Collaborating Authors

An ontology-based chatbot for crises management: use case coronavirus Artificial Intelligence

Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines. Obtaining the right information at the right time and place is the key to effective disaster management. The term "disaster management" encompasses both natural and human-caused disasters. To assist citizens, our project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours. With the growth in the World Wide Web, it is quite intelligible that users are interested in the swift and relatedly correct information for their hunt. A chatbot can be seen as a question-and-answer system in which experts provide knowledge to solicit users. This master thesis is dedicated to discuss COVID Assistant chatbot and explain each component in detail. The design of the proposed chatbot is introduced by its seven components: Ontology, Web Scraping module, DB, State Machine, keyword Extractor, Trained chatbot, and User Interface.

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction Artificial Intelligence

The ability to predict citywide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of citywide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a nontrivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors ( e.g., camera, ultrasonic sensor, and GPS). To this end, we propose S emi-supervised H iera rchical Re current Graph Neural Network (SHARE) for predicting citywide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial auto-correlation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.

Minimizing and Managing Cloud Failures

IEEE Computer

Guaranteeing high levels of availability is a huge challenge for cloud providers. The authors look at the causes of cloud failures and recommend ways to prevent them and to minimize their effects when they occur.

A framework to increase the safety of robots operating in crowded environments


Humans are innately able to adapt their behavior and actions according to the movements of other humans in their surroundings. For instance, human drivers may suddenly stop, slow down, steer or start their car based on the actions of other drivers, pedestrians or cyclists, as they have a sense of which maneuvers are risky in specific scenarios. However, developing robots and autonomous vehicles that can similarly predict human movements and assess the risk of performing different actions in a given scenario has so far proved highly challenging. This has resulted in a number of accidents, including the tragic death of a pedestrian who was struck by a self-driving Uber vehicle in March 2018. Researchers at Stanford University and Toyota Research Institute (TRI) have recently developed a framework that could prevent these accidents in the future, increasing the safety of autonomous vehicles and other robotic systems operating in crowded environments.


New Scientist

Ambient light may be all you need to charge your phone. Small, thin and flexible panels created with an inkjet printer can harvest energy from artificial light and sunlight. Conventional solar panels typically use silicon to capture the sun's energy. But Sadok Ben Dkhil from Dracula Technologies and his team have developed a conductive plastic that can capture a wider range of wavelengths. "Our material can capture energy from indoor light, which isn't possible with silicon," says Ben Dkhil.