A book that provides easy solutions to problems faced by language learners  English Bites! My Full pro of English Learning Formula is the story of a man who goes from being tongue-tied in school to becoming a smooth- talking banker. Through a series of hilarious personal adventures and misadventures, Manish Gupta provides easy solutions to problems faced by language learners. So, whether youre a vernacular speaker, a GRE/ GMAT/CAT/XAT aspirant or just a language nut, English Bites! will expand your vocabulary and improve your verbal ability. It may even help you love the English language a little more!   Add over 1000 new words to your vocabulary and figure out easy and effective ways to expand your word bank.  Combine etymology, mnemonics, jokes and anecdotes to better your understanding of the English language.  Differentiate between similar sounding words and learn to use them right.  Improve your general knowledge with trivia that spans brand names, automobiles, fine dining, love, money, banks, science and B-school jargon.

English Bites!: My Fullproof English Learning Formula

Gupta, Manish

Self-Help

India Penguin

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

Outlier Detection for Temporal Data (Synthesis Lectures on Data Mining and Knowledge Discovery, 8)

Computers & Technology

Aggarwal, Charu

Gao, Jing

Han,Jiawei

Databases & Big Data

Morgan & Claypool Publishers

Information retrieval with verbose natural language queries has been generating a lot of interest in recent years. The focus of many novel search applications has shifted from short keyword queries to verbose queries. Examples include question answering systems and dialogue systems, voice search on mobile devices, and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However, the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations.  Information Retrieval with Verbose Queries is the first monograph to provide a coherent and organized survey on this topic. It puts together the various research pieces of the puzzle, provides a comprehensive and structured overview of diverse proposed methods, and lists several application scenarios where effective verbose query processing can make a significant difference.  Information Retrieval with Verbose Queries is a very timely reference on this important topic. It is entirely based on previously published research and publicly available datasets and as such, it should prove useful for both practitioners and academic researchers interested in reproducing the reported results.

Information Retrieval with Verbose Queries (Foundations and Trends(r) in Information Retrieval)

Bendersky, Michael

Computer Science

Now Publishers

Outlier Detection for Temporal Data (Synthesis Lectures on Data Mining and Knowledge Discovery)

Springer

A comprehensive introduction to the know-how of HR analytics and its role in optimizing HR decision-making and driving organizational performance. In today’s technology-driven world, HR analytics is pivotal to measure, express and take focused HR decisions. Practical Applications of HR Analytics equips readers with the required tools and techniques to effectively identify, capture and utilize the vast amount of available data in making better informed business decisions. The text is lucid and packed with practical illustrative examples, hands-on exercises and datasets. It is an invaluable resource for management students and HR professionals who want to translate the benefits of HR analytics into practice, thus paving the way for evidence-based human resource management. Key Features: • Practical understanding of a range of open-source statistical GUI software. • Focus on both descriptive and predictive analytics in HR. • Chapter opening vignettes, industry examples and real-world case studies on application of HR analytics in organizations. • Supplementary exercises and soft copy of datasets provided on Google Drive Link to facilitate hands-on training.

Practical Applications of HR Analytics: A Step-by-Step Guide

Business & Money

Banerjee, Pratyush

Pandey, Jatin

Human Resources

SAGE Publications Pvt. Ltd

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