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Emotion modeling for social robotics has the great potential to improve the life quality for the elderly and individuals with disabilities by making communication, care, and interactions more effective. It can help individuals with communication difficulties express their emotions. It can also be used to monitor the emotional well-being of elderly persons living alone and alert caregivers or family members if there are signs of distress. More broadly, emotion modeling is necessary to design robots closer and closer to human beings that can naturally interact with them by understanding their behavior and reactions. Here, we propose a deep learning technique for emotion classification using electroencephalogram (EEG) signals. We aim to recognize valence, arousal, dominance, and likability. Our technique uses the spectrogram from each of the 32 electrodes applied in the skull area. Then, we employ a Resnet101 convolutional neural network to learn a model capable of predicting several emotions. We built and tested our model on the DEAP dataset.

Although ubiquitous and fast access to the Internet allows us to admire objects and artworks exhibited worldwide from the comfort of our home, visiting a museum or an exhibition remains an essential experience today. Current technologies can help make that experience even more satisfying. For instance, they can assist the user during the visit, personalizing her experience by suggesting the artworks of her higher interest and providing her with related textual and multimedia content. To this aim, it is necessary to automatically acquire information relating to the active user. In this paper, we show how a deep neural network-based approach can allow us to obtain accurate information for understanding the behavior of the visitor alone or in a group. This information can also be used to identify users similar to the active one to suggest not only personalized itineraries but also possible visiting companions for promoting the museum as a vehicle for social and cultural inclusion.

Current image/video acquisition and analysis techniques allow for not only the identification and classification of objects in a scene but also more sophisticated processing. For example, there are video cameras today able to capture micro facial expressions, namely, facial expressions that occur in a fraction of a second. Such micro expressions can provide useful information to define a person's emotional state. In this article, we propose to use these features to collect useful information for designing and implementing increasingly effective interactive technologies. In particular, facial micro expressions could be used to develop interfaces capable of fostering the social and cultural inclusion of users belonging to different realities and categories. The preliminary experimental results obtained by recording the reactions of individuals while observing artworks demonstrate the existence of correlations between the action units (i.e., single components of the muscular movement in which it is possible to break down facial expressions) and the emotional reactions of a sample of users, as well as correlations within some homogeneous groups of testers.

The DAtaset of Joint Educational Entities (DAJEE) is a repository which hosts more than 20,000 educational resources crawled from the MOOC platform Coursera. The resources are divided per category according to the MOOC categorization on Coursera, which is, however, very shallow. This contribution focuses on a more meaningful categorization of the resources in DAJEE, tailored to their content. To achieve such goal, our approach enriches the resources in DAJEE with semantic entities by applying state-of-the-art semantic techniques. The result is a significant improvement of the categorization of the resources in DAJEE than the previous version.

Learning Management Systems are among the most popular e-learning tools. Over the last few years, however, scientific research has made considerable progress in developing valuable resources currently unavailable in most Learning Management Systems, including solutions aimed at providing students with personalized support throughout the learning process, which is an essential requirement in continuing education. Observing and modelling the learner, and adapting their learning experience accordingly means opening up new technological and, above all, methodological perspectives in e-learning. The work described in this paper is part of the Open Learning project, in which business-based and university researchers aim to combine the most frequently used e-learning technologies, Learning Management Systems, with the benefits of customized systems so as to develop an innovative learning content delivery system based on the personalization of the learning experience. The proposed system integrates moodle with an engine, LS-Plan, which provides automated sequencing of the learning material based on the learner’s knowledge and learning styles. This paper describes the new system and presents the results of tests conducted in the domain of Italian Neorealist Cinema., Journal of e-Learning and Knowledge Society, Vol 7, No 1 (2011): Journal of e-Learning and Knowledge Society

This work can be seen as a rst approach to a new planning model that takes into account the possibility to express actions and uents with non-boolean values. According to this model, a planning problem is dened using both graded (multi-valued) and classical (boolean) uents. Moreover, actions that can have different application degrees can be defined. In this work a PDDL extension allowing to describe such new problems is proposed and a planning algorithm for such problems is presented.

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students' assessment and many others. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher's assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appear appropriate for concept maps. In concept maps, concepts exposed are at least as relevant as the structure that contains them. Neglecting the semantic and didactic aspect inevitably causes inaccuracies and the consequently limited applicability in Learning Analytics approaches. In this work, starting from an algorithm which compares didactic characteristic of concept maps, we present an extension which exploits a semantic approach to catch the actual meaning of the concepts expressed in the nodes of the map.

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.

