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ISBN: (数字)9781838983505

摘要： A practical guide simplifying discrete math for curious minds and demonstrating its application in solving problems related to software development, computer algorithms, and data science

Key Features

• Apply the math of countable objects to practical problems in computer science
• Explore modern Python libraries such as scikit-learn, NumPy, and SciPy for performing mathematics
• Learn complex statistical and mathematical concepts with the help of hands-on examples and expert guidance

Book Description

Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you’ll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you’ll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.

What you will learn

• Understand the terminology and methods in discrete math and their usage in algorithms and data problems
• Use Boolean algebra in formal logic and elementary control structures
• Impleme

关键词： Computer science   Finance   Economics   Fingers   Veins   Indexing   Forensics  

摘要： In second paragraph of the footnote on the first page of [1] , the institution information of Lu Yang and Xiaoming Xi is inaccurate. The correct institution name is “School of Computer Science and Technology, Shandong University of Finance and Economics.” So this paragraph should be corrected as:

关键词： 多样性   理论计算机科学   算法   体外化   人类世  

关键词： Cryptocurrency   Blockchain   Smart contracts   Bitcoin   Security   Peer-to-peer computing   Computer science   Blockchain   cryptocurrency   exchange   Ethereum   smart contracts  

摘要： Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve secure trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on an Ethereum blockchain and deployed on an Ethereum test network. In addition to implementing transactions between individual users, our scheme also allows transactions among multiple users. The experimental result proves that the cost of our scheme is acceptable.

关键词： Delays   Estimation   Bayes methods   Smart cards   Logic gates   Computer science   Clustering algorithms   AFC   automatic fare collection   AVL   automatic vehicle location   Bayes statistics   machine learning   missing data   smart card   train logs  

摘要： Smart card data from the Automatic Fare Collecting systems (AFC) and timetable information, such as Automatic Vehicle Location (AVL), are used in combination by practitioners and researchers to gain a deeper understanding of the public transit network. In some cases, AVL data are not available due to records being missing in the system. In such cases, people resort to the used schedule timetable such as General Transit Feed Specification (GTFS) to match smart card data to the transit network. Since delays or changes to the timetable are not contained in the scheduled timetable, it can result in wrong matches between the smart card data and the transit network. This paper shows how the uncertainty of arrival and departure times affects passengers to train assignments and proposes a method for estimating the missing arrival time of trains when the recorded timetable information is not available. The method uses the knowledge of how the tap-outs are distributed in a hierarchical, latent Bayesian model to predict the arrival times of trains. Evaluated on 15,136 train arrivals, the model can infer 70% of the arrivals times with an average error of 28 to 32 seconds depending on the station.

摘要： Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learners' behavior and assessing learners' performance for personalization. Hands-on labs are a critical learning approach for cybersecurity education. It provides real-world complex problem scenarios and helps learners develop a deeper understanding of knowledge and concepts while solving real-world problems. But there are unique challenges when using hands-on labs for cybersecurity education. Existing hands-on lab exercises materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. To solve these challenges, a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment is established. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. A knowledge graph in the cybersecurity domain is also constructed using Natural language processing (NLP) technologies including word embedding and hyperlink-based concept mining. This knowledge graph is then utilized during the regular learning process to build a personalized lab recommendation system by suggesting relevant labs based on students' past learning history to maxim

摘要： From Counter-Memory to Counter-Culture: Black Islam in the U.S. Through a Mundane Afrofuturist Lens is a multi-generational look at moments of Mundane Afrofuturism within the lives of Black Muslims in the U.S., emphasizing the concepts of counter-memory and counter-culture as key cultural and political approaches toward subversion and innovation. Consisting of three artworks, the exhibition space is intended to act as an unveiling of information, recognizing Black folks’ historical and ongoing relationship to accessing knowledge.

