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.

Workplace learning can be conceived as the set of processes related to learning and training activities at work. Typically, workplace learning includes formal, informal, and non-formal learning activities. Having a control on the whole learning process of each worker is a complex task. Indeed we have to align individual learning paths, real workers' needs (for instance in terms of tasks or projects to accomplish), career plans, and other organizational needs to activate virtuous knowledge flows. In order to accomplish this complex task a comprehensive framework is needed. This paper provides the definition of the aforementioned framework by exploiting semantic technologies in order to model (by means of Ontologies), represent, extract, and share knowledge within organizations. Although the proposed framework allows a wide range of workplace learning experiences, it mainly focuses on informal learning, on the ways it can be sustained by exploiting organizational resources, and on the capability of linking individual and organizational learning in the context of a widely accepted knowledge flow model like socialization, externalization, composition, and internalization.

Serendipitous Learning is the learning process occurring when hidden connections or analogies are unexpectedly discovered, mostly during searching processes (for instance on the Web) which are typical for informal learning activities, especially accomplished at the workplace context. Moreover, serendipitous processes have high probability to occur in the contexts where learners have high autonomy, more chances to intervene in different activities and to interact with resources and people. This paper proposes an approach based on the Social Semantic Web vision to sustain and improve Serendipitous Learning. The proposed approach considers two connected ontology layers to model knowledge by using several SemanticWeb vocabularies like SIOC, Dublin Core, SKOS, and so on. The SKOS role is particularly relevant because it allows connections among heterogeneous resources, also across multiple communities. The proposed approach models the above-mentioned connections at the conceptual level and facilitate learners in discovering them and following unexpected paths.

This paper discusses a new computational scheme based on functional networks and applies it to the problem of classification and quantification of gas species in a mixture. A generalized functional network as a new classifier is proposed to improve the potentialities of the standard functional network classifier. Both methodology and learning algorithm are derived. The performance of this new classifier is examined by using experimental applications. A comparative study with the most common classification algorithms is carried out by showing the high-quality performance of the proposed classifier. The classifier interacts with some quantifiers, again based on functional networks and finite differences. The scheme of the quantifiers was previously proposed for single gas exposure applications and is here extended to the multigas case. Numerical results show that our approach behaves quite satisfactorily.

Workforce Management (WFM) is becoming a core decisional approach for optimizing different enterprise processes such as operational activities needed to maintain a high production rate. However, in order to solve complex optimization problems it is necessary to analyze and deal with a plethora of distributed and semantically different information defining the collection of criteria from which enterprise activities depend. For this reason, this paper introduces a novel WFM system that, by using an ontological representation of knowledge related to the different aspects of an enterprise activity, exploits a multi-criteria decision making approach for selecting the most suitable strategies to face WFM issues.

The wealth of modern enterprises has progressively shifted from tangible assets (capital, resources,) into intangible ones (knowledge, reputation, skills management, innovation processes,). Intangibles are closely related to the natural interactions normally occurring among work practices. This is where ideas, innovation, learning, knowledge, social cohesion, and other diverse intangibles synergistically contribute to performance, competition differentiators and value creation. In order to make these intangibles productive for the organization, we have to define suitable models for their correct representation in real contexts, as well as tools for their proper management in the workplace environment in a transparent way. All these aspects are the foundation of the ARISTOTELE research project. In this paper, we address two issues: i) the use of semantic technologies for modeling and cross relating relevant organizational assets, namely knowledge, competency, worker and learning, ii) how to design a software architecture for managing these models through the integration of ad-hoc developed tools and commercial off-the-shelf platforms.

Workforce Management (WFM) is becoming a core decisional approach for optimizing different enterprise processes such as operational activities needed to maintain a high production rate. However, in order to solve complex optimization problems it is necessary to analyze and deal with a plethora of distributed and semantically different information defining the collection of criteria from which enterprise activities depend. For this reason, this paper introduces a novel WFM system that, by using an ontological representation of knowledge related to the different aspects of an enterprise activity, exploits a multi-criteria decision making approach for selecting the most suitable strategies to face WFM issues.

Assessing employees' competences to properly support Competence-based Management processes (e.g. Career Development, Workforce Planning, etc.) in Organizations is a complex task. Difficulties concern with both the right assessment methodology and the most effective tools. Moreover, the assessment process is time-consuming both for assessors and assessees and often it is performed at the wrong time with considerable costs for external resources. This work proposes a novel approach, based on semantic technologies, to enhance competence assessment in Organizations by analysing content produced, tasks completed and professional relationships established by employees in their day by day activities at the workplace.

