Since human beings have been writing it seems there has been plagiarism. It is not something that sprouted with the advent of the Internet. Teachers have been struggling for years in countries all over the globe to find good methods for dealing with the problem of plagiarizing students. How do we spot plagiarism? How do we teach them not to plagiarize? And how do we deal with those who have been found out to be plagiarists? The purpose of this book is to collect material on the various aspects of plagiarism in education with special attention given to the German problem of dissertation plagiarism. Since there is a wide-spread interest in the German plagiarism situation and in strategies for dealing with it, the book is written in English in order to be accessible to a larger audience.
In this book, the author has shown how the mental test idea was evolved out of the laboratory study of individual differences by psychologists, how the individual and then the group intelligence tests were developed, the application of statistical methods to the interpretation of the results, the creation of the different types of scales, the extension of the mental test idea in new directions, the technique and theory of the tests, the uses of the different types of mental tests, and their reliability, and has closed his treatment with two chapters on the interpretation of what the tests really measure and the nature of intelligence itself. The work of hundreds of individual investigators has been organized into a systematic treatise, and the place and work of each have been given their proper setting as parts of a great movement. The volume is accordingly offered to teachers of college and university classes in Mental Tests with confidence that it will prove as useful in this field as the texts now in use have done in the field of educational tests. (PsycINFO Database Record (c) 2005 APA, all rights reserved).
As heard on NPR's This American Life “Absorbing . . . Though it's non-fiction, The Feather Thief contains many of the elements of a classic thriller.” —Maureen Corrigan, NPR’s Fresh Air “One of the most peculiar and memorable true-crime books ever.” —Christian Science Monitor A rollicking true-crime adventure and a captivating journey into an underground world of fanatical fly-tiers and plume peddlers, for readers of The Stranger in the Woods, The Lost City of Z, and The Orchid Thief. On a cool June evening in 2009, after performing a concert at London's Royal Academy of Music, twenty-year-old American flautist Edwin Rist boarded a train for a suburban outpost of the British Museum of Natural History. Home to one of the largest ornithological collections in the world, the Tring museum was full of rare bird specimens whose gorgeous feathers were worth staggering amounts of money to the men who shared Edwin's obsession: the Victorian art of salmon fly-tying. Once inside the museum, the champion fly-tier grabbed hundreds of bird skins—some collected 150 years earlier by a contemporary of Darwin's, Alfred Russel Wallace, who'd risked everything to gather them—and escaped into the darkness. Two years later, Kirk Wallace Johnson was waist high in a river in northern New Mexico when his fly-fishing guide told him about the heist. He was soon consumed by the strange case of the feather thief. What would possess a person to steal dead birds? Had Edwin paid the price for his crime? What became of the missing skins? In his search for answers, Johnson was catapulted into a years-long, worldwide investigation. The gripping story of a bizarre and shocking crime, and one man's relentless pursuit of justice, The Feather Thief is also a fascinating exploration of obsession, and man's destructive instinct to harvest the beauty of nature.
Louise Erdrich meets Karen Russell in this deliciously strange and daringly original novel from Pulitzer Prize finalist Margaret Verble: An eclectic cast of characters--both real and ghostly--converge at an amusement park in Nashville, 1926.
The N*O*VO Nostalgia Movie Quiz and Information Book by David Cameron Dunn, Ph.D. The N*O*VO Nostalgia Movie Quiz and Information Book was written as a labor of love by a cinephile. Its intended audience is the kindred spirits out there who not only relish the “good, old films and players,” but who also enjoy being asked questions about them. The book goes a step further by quantifying the difficulty of the questions, providing the readers with the opportunity to record their “scores” and “batting averages.” Not only may the individual assess his or her personal expertise in the realm of motion picture lore, but also in the competitive way with family and friends as a game. In addition to the quiz section, which comprises the pronounced majority of the book, the readers will also find several interesting informational appendices. In view of the above, the reader will find this work to be set apart from the many other similar entries on the shelves!
Common as Air offers a stirring defense of our cultural commons, that vast store of art and ideas we have inherited from the past that continues to enrich our present. Suspicious of the current idea that all creative work is "intellectual property," Lewis Hyde turns to America's founding fathers—men like John Adams, James Madison, and Thomas Jefferson—in search of other ways to value the fruits of human wit and imagination. What he discovers is a rich tradition in which knowledge was assumed to be a commonwealth, not a private preserve. For the founding fathers, democratic self-governance itself demanded open and easy access to ideas. So did the growth of creative communities, such as that of eighteenth-century science. And so did the flourishing of public persons, the very actors whose "civic virtue" brought the nation into being. In this lively, carefully argued, and well-documented book, Hyde brings the past to bear on present matters, shedding fresh light on everything from the Human Genome Project to Bob Dylan's musical roots. Common as Air allows us to stand on the shoulders of America's revolutionary giants and to see beyond today's narrow debates over cultural ownership. What it reveals is nothing less than an inspiring vision of how to reclaim the commonwealth of art and ideas that we were meant to inherit.
Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life. In this provocative book, Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life. Rips argues that certain inference principles are so central to our notion of intelligence and rationality that they deserve serious psychological investigation to determine their role in individuals' beliefs and conjectures. Asserting that cognitive scientists should consider deductive reasoning as a basis for thinking, Rips develops a theory of natural reasoning abilities and shows how it predicts mental successes and failures in a range of cognitive tasks. In parts I and II of the book, Rips builds insights from cognitive psychology, logic, and artificial intelligence into a unified theoretical structure. He defends the idea that deduction depends on the ability to construct mental proofs—actual memory units that link given information to conclusions it warrants. From this base Rips develops a computational model of deduction based on two cognitive skills: the ability to make suppositions or assumptions and the ability to posit sub-goals for conclusions. A wide variety of original experiments support this model, including studies of human subjects evaluating logical arguments as well as following and remembering proofs. Unlike previous theories of mental proof, this one handles names and variables in a general way. This capability enables deduction to play a crucial role in other thought processes, such as classifying and problem solving. In part III, Rips compares the theory to earlier approaches in psychology which confined the study of deduction to a small group of tasks, and examines whether the theory is too rational or too irrational in its mode of thought.
Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. What's inside Using the tidyverse packages to process and plot your data Techniques for supervised and unsupervised learning Classification, regression, dimension reduction, and clustering algorithms Statistics primer to fill gaps in your knowledge About the reader For newcomers to machine learning with basic skills in R. About the author Hefin I. Rhys is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio. Table of contents: PART 1 - INTRODUCTION 1.Introduction to machine learning 2. Tidying, manipulating, and plotting data with the tidyverse PART 2 - CLASSIFICATION 3. Classifying based on similarities with k-nearest neighbors 4. Classifying based on odds with logistic regression 5. Classifying by maximizing separation with discriminant analysis 6. Classifying with naive Bayes and support vector machines 7. Classifying with decision trees 8. Improving decision trees with random forests and boosting PART 3 - REGRESSION 9. Linear regression 10. Nonlinear regression with generalized additive models 11. Preventing overfitting with ridge regression, LASSO, and elastic net 12. Regression with kNN, random forest, and XGBoost PART 4 - DIMENSION REDUCTION 13. Maximizing variance with principal component analysis 14. Maximizing similarity with t-SNE and UMAP 15. Self-organizing maps and locally linear embedding PART 5 - CLUSTERING 16. Clustering by finding centers with k-means 17. Hierarchical clustering 18. Clustering based on density: DBSCAN and OPTICS 19. Clustering based on distributions with mixture modeling 20. Final notes and further reading
