Investigando o uso do sentido háptico na visualização de dados multivariados em gráficos de dispersão bidimensionais

Data Visualization is widely used to facilitate the comprehension of information and find relationships between data. One of the most widely used techniques for multivariate data (4 or more variables) visualization is the 2D scatterplot. This technique associates each data item to a visual mark in the following way: two variables are mapped to Cartesian coordinates so that a visual mark can be placed on the Cartesian plane; the others variables are mapped gradually to visual properties of the mark, such as size, color, shape, among others. As the number of variables to be visualized increases, the amount of visual properties associated to the mark increases as well. As a result, the complexity of the final visualization is higher. However, increasing the complexity of the visualization does not necessarily implies a better visualization and, sometimes, it provides an inverse situation, producing a visually polluted and confusing visualization—this problem is called visual properties overload. This work aims to investigate whether it is possible to work around the overload of the visual channel and improve insight about multivariate data visualized through a modification in the 2D scatterplot technique. In this modification, we map the variables from data items to multisensoriy marks. These marks are composed not only by visual properties, but haptic properties, such as vibration, viscosity and elastic resistance, as well. We believed that this approach could ease the insight process, through the transposition of properties from the visual channel to the haptic channel. The hypothesis was verified through experiments, in which we have analyzed (a) the accuracy of the answers; (b) response time; and (c) the grade of personal satisfaction with the proposed approach. However, the hypothesis was not validated. The results suggest that there is an equivalence between the investigated visual and haptic properties in all analyzed aspects, though in strictly numeric terms the multisensory visualization achieved better results in response time and personal satisfaction.

Infoestrutura: um sistema em nuvem para prover contexto como serviço no cenário de internet das coisas integrado a ambientes veiculares

Through numerous technological advances in recent years along with the popularization of computer devices, the company is moving towards a paradigm “always connected”. Computer networks are everywhere and the advent of IPv6 paves the way for the explosion of the Internet of Things. This concept enables the sharing of data between computing machines and objects of day-to-day. One of the areas placed under Internet of Things are the Vehicular Networks. However, the information generated individually for a vehicle has no large amount and does not contribute to an improvement in transit, once information has been isolated. This proposal presents the Infostructure, a system that has to facilitate the efforts and reduce costs for development of applications context-aware to high-level semantic for the scenario of Internet of Things, which allows you to manage, store and combine the data in order to generate broader context. To this end we present a reference architecture, which aims to show the major components of the Infostructure. Soon after a prototype is presented which is used to validate our work reaches the level of contextualization desired high level semantic as well as a performance evaluation, which aims to evaluate the behavior of the subsystem responsible for managing contextual information on a large amount of data. After statistical analysis is performed with the results obtained in the evaluation. Finally, the conclusions of the work and some problems such as no assurance as to the integrity of the sensory data coming Infostructure, and future work that takes into account the implementation of other modules so that we can conduct tests in real environments are presented.

Cloud Stratus: uma plataforma de middleware para desenvolvimento de aplicações em nuvem

Cloud Computing is a paradigm that enables the access, in a simple and pervasive way, through the network, to shared and configurable computing resources. Such resources can be offered on demand to users in a pay-per-use model. With the advance of this paradigm, a single service offered by a cloud platform might not be enough to meet all the requirements of clients. Ergo, it is needed to compose services provided by different cloud platforms. However, current cloud platforms are not implemented using common standards, each one has its own APIs and development tools, which is a barrier for composing different services. In this context, the Cloud Integrator, a service-oriented middleware platform, provides an environment to facilitate the development and execution of multi-cloud applications. The applications are compositions of services, from different cloud platforms and, represented by abstract workflows. However, Cloud Integrator has some limitations, such as: (i) applications are locally executed; (ii) users cannot specify the application in terms of its inputs and outputs, and; (iii) experienced users cannot directly determine the concrete Web services that will perform the workflow. In order to deal with such limitations, this work proposes Cloud Stratus, a middleware platform that extends Cloud Integrator and offers different ways to specify an application: as an abstract workflow or a complete/partial execution flow. The platform enables the application deployment in cloud virtual machines, so that several users can access it through the Internet. It also supports the access and management of virtual machines in different cloud platforms and provides services monitoring mechanisms and assessment of QoS parameters. Cloud Stratus was validated through a case study that consists of an application that uses different services provided by different cloud platforms. Cloud Stratus was also evaluated through computing experiments that analyze the performance of its processes.

