Information technology as a technical resource for the memories: memories of UNATI-Marília in the virtual environment

UNATI (Open University of the Third Age), UNESP, Marília campus, has offered subsidies for the development of this work aimed at researching the existing relationships between information mediation processes and technological devices, especially computers, assuming that reading practices and textual construction in online environments could help the “third age” population to have access to these devices, thus promoting digital inclusion in this group. Mediation was presented as an interventionist action that, by introducing an intermediate element in the learning process, causes a rupture in the ways of living and personal digital inclusion processes hitherto experienced. In the context of a workshop, we found out that there is a physical relationship between subjects and technological supports and such a contact proved to be necessary, considering that handling a computer required knowledge of procedures, thus furthering a logic of use. It turned out to be necessary to develop actions that would enable the handling of a computer so as to bring about acceptance of these supports. Accordingly, activities were developed so as to articulate reminiscent processes, memories of older adults, the writing down of such memories and the creation of a blog to bring enhanced visibility to the content produced by older people. Such actions have shown that remembering, writing down and posting can reshape not only social relations but somehow significantly promote digital inclusion among older adults.

An esthetics rehabilitation with computer-aided design/ computer-aided manufacturing technology

This paper describes a case of a rehabilitation involving Computer Aided Design/Computer Aided Manufacturing (CAD-CAM) system in implant supported and dental supported prostheses using zirconia as framework. The CAD-CAM technology has developed considerably over last few years, becoming a reality in dental practice. Among the widely used systems are the systems based on zirconia which demonstrate important physical and mechanical properties of high strength, adequate fracture toughness, biocompatibility and esthetics, and are indicated for unitary prosthetic restorations and posterior and anterior framework. All the modeling was performed by using CAD-CAM system and prostheses were cemented using resin cement best suited for each situation. The rehabilitation of the maxillary arch using zirconia framework demonstrated satisfactory esthetic and functional results after a 12-month control and revealed no biological and technical complications. This article shows the important of use technology CAD/CAM in the manufacture of dental prosthesis and implant-supported.

Zirconia-based dental crown to support a removable partial denture: a three-dimensional finite element analysis using contact elements and micro-CT data

Veneer fracture is the most common complication in zirconia-based restorations. The aim of this study was to evaluate the mechanical behavior of a zirconia-based crown in a lower canine tooth supporting removable partial denture (RPD) prosthesis, varying the bond quality of the veneer/coping interface. Microtomography (μCT) data of an extracted left lower canine were used to build the finite element model (M) varying the core material (gold core - MAu; zirconia core - MZi) and the quality of the veneer/core interface (complete bonded - MZi; incomplete bonded - MZi-NL). The incomplete bonding condition was only applied for zirconia coping by using contact elements (Target/Contact) with 0.3 frictional coefficients. Stress fields were obtained using Ansys Workbench 10.0. The loading condition (L = 1 N) was vertically applied at the base of the RPD prosthesis metallic support towards the dental apex. Maximum principal (σmax) and von Mises equivalent (σvM) stresses were obtained. The σmax (MPa) for the bonded condition was similar between gold and zirconia cores (MAu, 0.42; MZi, 0.40). The incomplete bonded condition (MZi-NL) raised σmax in the veneer up to 800% (3.23 MPa) in contrast to the bonded condition. The peak of σvM increased up to 270% in the MZi-NL. The incomplete bond condition increasing the stress in the veneer/zirconia interface.

Minimizing the Number of Optical Amplifiers Needed to Support a Multi-Wavelength Optical LAN/MAN

Optical networks based on passive star couplers and employing wavelength-division multiplexing (WDhf) have been proposed for deployment in local and metropolitan areas. Amplifiers are required in such networks to compensate for the power losses due to splitting and attenuation. However, an optical amplifier has constraints on the maximum gain and the maximum output power it can supply; thus optical amplifier placement becomes a challenging problem. The general problem of minimizing the total amplifier count, subject to the device constraints, is a mixed-integer non-linear problem. Previous studies have attacked the amplifier placement problem by adding the “artificial” constraint that all wavelengths, which are present at a particular point in a fiber, be at the same power level. In this paper, we present a method to solve the minimum amplifier- placement problem while avoiding the equally powered- wavelength constraint. We demonstrate that, by allowing signals to operate at different power levels, our method can reduce the number of amplifiers required in several small to medium-sized networks.

Infrastructure Support for Controlled Experimentation with Software Testing and Regression Testing Techniques

Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the design of our infrastructure. The paper then describes the infrastructure that we are creating in response to these challenges, and that we are now making available to other researchers, and discusses the impact that this infrastructure has and can be expected to have.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.

