D-Learning and COVID-19 Crisis: Appraisal of Reactions and Means of Perpetuity.

Distance Learning (D-learning), as an alternative educational solution for students who cannot attend in-person classes, has been deployed during the COVID-19 pandemic to deliver the promises promoted long ago by technology and education experts. For many professors and students, the shift was a first as they had to resume their classes fully online despite not being academically competent to do so. This research paper examines the D-learning scenario introduced by Moulay Ismail University (MIU). It is based on the intelligent Association Rules method to identify relations between different variables. The significance of the method lies in its ability to assist in drawing relevant and accurate conclusions for decision-makers on how to rectify and adjust the adopted D-learning model in Morocco and elsewhere. The method also tracks the most probable future rules that govern the behavior of the population under study vis-à-vis D-learning; once these rules are outlined, the training quality can be dramatically improved by adopting better-informed strategies. The study concludes that most recurrent D-learning issues reported by students systematically interrelate with ownership of gadgets and that once specific procedures are implemented, reports concerning the D-learning experience at MIU are likely to be more comforting.

Rapid Localization and Mapping Method Based on Adaptive Particle Filters.

With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.