Suchergebnisse

Preface. I. THE R LANGUAGE. 1. Basics of R.1.1 What is R?1.2 Installing R.1.3 R Documentation. 1.4 Basics. 1.5 Getting Help. 1.6 Data Entry. 1.7 Tidying Up. 1.8 Saving and Retrieving the Workspace. 2. Summarising Statistical Data. 2.1 Measures of Central Tendency. 2.2 Measures of Dispersion. 2.3 Overall Summary Statistics. 2.4 Programming in R.3. Graphical Displays. 3.1 Boxplots. 3.2 Histograms. 3.3 Stem and Leaf. 3.4 Scatter Plots. 3.5 Graphical Display vs Summary Statistics. II: FUNDAMENTALS OF PROBABILITY. 4. Basics. 4.1 Experiments, Sample Spaces and Events. 4.2 Classical Approach to Probability. 4.3 Permutations and Combinations. 4.4 The Birthday Problem. 4.5 Balls and Bins. 4.6 Relative Frequency Approach to Probability. 4.7 Simulating Probabilities. 5. Rules of Probability. 5.1 Probability and Sets. 5.2 Mutually Exclusive Events. 5.3 Complementary Events. 5.4 Axioms of Probability. 5.5 Properties of Probability. 6. Conditional Probability. 6.1 Multiplication Law of Probability. 6.2 Independent Events. 6.3 The Intel Fiasco. 6.4 Law of Total Probability. 6.5 Trees. 7. Posterior Probability and Bayes. 7.1 Bayes' Rule. 7.2 Hardware Fault Diagnosis. 7.3 Machine Learning. 7.4 The Fundamental Equation of Machine Translation. 8. Reliability. 8.1 Series Systems. 8.2 Parallel Systems. 8.3 Reliability of a System. 8.4 Series-Parallel Systems. 8.5 The Design of Systems. 8.6 The General System. III: DISCRETE DISTRIBUTIONS. 9. Discrete Distributions. 9.1 Discrete Random Variables. 9.2 Cumulative Distribution Function. 9.3 Some Simple Discrete Distributions. 9.4 Benford's Law. 9.5 Summarising Random Variables: Expectation. 9.6 Properties of Expectations. 9.7 Simulating Expectation for Discrete Random Variables. 10. The Geometric Distribution. 10.1 Geometric Random Variables. 10.2 Cumulative Distribution Function. 10.3 The Quantile Function. 10.4 Geometric Expectations. 10.5 Simulating Geometric Probabilities and Expectations. 10.6 Amnesia. 10.7 Project. 11. The Binomial Distribution. 11.1 Binomial Probabilities. 11.2 Binomial Random Variables. 11.3 Cumulative Distribution Function. 11.4 The Quantile Function. 11.5 Machine Learning and the Binomial Distribution. 11.6 Binomial Expectations. 11.7 Simulating Binomial Probabilities and Expectations. 11.8 Project. 12. The Hypergeometric Distribution. 12.1 Hypergeometric Random Variables. 12.2 Cumulative Distribution Function. 12.3 The Lottery. 12.4 Hypergeometric or Binomial?.12.5 Project. 13. The Poisson Distribution. 13.1 Death by Horse Kick. 13.2 Limiting Binomial Distribution. 13.3 Random Events in Time and Space. 13.4 Probability Density Function. 13.5 Cumulative Distribution Function. 13.6 The Quantile Function. 13.7 Estimating Software Reliability. 13.8 Modelling Defects in Integrated Circuits. 13.9 Simulating Poisson Probabilities. 13.10Projects. 14. Sampling Inspection Schemes. 14.1 Introduction. 14.2 Single Sampling Inspection Schemes. 14.3 Acceptance Probabilities. 14.4 Simulating Sampling Inspections Schemes. 14.5 Operating Characteristic Curve. 14.6 Producer's and Consumer's Risks. 14.7 Design of Sampling Schemes. 14.8 Rectifying Sampling Inspection Schemes. 14.9 Average Outgoing Quality. 14.10Double Sampling Inspection Schemes. 14.11Average Sample Size. 14.12Single vs Double Schemes. 14.13Project. IV. CONTINUOUS DISTRIBUTIONS. 15. Continuous Distributions. 15.1 Continuous Random Variables. 15.2 Probability Density Function. 15.3 Cumulative Distribution Function. 15.4 The Uniform Distribution. 15.5 Expectation of a Continuous Random Variable. 15.6 Simulating Continuous Variables. 16. The Exponential Distribution. 16.1 Probability Density Function Of Waiting Times. 16.2 Cumulative Distribution Function. 16.3 Quantiles. 16.4 Exponential Expectations. 16.5 Simulating the Exponential Distribution. 16.6 Amnesia. 16.7 Simulating Markov. 17. Applications of the Exponential Distribution. 17.1 Failure Rate and Reliability. 17.2 Modelling Response Times. 17.3 Queue Lengths. 17.4 Average Response Time. 17.5 Extensions of the M/M/1 queue. 18. The Normal Distribution. 18.1 The Normal Probability Density Function. 18.2 The Cumulative Distribution Function. 18.3 Quantiles. 18.4 The Standard Normal Distribution. 18.5 Achieving Normality; Limiting Distributions. 18.6 Project in R.19. Process Control. 19.1 Control Charts. 19.2 Cusum Charts. 19.3 Charts for Defective Rates. 19.4 Project. V. TAILING OFF. 20. Markov and Chebyshev Bound. 20.1 Markov's Inequality. 20.2 Algorithm Run-Time. 20.3 Chebyshev's Inequality. Appendix 1: Variance derivations. Appendix 2: Binomial approximation to the hypergeometric. Appendix 3:. Standard Normal Tables.

