Cyber Situation Awareness requires effective methods to interpret complex, dynamic network data, and Granular Computing offers a powerful framework for managing such complexity through abstraction. In this work, we propose a granular computing-based approach for network activity classification that supports Cyber Situation Awareness by combining the Clustering-by-Time method with the principle of justifiable granularity. The system selects the most informative subsets of traffic within time windows, summarizes them into optimized frames, and trains a Random Forest classifier for anomaly detection. Evaluated on the LUFlow dataset, the approach achieves significant data reduction—up to 98%—while maintaining good detection accuracy. This enables scalable and efficient intrusion detection in complex network environments.

In a rapidly evolving global context — driven by technological innovation and societal change — the need for continuous reskilling and upskilling in education and training is more urgent than ever. To address this challenge, CONSALE (Constructing Situation Awareness in microLearning Environments) offers a structured framework for adaptive microlearning that aligns instructional goals with cognitive processes. By integrating Understanding by Design with Situation Awareness-Oriented Design, CONSALE enables the creation of personalized, context-aware learning experiences. Its implementation in Moodle — via a plugin-based architecture — supports dynamic learner profiling (based on the Felder-Silverman model), behavioral adaptation, and cognitively tagged content delivery, enhancing engagement and learning outcomes.

Accurately identifying and profiling users is one of the primary challenges of many modern web applications. This paper presents an approach for user profiling that utilizes Fuzzy User Signatures combined with browser fingerprinting techniques. Our approach analyzes users' web domain visit frequencies and categories to determine their preferences and behaviors. Fuzzy User Signatures provide a condensed representation of user activities, enabling a framework for assessing user similarity. This method can significantly improve web navigation experiences by allowing for personalized content and product recommendations. The approach has been evaluated on a dataset comprising users' web activities combined with browser fingerprints, achieving overall good performances.

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset.

Situation awareness of human and artificial agents can be improved by the recognition and adequate representation of real-life situations. The lack of easy-understandable, easy-to-use, and effective computational models of situations hindered the adoption and diffusion of situation awareness approaches in modern human-machine systems. Context Space Theory is a context awareness approach that uses a geometric metaphor to provide integrated mechanisms for representing contexts and situations. A drawback of this approach is the expert-based definition of context and situation spaces. This process can be time-consuming and expensive. In this paper, we propose a novel approach, namely Machine Learning-based Context Space Theory, which adopts machine learning techniques and, in particular, decision trees, to semi-automatically define context spaces and situation spaces with a data-driven approach. A case study related to the monitoring and control of the Covid-19 pandemic in Italy is proposed to demonstrate the feasibility and benefits of the proposed approach.

The lack of motivation and engagement is recognized as one of the main causes of learners dropping out of e-learning systems. In this paper, an Adaptive Learning System, based on the principles of situation awareness, is proposed to tackle such an issue. The work proposes a situation model based on motivation and engagement. A technique based on Fuzzy Cognitive Map (FCM) has been defined to identify the current situation by tracking the behavior and the interactions of the learner with the system. The FCM drives the process of feedback generation to improve the situation awareness of the learner, and therefore their motivation and engagement. The system has been evaluated using the Situation Awareness Global Assessment Technique, involving students and teachers. The experimental results demonstrate that the system is able to significantly improve the situation awareness of both learners and teachers, reducing the risk of learner dropout.

Social networks are becoming increasingly important in many fields, from marketing analysis to bioinformatics. Link prediction processes are essential tasks required for analysis of the networks' structures. In this paper, we propose a fuzzy computational model, called Fuzzy Social Signature, to represent a network from the perspective of a single user. This model assumes that not all links are equally important and that the relationships between nodes of a social network can be vague and uncertain. Based on the proposed Fuzzy Social Signature, a preliminary technique for link prediction between users performing same activities is proposed. Encouraging results have been obtained with an initial set of experiments using a real-world dataset.

