Omar Abou Khaled
University of Applied Sciences and Arts Western Switzerland, Fribourg Campus
HumanTech Institute
E-mails: Omar.Aboukhaled@hefr.ch | Omar.Aboukhaled@hes-so.ch
ORCID: https://orcid.org/0000-0002-0178-9037
DBLP: https://dblp.dagstuhl.de/pers/hd/k/Khaled:Omar_Abou
Google Scholar ID: https://scholar.google.com/citations?user=AYzqC1wAAAAJ
People HES-SO: https://people.hes-so.ch/fr/profile/omar.aboukhaled?type=direct
NLU on the edge Bachelor project
31.08.2020La 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.
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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.Student(s): Nicolas Crausaz
Norovirus outbreak detection using tweets Internship project
28.08.2020A 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.
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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.Student(s): Jean Khoury
Cyberbullying and Social Networks Semester project
08.06.2020More informationSocial 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.
Student(s): Charles PerriardSupervisor(s): Jacky Casas, Omar Abou Khaled
Integrated NLP Pipeline Master project
05.06.2020More informationThe 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.
Student(s): Cheryl Sarrouh, Toufic Yammine
BombusCar II Semester project
13.05.2020Le 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 informationStudent(s): Quentin Seydoux34 match II Semester project
13.05.2020More informationLa 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.
Student(s): Dorian Saudan
Détection des émotions via les micro-expressions pour réduire le stress Master project
30.09.2019L'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 informationStudent(s): Wendy Mikhael El MurrChat conversation analysis Bachelor project
02.09.2019Privacy 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.
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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.Student(s): Samuel Torche
Virtual Reality in Art Internship project
30.08.2019Ce projet, en collabaration avec Monsieur Hafis Bertschinger, implémente la réalité virtuelle dans un musée virtuel représentant la Phénicie.
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Détection des émotions cachées Internship project
30.08.2019Le 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.
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Alphabet phénicien en réalité augmentée Internship project
30.08.2019Ce projet, en collabaration avec Monsieur Hafis Bertschinger, fusionne l’art de cet artiste avec la technologie moderne par le biais de la réalité augmentée.
More informationHumanTech chatbot Internship project
23.08.2019The purpose of this project is to enhance the interaction between the user and HumanTech website by integrating a chatbot in HumanTech website that will be able to answer questions about the institute.
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MeccanoLego 2.2 Bachelor project
22.05.2019More informationMeccanoLEGO is a VR workbench simulation program. Its main goal is to make using tools like laser cutters accessible and easy to use thanks to a virtual space in which the user can create their own three-dimensional models and generate flat cutouts that fold into the desired form.
Student(s): Sébastien FragnièreCommunication entre un simulateur de conduite et une application mobile Semester project
10.05.2019Ce projet de semestre s'inscrit dans l'avancement du projet AdVitam, mené par l’institut HumanTech, en collaboration avec l’HE-Arc, l’Université de Fribourg et l'EPFL+ECAL Lab. Le but de ce projet de semestre est d'implémenter la communication entre le simulateur de conduite servant à faire des tests et une application mobile pour tablette dévelopéee dans le cadre du projet.More informationStudent(s): Alain SchallerSupervisor(s): Omar Abou Khaled, Elena Mugellini, Leonardo Angelini, Marine Capallera, Quentin Meteier
Evaluation of Deep Learning Methods on SEM Images of Asbestos Master project
06.05.2019Asbestos is a highly toxic silicate mineral that has been used widely in many products for its insulating, non-flammable and heat resistant properties. Exposition to high concentrations of asbestos may lead to chronic inflammation of the lungs and cancer. After the toxicity has become known, much effort was undertaken - and is up to this day - in removing asbestos from buildings, roofs and other materials used in industry and in the public. Asbestos detection is a manual, complex and time-intensive process, that requires an experienced expert in order to have consistent and correct results. In an attempt to reduce manual labor and increase consistency in detection, machine learning models have been recently adopted to automate the detection process.
More informationStudent(s): Thomas Kolonko