The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners' activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this point of view, the use of different cooperative intelligent agents can be exploited to analyze learner's preferences and generate high quality learning presentations which provide attractive learning solutions. In particular, to achieve this goal this paper exploits an ontological representation of the learning environment and an adaptive memetic algorithm based on a cooperative multi-agent framework. In this framework different agents analyze the e-learning instance and solve it in a parallel way, cooperating among them. This cooperation is performed by jointly exploiting data mining, via fuzzy decision trees, together with a decision making framework exploiting fuzzy methodologies. As will be shown in the experimental results section, this multi-agent strategy is capable of speeding up the convergence to high-quality personalized e-learning experiences.

In an era in which organizations increasingly consider the competencies of their employees as a crucial resource, human management becomes a key activity for improving staff and business performances. Important is also knowing who knows what inside the organization, so that project teams are assembled as the right mix of skill, knowledge and workforce abilities. At the same time, trust, an essential component, related to understanding interpersonal and group behavior, is an indisputable prerequisite for organizational effectiveness, in terms of social-cognitive capital, global competency as well as economic exchange and social and political stability. This paper defines an approach to support competency-based management by providing recommendations about the reliability of a worker in terms of trust information and own competencies. The approach lies on an agent-based architecture which supervises the Human Resources Management (HRM). Task-oriented agents monitor the employees' profiles and capabilities by maintaining update the competencies and the trusts in the organizational social network. Particularly an agent endowed by fuzzy reasoning capabilities provides recommendation about workers' competencies in HRM decision-making processes.

Nowadays, the importance of knowledge management is well understood by managers in the organizations and, at the same time, the great significance of trust, in enabling effective knowledge sharing, is emerging. Presence or lack of trust can have serious implications for organizations with respect to the quality and of their business processes. Several scientific works have confirmed the direct correlation between social-cognitive capital, in terms of competencies and experiences, and a feeling of trust in both learning and working collaborative environments. On the other hand, Competency-Based Management allows organizations to link human resources processes to competencies in order to shape its workforce capabilities and to achieve better results. Typically, employees' competencies (and proficiency levels) are stored and used by specific enterprise software, whereas the trust is not considered or left to managers' feeling. This work proposes an approach to the improvement of collaborative learning activities by refining and allowing (at workers' level) competency-finding processes through the social calculus of trust-in-competencies degree.

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Computational Intelligence methodologies can support e‐Learning system designers in two different aspects: (1) they represent the most suitable solution able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop “in time” e‐Learning environments. This article attempts to achieve both results by exploiting an ontological representations of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework. This synergy enables multi‐island memetic approach managing a collection of models and processes for adapting an e‐Learning system to the learner expectations and to formulate objectives in an effective and dynamic intelligent way. More precisely, our proposal exploits ontological representations of learning environment and a memetic distributed problem‐solving approach to generate the best learning presentation and, at the same time, minimize the computational efforts necessary to compute optimal learning experiences.

Ontologies have been frequently employed in order to solve problems derived from the management of shared distributed knowledge and the efficient integration of information across different applications. However, the process of ontology building is still a lengthy and error-prone task. Therefore, a number of research studies to (semi-)automatically build ontologies from existing documents have been developed. In this paper, we present our approach to extract relevant ontology concepts and their relationships from a knowledge base of heterogeneous text documents. We also show the architecture of the implemented system and discuss the experiments in a real-world context.

Nowadays, we are witnesses of a transformation in the e-learning arena. This transformation has different drivers involving all the actors in the learning value chain, from final users to learning institutions through technology providers. All those actors share a common goal: making the learning processes more effective through the information and communication technologies. This is happening through the promotion of a paradigm shift from content-centered to process-centered solutions. In this paper, we present the results from the European Learning Grid Infrastructure project concerning models, processes, and services supported by a service-oriented software architecture for creating dynamic and adaptive virtual organizations for learning using Grid technologies.

The term Enterprise 2.0 applies to the use of Web 2.0 technologies as a support for business activities within the organizations. These technologies are exploited to foster inter-persons collaboration, information exchange and knowledge sharing, also outside the organization, to establish relationships based on conversational modalities rather than on traditional business communication. The vision of Enterprise 2.0 places a high value on the importance of social networks inside and outside the organization stimulating flexibility, adaptability and innovation between workers, managers, customers, suppliers and consultants. The integration between the Web 2.0 tools with traditional enterprise software, the aggregation of organization inner data with external data and the choice of adequate knowledge representations are critical aspects to be faced in order to further the growth of smart applications in the Enterprise 2.0 context. In this work we propose an approach, based on Semantic Web techniques, to relax the aforementioned critical issues.

Learning is a mechanism to acquire new knowledge and to enhance individual skills in industrial and academic environments. In particular, employing learning methods in an industrial context supports the overall business competitiveness in the new economy. Currently, the e-Learning systems provide a simple “digitalization” of the learning process where the focus is on the educational resources, which are only an input of the whole learning process, and on their presentation (delivery). Computational Intelligence methodologies can overcome current learning systems limitations attaining to personalize learning content and activities to specific preferences of the learner and to assist designers with computationally efficient methods to develop “in time” e-Learning environments. This paper shows how to achieve both results exploiting an ontological representation of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework.