A Concept Map provides a graphical, hierarchical, representation of a domain of knowledge, and can be used by teachers to define and organize the content of a course. The use of existing concept maps, possibly implemented by means of tested and suitable learning material, can be of great help for the teacher, while defining her/his own concept map. In this context, being able to establish the similarity between two concept maps is a significant asset, allowing to limit the analysis of other maps, and focus on those that can be of actual help for the teacher. This is true in a special way if the measure is computed 'according to one among many possible pedagogical criteria of similarity'. We are working on the definition of measures entailing similarity based on different pedagogical criteria. In this paper, we meet the problem of experimenting such with measures, and comparing them with baseline measures, while investigating the effectiveness of a new measure. To support this kind of analysis we propose here a web-based system. We show the characteristics of the system, and use it to compare the behaviour of three measures of similarity: the pre-requisite measure (also presented) computes the similarity of two maps with respect to a particular pedagogical aspect. The other two measures are used as a baseline. We conclude that the system has shown its usefulness and can be used for more extensive analysis of other measures.

The increasing trend of sharing educational resources on the World Wide Web has attracted several contributions from the research community. Since most Technology Enhanced Learning users retrieve resources from the Web for teaching or learning, the Web is recognised as a repository of educational material. A big issue in automatic retrieval of online resources is that the Web is a huge and mostly unorganised space. Hence, there is no guarantee that items retrieved by current search engines are appropriate for educational uses. Automatically identifying Web-content suitable for education is one of the most challenging objectives because it requires extraordinary attention. Studies in Technology Enhanced Learning have proposed several solutions to support the teaching and learning needs of instructors and pupils within an enclosed platform. Other studies offer different techniques for collecting resources from the open Web that have specific characteristics. This work aims to gather findings and results from such studies for establishing a set of guidelines for researchers in education, expecting to improve the development of future proposals in this field.

The Web offers an unprecedented number of resources and has become the most popular source of information for students shaping their understanding of a new topic, and for instructors selecting relevant material for learning and teaching activities. Even though search engines are the most widely used tools for searching for educational content, the realities of the learning and teaching processes make the retrieval and evaluation of educational resources more complex than they are for other goods or services. The lack of recourse to educational metadata in web pages, as well as the size of the Web itself, call for specific techniques to be adopted for a more effective ranking of educational content. In this study, we propose an innovative approach based on semantic technologies. The SemanticSearch approach described in this paper leverages knowledge graph representation of teaching contexts and proposes a new ranking method for rating educational web content. In the literature we find an Educational Ranking Principle that ranks web pages for a specific teaching context. In this study, we integrate the Educational Ranking Principle with semantic data to extend the experimentation and analyse performance further. We undertake an evaluation involving university teachers, considering more than 70 queries to measure the SemanticSearch performance against the Educational Ranking Principle in addition to two state-of-the-art methodologies: Tf-Idf and BM25F. Paired t-tests of four accuracy measures provide statistical evidence for improvements made by using SemanticSearch method when compared to the three baselines.

A concept map provides a graphic hierarchical means of representing how knowledge is structured in a domain. It visually organizes a set of concepts showing their mutual relations. An analysis of the similarities between two concept maps can produce significant results in any fields where Intelligent Knowledge Management is used, such as Healthcare, Policy Development, Energy and Waste Management, Resource Consumption Sustainability, Mobility, Safety, Citizen Empowerment, and, of course, Education. In an educational setting, a concept map conveys the various concepts connected by relations of dependence that a course must cover. However, the similarity between two concept maps for education has to be measured according to criteria that take into consideration the pedagogical properties of the maps, i.e., not only considering the structural aspects of the maps themselves. An automated analysis of the similarity between two concept maps can allow the teacher to reflect on different interpretations of the knowledge domain of a certain course as well as to assess how existing learning material can be implemented in a new course. Research into this aspect of concept mapping appears to be relatively scarce. This paper proposes criteria to assess the similarity of two concept maps, also based on pedagogical features, with the aim of providing teachers with better support during the course creation process. Each criterion is implemented through a specific measure function. The measures are then shown to be sensitive to their criterion rationale by evaluating them against a collection of random case studies.

Among more dramatic effects, the COVID-19 scenario also raised the need for new online information and communication services, promoting the spread of software solutions whose usefulness will last well beyond the pandemic situation. Particularly in the cultural heritage domain, it has been unveiled the relevance of new AI-based approaches, able to dynamically aggregate information and making them available for a customized fruition aimed to the individual cultural growth. Here, we integrate machine learning techniques for the automatic generation of contents for an intelligent tutoring system grounded on automated planning techniques. We present a solution for semantic, intelligent creation of personalized cultural contents, born as a lesson-making assistant, but developed as to become a multi-function “cultural crossover”, useful in the frame of a wide range of planning, dissemination, and managing activities for cultural heritage contents. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

We present a method for analyzing the dynamics of a Z document describing a non-sequential system. First a formal operational semantics based on the chemical metaphor is given to Z. Then, some Unity-like temporal logic constructs are defined on such a formal operational semantics in order to allow the specification and analysis of dynamic and temporal properties of concurrent systems, such as safety and liveness properties.