Electric Ventilation: indications for and technical aspects of diaphragm pacing stimulation surgical implantation

Objective: Patients with high cervical spinal cord injury are usually dependent on mechanical ventilation support, which, albeit life saving, is associated with complications and decreased life expectancy because of respiratory infections. Diaphragm pacing stimulation (DPS), sometimes referred to as electric ventilation, induces inhalation by stimulating the inspiratory muscles. Our objective was to highlight the indications for and some aspects of the surgical technique employed in the laparoscopic insertion of the DPS electrodes, as well as to describe five cases of tetraplegic patients submitted to the technique. Methods: Patient selection involved transcutaneous phrenic nerve studies in order to determine whether the phrenic nerves were preserved. The surgical approach was traditional laparoscopy, with four ports. The initial step was electrical mapping in order to locate the "motor points" (the points at which stimulation would cause maximal contraction of the diaphragm). If the diaphragm mapping was successful, four electrodes were implanted into the abdominal surface of the diaphragm, two on each side, to stimulate the branches of the phrenic nerve. Results: Of the five patients, three could breathe using DPS alone for more than 24 h, one could do so for more than 6 h, and one could not do so at all. Conclusions: Although a longer follow-up period is needed in order to reach definitive conclusions, the initial results have been promising. At this writing, most of our patients have been able to remain ventilator-free for long periods of time.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

SED Stroop Effect Detector prototype

[ES] El Detector de Efectos Stroop (SED - Stroop Effect Detector), es una herramienta informática de asistencia, desarrollada a través del programa de investigación de Desarrollo Tecnológico Social de la Universidad de Las Palmas de Gran Canaria, que ayuda a profesionales del sector neuropsicológico a identificar problemas en la corteza orbitofrontal de un individuo, usándose para ello la técnica ideada por Schenker en 1998. Como base metodológica, se han utilizado los conocimientos adquiridos en las diferentes materias de la adaptación al grado en Ingeniería Informática como Gestión del Software, Arquitectura del Software y Desarrollo de Interfaces de Usuario así como conocimiento adquirido con anterioridad en asignaturas de Programación e Ingeniería del Software I y II. Como para realizar este proyecto sólo el conocimiento informático no era suficiente, he realizado una labor de investigación acerca del problema, teniendo que recopilar información de otros documentos científicos que abordan el tema, consultas a profesionales del sector como son el Doctor Don Ayoze Nauzet González Hernández, neurólogo del hospital Doctor Negrín de Las Palmas de Gran Canaria y el psicólogo Don José Manuel Rodríguez Pellejero que habló de este problema en clase del máster de Formación del Profesorado y que actualmente estoy cursando. Este trabajo presenta el test de Stroop con las dos versiones de Schenker: RCN (Reading Color Names) y NCW (Naming Colored Words). Como norma general, ambas pruebas presentan ante los sujetos estudios palabras (nombres de colores) escritas con la tinta de colores diferentes. De esta forma, el RCN consiste en leer la palabra escrita omitiendo la tonalidad de su fuente e intentando que no nos influya. Por el contrario, el NCW requiere enunciar el nombre del color de la tinta con la que está escrita la palabra sin que nos influya que ésta última sea el nombre de un color.

Design, implementation and evaluation of a virtual laboratory for Computer Engineering Education

Broad consensus has been reached within the Education and Cognitive Psychology research communities on the need to center the learning process on experimentation and concrete application of knowledge, rather than on a bare transfer of notions. Several advantages arise from this educational approach, ranging from the reinforce of students learning, to the increased opportunity for a student to gain greater insight into the studied topics, up to the possibility for learners to acquire practical skills and long-lasting proficiency. This is especially true in Engineering education, where integrating conceptual knowledge and practical skills assumes a strategic importance. In this scenario, learners are called to play a primary role. They are actively involved in the construction of their own knowledge, instead of passively receiving it. As a result, traditional, teacher-centered learning environments should be replaced by novel learner-centered solutions. Information and Communication Technologies enable the development of innovative solutions that provide suitable answers to the need for the availability of experimentation supports in educational context. Virtual Laboratories, Adaptive Web-Based Educational Systems and Computer-Supported Collaborative Learning environments can significantly foster different learner-centered instructional strategies, offering the opportunity to enhance personalization, individualization and cooperation. More specifically, they allow students to explore different kinds of materials, to access and compare several information sources, to face real or realistic problems and to work on authentic and multi-facet case studies. In addition, they encourage cooperation among peers and provide support through coached and scaffolded activities aimed at fostering reflection and meta-cognitive reasoning. This dissertation will guide readers within this research field, presenting both the theoretical and applicative results of a research aimed at designing an open, flexible, learner-centered virtual lab for supporting students in learning Information Security.

A Design Methodology for Computer Security Testing

The field of "computer security" is often considered something in between Art and Science. This is partly due to the lack of widely agreed and standardized methodologies to evaluate the degree of the security of a system. This dissertation intends to contribute to this area by investigating the most common security testing strategies applied nowadays and by proposing an enhanced methodology that may be effectively applied to different threat scenarios with the same degree of effectiveness. Security testing methodologies are the first step towards standardized security evaluation processes and understanding of how the security threats evolve over time. This dissertation analyzes some of the most used identifying differences and commonalities, useful to compare them and assess their quality. The dissertation then proposes a new enhanced methodology built by keeping the best of every analyzed methodology. The designed methodology is tested over different systems with very effective results, which is the main evidence that it could really be applied in practical cases. Most of the dissertation discusses and proves how the presented testing methodology could be applied to such different systems and even to evade security measures by inverting goals and scopes. Real cases are often hard to find in methodology' documents, in contrary this dissertation wants to show real and practical cases offering technical details about how to apply it. Electronic voting systems are the first field test considered, and Pvote and Scantegrity are the two tested electronic voting systems. The usability and effectiveness of the designed methodology for electronic voting systems is proved thanks to this field cases analysis. Furthermore reputation and anti virus engines have also be analyzed with similar results. The dissertation concludes by presenting some general guidelines to build a coordination-based approach of electronic voting systems to improve the security without decreasing the system modularity.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.