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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

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Twitter mood predicts the stock market.

The productivity of work meetings is mostly assessed through post-hoc questionnaires. These questionnaires are impractical as they require additional time after the meeting has ended. Thus, measuring meeting productivity in a non-intrusive manner is of practical and theoretical importance. Extending research on physiological arousal and the healthy physiological variability thesis to the context of work meetings, we take a novel approach and investigate whether physiological arousal and the variability in implicit body signals of meeting participants (heart rate, arm movements, and speech intensity) can be accurate predictors of perceived meeting productivity. In a preliminary field study, we used smartwatches and tracked the body signals of 16 team members in 26 team meetings. The perceived meeting productivity was assessed at the end of the meetings. Partly supporting our assumptions, multilevel analysis showed that the variance in arm acceleration was a significant predictor of perceived meeting productivity. Further, using a random forest classifier, we accurately predicted perceived meeting productivity in roughly 60% of the cases with body signals. This study adds to previous work on meeting effectiveness by tapping into the potential of wearables to provide valid information about perceived meeting productivity. Cultivating our findings, we discuss lessons learned for future research. 

Your body tells more than words – predicting perceived meeting productivity through body signals

Social Network Analysis" or SNA is the science that makes "networking" measurable. SNA tracks relations between different people through the structure of their network. SNA applies graph theory to determine the strength of interactions between individuals. This chapter introduces work done in the last twenty years analyzing email and other electronic communication archives using SNA with the Web-based Griffin analysis tool, a Griffin version is freely available. Griffin can be used to create a virtual mirror of one's own communication behavior by analyzing individual email. 

Measuring social network structure

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a "proof of concept" system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a faster region-based convolutional neural network that detects horses in an image. The second one, the model, is a convolutional neural network that predicts the emotion of those horses. These two models were trained with multiple images of horses until they achieved high accuracy in their tasks, creating therefore the desired system. 400 images of horses were used to train both the detector and the model while 80 were used to validate the system. Once the two components were validated they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through head, neck, ear, muzzle, and eye position. The system showed an accuracy of between 69% and 74% on the validation set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. It is a first "proof of concept" approach that can be enhanced in many ways. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

Emotion Recognition in Horses with Convolutional Neural Networks

This chapter shows how computers and the Internet empower us to measure inter-human interaction on a high level of granularity and detail. Sensors combined with AI give the capability to constantly analyze and interpret communication. Wearable technologies, cloud computing, and artificial intelligence measure happiness, wellbeing, workplace satisfaction, and stress, and mirror back these measurements to the individual. This will lead to more connected, collectively aware, entangled team members, and thus to teams collaborating in groupflow. 

AI makes emotions measurable by aggregating the wisdom of the crowd

This paper describes our experience teaching a distributed virtual course with teams made up by students from China and Germany. It is based on a distributed course about Collaborative Innovation Networks (COINs) which has been successfully taught for the last 12 years to students from Germany, the US, Finland, and Switzerland. In this course, students form teams from different locations and languages, and together complete a complex project analyzing online social media. In 2016–2017, we applied the same course framework to participants from China and Germany. To gather insights from the course, we follow a mixed method study design by analyzing qualitative interviews with course participants and quantitative communication data of course participants. We find that combining members from China and Germany into the same team poses a set of unique intercultural challenges, overcoming language and behavioral differences. We present key lessons learned to inform future courses combining participants from the East and the West.

Peter A. Gloor

Papers

Your body tells more than words – predicting perceived meeting productivity through body signals

Measuring social network structure

Emotion Recognition in Horses with Convolutional Neural Networks

AI makes emotions measurable by aggregating the wisdom of the crowd

“German Association or Chinese Emperor?” Building COINs Between China and Germany