Segmentação fuzzy de objetos tridimensionais com propriedades texturais

Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been used successfully in the segmentation of images from several modalities. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully described by simple statistics computed over a very restricted area. In this paper we present an extension of the fuzzy segmentation algorithm that achieves the segmentation of textures by employing adaptive affinity functions as long as we extend the algorithm to tridimensional images. The adaptive affinity functions change the size of the area where they compute the texture descriptors, according to the characteristics of the texture being processed, while three dimensional images can be described as a finite set of two-dimensional images. The algorithm then segments the volume image with an appropriate calculation area for each texture, making it possible to produce good estimates of actual volumes of the target structures of the segmentation process. We will perform experiments with synthetic and real data in applications such as segmentation of medical imaging obtained from magnetic rosonance

Novas abordagens para configurações automáticas dos parâmetros de controle em comitês de classificadores

Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

Uma máquina de redução de grafos extensível para a implementação de fluxos de trabalho

Graph Reduction Machines, are a traditional technique for implementing functional programming languages. They allow to run programs by transforming graphs by the successive application of reduction rules. Web service composition enables the creation of new web services from existing ones. BPEL is a workflow-based language for creating web service compositions. It is also the industrial and academic standard for this kind of languages. As it is designed to compose web services, the use of BPEL in a scenario where multiple technologies need to be used is problematic: when operations other than web services need to be performed to implement the business logic of a company, part of the work is done on an ad hoc basis. To allow heterogeneous operations to be part of the same workflow, may help to improve the implementation of business processes in a principled way. This work uses a simple variation of the BPEL language for creating compositions containing not only web service operations but also big data tasks or user-defined operations. We define an extensible graph reduction machine that allows the evaluation of BPEL programs and implement this machine as proof of concept. We present some experimental results.

An augmented reality pipeline to create scenes with coherent illumination using textured cuboids

Shadows and illumination play an important role when generating a realistic scene in computer graphics. Most of the Augmented Reality (AR) systems track markers placed in a real scene and retrieve their position and orientation to serve as a frame of reference for added computer generated content, thereby producing an augmented scene. Realistic depiction of augmented content with coherent visual cues is a desired goal in many AR applications. However, rendering an augmented scene with realistic illumination is a complex task. Many existent approaches rely on a non automated pre-processing phase to retrieve illumination parameters from the scene. Other techniques rely on specific markers that contain light probes to perform environment lighting estimation. This study aims at designing a method to create AR applications with coherent illumination and shadows, using a textured cuboid marker, that does not require a training phase to provide lighting information. Such marker may be easily found in common environments: most of product packaging satisfies such characteristics. Thus, we propose a way to estimate a directional light configuration using multiple texture tracking to render AR scenes in a realistic fashion. We also propose a novel feature descriptor that is used to perform multiple texture tracking. Our descriptor is an extension of the binary descriptor, named discrete descriptor, and outperforms current state-of-the-art methods in speed, while maintaining their accuracy.

Contribuições para o processo de verificação de satisfatibilidade módulo teoria em Event-B

Event-B is a formal method for modeling and verification of discrete transition systems. Event-B development yields proof obligations that must be verified (i.e. proved valid) in order to keep the produced models consistent. Satisfiability Modulo Theory solvers are automated theorem provers used to verify the satisfiability of logic formulas considering a background theory (or combination of theories). SMT solvers not only handle large firstorder formulas, but can also generate models and proofs, as well as identify unsatisfiable subsets of hypotheses (unsat-cores). Tool support for Event-B is provided by the Rodin platform: an extensible Eclipse based IDE that combines modeling and proving features. A SMT plug-in for Rodin has been developed intending to integrate alternative, efficient verification techniques to the platform. We implemented a series of complements to the SMT solver plug-in for Rodin, namely improvements to the user interface for when proof obligations are reported as invalid by the plug-in. Additionally, we modified some of the plug-in features, such as support for proof generation and unsat-core extraction, to comply with the SMT-LIB standard for SMT solvers. We undertook tests using applicable proof obligations to demonstrate the new features. The contributions described can potentially affect productivity in a positive manner.

