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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.

The personalized fruition of cultural heritage, especially with the health crisis we are currently experiencing, poses challenges to the learning and educational processes that could benefit from the use of intelligent tools. By emphasizing the interactivity of the learning process, in particular, the recent Technology Enhanced Learning (TEL) methodologies can represent an enabling factor for the construction of the common knowledge. This paper pursues the idea of integrating different heterogeneous artificial intelligence techniques for the personalized administration of stimuli in a dynamic learning environment. With this in mind, this paper describes a system, called WikiTEL and its first instantiation to Cultural Heritage visits in the Matera city.

The retrieval and composition of educational material are topics that attract many studies from the field of Information Retrieval and Artificial Intelligence. The Web is gradually gaining popularity among teachers and students as a source of learning resources. This transition is, however, facing skepticism from some scholars in the field of education. The main concern is about the quality and reliability of the teaching on the Web. While online educational repositories are explicitly built for educational purposes by competent teachers, web pages are designed and created for offering different services, not only education. In this study, we analyse if the Internet is a good source of teaching material compared to the currently available repositories in education. Using a collection of 50 queries related to educational topics, we compare how many useful learning resources a teacher can retrieve in Google and three popular learning object repositories. The results are very insightful and in favour of Google supported by the t-tests. For most of the queries, Google retrieves a larger number of useful web pages than the repositories (\(p < .01\)), and no queries resulted in zero useful items. Instead, the repositories struggle to find even one relevant material for many queries. This study is clear evidence that even though the repositories offer a richer description of the learning resources through metadata, it is time to undertake more research towards the retrieval of web pages for educational applications.

Wikipedia has become the mostused source of didactic materials with its immense number of Wiki pages,freely available to anyone. Wiki Course Builder is a Web platform designed to help teachers building courses composed by Wikipedia pages only,directly taken by the Wikipedia free encyclopedia.One of the interesting and novel features is the Community module, that provides a collaborative environment, through a suitable Graphic User Interface, where teachers can interact with all the courses in the platform. The Community is a graphical visualization of the Wikipedia pages used by all the teachers in the systems, grouped in clusters, each representing a particular topic. Another characteristic is the information about the Teaching Styles of those teachers that have used that page in their courses. This feature has a twofold advantage. First, each Wikipedia page is tagged with the Teaching Styles. Second, this social aspect gives useful suggestions for possible extensions or changes in the courses. We present a complete use case showing the usefulness of the social Community environment to speed up and improve the course building activity.

The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning. In particular for the teaching activity, the numerous standard compliant Learning Object Repositories available via the Internet, and Open Educational Resources repositories, provide formidable support to teachers when they need to develop a course that can also make use of already available learning materials. The search and selection of Learning Objects, however, can be an inherently complex operation involving accessing various repositories, each potentially involving different software tools, and different organization and specification formats for the learning resources. This complexity may hinder the very success of an e-learning course. Cross-repository aggregators, i.e., systems that can roam through different repositories to satisfy the user's/teacher's query, can help to reduce such complexity, although problems of course delivery may remain. This paper proposes a hybrid recommender system, MoodleRec, implemented as a plug-in of the Moodle Learning Management System. MoodleRec can sort through a set of supported standard compliant Learning Object Repositories, and suggest a ranked list of Learning Objects following a simple keyword-based query. The various recommendation strategies operate on two levels. First, a ranked list of Learning Objects is created, ordered by their correspondence to the query, and by their quality, as indicated by the repository of origin. Social generated features are then used to show the teacher how the Learning Objects listed have been exploited in other courses. A real life experimental study is also presented, and the validity of the MoodleRec approach discussed.

This paper describes a methodology based on the object-oriented programming paradigm, to support the design and implementation of a symbolic computation system. The requirements of the system are related to the specification and treatment of mathematical structures. This treatment is considered from both the numerical and the symbolic points of view. The resulting programming system should be able to support the formal definition of mathematical data structures and methods at their highest level of abstraction, to perform computations on instances created from such definitions, and to handle abstract data structures through the manipulation of their logical properties. Particular consideration is given to the correctness aspects. Some examples of convenient application of the proposed design methodology are presented.