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Cultural heritage is an important resource that allows us to know and promote a territory. In this respect, it is important to experiment with the enhancement of cultural heritage by adopting approaches that meet the dynamic needs of various types of users. The aim of this paper is to introduce a recommender system capable of developing adaptive tourist routes. In fact, the proposed system suggests points of interest and related services according to both the profile of the tourist and contextual aspects. In particular, the interaction of the user with the system occurs through a chatbot that allows to build a real dialog. In order to show the potential of the proposed approach, a prototype was developed to support the user in building a customized tourist route related to some of the most important cultural sites in Campania (a region in Southern Italy): Herculaneum, Paestum and Pompeii.

The cultural tourism market is evolving towards a dimension of complete fulfilment of the tourist’s needs. On the one hand, it is enhancing the centrality of the cultural aspect in a 360° travel experience and on the other is paying attention to the choice of one’s journey, completed by, according to each context, all needed services. The information overload, the substantial changes of the traveller’s decisions and the enlargement of the tools of Knowledge available to each connected user are today’s main levers of change and require the use of Context-Aware Systems. In this scenario, a context-aware framework for the fruition of e-Tourism contents and services will be discussed. The proposed system is able of supporting not only tourists but also Public Institutions and professionals, through the dynamic design of core and services for tourism promotion. In order to test the proposed methodology, a prototype has been developed, obtaining promising results.

In the panorama of Italian coastal tourism, there are many unique and unexplored places. These places, which suffer from the lack of government investment, present the need to be promoted through low consumption systems and widely used: distributed applications.

Within what is called the Fourth Industrial Revolution, one of the problems that companies encounter frequently, in order to ensure themselves and their products and services over time, is the need for continuous training of the people who work in synergy for them. If continuing education is a problem, e-learning is its natural solution. The research concerns the realization of a system capable of providing a constant, reliable, and friendly help through a practical and nice chatbot based on context processing. In particular, the proposed chatbot acts as a reminder and follows the user during his personal corporate training, ready to provide, when needed, the necessary and useful teaching material to complete the educational course.

The cultural heritage is one of the most important resources of the territory. It represents one of the scenarios where new technologies can provide more interesting contributions. In particular, adaptive systems and related services may increase the promotion of cultural heritage. In fact, tourists can use several services able to filter the huge amount of data present on the network in order to only provide relevant information. The aim of this paper is to introduce a chatbot based on a Context-Aware System. This chatbot recommends contents and services according to tourist profiles and context. For testing the proposed architecture, a prototype was developed in order to support tourists during a visit to some cultural sites in Campania: Paestum, Pompeii and Herculaneum. The first experimental results are encouraging and show the potential of the proposed approach.

Finding the information and making it available today is a very complex challenge. On the one hand, the large amount of available data requires a great ability to manage information; on the other hand, understanding the real needs of users requires complex systems that can provide contextual information. Italy's economy is based on tourism, thanks to its cultural heritage visited by millions of people every day. The goal of this paper is to create a recommender system to furnish a tailor-made story for the user based on the context in which it is located, thanks to a smart app that can provide contextual information. Using a Chatbot, this system also provides a context awareness data in order to enhance the tourist experience.

E-Learning is becoming one of the most effective training approaches. In particular, the blended learning is considered a useful methodology for supporting and understanding students and their learning issues. Thanks to e-Learning platforms and their collaborative tools, students can interact with other students and share doubts on certain topics. However, teachers often remain outside of this process and do not understand the learning problems that are in their classrooms. A solution for ensuring the privacy of communication among students could be the adoption of a Sentiment Analysis methodology for the detection of the classroom mood during the learning process. In this paper, we investigate the adoption of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment Grabber. The proposed approach can detect the mood of students on the various topics and teacher can better tune his/her teaching approach. The proposed method has been tested in real cases with effective and satisfactory results.