Introduction of graph-based data representation formats, that resulted in Knowledge Graphs and Linked Open Data, enables new ways of processing and analyzing relations between individual pieces of data. One of the most important features of such representation is its ability to represent data semantics. We state that an important step towards obtaining a full utilization of graph-based semantics is to create a formal process of extracting underlying structures of data from Knowledge Graphs and Linked Open Data, as well as building data models. The paper proposes a methodology, based on category theory, for representing graph-based data as a topos category. Construction of topos give us the ability to identify two types of features: ones that are involved in definitions of other concepts; and ones that show how other concepts are involved in a definition of a given concept. Topos and structures of features allow for reasoning about concepts and their interrelations. Further, mechanisms of category theory enable to synthesize new concepts. A simple example is included.

We present a methodology to support urban planners and decision makers in obtaining a good awareness of how city assets (points of interest) are perceived by a community, and on the impact and influence that this collective perception can have on other city assets and city issues such as mobility, environment, security. The methodology employees Fuzzy Cognitive Maps and Fuzzy sets. Fuzzy Cognitive Maps are used to model the relationships between elements of mental representations that different communities have with regards to city issues. The concept of signature as relation between two fuzzy sets is adopted, in analogy to what proposed by Yager and Reformat [1], to characterize a point of interest. Different signatures are subsequently grouped to characterize an area and adopted, in combination with sentiment analysis, to derive a measure of collective perception on the quality of the area. This measure is used to activate some qualitative concept of a Fuzzy Cognitive Map and perform what-if analysis. The methodology has been applied to a sample of three POIs (representing three attractions of the city of Salerno) by using data gathered from the Web and involving some real citizens. Our preliminary results are encouraging with regards to the possibilities offered by our approach of enforcing city decision makers with a good awareness on how changes in the perception of quality of urban areas can influence other city related issues.

Events that deal with Group Decision Making are continuously studied in order to provide a suitable representation of different opinions, with the aim of reaching the consensus of all experts involved in decision processes. In this paper, the authors, focusing on employees' evaluations inside Italian companies, propose an extension of a fuzzy consensus model dealing with a feedback process to guide the decisions. Precisely, a fuzzy logic approach is used to compute the importance degree of the experts considering, besides their experiences and roles, the profile of the resource to evaluate, i.e. a factor that indicates the working trend of the employee. This allows more fair evaluations of resources, as the importance of each expert also considers the behavior of employees during their whole working period. A case study, that focus on the evaluations inside a real Italian company, is useful to analyze the proposed approach.

We present an application of Granular Computing and Three-way decisions to intelligence analysis. In particular we extend the Analysis of Competing Hypotheses with an additional perspective devoted to support analysts in reasoning with groups of hypotheses that can be equivalent on the basis of partial and incomplete evidence, and in classifying these groups of hypotheses with respect to a decisional attribute of interest for the analyst, such as dangerous or safe. Creating and reasoning with granules and multi-level granular structures give to our approach an added value when dealing with a large number of evidence and hypotheses. Three-way decision making offers the possibility of a rapid understanding of how granules of hypotheses approximate a class of dangerous hypotheses, with clear benefits when analysts have to take decision on classifying a group of hypotheses or setting a proper level of attention to group of equivalent hypotheses.

Situation identification is a complex task that is usually employed in order to sustain the work of Decision Support Systems in several and heterogeneous application scenarios like, for instance, Emergency Management, Safety and Security. Typically, situation awareness systems gather and process raw sensor data by means of different techniques. In this context, it is fundamental to exploit qualitative sensor data in order to guarantee the reliability of the situation identification task results. The consolidation of Internet of Things and the growth of the Linked Sensor Data ecosystem provide us with different degrees of availability and, sometimes, redundancy of sensor observations that could be conflicting. This could be caused by sensor failures due to contextual factors, malicious attacks, faults. This paper proposes an approach based on Fuzzy Consensus to assess data coming from a group of redundant sensors and provide reliable observations to be exploited for situation identification. Lastly, Granular Computing paradigm is adopted to handle multigranularity of information, i.e., To manage observations assessed in different linguistic term sets.

The concept of Seamless Learning is becoming more and more effective because the newer technologies are able to meet the personal needs of the people and really support them in their learning processes. Thus, the learning experience is a moment in the everyday life strongly related with the situation each person is dealing with. The main idea of this work is to define a flexible seamless learning environment able to identify the context where a learner is deepened in and to apply an adaptation by respecting her learning goals. The proposed approach leverages on three main aspects: situation awareness, adaptive learning and semantic technologies.