Abstract
Covid-19 posed several challenges to all organisations in general and to social solidarity cooperatives in particular. However, the challenges faced by these cooperatives have unique features arising from their special characteristics compared to other types of cooperatives. Therefore it is vital to study these challenges and the impacts of covid-19. This study has as main goal to understand those challenges and their impact. An exploratory study was undertaken by applying 11 interviews to 11 social solidarity cooperatives. The cooperatives were chosen to be heterogeneous among the existent cooperatives in Portugal. This study corresponds to the first phase of a project that is still underway. This article presents the main results of the content analysis of the data collected from the interviews. Data show cooperatives could promptly adapt and continue their mission under pressure from the pandemic despite the first difficulties encountered in a new and unknown situation, showing a capacity to adapt and serve their members. However, these members were also submitted to several increasing and new challenges. The adaptations were possible due to legal changes in the work organisation law, from layoff to telework, government support involving financial programs, VAT, and other tax relaxation, as well as due to human resources reorganisation and the cooperatives' staff positive attitude towards the difficulties (both leaders and general workers). Differences between the social solidarity cooperatives under study concerning digital technologies showed that those already having some infrastructure had minor adapting difficulties.

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
Over the years, workers have joined in producer organizations to face the difficulties that the capitalist market poses to them. Together they can gain efficiency and equity compared to big companies, and they can gain bargaining power over the product market. In our case, we target smallholder farmers who face many difficulties in increasing their welfare. To overcome them, they group together in producer organizations such as cooperatives. With the development of technology, it became possible for these cooperatives of workers to use the Web to operate - such type of organization and operation is called a Platform Cooperative (PC). This paper presents a multi-agent based modeling of Farmers' Coalition Formation (FCF) for smallholder farmers so that they can operate by means of a Platform cooperative. We present the design of a characteristic function that calculates the coalition values in this context, finds the best way of partitioning the farmers into smaller groups and divides the payoff in a stable manner. We empirically analyze the model using value distributions. The results show that forming coalitions is profitable for farmers. We also proved that the model ensures a fair distribution of the payoff among the farmers.

Abstract
There are many information retrieval tasks over the Web, which cannot be attended with a simple keyword-based lookup search. Such an important exploratory search problem is the comparison of two Web resources. To manually compare two data resources by looking for information from one Web page to another without any software support is inefficient and time-consuming. This paper discusses a solution to automatize the comparison of two data resources present in a RDF graph. In our work, we provide an improvement over the current state-of-the-art method, by reverse engineering SPARQL queries using a hashing based recursive procedure. We empirically verify how hashing could largely benefit in reducing the size of the returned query and hence making it practically comprehensible for users or agents to understand the similarity concepts returned.

Abstract
The integration of meta-knowledge on the Web of data is essential to support trustworthiness. This is in fact an issue because of the enormous amount of data that exists on the Web of Data. Meta-knowledge describes how the data is generated, manipulated, and disseminated. In the last few years, several approaches have been proposed for tracing and representing meta-knowledge efficiently on a statement or on a set of statements in the Semantic Web. The approaches differ significantly; for instance, in terms of modelling patterns, the number of statements generation, redundancy of the resources, query length, or query response time. This article reports a systematic review of the various approaches of the four dimensions (namely time, trust, fuzzy, and provenance) to provide an overview of the meta-knowledge assertion techniques in the field of the Semantic Web. Some experiments are conducted to analyze the actual performance of the approaches of meta-knowledge assertion considering the provenance dimension. These experiments are based on specific parameters such as graph size, number of statements generation, redundancy, query length, and query response time. All the experiments are done with real-world datasets. The semantics of the different approaches are compared to analyze the methodology of the approaches. Our study and experiments highlight the advantages and limitations of the approaches in terms of the parameters mentioned above.

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.