Wireless sensor networks   Probabilistic logic   Schedules   Low latency communication   Wireless communication   Computer science   Asynchronous   energy-efficient   neighbor discovery protocol   proactive   wireless sensor network  

摘要： Mobile Low-Duty-Cycle Wireless Sensor Network (MLDC-WSN) is a new type of wireless multi-hop network composed of sensor nodes that have mobility and sleep for a long time in a wireless self-organizing manner. Neighbor discovery protocols are used for sensor nodes to discover neighbor nodes within their communication range and can provide neighbor information to other protocols. MLDC-WSN uses low duty cycle technology to reduce energy consumption, and captures dynamic environmental information through the movement of nodes. However, the random mobility and long dormancy of nodes lead to frequent changes in the network topology, and nodes need to consume a lot of energy and time to discover all their neighbors. The sensor nodes are often powered by energy-limited batteries, and battery replacement is difficult. How to implement efficient neighbor discovery with low-latency and low-energy is a challenge of current research. Different from the previous slot model that couples the listening and transmitting of beacons in one slot, this paper proposes a new discovery schedule based on the slot model that separates the listening and transmitting of beacons. The discovery schedule can guarantee unidirectional discovery. And the node proactively transmits the beacon to accelerate bidirectional discovery after unilaterally discovering its neighbor nodes. On this basis, a proactive asynchronous neighbor discovery protocol called Fedab is proposed in this paper. Ignoring beacon collisions and other realistic interference factors, Fedab can enable neighbor discovery within the theoretical worst-case discovery latency. Moreover, Fedab is extended to the scenario where nodes perform different duty cycles, i.e., asymmetric scenarios. After experimental simulations and numerical analysis, the Fedab protocol has better performance compared to other existing neighbor discovery protocols.

ISBN: (数字)9783111081670;9783111082011 ISBN: (纸本)9783111080567

摘要： This book analyzes the generation of the arrow-categories of a given category, which is a foundational and distinguishable Category Theory phenomena, in analogy to the foundational role of sets in the traditional set-based Mathematics, for defi nition of natural numbers as well. This inductive transformation of a category into the infinite hierarchy of the arrowcategories is extended to the functors and natural transformations. The author considers invariant categorial properties (the symmetries) under such inductive transformations. The book focuses in particular on Global symmetry (invariance of adjunctions) and Internal symmetries between arrows and objects in a category (in analogy to Field Theories like Quantum Mechanics and General Relativity). The second part of the book is dedicated to more advanced applications of Internal symmetry to Computer Science: for Intuitionistic Logic, Untyped Lambda Calculus with Fixpoint Operators, Labeled Transition Systems in Process Algebras and Modal logics as well as Data Integration Theory.

关键词： Dimensionality reduction   Principal component analysis   Image reconstruction   Decoding   Computer science   Time series analysis   Discrete wavelet transforms   Time-series data   dimensionality reduction   high-dimensional data   machine learning  

摘要： Data analysis in modern times involves working with large volumes of data, including time-series data. This type of data is characterized by its high dimensionality, enormous volume, and the presence of both noise and redundant features. However, the "curse of dimensionality" often causes issues for learning approaches, which can fail to capture the temporal dependencies present in time-series data. To address this problem, it is essential to reduce dimensionality while preserving the intrinsic properties of temporal dependencies. This will help to avoid lower learning and predictive performances. This study presents twelve different dimensionality reduction algorithms that are specifically suited for working with time-series data and fall into different categories, such as supervision, linearity, time and memory complexity, hyper-parameters, and drawbacks.