ArchWiSeN: uma estratégia baseada em modelos para desenvolvimento de aplicações para redes de sensores e atuadores sem fio

Wireless Sensor and Actuator Networks (WSAN) are a key component in Ubiquitous Computing Systems and have many applications in different knowledge domains. Programming for such networks is very hard and requires developers to know the available sensor platforms specificities, increasing the learning curve for developing WSAN applications. In this work, an MDA (Model-Driven Architecture) approach for WSAN applications development called ArchWiSeN is proposed. The goal of such approach is to facilitate the development task by providing: (i) A WSAN domain-specific language, (ii) a methodology for WSAN application development; and (iii) an MDA infrastructure composed of several software artifacts (PIM, PSMs and transformations). ArchWiSeN allows the direct contribution of domain experts in the WSAN application development without the need of specialized knowledge on WSAN platforms and, at the same time, allows network experts to manage the application requirements without the need for specific knowledge of the application domain. Furthermore, this approach also aims to enable developers to express and validate functional and non-functional requirements of the application, incorporate services offered by WSAN middleware platforms and promote reuse of the developed software artifacts. In this sense, this Thesis proposes an approach that includes all WSAN development stages for current and emerging scenarios through the proposed MDA infrastructure. An evaluation of the proposal was performed by: (i) a proof of concept encompassing three different scenarios performed with the usage of the MDA infrastructure to describe the WSAN development process using the application engineering process, (ii) a controlled experiment to assess the use of the proposed approach compared to traditional method of WSAN application development, (iii) the analysis of ArchWiSeN support of middleware services to ensure that WSAN applications using such services can achieve their requirements ; and (iv) systematic analysis of ArchWiSeN in terms of desired characteristics for MDA tool when compared with other existing MDA tools for WSAN.

Utilizando comitês de classificadores para predição de rendimento escolar

Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student’s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student’s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.

Mecanismo de tolerância a falhas através de escalonamento para uma arquitetura reconfigurável de grão grosso

The continuous evolution of integrated circuit technology has allowed integrating thousands of transistors on a single chip. This is due to the miniaturization process, which reduces the diameter of wires and transistors. One drawback of this process is that the circuit becomes more fragile and susceptible to break, making the circuit more susceptible to permanent faults during the manufacturing process as well as during their lifetime. Coarse Grained Reconfigurable Architectures (CGRAs) have been used as an alternative to traditional architectures in an attempt to tolerate such faults due to its intrinsic hardware redundancy and high performance. This work proposes a fault tolerance mechanism in a CGRA in order to increase the architecture fault tolerance even considering a high fault rate. The proposed mechanism was added to the scheduler, which is the mechanism responsible for mapping instructions onto the architecture. The instruction mapping occurs at runtime, translating binary code without the need for recompilation. Furthermore, to allow faster implementation, instruction mapping is performed using a greedy module scheduling algorithm, which consists of a software pipeline technique for loop acceleration. The results show that, even with the proposed mechanism, the time for mapping instructions is still in order of microseconds. This result allows that instruction mapping process remains at runtime. In addition, a study was also carried out mapping scheduler rate. The results demonstrate that even at fault rates over 50% in functional units and interconnection components, the scheduler was able to map instructions onto the architecture in most of the tested applications.

Uma abordagem para avaliação e tratamento de exceções propagadas no uso de serviços Web em .Net

High dependability, availability and fault-tolerance are open problems in Service-Oriented Architecture (SOA). The possibility of generating software applications by integrating services from heterogeneous domains, in a reliable way, makes worthwhile to face the challenges inherent to this paradigm. In order to ensure quality in service compositions, some research efforts propose the adoption of verification techniques to identify and correct errors. In this context, exception handling is a powerful mechanism to increase SOA quality. Several research works are concerned with mechanisms for exception propagation on web services, implemented in many languages and frameworks. However, to the extent of our knowledge, no works found evaluates these mechanisms in SOA with regard to the .NET framework. The main contribution of this paper is to evaluate and to propose exception propagation mechanisms in SOA to applications developed within the .NET framework. In this direction, this work: (i)extends a previous study, showing the need to propose a solution to the exception propagation in SOA to applications developed in .NET, and (ii) show a solution, based in model obtained from the results found in (i) and that will be applied in real cases through of faults injections and AOP techniques.

