Empathic Labs is a community of researchers, entrepreneurs and enthusiasts who share our belief that the future lies in empathy. Our mission is to tackle society problems by integrating empathic machines into daily use through a multidisciplinary approach with the help of our partners.
Check us out on empathiclabs.ch or read our articles on Medium.
Our empathic system is a system that provides multiple scientifically proven techniques for stress reducing and productivity increase for users in occupational health sector. The main objective of this system is to allow the users to use the provided techniques in multiple situations (i.e. car, office, remote work) in a way that almost all users can benefit from them without being limited to have some extra stuff in order to practice them. Our proposed system is divided into two main components
More informationThis project lies in the context of a collaboration between the HumanTech Institute and Kare Knowledgeware. Kare’s product is an automated knowledge retrieval conversational tool. The goal of the collaboration is to enhance the customer experience by adding empathy into the system. Empathy is possible by following two steps, first understanding the user intention, and second answering him accordingly.
The main objective is to develop a tool that will allow a user to extract useful information about his query: intent (informational vs emotional), sentiment (happy, upset). A user query enters the system, and this same query exits the system with annotations.
To achieve this goal, a system must be developed. This system has to:
1. Extract the intent from the query
2. Extract the emotion from the query
The current solution meets the initial objective. The intent classification model reaches 85% accuracy. The deep learning model for the emotion detection reaches 80% accuracy and the more simple model using hand-crafted features for the emotion detection reaches 60% accuracy, both on a problem consisting of 4 emotions so 4 classes: joy, anger, fear, sadness. Kare Knowledgeware can try these models on their data and see how the model performs. Hopefully, the models will perform well on their data too and they can work on answering user queries with more empathy.
Social networks, and digital communication in general, have evolved at an impressive speed in recent years. They have enabled everyone to stay in constant contact with family members, co-workers or classmates. This technological progress has also brought with it a number of disadvantages, one of them being cyberbullying.
Cyberbullying, which is simply bullying that occurs on digital devices, is primarily directed at teens. In the past, this problem was more or less limited to school boundaries. But unfortunately, technology has removed these boundaries and so the bullying continues unabated, leaving no respite for the victims. The consequences are numerous and this phenomenon has already led to many suicides. It is therefore necessary to be able to detect cyberbullying on social networks and take action accordingly.
During this project, it soon became clear that the lack of existing resources, including datasets containing relatively recent cyberbullying texts, would complicate the task. Therefore, a slightly different approach has been adopted. Indeed, the objective has been changed. It was no longer a question of detecting cyberbullying, but rather of finding out whether or not a text containing insults was hateful.
To do so, around 4’000 tweets have been collected and labelled. From this dataset, different features have been extracted and different predictions, mainly based on random forest and neural networks models, have been realized. This process made it possible to identify the most useful features which were none other than the TFIDF values. Combining these features with a few others made it possible to reach an accuracy of 72.76%, a relatively low score for a binary classification problem.
It is possible that the model currently in use relies too much on the statistics of the various insults. For example, if one insult appears predominantly in positive samples rather than negative ones, the model will have difficulty in correctly predicting the samples containing this insult but whose class should be positive. Of course, the opposite is also true.
Multilingual Appointment Chatbot is a project in collaboration with a Swiss startup called Deeplink specialized in chatbot technologies. For one of their customers, Deeplink requires a chatbot being able to detect if there is a time and a date in a text message sent by a human to the bot and to respond with a proper answer. This chatbot will be used in order to take appointment with customers.
The goal of the project is to compare several popular Natural Language Processing al- gorithms with text in French translated by a translation service. After testing those algorithms, a scoreboard will be made with specific criteria to find the best viable solu- tion.
After that, it is planned to create a Telegram bot in order to interact with the com- pany schedule and the customer.
Nombreux sont les bots qui lorsqu’ils ne comprennent pas l’utilisateur (la phrase, l’intention) répondent par la phrase bateau “Could you rephrase, I don’t understand”. Cela peut vite deve- nir ennuyant pour l’utilisateur.
C’est là qu’intervient l’idée folle du projet Movie Dialog Bot. Afin de garder l’utilisateur captivé, le bot doit lui répondre par un message divertissant. L’idée est justement d’afficher à l’utilisa- teur une célèbre citation de film avec en plus, les informations concernant l’acteur, le caractère et le nom du film. La citation n’est pas affichée au hasard ! C’est à l’aide du machine learning qu’on va définir la meilleure citation à afficher en fonction de la phrase envoyée par l’utilisateur.
L'objectif du projet est de détecter les émotions induites par le stress à travers les micro-expressions révélées par le visage humain, dans le but de réduire le stress dans la vie quotidienne. Par ailleurs, cette étude développe un logiciel informatique capable de réagir en fonction de l'état affectif de l'utilisateur et de prendre des décisions intelligentes basées sur des indices non verbaux.
More information
Privacy is a widely publicized topic. Personal data are scattered everywhere, often in possession of big companies like Google and Facebook. Messaging applications are particularly affected as we communicate on a daily basis with our loved ones through these applications.
The main objective is to develop a tool that will allow an user to fetch the important data from his conversations, like the locations mentioned, the list of people with whom the person discusses the most, the emotions, the personality, etc.
Le but de ce projet est de mettre en place un système capable de détecter le langage du haut du corps, afin de détecter et de reconnaître les émotions cachées.
More informationLe stress et les émotions sont étroitement liés car le stress négatif se manifeste par des émotions négatives et le stress positif par des émotions positives. De nos jours, certaines machines sont capables de comprendre leur utilisateur, par exemple elles sont capables de reconnaître que l’utilisateur est stressé, et peuvent faire preuve d’empathie, par exemple aider l’utilisateur stressé à se calmer. Ainsi, ces machines, ou autrement dit compagnons empathiques virtuels, sont un moyen de réduire le mauvais stress et d’augmenter le bon en fournissant à l’utilisateur une réponse empathique. Par conséquent, cette thèse explore les effets de la lumière et de la couleur, ou plus précisément l’effet de la lumière bleue, comme réponse empathique dans le but de réduire le stress.
More informationIl s’agit d’un chatbot qui vise à aider les personnes qui souffrent d’un trouble de comportement alimentaire (TCA). L’application se chargera d’interroger d’utilisateur afin de sauvegarder les détails de chaque crise dans une base de données, de proposer des stratégies de prévention adéquates ainsi que de rester à l’écoute de l’utilisateur en cas de problème.
Le but de ce stage était de concevoir puis développer un chatbot qui joue le rôle d’un compagnon nutritionnel. Le bot doit récolter des données sur le contenu des repas des utilisateurs (sous forme de texte dans un premier temps et d’image par la suite) et les stocker pour ensuite en retirer de la valeur pour l’utilisateur.
The goal of this project is to explore the current state of the art of conversational agents, and then to proceed to
the conception and implementation of a textual, english speaking, chatbot based on the Game of Thrones TV show.
Jacky Casas
HEIA-FR
Bouvard de Pérolles 80
1700 Fribourg
Switzerland
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