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Highlights • Relative optimized parameters and plants used as green manure. • Innovative technique for extraction of knowledge using networks. • Information visualization technique that facilitates the knowledge discovery. • Optimization of agricultural data mining and discovery of new hypotheses for future work. Abstract The food sector is one of the most critical areas of the economy, and consumers are seeking safer, more readily available, more affordable, and better quality food. Therefore, organic agriculture has become a possible approach for optimizing the characteristics of processed foods. Vegetables have essential uses as green manure, but the greatest difficulty encountered when using these species is related to the time required for their residues to decompose. Computational intelligence and data mining techniques are used widely in agricultural studies and for process improvement purposes. Association rules are used as data mining techniques to identify patterns in large databases, where the patterns identified are analyzed and transformed into a graph to facilitate further analyses. In this study, we employed the patterns identified by association rule networks (ARNs) to determine directly the key parameters related to the half-life decomposition times of legumes used as green manure in northeast Brazil. We validated this method by comparing the results obtained by the ARNs and a decision tree. The proposed approach obtained promising results, thereby demonstrating its capacity to describe a set of objective items and facilitate the development of more integrated hypotheses. Thus, it was possible to optimize the discovery of the key parameters related to green fertilizers in order to identify the best species according to its culture requirements and to enhance productivity. [ABSTRACT FROM AUTHOR]

• Event analysis from news is a promising task to understand complex social phenomena. • Heterogeneous networks represent events by combining text, time and location features. • We present some grand challenges for event classification in heterogeneous networks. • We propose an entropy-based scheme to weight the importance of the network relations. • Our proposed two-stage regularization framework outperforms existing approaches. Event analysis from news and social networks is a promising way to understand complex social phenomena. Each event consists of different components, which indicate what happened, when, where, and the people and organizations involved. Heterogeneous networks are useful for modeling large event datasets, where we map different types of objects (e.g. events and their components), as well as the different relationships between objects. Such networks enable the identification of related events, in which users label some events in categories and then use the network's topological structure to find other events of interest. Although this process can be automated, there is a lack of machine learning methods to properly handle event classification from heterogeneous networks. In this paper, we present the framework named Heterogeneous Event Network Regularization in Two-stages (HENR2). The first stage of HENR2 aims to learn the importance level of each relationship between events and their components. In the second stage, the regularization process considers the importance levels of each relationship to propagate labels on the network. Thus, the classification process is improved by considering the domain characteristics of the event dataset, such as temporal seasonality and geographical distribution. In both stages, our approach also deals with noisy data through parameters that define the confidence level of labeled events during label propagation. Experimental results involving twelve event networks from different application domains show that our proposal outperforms existing regularization frameworks. [ABSTRACT FROM AUTHOR]

Complex and large software-intensive systems are increasingly present in several application domains, including Industry 4.0, connected health, smart cities, and smart agriculture, to mention a few. These systems are commonly composed of diverse other systems often developed by different organizations using various technologies and, as a consequence, interoperability among these systems becomes difficult. Many architectural strategies for interoperability have already been proposed; however, selecting adequate strategies is challenging. Additionally, it lacks an overview of such strategies. This work presents TASIS, a typology of architectural strategies for the interoperability of software-intensive systems. We also validated it with 33 practitioners from different countries with an extensive experience in integration projects. This work also offers 12 industry-based association rules that suggest how to combine those strategies to mitigate issues at different interoperability levels. As a result, our typology can serve as a starting point to further aggregate new strategies and, ultimately, supports software architects in designing interoperability-driven architectural solutions. • It is a typology with architectural strategies and their levels of interoperability. • The typology was created from evidence in literature and validated and refined it. • We offer strategy combinations proven in integration projects, derived from association rules. • Software architects can use it to select architectural strategies for their projects. [ABSTRACT FROM AUTHOR]

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context‐aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context‐aware recommendation method based on text mining (CARM‐TM). The method includes two techniques to extract context from reviews: CIET.5embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect‐based opinions. We call our extension of CARM‐TOM (context‐aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context‐aware recommender based on matrix factorization. To evaluate CARM‐TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM‐TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context‐aware recommender system. [ABSTRACT FROM AUTHOR]

The quality of any text mining technique is highly dependent on the features that are used to represent the document collection. A classical form of document representation is the vector space model (VSM), according to which the documents are represented as vectors of weights that correspond to the features of the documents. The bag-of-words model is the most popular VSM approach due to its simplicity and general applicability, but this model does not include term dependency and has a high dimensionality. In the literature, several models for document representation have been proposed in order to capture the dependency of terms. Among them, the topic model representation is one of the most interesting approaches - since it describes the collection of documents in a way that reveals their internal structure and the interrelationships therein, and also provides a dimensionality reduction. However, even for topic models, the efficient extraction of information concerning the relations among terms for document representation is still a major research challenge. In order to address this issue, we proposed thelatent association rule cluster based model (LARCM). The LARCM is a non-probabilistic topic model that makes use of association rule clustering to build a document representation with low dimensionality in such a way that each feature (i.e., topic) is comprised of information concerning relations among the terms. We evaluated the interpretability of the topics obtained by using our proposed model against the ones provided by the traditional latent dirichlet allocation (LDA) model and the LDA model using a document representation that includes correlated terms (i.e., bag-of-related-words). The experimental results indicated that the LARCM provides topics with better interpretability than the LDA models. Additionally, we used the topics obtained by the LARCM in two different applications: text classification and page recommendation. With respect to text classification, the topics were used to improve document collection representation. Concerning page recommendation, topics were used as contextual information in context-aware recommender systems. Results have shown that the topics provided by the LARCM can be used to improve both applications. [ABSTRACT FROM AUTHOR]

Machine learning methods to detect fake news typically use textual features and Binary or Multi-class classification. However, accurately labeling a large news set is still a very costly process. On the other hand, one of the prominent approaches is One-Class Learning (OCL). OCL requires only the labeling of fake news, minimizing data labeling efforts. Although we eliminate the need to label non-interest news, the efficiency of OCL algorithms depends directly on the data representation model adopted. Most existing methods in the OCL literature explore representations based on one modality to detect fake news. However, different text features can be the reason for the news to be fake, such as topic or linguistic features. We model this behavior as different modalities for news to represent different textual feature sets. Thus, we present the MVAE-FakeNews, a multimodal method to represent the texts in the fake news detection through OCL that learns a new representation from the combination of promising modalities for news data: text embeddings, topic, and linguistic information. We used real-world fake news datasets in Portuguese and English in the experimental evaluation. Results show that MVAE-FakeNews obtained a better F 1 -Score and AUC-ROC, outperforming another fourteen methods in three datasets and getting competitive results on the other three. Moreover, our MVAE-FakeNews, with only 3% of labeled fake news, obtained comparable or higher results than other methods. To improve the experimental evaluation, we also propose the Multimodal LIME for OCL to identify how each modality is associated with the fake news class. [ABSTRACT FROM AUTHOR]

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system. [ABSTRACT FROM AUTHOR]

Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user's reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works. [ABSTRACT FROM AUTHOR]