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关键词： Constraint satisfaction problems   Hrushovski-Fraïssé Classes   Omega-categoricity   Triangle-free graphs   Unary expansions  

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.

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： 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.

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.

关键词： Computer Science   Logic in Computer Science  

摘要： A linear inference is a valid inequality of Boolean algebra in which each variable occurs at most once on each side. In this work we leverage recently developed graphical representations of linear formulae to build an implementation that is capable of more efficiently searching for switch-medial -independent inferences. We use it to find four 'minimal' 8-variable independent inferences and also prove that no smaller ones exist;in contrast, a previous approach based directly on formulae reached computational limits already at 7 variables. Two of these new inferences derive some previously found independent linear inferences. The other two (which are dual) exhibit structure seemingly beyond the scope of previous approaches we are aware of;in particular, their existence contradicts a conjecture of Das and Strassburger. We were also able to identify 10 minimal 9-variable linear inferences independent of all the aforementioned inferences, comprising 5 dual pairs, and present applications of our implementation to recent 'graph logics'.

关键词： Social networking (online)   Mathematical models   Computer science   Predictive models   Market research   Behavioral sciences   Aging   Temporal bipartite networks   ranking   popularity prediction  

摘要： One of the big challenges of our modern life is to find the right items or contents on the Internet and particularly in social media. One way of addressing the information overload problem in social media is to predict the future trends and popularity of online items. The popularity of an item can be measured by its attractiveness, i.e., the number of times it is being used. This popularity prediction can be translated to a link prediction and ranking problem, which aims to predict the link gain of the items in a user-item interaction network. User-item interactions in an online environment can be modelled as a bipartite network, where a link represents an event, reflecting a user buys or collects an item. Popularity prediction problem in temporal bipartite networks is of great interest to researchers. In this study, we propose a heuristic based model which only consider nodes collective link gain in a recent past time window of time as well as total link gain. To evaluate our model's efficiency, we tested them on co-evolving social media items. We also evaluated the models' performance on five information retrieval metrics (i.e., Area Under the Receiver Operating Characteristic, Kendall's rank correlation tau, Precision, Novelty, and temporal novelty). The proposed model does not need hyper-parameter learning, which makes it the best choice for highly temporal and data streaming scenarios.

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关键词： Smart contracts   algorithmic stablecoin   financial stability   DeFi   cryptocurrency  

摘要： Computer science uses abstractions as a tool for reasoning. It is no surprise that computer science might have something valuable to lend to the world of decentralized stablecoin design, as it is a "computing" problem. In this paper, we examine the possibility of a decentralized and capital-efficient stablecoin using smart contracts that algorithmically trade to maintain stability. By exploiting traditional abstractions from computer science, we show that a capital-efficient algorithmic stablecoin cannot be provably stable. Additionally, we provide a formal exposition of the workings of Central Bank Digital Currencies, connecting this framing to the space of possible stablecoin designs. We then discuss several outstanding conjectures from both academics and practitioners and finally highlight the regulatory similarities between money-market funds and working stablecoins. Our work builds upon the current and growing interplay between the realms of engineering and financial services, and it also demonstrates how ways of thinking as a computer scientist can aid practitioners. We believe this research is vital for understanding and developing the future of financial technology.

关键词： Adaptation models   Training   Task analysis   Labeling   Computer science   Kernel   Feature extraction   Crowd counting   multi-head parallel network   need only one more point   perspective-adaptive  

摘要： Recently, solving the crowd counting problem under occlusion and complex perspective is a hot but difficult topic. Existing methods mainly constructed counters in parallel perspective, but when facing complex perspective, such as the influences of height difference and heavy occlusions, they fail to get good accuracy. To alleviate these problems, this work proposes a novel and interesting framework NOOMP (Need Only One More Point) for perspective adaptation crowd counting task in complex nature scenes. Firstly, this work considers that the common scenes in our daily life usually have the height difference, which brings complex perspective to crowd counting. So, a new labeled method, Absolute-geometry Gaussian Generation is proposed, which only needs one more point for each person in image and gets better accuracy. Secondly, the NOOMP framework consists of meta-learning structure and uses the few-shot way to train the counting model, which can implement the perspective adaptation effective and solve the problem of high label cost. Thirdly, for fitting the characteristic of few-shot learning, this work proposes a new Multi-head Parallel Network (MPNet) for NOOMP. The feature of crowd is extracted by MPNet, which is a hybrid structure composed of shallow network and deep network. This network can save the features of shallow network and the deeper network effectively, which makes MPNet performs well in NOOMP. In addition, this work collects a new dataset, named Multiple Height Differences in Mall (MHDM) for NOOMP, which contains images of different views and height differences from shopping malls and supermarkets. Experiments based on MHDM and other benchmarks show that the NOOMP has good performances in model accuracy and works well for solving perspective change problem.