Abstract
Influential spreaders contribute substantially to managing and optimizing any spreading process in a network. Influential spreaders are nodes that hold importance within the network. Identifying them is a challenging task. Some encysting methods for such identification include local-structure-based, global-structure-based, semi-global-structure-based, and hybrid-structure-based methods. Semi-global structure-based methods show significant potential in identifying influential nodes in different network structures. However, existing semi-global structure-based methods often identify nodes from the network's periphery, where nodes are loosely connected, and their collective influence in spreading processes is minimal. This paper presents a novel method called Semi-global triangular and degree centrality (STC + K) to overcome this limitation by considering a node's degree, the number of triangles, and the third hop of neighbourhood connectivity information. The proposed novel method outperforms the existing noteworthy indexing methods regarding ranking performance. The experimental results show better performance, as indicated by two performance metrics: recognition rate and improvement percentage. By virtue of the fact that the empirically set free parameters are absent, our method eliminates the need for time-consuming preprocessing to select optimal parameter values for ranking nodes in large networks.

Abstract
Digital Transformation (DT) has become an important issue for organisations. It is proven that DT fuels Digital Innovation in organisations. It is well-known that technologies and practices such as distributed ledger technologies, open source, analytics, big data, and artificial intelligence (AI) enhance DT. Among those technologies, AI provides tools to support decision-making and automatically decide. Cooperatives are organisations with a mutualistic scope and are characterised by having participatory cooperative governance due to the principle of democratic control by the members. In a context where DT is here to stay, where the dematerialisation of processes can bring significant advantages to any organisation, this article presents a critical reflection on the dangers of using AI technologies in cooperatives. We base this reflection on the Portuguese cooperative code. We emphasise that this code is not very different from the ones of other countries worldwide as they are all based on the Statement of Cooperative Identity defined by the International Cooperative Alliance. We understand that we cannot stop the entry of AI technologies into the cooperatives. Therefore, we present a framework for using AI technologies in cooperatives to avoid damaging the principles and values of this type of organisations.

Abstract
Agricultural cooperatives remain a significant component of the food and agriculture industry to help the stakeholders to provide services and have opportunities for themselves. One of the aims of an agricultural cooperative is to answer to the needs within the communities of the farmers. Agricultural cooperatives enable individual farmers to increase productivity and maximise their social welfare. Together the farmer members of an agricultural cooperative can buy input supplies cheaper and sell more of their products in larger markets at higher prices, which is not possible for an individual smallholder farmer otherwise. Some studies have shown that farmers who were members of cooperatives have gained higher revenue for their products and spent less on input. However, organising the hundreds of farmers into smaller groups to perform collective farming and marketing is crucial to strengthening their position in the food and agriculture industry. Thereby, in our work, we consider an agricultural cooperative of smallholder farmers as a multi-agent based coalitional model, where coalitions are formed based on the similarity among the smallholder farmers. In this paper, we propose a model and implement a heuristic-based algorithm to find the disjoint partition of the agents set. We evaluate the model and the algorithm based on the following criteria: (i) individual gain, (ii) runtime analysis, (iii) solution quality, and (iv) scalability. We theoretically prove that our coalitional model of an agricultural cooperative has conciseness, expressiveness and efficiency properties. Experimental results confirm that our algorithm is time efficient and scalable. We show, both empirically and theoretically, that our algorithm generates a solution within a bound of the optimal solution. We also show that our coalition model generates positive revenue for the smallholder farmers and the payoff division rule is individual rational. In addition, we generate a new dataset in the context of an agricultural cooperative to show the effectiveness and efficiency of the proposed coalitional model of the cooperative.

Abstract
Judea Pearl's ladder of causation framework has dramatically influenced the understanding of causality in computer science. Despite artificial intelligence (AI) advancements, grasping causal relationships remains challenging, emphasizing the causal revolution's significance in improving AI's understanding of cause and effect. The work presents a novel taxonomy of causal inference methods, clarifying diverse approaches for inferring causality from data. It highlights the implications of causality in responsible AI and explainable AI (xAI), addressing bias in AI systems. The chapter points out causality as the next step in AI for creating new questions, developing causal tools, and clarifying opaque models with xAI approaches. The work clarifies causal models' significance and implications in various AI subareas. © 2023, IGI Global. All rights reserved.

Abstract
Open and networked education at Portuguese Open University is made up of a set of idiosyncrasies that are not immediately perceptible by new professors coming from face-to-face universities or by evaluators from A3ES (Agency for the Evaluation and Accreditation of Higher Education). This work presents the essential concepts of Distance Education with the fewest words and avoiding synonyms. In the teaching-learning process, the teacher’s role in digital contexts is detailed and the notion of learning is discussed. A teaching taxonomy is presented with an emphasis on practice in virtual communities. © 2023, Edicoes Universitarias Lusofonas. All rights reserved.

Abstract
Data quality is essential for a correct understanding of the concepts they represent. Data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.