• NLU on the edge  Bachelor project

    La société Deeplink est une startup d’intelligence artificielle spécialisée dans la technologie "chatbot". Cette entreprise utilise beaucoup le NLU (Natural Language Understanding) pour déterminer l’intention de l’utilisateur lorsqu’il communique avec le "chatbot". Actuellement la solution NLU est hébergée sur un serveur dédié et les échanges entre celle-ci et le client se font par requêtes http.

    L’idée de ce projet consiste à faire tourner directement la solution NLU sur le navigateur du client. Avec une idée pareille, on assure qu’aucune requête contenant des données sensibles ne soit envoyée vers un serveur externe puisque l’inférence du modèle se fait sur le navigateur. En plus d’assurer une confidentialité bien plus sécurisée, si on déplace l’inférence du modèle on devrait obtenir de meilleure performance en termes de latence, d’extensibilité et on pourrait dans le futur développer des modèles personnaliser pour chaque personne.

    More information
    Student(s): Nicolas Crausaz

  • Norovirus outbreak detection using tweets  Internship project

    A norovirus outbreak happened in France and was caused by raw shellfish and oysters. Switzerland, Sweden, Italy and the Netherlands have all also reported outbreaks linked to live oysters from France. Symptoms such as diarrhea, vomiting and incubation times, are consistent with norovirus or other enteric virus infections.

    The number of people in France who have become ill after eating contaminated raw shellfish has jumped to more than 1,000. The outbreak has spurred international product recalls and many medias have talked about the Norovirus epidemic.

    Social media services such as Twitter are valuable sources of information for surveillance systems. A digital syndromic surveillance system has several advantages including its ability to overcome the problem of time delay in traditional surveillance systems.

    In this project, a Twitter-based data analysis system was developed to analyze the textual content of a set of tweets and see if there are any evidence of the Norovirus outbreak in France or Switzerland.

    More information
    Student(s): Jean Khoury

  • Deep understanding of written user query  Semester project

    This 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.

    More information
    Student(s): Samuel Torche

  • Cyberbullying and Social Networks  Semester project

    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.

    More information
    Student(s): Charles Perriard

  • Integrated NLP Pipeline  Master project

    The issue of the structuration of textual data is very paramount in today’s technological reality. Textual data is a part of our daily life, we emit it, text it, tweet it, and receive it in huge bulks regularly, making the realm of data largely dependent on textual content that is highly unstructured by nature and unexploitable. In a world where Machine Learning, Big Data, and smart assistants are becoming the trend, companies are relying on these concepts to flourish their businesses. Therefore, the need for a platform permitting textual analytics techniques for all, becomes essential. This is where Wisely comes in handy. In a nutshell, Wisely provides two of the most used branches of Natural Language Processing: Named Entity Recognition and Natural Language Understanding. Using our platform, a non-technical user can import their own dataset, do the wanted treatments and export the results for future usages. This report has the intention of helping you get a better understanding of how Wisely works by giving you its implementation details from all the aspects.

    More information
    Student(s): Cheryl Sarrouh, Toufic Yammine

  • Multilingual Appointment Chatbot  Semester project

    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.

    More information
    Student(s): Luke Perrottet

  • Movie Dialog bot  Semester project

    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.

    More information
    Student(s): Nicolas Crausaz

  • BombusCar II  Semester project

    Le coût environnemental et sociétal de la logistique traditionnelle, basée sur le modèle « Hub and spoke », est de plus en plus défavorable. La digitalisation permet de nouvelles approches qui ont montré leur efficacité dans le transport de personne (p.ex. BlaBlaCar) mais il reste une opportunité à saisir dans le domaine du transport de marchandises.

    More information
    Student(s): Quentin Seydoux

  • 34 match II  Semester project

    La collecte de cellules souches de cordon ombilical est précieuse. Elles permettent de traiter plu- sieurs maladies du sang, comme la leucémie, mais manque de moyens partout dans le monde. Que cela concerne un usage interne à sa famille ou un don à la collectivité, ces cellules sont souvent jetées faute d’argent pour les recueillir.


    More information
    Student(s): Dorian Saudan

  • Application mobile pour augmenter l’attention des conducteurs des futures voitures semi-autonomes  Semester project

    L’objectif principal de ce projet est d’améliorer l’application existante en apportant des idées créatives pour augmenter l’attention du conducteur et pour que l’application reste attrayante et convient tout à fait aux besoins du client. Pour y parvenir, des analyses ont été faites sur l’outil de développement, l’application existante et les propositions d’amélioration se basant sur des études sûres. Deux prototypes ont été décidés en conséquence.

    Le premier se base sur un nouveau mode de l’application (avec une interface améliorée) qui est l’overlay. Le second se repose sur un jeu de reflexe qui va rendre le conducteur plus éveillé avant qu’il doive reprendre le contrôle. Ces deux prototypes ont pu être été réalisés grâce à notre analyse, conception et surtout grâce aux tests que nous avons faits.

    More information
    Student(s): Yulia Termkhitarova, Samoelina Hana Ranaivo

  • DriveSim  Semester project

    Création d'un scénario pour simulateur de conduite semi-autonome

    More information
    Student(s): Gaëtan Spada, Mickael Reynaud

  • Détection des émotions via les micro-expressions pour réduire le stress  Master project

    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
    Student(s): Wendy Mikhael El Murr

  • Chat conversation analysis  Bachelor project

    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.

    More information
    Student(s): Samuel Torche

  • Virtual Reality in Art  Internship project

    Ce projet, en collabaration avec Monsieur Hafis Bertschinger, implémente la réalité virtuelle dans un musée virtuel représentant la Phénicie.

    More information
    Student(s): Chris Lteif

  • Détection des émotions cachées  Internship project

    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 information