Our daily life is characterized by countless human relationships. Every day we go to work, do shopping, partake in leisure time. At the base, there are some choices that lead us to make a series of activities; In which we interact and relate with many people. What influences the choices we make? Answering this question can be a very daunting task. However, it can help us to identify the main key elements that influence the behaviour of people. Arguably the weather conditions have a major impact in our daily decisions and events, with the more extreme weather having a stronger influence our lives. How can we tie Human behaviour to environmental conditions? Can we determine the human behaviour during a catastrophic environmental event? To answer these questions, we can query various information sources including social networks. These represent a good starting point with respect to the analysis of a human behaviour aspect. The aim of the paper is to develop a methodology in gaining an understanding to what is happening on the territory from user behaviours and vice versa. The paper will conclude with a discussion of the results generated from the application of this methodology to real cases.

&laquo, You take delight not in a city&rsquo, s seven or seventy wonders, but in the answer it gives to a question of yours&raquo, Those are the words used by Italo Calvino in his book &ldquo, Invisible Cities&rdquo, to give us a key aspect of a city, and it is from this consideration that this research work starts. In particular, the aim is to study and develop innovative solutions that guarantee value for the territory and for the cultural and landscape assets that insist on it. At the same time, such innovative solutions should be able to make the answers, connected to these resources, &ldquo, tailored&rdquo, to the user who requested it. For this reason, an architecture will be described, which allows content generating actors and users to operate by means of a broker-platform. Through this platform, it is possible to search a content within a Knowledge Base and, through the automatic orchestration of services, to activate in a controlled way the access and fruition of the information contained in it.

Several applications require a distributed measurement system able to measure the same or different parameters at different points in a wide area. In recent years, a wide variety of solutions have been proposed for the remote measurement and data transmission. In this paper an architecture of a real-time energy management is presented. It provides several Smart Meters, that continuously monitor connected loads and communicate with a Smart Concentrator via CAN bus. The main task of SC is to temporarily store the data forwarded from a single Smart Meters, in order to make this information available for HTTP request/response. The paper illustrates the hardware architecture, discusses the adopted communication protocol solutions and is completed with an example of energy monitoring for a smart utility through a Web-based application.

Text classification methods have been evaluated on supervised classification tasks of large datasets showing high accuracy. Nevertheless, due to the fact that these classifiers, to obtain a good performance on a test set, need to learn from many examples, some difficulties may be found when they are employed in real contexts. In fact, most users of a practical system do not want to carry out labeling tasks for a long time only to obtain a better level of accuracy. They obviously prefer algorithms that have high accuracy, but do not require a large amount of manual labeling tasks. In this paper we propose a new supervised method for single-label text classification, based on a mixed Graph of Terms, that is capable of achieving a good performance, in term of accuracy, when the size of the training set is 1% of the original. The mixed Graph of Terms can be automatically extracted from a set of documents following a kind of term clustering technique weighted by the probabilistic topic model. The method has been tested on the top 10 classes of the ModApte split from the Reuters-21578 dataset and learned on 1% of the original training set. Results have confirmed the discriminative property of the graph and have confirmed that the proposed method is comparable with existing methods learned on the whole training set.

It is well known how the use of additional knowledge, coded through ontologies, can improve the quality of the results obtained, in terms of user satisfaction, when seeking information on the web. The choice of a knowledge base, as long as it is reduced to small domains, is still manageable in a semi-automatic mode. However, in wider contexts, where a higher scalability is required, a fully automatic procedure is needed. In this paper, we show how a procedure to extract an ontology from a collection of documents can be completely automatised by making use of an optimization procedure. To this aim, we have defined a suitable fitness function and we have employed a Random Mutation Hill-Climbing algorithm to explore the solution space in order to evolve a near-optimal solution. The experimental findings show that our method is effective. © 2011 Springer-Verlag Berlin Heidelberg.

It is well known that one way to improve the accuracy of a text retrieval system is to expand the original query with additional knowledge coded through topic-related terms. In the case of an interactive environment, the expansion, which is usually represented as a list of words, is extracted from documents whose relevance is known thanks to the feedback of the user. In this paper we argue that the accuracy of a text retrieval system can be improved if we employ a query expansion method based on a mixed Graph of Terms representation instead of a method based on a simple list of words. The graph, that is composed of a directed and an undirected subgraph, can be automatically extracted from a small set of only relevant documents (namely the user feedback) using a method for term extraction based on the probabilistic Topic Model. The evaluation of the proposed method has been carried out by performing a comparison with two less complex structures: one represented as a set of pairs of words and another that is a simple list of words.