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$80.00 cloth, $34.95 paper, e-book available.. By Sofian Audry. $45.00 cloth, e-book available. Working Backstage: A Cultural History and Ethnography of Technical Theater Labor. [Extracted from the article]

Group sentiment analysis in today's era is influencing many applications. Sentiment recognition is applicable for individuals, and group as well as crowd. This paper aims to highlight the ongoing present research scenario on perceived group sentiment analysis. The paper provides a brief overview of the strengths and weaknesses of well-known group sentiment analysis approaches. These approaches are broadly split into two basic categories: top-down and bottom-up approaches. A combination of top-down and bottom-up approaches is found superior in performance. This comprehensive review also discusses the available dataset with taxonomic representation and the importance of fusion techniques in group emotion recognition. This paper also narrates the novel attempt to demarcate the group emotion recognition approaches into the categories evolved by the influence of machine learning and deep learning at the feature extraction level and at the classification level. The multi-modality based group sentiment analysis has a great scope to overcome the challenges and improve the performance on real time applications. • Comprehensive survey on perceived group sentiment analysis. • Taxonomic representation of available group emotion datasets. • Use of various attributes which utmost affect a person's emotion in a crowd or group. • Use of fusion methods at feature and decision level in GER. • Performance comparison of the existing methodology. [ABSTRACT FROM AUTHOR]

The article is a review of a book titled "Advances in Business Statistics, Methods and Data Collection" published by Wiley in 2023. The book provides an extensive overview of methods and developments in producing high-quality establishment statistics. The review focuses on section 5 of the book, which discusses topics related to the use of new data sources and technologies, particularly in the field of data science, machine learning, and big data. The review highlights the challenges of presenting machine learning to statisticians and suggests a more methodological approach. It also provides a brief introduction to machine learning from a statistical perspective and discusses the chapters on machine learning and the use of secondary data in official statistics. Overall, the review finds the chapters engaging and recommends the book for young data scientists and statisticians interested in data science. [Extracted from the article]

The article reviews the book "The Refugee Convention at Fifty," by Joanne van Selm, Khoti Kamanga, John Morrison, and Aninia Nadig.

It demystified what I had heard about Machine Learning in finance: Making money from nothing by copy-pasting the success of Machine Learning in other domains such as driving cars. This gives hope that academics will continue to create fruitful applications of Machine Learning - also in areas of finance other than asset pricing. [Extracted from the article]

The article reviews the book "Predicting Structured Data," by Gokhan Bakir, Thomas Hofmann, Bernard Scholkopf, Alexander J. Smola, Ben Taskar, and S. V. N. Vishwanathan.

As a field, Machine Learning (ML) has captured a lot of attention in both academia and practice in the recent decade. Unsupervised machine learning model using JMP In this section, I first describe a motivating example and then highlight the use of JMP for clustering using an unsupervised ML model. Supervised parametric machine learning model using JMP In this section, I first describe a motivating example and then highlight the use of JMP for numeric prediction using a supervised parametric ML model. Machine learning for a citizen data scientist with JMP In this section, I describe four key features of JMP that makes it an effective tool for a citizen data scientist: (1) supports non-linear, dynamic thinking; (2) uses visual metaphors that reduce cognitive burden; (3) is customizable, expandable and flexible; and (4) has low barrier of entry. [Extracted from the article]

The article reviews the book "Literature-Based Discovery," edited by Peter Bruza and Marc Weeber.

The article reviews the book "Learning Machine Translation," edited by Cyril Goutte, Nicola Cancedda and Marc Dymetman.

This book introduces and leverages concepts from reinforcement learning (RL). RL methods have been formalized by the computational intelligence community based on the conditioned reflex concept and serve as the bridge between adaptive and optimal control methods. In this book, the authors focus on RL methods to design adaptive controllers that learn approximate optimal solution methods. These optimal adaptive control approaches make use of neural networks (NNs) that serve as the actor-critic for discrete and continuous optimal control and differential game problems. The book contains 11 chapters, with most chapters representing the results from recent journal publications. For readers with an understanding of concepts such as Lyapunov-based analysis and adaptive and optimal control methods, this book presents a foundation for online optimal control solutions for continuous systems. The text uses RL to merge concepts from optimal and adaptive control, and the results are solutions to a class of challenging and highly relevant optimal control problems. The text is the first of its kind specialized for the control systems community. The mixed use of typical state feedback and sampled past data to adjust the adaptive update laws presents an exciting new domain rich for new contributions. As control practitioners seek to apply the approaches in this text to more complex, higher-dimensional systems, new challenges will likely arise such as finding appropriate basis functions for the learning laws; developing alternatives to or ensuring persistence of excitation, extending to interconnected (potentially distributed) networks; and applying to systems where the uncertainty enters the dynamics in unique ways. In summary, this book presents an exciting blend of ideas and methods developed in machine learning and adaptive control to provide new inroads into online approximate optimal control for complex and potentially uncertain systems. [ABSTRACT FROM PUBLISHER]

Brunton and Kutz have written a wonderful book about the mathematical foundation of a large part of data science, focussing specifically on methods related to dynamical systems and control with applications primarily in engineering. Of course, having the broader data science community in mind, Python might have been a better choice, given that it is not only the lingua franca of data science but also available completely free of charge. I recommend the textbook to graduate students, researchers and practitioners alike to get an overview of the latest data-driven methods in engineering. [Extracted from the article]

In modern developments of statistics, machine learning has become more and more popular. For the contents in detail, in Chapter 1, the authors first introduce some fundamental concepts of machine learning and present the outlined topics of the remaining chapters. Different from several "preliminaries" in previous chapters, the authors start introducing several well-known methods in machine learning and focus on the classification problem. [Extracted from the article]

Chapter 2 "Linear Regression Models" discusses the properties of Least Squares (LS) as an estimator of the conditional mean of HT ht given HT ht . An important statement is that "This book recommends using confidence intervals instead of HT ht -values". Curiously, neither more sophisticated outlier detection methods nor more reliable robust methods are mentioned (the HT ht estimator is known to lack robustness towards high-leverage outliers). [Extracted from the article]