User-centric applications have recently gained popularity in particular in the tourism domain, in order to satisfy the individual needs of users and to provide personalized information. In turn, there is a need on modeling user profiles, considering different aspects of the information related to the user himself and his context. However, there is a lack of standardization to represent such information. The Semantic Web seems to be a clear solution for the formal representation of this knowledge, due to its capacity for organization and reasoning, in particular through ontologies. There are works that propose ontologies to model the user profile, but only cover partial aspects of the information required and are only applicable to specific applications, they do not propose a generalized user profile model applicable to the domain of tourism. This work proposes the development of GENTE ontology, a GENeral ontology for Tourism Environments, that represents the different dimensions of the information related to users and their context. In addition, techniques to infer characteristics, preferences, interests, and behaviors of users, from their social networks are proposed and developed.

In robotics, object detection in images or videos, obtained in real-time from sensors of robots can be used to support the implementation of service robot tasks (e.g., navigation, model its social behavior, recognize objects in a specific domain), usually accomplished in indoor environments. However, traditional deep learning based object detection techniques present limitations in such indoor environments, specifically related to the detection of small objects and the management of high density of multiple objects. Coupled with these limitations, for specific domains (e.g., hospitals, museums), it is important that the robot, apart from detecting objects, extracts and knows information of the targeted objects. Ontologies, as a part of the Semantic Web, are presented as a feasible option to formally represent the information related to the objects of a particular domain. In this context, this work proposes an object detection and recognition process based on a Deep Learning algorithm, object descriptors, and an ontology. ODROM, an Object Detection and Recognition algorithm supported by Ontologies and applied to Museums, is an implementation to validate the proposal. Experiments show that the usage of ontologies is a good way of desambiguating the detection, obtained with a and$\mathbf{mAP}{@}0.5=0.88$and a$\mathbf{mAP}{@}[0.5:0.95]=61\%$.

Representation of the knowledge related to any domain with flexible and well-defined models, such as ontologies, provides the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. It is necessary to define how to compare such ontologies to decide which one is the most suitable for specific needs of users/developers. Since the emerging developing of ontologies, several studies have proposed criteria to evaluate them. Nevertheless, there is still a lack of practical and reproducible guidelines to drive a comparative evaluation of ontologies as a systematic process. In this paper, we propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural, and Domain Knowledge levels, considering Correctness and Quality perspectives. Since the evaluation methods of our proposal are based in a golden-standard, it can be customized to compare ontologies in any domain. To show the suitability of our proposal, we apply our methodological approach to conduct a comparative study of ontologies in the robotic domain, in particularly for the Simultaneous Localization and Mapping (SLAM) problem. With this study case, we demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domain (SLAM for our study case).

Twitter is one of the most popular information source available on the Web. Thus, there exist many studies focused on analyzing the credibility of the shared information. Most proposals use either Twitter API or web scraping to extract the data to perform such analysis. Both extraction techniques have advantages and disadvantages. In this work, we present a study to evaluate their performance and behavior. The motivation for this research comes from the necessity to know ways to extract online information in order to analyze in real-time the credibility of the content posted on the Web. To do so, we develop a framework which offers both alternatives of data extraction and implements a previously proposed credibility model. Our framework is implemented as a Google Chrome extension able to analyze tweets in real-time. Results report that both methods produce identical credibility values, when a robust normalization process is applied to the text (i.e., tweet). Moreover, concerning the time performance, web scraping is faster than Twitter API, and it is more flexible in terms of obtaining data; however, web scraping is very sensitive to website changes.

In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we present an R DF D atatype in F erring F ramework, called RDF-F, which provides two independent datatype inference processes: 1) a four-step process consisting of (i) a predicate information analysis (i.e., deduce the datatype from existing range property), (ii) an analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical space), (iii) a semantic analysis of the predicate name and its context, and (iv) generalization of Numeric and Binary datatypes to ensure the integration; and 2) a non-ambiguous lexical-space-matching process, where literal values are inferred by the modification of their representation, following new lexical spaces. We evaluated the performance and the accuracy of both processes with datasets from DBpedia. Results show that the execution time of both indicators is linear and their accuracy can increase up to 97.10 and 99.30%, respectively.

Multi-Agent Systems (MASs) have received much attention in recent years because of their advantages on modeling complex distributed systems, such Digital Ecosystems (DESs). Many existing modeling languages that support the design of such systems are based on ontologies to assist the representation of agents knowledge. However, in the context of DESs, there is still a need for more general conceptual models to represent the specific characteristics of DESs in terms of win-win interaction, engagement, equilibrium, and self-organization. Then, concepts such behavior, roles, rules, and environment are needed. This paper describes an ontology-based approach by proposing MAS2DES-Onto, as the conceptual model, which considers the essential static and dynamic aspects of MASs by a clear representation of their concepts and relationships to support the design and development of DESs. To validate and conduct experimental tests, we integrate MAS2DES-Onto into a framework to automatically generate MAS-based DESs. Results show the efficiency and effectiveness of our approach.

In this work, we propose the implementation of a Distributed Platform for Massively Emails distribution, called OPTIMUS (OPTImized Mail distribUtion System). It allows efficient workload distribution among multiples email servers, optimizes the use of available resources, and provides fault tolerance in a robust distribution platform. OPTIMUS architecture extends the Postfix server implementation to incorporate data base management, workload balance, and fault tolerance. It is composed by several extended Postfix servers, connected in a Peer-to-Peer platform. Experimental results show that with a few amount of emails, Postfix (with its original centralized implementation) and OPTIMUS have similar performance. However, with huge amount of emails, OPTIMUS highly overcomes the performance of the original centralized Postfix version.

Dynamic load balancing on a cluster computing consists on evenly dividing at any moment of time the workload to be distributed among the nodes, in order to avoid load unbalance. Load unbalance can be resolved if nodes can migrate some of their work. Using a stability criterion to allow the analysis of stability in the cluster is a way to decide the migration tasks. The Routh-Hurwitz criterion applied to the equation feature allows knowing whether a system is stable or not. The aim of this work is to develop a mathematical model based on linear differential equations representing the load, execution, and migration of task on a cluster computing. With the model, it is possible to determine the stability of the cluster. If the workload on the nodes tends to their equilibrium, new migration tasks do not need to check the stability of the nodes to be executed.

QoS parameters are used to describe services in terms of their behavior and can be used to rank services according to non-functional criteria. To provide an accurate characterization of the quality of a service, we propose a sampling-based technique. The proposed technique uses Adaptive and Sequential Sampling strategies to estimate the QoS parameters that satisfy the required confidence levels while the size of the sample remains small. QoS estimates are used by a hybrid composer, named PT-SAM, to identify the service compositions that satisfy a functional condition and best meet non-functional criteria of a user query. PT-SAM adapts a Petri-Net unfolding algorithm to find a desired marking from an initial state by using a utility function defined on QoS estimates and functional properties of the available services. PT-SAM uses a QoS-based utility function to guide the search into portions of good quality service compositions; thus, PT-SAM is able to scale up to large-scale search spaces of services. We report on the quality of the sampling techniques and the performance of the composer. First, we show correlation between the estimates and the real values of the QoS parameters; then, we report on the benefits of using these estimates to traverse large search spaces of service compositions (e.g., in the range of 1,000 to 100,000 services). Our experiments show that the quality of the compositions identified by our algorithm is close to the optimal solution produced by the exhaustive algorithm.