Classificação de técnicas e auxílio à navegação de busca com base em sua carga cognitiva, precisão e eficiência

Navigation, in both virtual and real environments, is the process of a deliberated movement to a specific place that is usually away from the origin point, and that cannot be perceived from it. Navigation aid techniques (TANs) have as their main objective help finding a path through a virtual environment to a desired location and, are widely used because they ease the navigation on these unknown environments. Tools like maps, GPS (Global Positioning System) or even oral instructions are real world examples of TAN usage. Most of the works which propose new TANs for virtual environments aim to analyze their impact in efficiency gain on navigation tasks from a known place to an unknown place. However, such papers tend to ignore the effect caused by a TAN usage over the route knowledge acquisition process, which is important on virtual to real training transfer, for example. Based on a user study, it was possible to confirm that TANs with different strategies affects the performance of search tasks differently and that the efficiency of the help provided by a TAN is not inversely related to the cognitive load of the technique’s aids. A technique classification formula was created. This formula utilizes three factors instead of only efficiency. The experiment’s data were applied to the formula and we obtained a better refinement of help level provided by TANs.

Cloud query manager: uso de web semântica para evitar o problema de aprisionamento em IaaS

Cloud computing can be defined as a distributed computational model by through resources (hardware, storage, development platforms and communication) are shared, as paid services accessible with minimal management effort and interaction. A great benefit of this model is to enable the use of various providers (e.g a multi-cloud architecture) to compose a set of services in order to obtain an optimal configuration for performance and cost. However, the multi-cloud use is precluded by the problem of cloud lock-in. The cloud lock-in is the dependency between an application and a cloud platform. It is commonly addressed by three strategies: (i) use of intermediate layer that stands to consumers of cloud services and the provider, (ii) use of standardized interfaces to access the cloud, or (iii) use of models with open specifications. This paper outlines an approach to evaluate these strategies. This approach was performed and it was found that despite the advances made by these strategies, none of them actually solves the problem of lock-in cloud. In this sense, this work proposes the use of Semantic Web to avoid cloud lock-in, where RDF models are used to specify the features of a cloud, which are managed by SPARQL queries. In this direction, this work: (i) presents an evaluation model that quantifies the problem of cloud lock-in, (ii) evaluates the cloud lock-in from three multi-cloud solutions and three cloud platforms, (iii) proposes using RDF and SPARQL on management of cloud resources, (iv) presents the cloud Query Manager (CQM), an SPARQL server that implements the proposal, and (v) comparing three multi-cloud solutions in relation to CQM on the response time and the effectiveness in the resolution of cloud lock-in.

Distribuição de derivados de petróleo por redes de polidutos: uma abordagem através de algoritmos evolucionários híbridos para um problema triobjetivo

An important problem faced by the oil industry is to distribute multiple oil products through pipelines. Distribution is done in a network composed of refineries (source nodes), storage parks (intermediate nodes), and terminals (demand nodes) interconnected by a set of pipelines transporting oil and derivatives between adjacent areas. Constraints related to storage limits, delivery time, sources availability, sending and receiving limits, among others, must be satisfied. Some researchers deal with this problem under a discrete viewpoint in which the flow in the network is seen as batches sending. Usually, there is no separation device between batches of different products and the losses due to interfaces may be significant. Minimizing delivery time is a typical objective adopted by engineers when scheduling products sending in pipeline networks. However, costs incurred due to losses in interfaces cannot be disregarded. The cost also depends on pumping expenses, which are mostly due to the electricity cost. Since industrial electricity tariff varies over the day, pumping at different time periods have different cost. This work presents an experimental investigation of computational methods designed to deal with the problem of distributing oil derivatives in networks considering three minimization objectives simultaneously: delivery time, losses due to interfaces and electricity cost. The problem is NP-hard and is addressed with hybrid evolutionary algorithms. Hybridizations are mainly focused on Transgenetic Algorithms and classical multi-objective evolutionary algorithm architectures such as MOEA/D, NSGA2 and SPEA2. Three architectures named MOTA/D, NSTA and SPETA are applied to the problem. An experimental study compares the algorithms on thirty test cases. To analyse the results obtained with the algorithms Pareto-compliant quality indicators are used and the significance of the results evaluated with non-parametric statistical tests.