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One of the major problems facing our cities is the disposal of the huge amount of waste produced every day. A possible solution is represented by recycling. In this article, we propose a system for automatic recognition and extraction of materials from the unsorted waste, which takes advantage of Computer Vision and Machine Learning techniques. The system can classify the material of incoming objects and grasp, and insert them into proper bins. For the material classification phase, the system analyzes the information captured by a Near-Infrared (NIR) camera and an RGB camera. Experimental tests performed on real-world datasets show encouraging accuracy values.

Curriculum Sequencing is one of the most interesting challenges in learning environments, such as Intelligent Tutoring Systems and e-learning The goal is to automatically produce personalized sequences of didactic materials or activities, on the basis of each individual student's model In this paper we present the extension of the LS-Lab framework, supporting an automated and flexible comparison of the outputs coming from a variety of Curriculum Sequencing algorithms over the same student models The main aim of LS-Lab is to provide researchers or teachers with a ready-to-use and possibly extensible environment, supporting a reasonably low-cost experimentation of several sequencing algorithms The system accepts a student model as input, together with the selection of the algorithms to be used and a given learning material; then the algorithms are applied, the resulting courses are shown to the user, and some metrics computed over the selected characteristics are presented, for the user's appraisal.

Visiting a real or virtual museum or an archaeological site can be a hard task, especially in case of large sites provided with many works of art or ancient ruins. For this reason most historical sites provide guided tours, to improve visitors satisfaction and interest. In this work we explore the use of an e-learning environment, called Lecomps5 , to provide museums or other cultural sites with the capability of automatically planning personalized tours, according to visitors needs and interests. Lecomps5 allows a domain expert, through a suitable GUI, to build a pool of learning components concerning a given site. Then the system, by means of an embedded planner, generates a personalized tour through the works of art, on the basis of the visitor's artistic interests and needs. We propose a first application of this system to an ancient archaeological site called Lucus Feroniae , showing how an e-learning platform can be successfully used for guiding visitors as well.

In the context of distance learning, Adaptive Web-based Educational System focus on personalization and adaptation, that is on “learner’s satisfaction”. In this paper we address the other side of the coin, that is the "teacher’s satisfaction" problem, which is quite seldom taken into account in educational systems. We present a new version of the Lecomps5 Web-based Educational System, a system capable of providing personalization and adaptation on the basis of learner’s knowledge, learning styles and learning progresses. In this new version, a framework provides the teacher with an easy and flexible tool for managing learning material, expressing different didactic strategies and sequencing personalized courses by means of an embedded planner. Such functionalities are supported by the system basing on evaluations of learner’s knowledge, learning styles, and learning progresses. We report on a first controlled experiment, we made to evaluate the “teacher’s satisfaction”.