Situation Awareness is defined by Endsley as ''the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future'' and it deals with the continuous extraction of environmental information and its integration with prior knowledge for directing further perception and anticipating future events. To realize systems for Situation Awareness, individual pieces of raw information (e.g. sensor data) should be interpreted into a higher, domain-relevant concept called ''situation'', which is an abstract state of affairs interesting to specific applications. The power of using ''situations'' lies in their ability to provide a simple, human-understandable representation of, for instance, sensor data. The aim of this work is to propose an overview of the applications of Computational Intelligence and Granular Computing for the implementation of systems supporting Situation Awareness. In this scenario, several and heterogeneous Computational Intelligence models and techniques (e.g. Fuzzy Cognitive Maps, Fuzzy Formal Concept Analysis, Dempster-Shafer Theory of Evidence, Ontologies, Knowledge Reasoning, Evolutionary Computing, Intelligent Agents) can be employed to implement such systems. Moreover, in a Situation Identification process, huge volumes of heterogeneous data need processing (e.g. fusion). With respect to this issue, Granular Computing is an information processing theory for using ''granules'' (e.g. subsets, intervals, fuzzy sets) effectively to build an efficient computational model for dealing with the above-mentioned data. The overview is proposed coherently to both methodological and architectural viewpoints for Situation Awareness.

Since the harmful consequences of the online publication of fake news have emerged clearly, many research groups worldwide have started to work on the design and creation of systems able to detect fake news and entities that share it consciously. Therefore, manifold automatic, manual, and hybrid solutions have been proposed by industry and academia. In this article, we describe a deep investigation of the features that both from an automatic and a human point of view, are more predictive for the identification of social network profiles accountable for spreading fake news in the online environment. To achieve this goal, the features of the monitored users were extracted from Twitter, such as social and personal information as well as interaction with content and other users. Subsequently, we performed (i) an offline analysis realized through the use of deep learning techniques and (ii) an online analysis that involved real users in the classification of reliable/unreliable user profiles. The experimental results, validated from a statistical point of view, show which information best enables machines and humans to detect malicious users. We hope that our research work will provide useful insights for realizing ever more effective tools to counter misinformation and those who spread it intentionally.

Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL). INDEX TERMS Classification rule mining, intelligent tutoring systems, ontologies, pedagogical rules, semantic web, web ontology language (OWL).

A smart city can be defined as a city exploiting information and communication technologies to enhance the quality of life of its citizens by providing them with improved services while ensuring a conscious use of the available limited resources. This paper introduces a conceptual framework for the smart city, namely, the Smart City Service System. The framework proposes a vision of the smart city as a service system according to the principles of the Service-Dominant Logic and the service science theories. The rationale is that the services offered within the city can be improved and optimized via the exploitation of information shared by the citizens. The Smart City Service System is implemented as an ontology-based system that supports the decision-making processes at the government level through reasoning and inference processes, providing the decision-makers with a common operational picture of what is happening in the city. A case study related to the local public transportation service is proposed to demonstrate the feasibility and validity of the framework. An experimental evaluation using the Situation Awareness Global Assessment Technique (SAGAT) has been performed to measure the impact of the framework on the decision-makers’ level of situation awareness.

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