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With the advent of distributed computing, the need for frameworks that facilitate its programming and management has also appeared. These tools have typically been used to support the research on application areas that require them. This poses good initial conditions for translational computer science (TCS), although this does not always occur. This article describes our experience with the PyCOMPSs project, a programming model for distributed computing. While it is a research instrument for our team, it has also been applied in multiple real use cases under the umbrella of European Funded projects, or as part of internal projects between various departments at the Barcelona Supercomputing Center. This article illustrates how the authors have engaged in TCS as an underlying research methodology, collecting experiences from three European projects. ; This work was supported in part by Spanish Government under Contract TIN2015-65316-P, in part by the Generalitat de Catalunya under Contract 2014-SGR-1051, and in part by the European Commission's Horizon 2020 Framework program through BioExcel Center of Excellence under Contract 823830 and Contract 675728, in part by the ExaQUte Project under Contract 800898, in part by the European High-Performance Computing Joint Undertaking (JU) under Grant 955558, in part by the MCIN/AEI/10.13039/501100011033, and in part by the European Union NextGenerationEU/PRTR. ; Peer Reviewed ; Postprint (author's final draft)

Major Speakers -- Networks of Quasi-Reversible Nodes -- The c — Server Queue with Constant Service Times and a Versatile Markovian Arrival Process -- Simulation Output Analysis for General State Space Markov Chains -- Models and Problems of Dynamic Memory Allocation -- Probabilistic Analysis of Algorithms -- Point Process Method in Queueing Theory -- Error Minimization in Decomposable Stochastic Models -- Computational Methods for Product Form Queueing Networks: Extended Abstract -- Networks of Queues, I Richard Muntz, Chairman -- Closed Multichain Product Form Queueing Networks with Large Population Sizes -- The Significance of the Decomposition and the Arrival Theorems for the Evaluation of Closed Queueing Networks -- On Computing the Stationary Probability Vector of a Network of Two Coxian Servers -- Performance and Reliability Donald Gross, Chairman -- Fitting of Software Error and Reliability Models to Field Failure Data -- Performance Evaluation of Voice/Data Queueing Systems -- Probabilistic Aspects of Simulation Peter Lewis, Chairman -- On a Spectral Approach to Simulation Run Length Control -- Generation of Some First-Order Autoregressive Markovian Sequences of Positive Random Variables with Given Marginal Distributions -- Testing for Initialization Bias in the Mean of a Simulation Output Series: Extended Abstract -- Queueing Models in Performance Analysis, I Daniel Heyman, Chairman -- Response Time Analysis for Pipelining Jobs in a Tree Network of Processors -- Mean Delays of Individual Streams into a Queue: The ?GIi/M/1 Queue -- Probabilistic Models in Performance Analysis of Computer Systems and Communication Networks Donald Gaver, Chairman -- Analysis and Design of Processor Schedules for Real Time Applications -- Modeling Real Dasd Configurations -- Bottleneck Determination in Networks of Queues -- Probabilistic Analysis of Algorithms Dave Liu, Chairman -- On the Average Difference Between the Solutions to Linear and Integer Knapsack Problems.

Aspiring Minds Computer Adaptive Test (AMCAT) is a computer adaptive test used widely in India to assess the employability skill level of engineering students. Prefinal and final year students belonging to different streams take up this online test. The present study investigates the perception of students regarding the conduct of AMCAT. Furthermore, the study explores the benefits that they obtain through the test. The comparison is made among the students belonging to the two different streams, engineering and computer science. The conclusion drawn facilitates the readers to understand the utility of AMCAT.

A description of the trend toward cooperative research efforts between academic, government, and entrepreneurial partners in the field of science to expand technological innovation and research.

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.