关键词： Computer science   Robotics   Artificial intelligence   Robot hands--Design and construction   Tactile sensors  

摘要： Robotic grasping and manipulation remains an active area of research despite significant progress over the past decades. Many existing solutions still struggle to robustly handle difficult situations that a robot might encounter even in non-contrived settings. For example, grasping systems struggle when the object is not centrally located in the robot's workspace. Also, grasping in dynamic environments presents a unique set of challenges. A stable and feasible grasp can become infeasible as the object moves; this problem becomes pronounced when there are obstacles in the scene. This research is inspired by the observation that object-manipulation tasks like grasping, pick-and-place or insertion require different forms of awareness. These include reachability awareness -- being aware of regions that can be reached without self-collision or collision with surrounding objects; tactile awareness-- ability to feel and grasp objects just tight enough to prevent slippage or crushing the objects; and 3D awareness -- ability to perceive size and depth in ways that makes object manipulation possible. Humans use these capabilities to achieve a high level of coordination needed for object manipulation. In this work, we develop techniques that equip robots with similar sensitivities towards realizing a reliable and capable home-assistant robot. In this thesis we demonstrate the importance of reasoning about the robot's workspace to enable grasping systems handle more difficult settings such as picking up moving objects while avoiding surrounding obstacles. Our method encodes the notion of reachability and uses it to generate not just stable grasps but ones that are also achievable by the robot. This reachability-aware formulation effectively expands the useable workspace of the robot enabling the robot to pick up objects from difficult-to-reach locations. While recent vision-based grasping systems work reliably well achieving pickup success rate higher than 90% in cluttered scen

关键词： Computational methods   Computer science   Deep learning   Engineering science and mechanics   Sustainable energy   Wind engineering  

摘要： Large variations in wind energy production over a period of minutes to hours is a challenge for electricity balancing authorities. The use of reliable tools for the prediction of wind power and wind power ramp events is essential for the operator of the electrical system. The main objective of this work is to analyze the wind power and wind power ramp forecasting at Brazil and Uruguay. To achieve this goal the wavelet decomposition applying 48 different mother wavelet functions and deep learning techniques are used. The recurrent neural network was trained to perform the forecasting of 1 h ahead, and then, using it, the trained network was applied to recursively infer the forecasting for the next hours of the wind speed. After this computational procedure, the wind power and the wind power ramp were predicted. The results showed good accuracy and can be used as a tool to help national grid operators for the energy supply. The wavelet discrete Meyer family (dmey) demonstrates greater precision in the decomposition of the wind speed signals. Therefore, it is proven that the wavelet dmey is the most accurate in the decomposition of temporal wind signals, whether using signals from tropical or subtropical regions. • Nowcasting wind prediction in tropical and subtropical sites using AI and Wavelet. • An ANN approach for the estimation of wind power ramp using deep learning. • Modeling of wind using atmospheric factors in tropical and subtropical sites. • Wind power and wind power ramp forecasting applying 48 mother Wavelet functions. [ABSTRACT FROM AUTHOR]

摘要： A large part of science involves building and investigating models. One key feature of model-based science is that one thing is studied as a means of learning about some rather different thing. How scientists make inferences from a model to the world, then, is a topic of great interest to philosophers of science. An increasing number of models are specified with very complex computer programs. In this thesis, I examine the epistemological issues that arise when scientists use these computer simulation models to learn about the world or to think through their ideas. I argue that the explosion of computational power over the last several decades has revolutionised model-based science, but that restraint and caution must be exercised in the face of this power. To make my arguments, I focus on two kinds of computer simulation modelling: climate modelling and, in particular, high-fidelity climate models; and agent-based models, which are used to represent populations of interacting agents often in an ecological or social context. Both kinds involve complex model structures and are representative of the beneficial capacities of computer simulation. However, both face epistemic costs that follow from using highly complex model structures. As models increase in size and complexity, it becomes far harder for modellers to understand their models and why they behave the way they do. The value of models is further obscured by their proliferation, and a proliferation of programming languages in which they can be described. If modellers struggle to grasp their models, they can struggle to make good inferences with them. While the climate modelling community has developed much of the infrastructure required to mitigate these epistemic costs, the less mature field of agent-based modelling is still struggling to implement such community standards and infrastructure. I conclude that modellers cannot take full advantage of the representational capacities of computer simulations unless