Using the Myers-Briggs Type Indicator (MBTI) for Modeling Multiagent Systems

The formation of high-performance teams has been a constant challenge for organizations, which despite considering human capital as one of the most important resources, it still lacks the means to allow them to have a better understanding of several factors that influence the formation of these teams. In this sense, studies also demonstrate that teamwork has a significant impact on the results presented by organizations, in which human behavior is highlighted as one of the main aspects to be considered in the building of work teams. The Myers-Briggs Type Indicator seeks to classify the behavioral preferences of individuals around eight characteristics, which grouped as dichotomies, describe different psychological types. With it, researchers have sought to expand the ability to understand the human factor, using strategies with multiagent systems that, through experiments and simulations, using computer resources, enable the development of artificial agents that simulate human actions. In this work, we present an overview of the research approaches that use MBTI to model agents, aiming at providing a better knowledge of human behavior. Additionally, we make a preliminary discussion of how these results could be explored in order to advance the studies of psychological factors' influence in organizations' work teams formation.

A Novel Approach to the Measurement of Bourdieusian Social Capital within Institutional Pages and Profiles

We present in this paper a novel approach for measuring Bourdieusian Social Capital (BSC) within Institutional Pages and Profiles. We analyse Facebook's Institutional Pages and Twitter's Institutional Profiles. Supported by Pierre Bourdie's theory, we search for directions to identify and capture data related to sociability practices, i. e. actions performed such as Like, Comment and Share. The system of symbolic exchanges and mutual recognition treated by Pierre Bourdieu is represented and extracted automatically from these data in the form of generalized sequential patterns. In this format, the social interactions captured from each page are represented as sequences of actions. Next, we also use such data to measure the frequency of occurrence of each sequence. From such frequencies, we compute the effective mobilization capacity. Finally, the volume of BSC is computed based on the capacity of effective mobilization, the number of social interactions captured and the number of followers on each page. The results are aligned with Bourdieu's theory. The approach can be generalized to institutional pages or profiles in Online Social Networks.

SSM: A Semantic Metasearch Platform for Scientific Data retrieval

Scientific research in all fields has advanced in complexity and in the amount of data generated. The heterogeneity of data repositories, data meaning and their metadata standards makes this problem even more significant. In spite of several proposals to find and retrieve research data from public repositories, there is still need for more comprehensive retrieval solutions. In this article, we specify and develop a mechanism to search for scientific data that takes advantage of metadata records and semantic methods. We present the conception of our architecture and how we have implemented it in a use case in the agriculture domain.

A Conceptual Model for Situating Purposes in Artificial Institutions

{In multi-agent systems, artificial institutions connect institutional concepts, belonging to the institutional reality, to the concrete elements that compose the system. The institutional reality is composed of a set of institutional concepts, called Status-Functions. Current works on artificial institutions focus on identifying the status-functions and connecting them to the concrete elements. However, the functions associated with the status-functions are implicit. As a consequence, the agents cannot reason about the functions provided by the elements that carry the status-functions and, thus, cannot exploit these functions to satisfy their goals. Considering this problem, this paper proposes a model to express the functions -- or the purposes -- associated with the status-functions. Examples illustrate the application of the model in a practical scenario, showing how the agents can use purposes to reason about the satisfaction of their goals in institutional contexts.

An Adaptive Trust Model Based on Fuzzy Logic

In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly.

Integrating Embedded Multiagent Systems with Urban Simulation Tools and IoT Applications

The smart city systems development connected to the Internet of Things (IoT) has been the goal of several works in the multi-agent system field. Nevertheless, just a few projects demonstrate how to deploy and make the connection among the employed systems. This paper proposes an approach towards the integration of a MAS through the JaCaMo framework plus an Urban Simulation Tool (SUMO), IoT applications (Node-RED, InfluxDB, and Grafana), and an IoT platform (Konker). The integration presented in this paper applies in a Smart Parking scenario with real features, where is shown the integration and the connection through all layers, from agent level to artifacts, including real environment and simulation, as well as IoT applications. In future works, we intend to establish a methodology that shows how to properly integrate these different applications regardless of the scenario and the used tools.

System for Identifying Pests and Diseases in Soybean Crop through Natural Language Processing

The presence of technologies in the agronomic field has the purpose of proposing the best solutions to the challenges found in agriculture, especially to the problems that affect cultivars. One of the obstacles found is to apply the use of your own language in applications that interact with the user in Brazilian Agribusiness. Therefore, this work uses Natural Language Processing techniques for the development of an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in soybean crop, stored in a non-relational database repository to provide accurate diagnostics to simplify the work of the farmer and the agricultural stakeholders who deal with a lot of information. In order to build dialogues and provide rich consultations, from agriculture manuals, a data structure with 108 pests and diseases with their information on the soybean cultivar and through the spaCy tool, it was possible to pre-process the texts, recognize the entities and support the requirements for the development of the conversacional system.

Efficient Breast Cancer Classification Using Histopathological Images and a Simple VGG

Breast cancer is the second most deadly disease worldwide. This severe condition led to 627,000 people dying in 2018. Thus, early detection is critical for improving the patients' lifetime or even curing them. In this context, we can appeal to Medicine 4.0, which exploits machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.

Hash Tables for a Digital Lexicon

This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lexicon of Herbs), improving performance results of several implemented hash functions. Such structure has achieved satisfactory results in terms of speed and storage when compared to conventional databases and can work in various media, such as desktop, Web and mobile.

Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation

Clustering on target positions is a class of centralized algorithms used to calculate the surveillance robots' displacements in the Cooperative Target Observation (CTO) problem. This work proposes and evaluates Fuzzy C-means (FCM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with K-means (DBSk) based self-tuning clustering centralized algorithms for the CTO problem and compares its performances with that of K-means. Two random motion patterns are adopted for the targets: in free space or on a grid. As a contribution, the work allows identifying ranges of problem configuration parameters in which each algorithm shows the highest average performance. As a first conclusion, in the challenging situation in which the relative speed of the targets is high, and the relative sensor range of the surveillance is low, for which the existing algorithms present a substantial drop in performance, the FCM algorithm proposed outperforms the others. Finally, the DBSk algorithm adapts very well in low execution frequency, showing promising results in this challenging situation.