In an enterprise context, competencies are often dispersed across different teams. A specific need within the enterprise could not be satisfied only because of a lack of awareness about real competencies owned by employees. The aforementioned problem involves two main critical aspects: the difficulty to manage employees' competencies in order to constantly keep them up-to-date and the ability to agilely share employees' profiles across the organization in order to support competency finding. This work proposes an approach to relax the above critical points by integrating a semantic web-based educational system within a social network system applied to the enterprise context. The integration glue is provided by using and harmonizing several existing upper ontologies also furthering semantic interoperability.

In Ambient Intelligence (AmI) vision, people should be able to seamlessly and unobtrusively use and configure the intelligent devices and systems in their ubiquitous computing environments without being cognitively and physically overloaded. In other words, the user should not have to program each device or connect them together to achieve the required functionality. However, although it is possible for a human operator to specify an active space configuration explicitly, the size, sophistication, and dynamic requirements of modern living environment demand that they have autonomous intelligence satisfying the needs of inhabitants without human intervention. This work presents a proposal for AmI fuzzy computing that exploits multiagent systems and fuzzy theory to realize a long-life learning strategy able to generate context-aware-based fuzzy services and actualize them through abstraction techniques in order to maximize the users' comfort and hardware interoperability level. Experimental results show that proposed approach is capable of anticipating user's requirements by automatically generating the most suitable collection of interoperable fuzzy services.

The success of today enterprises depends on their capability to respond on demand to challenging customers' request. At the same time, the enterprise marketplace is characterized by high dynamicity and heterogeneous requests. This makes difficult to provide in house cost-effective services able to meet them and, in many cases, the only solution is to aggregate different services at this purpose. In this context, the availability of an efficient service discovery mechanism is a key feature in order to enable the sketched context. In this paper, we present an architectural pattern for web service discovery following the principle of separation of concerns and based on the definition of discovery queries driven by the customers' needs.

Nowadays, the semantic Web technologies are exploited also in the e-learning domain in order to provide personalized and adaptive learning experiences, semantic annotation of learning contents and learner profiling. The approaches of the Web 2.0, instead, are used to implement and deploy knowledge exchange services based on the concept of social collaboration. In this work, we propose an approach resulted from the convergence between semantic Web and social Web to manage, agilely and easily, the contingent learning needs of workers within organizations. Our intention is to support the use of natural languages to express the learning needs for either driving the automatic generation of learning units or effectively adapting learning pathways.

Nowadays, the emphasis on Web 2.0 is specially focused on user generated content, data sharing and collaboration activities. Protocols like RSS (Really Simple Syndication) allow users to get structured web information in a simple way, display changes in summary form and stay updated about news headlines of interest. In the e-Learning domain, RSS feeds meet demand for didactic activities from learners and teachers viewpoints, enabling them to become aware of new blog posts in educational blogging scenarios, to keep track of new shared media, etc. This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through the RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the definition and design of a system for supporting learners in the didactic activities. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics are specific of a given educational domain. Then, during the execution of a specific learning path, the agents suggest the most appropriate feeds with respect to the subjects in which the students are currently engaged in.

e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way.

In this paper, starting from the limitations and constrains of traditional human learning approaches, we outline new suitable approaches to education and training in future knowledge based society. In our vision, learning and teaching are no longer standalone activities but complex, conversational and experiential-based processes implying collaboration, direct experience, mutual trust and shared interests. We identify characteristics of the environments suitable for these processes, and we compare different enabling technology infrastructures in order to justify why the semantic grid for human learning, that is a particular enhanced instance of the traditional semantic grid, is the most appropriate infrastructure to build our vision on. Finally, we present a realistic learning scenario as a case study, proving the effectiveness of our innovative learning approaches for future education and training.

In this article we examine the integration of three emerging trends in information technology (utility computing, grid computing, and Web services) new computing paradigm (grid-based application service provision) that is taking place in the context of the European research project GRASP. In the first of the paper, we explain how the integration of emerging trends can support enterprises in creating competitive advantage. In the second part, we focus on grid-based application service provision (GRASP), which builds a new technology-driven business paradigm on top of such integration. We conclude by outlining a plan for prototyping a GRASP platform in the context of an ongoing European research project.

In this paper we present an open source platform supporting distributed software engineering processes, which is currently under development in the GENESIS project (generalised environment for process management in cooperative software engineering). It supports the definition, enactment and control of software processes in a distributed manner and the formal and informal communication among distributed software engineer teams using workflow and document management technologies. We make use of software agents as technological glue to control and monitor the activities execution at different sites (low invasive approach). The highly flexible process definition language allows the project manager to define a software process at different levels of detail supporting both iterative refinement and on the fly activities flow modification.