Business platform models frequently require continuous adaptation and agility to allow new experiences to be created and delivered to customers. To understand user behavior in online systems, researchers have taken advantage of a combination of traditional and recently developed analysis techniques. Earlier studies have shown that user behavior monitoring data, as obtained by mouse tracking, can be utilized to improve user experience (UX). Many mouse-tracking solutions exist; however, the vast majority is proprietary, and open-source packages do not provide the resources and data needed to support UX research. Thus, this paper presents: 1) the development of an interaction monitoring application titled Artificial Intelligence and Mouse Tracking-based User eXperience Tool (AIMT-UXT); 2) the validation of the tool in a case study conducted on the Website of the Brazilian Federal Revenue Service (BFR); 3) the definition of a new relationship pattern of variables that determine user behavior; 4) the construction of a fuzzy inference system for measuring user performance using the defined variables and the data captured in the case study; and 5) the application of a clustering algorithm to complement the analysis. A comparison of the results of the applied quantitative methodologies indicates that the developed framework was able to infer UX scores similar to those reported by users in questionnaires.

Handover (HO) is designed to facilitate user mobility and ensure quality of service in mobile networks. In multiple base station (eNodeBs) scenarios, the HO priority process is a problem that has been studied in many surveys, as neglecting the use of priority-based schemes can result in high amounts of HO and, consequently, a decrease in the quality of services provided. This paper presents a Heuristic for Handover based on AHP-TOPSIS-FUZZY (H 2 ATF), which generates a priority ranking of eNodeBs from the use of (a) the analytical hierarchical process (AHP) to define the weights of the criteria; (b) the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the selected target cells; and (c) the use of an adaptive hysteresis calculated through a fuzzy inference system based on parameters that directly impact the HO process. Through this proposal, it was possible to define the best time and, together, the best antenna to perform the HO. The results demonstrate a decrease of up to 43% in HO ping pong (HPP), a widely used metric in the literature to evaluate HO heuristics. INDEX TERMS Handover, priority, heterogeneous networks, mobile networks, AHP-TOPSIS, fuzzy logic.

Trigona spinipes (abelhas-arapuá) atacam o broto apical da planta de Khaya ivorensis (mogno-africano), causando atrofia e brotação, provocando ramificações que irão depreciar o fuste se não manejado. Danos à cultura de K. ivorensis já foram relatados para o Brasil, porém nunca antes para a savana brasileira. O objetivo desta pesquisa foi realizar o levantamento do ataque de Trigona spinipes e relatar como primeiro registro a presença e o dano causado um plantio de mogno-africano na savana brasileira. A área apresenta cerca de 16,6 hectares de monocultivo de mogno-africano no município de Piracanjuba, Goiás, sendo utilizado 21 parcelas de 400 m², pré-definidas e o método de amostragem aleatória simples, nas quais foi realizado inventário florestal e observação de rebrotas da parte apical do caule e consequente desrama artificial das rebrotas. Também foram visualizadas as abelhas realizando atividade de forrageamento cortando os brotos de K. ivorensis. No plantio, 6,14% das árvores apresentaram rebrotas, podendo indicar a partir deste percentual a quantidade de árvores atacadas. O total de árvores com rebrota representa uma grande quantidade de árvores que poderão se desenvolver com problemas, gerando mais de um fuste ou galhos, assim impossibilitando que a madeira afetada seja utilizada para a finalidade de movelaria.

Mobile Edge Computing (MEC) extends Cloud Computing to the edge of the network, creating a hierarchy of data centers. This paradigm provides computing capacity close to final users, relieves backhaul and the leading network, and serves latency-sensitive applications. When providing computing services at different network levels, it becomes necessary to carry out a more efficient distribution of the resources that come to coexist. A random allocation of these resources can lead to a low service acceptance rate and backhaul overhead problems. Problems like these can be solved with MEC. To maximize the service acceptance and ensure a fair distribution of services in the kinds of servers guaranteeing their QoS requirements, we propose a MILP (Mixed Integer Linear Programming). The model performs an optimal allocation of applications in a two-level hierarchy of data centers: (i) MEC and (ii) cloud computing. On a large scale, the use of MILP becomes unfeasible due to the high computational cost, so we propose a heuristic based on the application profiles. We compare the proposed heuristic with two metaheuristics: (i) Genetic Algorithm and (ii) Particle Swarm Optimization. The solutions are compared in terms of service acceptance rate, largescale performance, and efficient use of available resources. Results show that the proposed heuristic reaches 91% of the optimal solution and over 140% compared to AG and PSO solutions.

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Agradeço à minha família, pela compreensão e apoio que me deram durante todo esse período. À orientadora prof. a Maria Emília, por ter acolhido e acreditado no projeto. Por todo apoio, confiança e paciência depositada em mim, desde à iniciação científica até o presente momento. À prof. a Célia Ralha, também pela sua confiança, pelos conselhos e direções ao longo de todos esses anos. Aos pesquisadores da Embrapa Roberto Togawa, Natália Martins e Priscila Grynberg, pelas valiosas contribuições e que, desde sempre, confiaram e acompanharam o desenvolvimento deste trabalho. À Tainá Raiol, também por suas valiosas contribuições, sendo uma delas, o ponto de engate que levou a concretização deste trabalho. À meu amigo Marcius Marques, por toda ajuda e foco nos estudos durante esses anos. Aos amigos do IFG Thiago Peixoto e Waldeyr Mendes, pela amizade e apoio durante o mestrado. Ao meu amigo João Victor, que sempre esteve presente para fornecer algum apoio, não apenas nos estudos. Ao CNPq, pelo apoio à pesquisa. v

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