Cross disciplinary research is essential for technological innovation. For decades, computer science (Comp Sci) has leveraged behavior science (Behav Sci) research to create innovative products and improve end user experience. Despite the natural challenges that come with cross disciplinary work, there are no published manuscripts outlining how to responsibly integrate Behav Sci into Comp Sci research and development. This publication fills this critical gap by discussing important differences between Behav Sci and Comp Sci, particularly with regard to how each field fits under the umbrella of science and how each field conceptualizes data. We then discuss the consequences of misusing Behav Sci and provide examples of technology efforts that drew inappropriate or unethical conclusions about their behavioral data. We discuss in detail common errors to avoid at each stage of the research process, which we condensed into a useful checklist to use as a tool for teams integrating Behav Sci in their work. Finally, we include examples of good applications of Behav Sci into Comp Sci research, the design of which can inform and strengthen digital government, e-commerce, defense, and many other areas of information technology.

This report is one of the deliverables for the Ethics4EU project. It presents results obtained from a survey conducted in early 2020 that polled faculty from Computer Science and related disciplines on teaching practices in Computer Ethics in Computer Science across Europe. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not Computer Ethics is taught to Computer Science students at each institution, the reasons why Computer Ethics is or is not taught, how Computer Ethics is taught (for example, as a standalone course or embedded within other courses), the background of staff who teach Computer Ethics and the scope of Computer Ethics curricula. Data was also gathered on teaching and learning methods used (theory, case studies, practical work) and how Computer Ethics is assessed. The results of the survey are a comprehensive insight into teaching practices for Computer Ethics in Computer Science and related disciplines and will inform the development of new curricula and learning resources for Digital Computer Ethics as part of the Computer Ethics4EU project.

Over the last twenty years, computer science has relied on concepts borrowed from game theory and economics to reason about applications ranging from internet routing to real-time auctions for online advertising. More recently, ideas have increasingly flowed in the opposite direction, with concepts and techniques from computer science beginning to influence economic theory and practice. In this lecture, I will illustrate this point with a detailed case study of the 2016-2017 Federal Communications Commission incentive auction for repurposing wireless spectrum. Computer science techniques, ranging from algorithms for NP-hard problems to nondeterministic communication complexity, have played a critical role both in the design of the reverse auction (with the government procuring existing licenses from television broadcasters) and in the analysis of the forward auction (when the procured licenses sell to the highest bidder).

International audience ; Official European statistics of education indicate that the number of students entering tertiary education have significantly increased between 2000 and 2006 [1], and indicate a trend that will continue. However, this increase is not reflected in every field of study; computer science and engineering are among those that have decreased each year, evidence of a decline of interest in following this career on the part of students. As a response to this disturbing fact, this paper aims to identify some of the possible consequences that this trend could produce in Europe. It will highlight the impacts in economic, social, political and pedagogical fields and explain how these segments will be affected if the decline in computer science persists. Supported by previous investigations and official reports, this analysis provides some examples of the problems already produced by the declining interest in computer science in Europe and proposes solutions such as teaching methods and learning strategies to attract more students to this field and therefore limit the negative effects in a near future.

The field of Graph Theory plays a vital role in various fields. In Graph theory main problem is graph labeling. Graph Labeling is the assignment of integer's form 1 to n for vertex, edges and both of the graphs respectively. One of the important areas in graph theory is Graph Labeling which is used in many applications like coding theory, radar, astronomy, circuit design, missile guidance, communication network addressing, x-ray crystallography, data base management. Here we would like to enhance the graph labeling applications in the field of computer science. This paper gives an overview of labeling of graphs in heterogeneous fields to some extent, but mainly focuses on important major areas of computer science like data mining, image processing, cryptography, software testing, information security, communication networks etc….These are various subjects in engineering studies and these are more efficiently used in various sectors like government sectors, corporate sectors like that. In these subjects every subject has their concept and gave their usage related to graph labeling. Future enhancements for the graph labeling should be used in cloud computing, signal processing etc… Various papers based on graph theory and graph labeling applications have been studied and we explore the usage of Graph Labeling in several areas like data mining, communication networks, image processing, cryptosystems, computer science applications and an overview has been proposed here.

One of the basic tasks of computer science is to rewrite models derived from other scientific disciplines so that they can be represented and processed on computers. If such a reconstruction process is only partially successful or fails entirely, the modification of the initial model becomes an interdisciplinary re-search task. The modelling task is to be seen as an application of knowledge representation and processing. We distinguish between aiming at models of something or models for some purpose. Modelling of given domains starts with the construction of a formal ontology. To support issues such as modularity and interoperability, in particular in a web-based environment, the idea of reference ontologies came up. For object-based research in the humanities, the Conceptual Reference Model (CRM) by ICOM/CIDOC is such a reference ontology which has become an ISO standard.

Probability and random variables -- Probability distributions -- Stochastic processes -- Queueing theory -- Queueing theory models of computer systems -- Estimation -- Hypothesis testing -- Appendix A. Statistical tables -- Appendix B. APL programs -- Appendix C. Queueing theory definitions and formulas.