Unsupervised Canine Emotion Recognition Using Momentum Contrast.

We describe a system for identifying dog emotions based on dogs' facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist and psychobiologist Jaak Panksepp as 'Exploring', 'Sadness', 'Playing', 'Rage', 'Fear', 'Affectionate' and 'Lust'. We modified the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieved an accuracy of 43.

Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood.

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce.

Can Plants Perceive Human Gestures? Using AI to Track Eurythmic Human-Plant Interaction.

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves.

Leveraging the Sensitivity of Plants with Deep Learning to Recognize Human Emotions.

Recent advances in artificial intelligence combined with behavioral sciences have led to the development of cutting-edge tools for recognizing human emotions based on text, video, audio, and physiological data. However, these data sources are expensive, intrusive, and regulated, unlike plants, which have been shown to be sensitive to human steps and sounds. A methodology to use plants as human emotion detectors is proposed.

Can Plants Sense Humans? Using Plants as Biosensors to Detect the Presence of Eurythmic Gestures.

This paper describes the preliminary results of measuring the impact of human body movements on plants. The scope of this project is to investigate if a plant perceives human activity in its vicinity. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans.

Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions.

This paper presents novel preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. We have developed a real-time software to calculate the physical synchronicity of team members by tracking the difference in arm, leg, and head movements using Lightweight OpenPose. We employ facial expression recognition to evaluate the musicians' collective emotions.

Measuring ethical behavior with AI and natural language processing to assess business success.

Everybody claims to be ethical. However, there is a huge difference between declaring ethical behavior and living up to high ethical standards. In this paper, we demonstrate that "hidden honest signals" in the language and the use of "small words" can show true moral values and behavior of individuals and organizations and that this ethical behavior is correlated to real-world success; however not always in the direction we might expect.

"Towards Re-Inventing Psychohistory": Predicting the Popularity of Tomorrow's News from Yesterday's Twitter and News Feeds.

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past. In this work, we present a deep learning forecasting framework which is capable to predict tomorrow's news topics on Twitter and news feeds based on yesterday's content and topic-interaction features. The proposed framework starts by generating topics from words using word embeddings and K-means clustering.

Co-Designing a Collaborative Chronic Care Network (C3N) for Inflammatory Bowel Disease: Development of Methods.

Background: Our health care system fails to deliver necessary results, and incremental system improvements will not deliver needed change. Learning health systems (LHSs) are seen as a means to accelerate outcomes, improve care delivery, and further clinical research; yet, few such systems exist. We describe the process of codesigning, with all relevant stakeholders, an approach for creating a collaborative chronic care network (C3N), a peer-produced networked LHS.

Inequalities in Open Source Software Development: Analysis of Contributor's Commits in Apache Software Foundation Projects.

While researchers are becoming increasingly interested in studying OSS phenomenon, there is still a small number of studies analyzing larger samples of projects investigating the structure of activities among OSS developers. The significant amount of information that has been gathered in the publicly available open-source software repositories and mailing-list archives offers an opportunity to analyze projects structures and participant involvement. In this article, using on commits data from 263 Apache projects repositories (nearly all), we show that although OSS development is often described as collaborative, but it in fact predominantly relies on radically solitary input and individual, non-collaborative contributions.

Exploration of Online Culture Through Network Analysis of Wikipedia.

Understanding online culture is becoming crucial in the global and connected world. Contrary to conventional attitudinal surveys used in cultural research, this study uses the approach of directly observing culture-specific behavior that emerges from online collaboration on the Internet. The editing data of Wikipedia were analyzed in 12 languages.

Communication, opponents, and clan performance in online games: a social network approach.

Online gamers form clans voluntarily to play together and to discuss their real and virtual lives. Although these clans have diverse goals, they seek to increase their rank in the game community by winning more battles. Communications among clan members and battles with other clans may influence the performance of a clan.

Process efficiency. Redesigning social networks to improve surgery patient flow.

We propose a novel approach to improve throughput of the surgery patient flow process of a Boston area teaching hospital. A social network analysis was conducted in an effort to demonstrate that process efficiency gains could be achieved through redesign of social network patterns at the workplace; in conjunction with redesign of organization structure and the implementation of workflow over an integrated information technology system. Key knowledge experts and coordinators in times of crisis were identified and a new communication structure more conducive to trust and knowledge sharing was suggested.