Análise da estrutura e transporte do zooplâncton no estuário do Rio Caravelas (Bahia-Brasil) através do uso do zooscan

Spatial-temporal dynamics of zooplankton in the Caravelas river estuary (Bahia, Brazil). The survey was conducted in order to describe the zooplankton community of the estuary Caravelas (Bahia, Brazil), to quantify and relate the patterns of horizontal and vertical transport with the type of tide (neap and spring) and tidal phase (flood and ebb). Zooplankton samples were collected with the aid of a suction pump (300L), filtered in plankton nets (300μm) and fixed in saline formalin 4%. Samples were collected at a fixed point (A1), near the mouth of the estuary, with samples taken at neap tides and spring tides during the dry and rainy seasons. Samples were collected for 13 hours, at intervals of 1 hour in 3 depths: surface, middle and bottom. Simultaneous collection of biological, we measured the current velocity, temperature and salinity of the water through CTD. In the laboratory, samples were selected for analysis in estereomicroscope, with 25 groups identified, with Copepoda getting the highest number of species. The 168 samples obtained from temporal samples were subsampled and processed on equipment ZooScan, with the aid of software ZooProcess at the end were generated 458.997 vingnettes. 8 taxa were identified automatically, with 16 classified as a semi-automatic. The group Copepoda, despite the limited taxonomic refinement ZooScan, obtained 2 genera and 1 species identified automatically. Among the seasons dry and wet groups Brachyura (zoea), Chaetognatha, and the Calanoid copepods (others), Temora spp., Oithona spp. and Euterpina acutifrons were those who had higher frequency of occurrence, appearing in more than 70% of the samples. Copepoda group showed the largest percentage of relative abundance in both seasons. There was no seasonal variation of total zooplankton, with an average density of 7826±4219 org.m-3 in the dry season, and 7959±3675 org.m-3 in the rainy season, neither between the types and phases of the tides, but seasonal differences were significant recorded for the main zooplankton groups. Vertical stratification was seen for the major zooplankton groups (Brachyura, Chaetognatha, Calanoida (other), Oithona spp, Temora spp. e Euterpina acutifrons). The scale of this stratification varied with the type (square or tide) and tidal phase (flood or ebb). The instantaneous transport was more influenced by current velocity, with higher values observed in spring tides to the total zooplankton, however, there was a variation of this pattern depending on the zooplankton group. According to the data import and export of total zooplankton, the outflow of organisms of the estuary was higher than the input. The results suggest that the estuary of Caravelas may influence the dynamics of organic matter to the adjacent coast, with possible consequences in National Marine Park of Abrolhos

System-on-a-Chip (SoC) based Hardware Acceleration in Register Transfer Level (RTL) Design

Today, modern System-on-a-Chip (SoC) systems have grown rapidly due to the increased processing power, while maintaining the size of the hardware circuit. The number of transistors on a chip continues to increase, but current SoC designs may not be able to exploit the potential performance, especially with energy consumption and chip area becoming two major concerns. Traditional SoC designs usually separate software and hardware. Thus, the process of improving the system performance is a complicated task for both software and hardware designers. The aim of this research is to develop hardware acceleration workflow for software applications. Thus, system performance can be improved with constraints of energy consumption and on-chip resource costs. The characteristics of software applications can be identified by using profiling tools. Hardware acceleration can have significant performance improvement for highly mathematical calculations or repeated functions. The performance of SoC systems can then be improved, if the hardware acceleration method is used to accelerate the element that incurs performance overheads. The concepts mentioned in this study can be easily applied to a variety of sophisticated software applications. The contributions of SoC-based hardware acceleration in the hardware-software co-design platform include the following: (1) Software profiling methods are applied to H.264 Coder-Decoder (CODEC) core. The hotspot function of aimed application is identified by using critical attributes such as cycles per loop, loop rounds, etc. (2) Hardware acceleration method based on Field-Programmable Gate Array (FPGA) is used to resolve system bottlenecks and improve system performance. The identified hotspot function is then converted to a hardware accelerator and mapped onto the hardware platform. Two types of hardware acceleration methods – central bus design and co-processor design, are implemented for comparison in the proposed architecture. (3) System specifications, such as performance, energy consumption, and resource costs, are measured and analyzed. The trade-off of these three factors is compared and balanced. Different hardware accelerators are implemented and evaluated based on system requirements. 4) The system verification platform is designed based on Integrated Circuit (IC) workflow. Hardware optimization techniques are used for higher performance and less resource costs. Experimental results show that the proposed hardware acceleration workflow for software applications is an efficient technique. The system can reach 2.8X performance improvements and save 31.84% energy consumption by applying the Bus-IP design. The Co-processor design can have 7.9X performance and save 75.85% energy consumption.

What Influences Information Security Behavior? A Study with Brazilian Users

The popularization of software to mitigate Information Security threats can produce an exaggerated notion about its full effectiveness in the elimination of any threat. This situation can result reckless users behavior, increasing vulnerability. Based on behavioral theories, a theoretical model and hypotheses were developed to understand the extent to which human perception of threat, stress, control and disgruntlement can induce responsible behavior. A self-administered questionnaire was created and validated. The data were collected in Brazil, and complementary results regarding similar studies conducted in USA were found. The results show that there is influence of information security orientations provided by organizations in the perception about severity of the threat. The relationship between threat, effort, control and disgruntlement, and the responsible behavior towards information security was verified through linear regression. The contributions also involve relatively new concepts in the field and a new research instrument.

Prevalencia de síntomas de fatiga crónica / encefalomielitis miálgica (SFC/EM) y su relación con factores ocupacionales en trabajadores en una empresa de vigilancia en Bogotá, Colombia, 2016

Antecedentes: El síndrome de fatiga crónica/encefalomielitis miálgica (SFC/EM), un trastorno debilitante y complejo que se caracteriza por un cansancio intenso, ha sido estudiado en población general, sin embargo, su exploración en población trabajadora ha sido limitada. Objetivo: Determinar la prevalencia de síntomas asociados a SFC/EM y su relación con factores ocupacionales en personal de una empresa de vigilancia en Bogotá, durante el año 2016. Materiales y métodos: Estudio de corte transversal en una empresa de vigilancia, utilizando como instrumento para la recolección de datos la historia clínica-ocupacional. En las variables cualitativas se obtuvieron frecuencias simples y porcentajes y en las variables cuantitativas medidas de tendencia central y de dispersión. Se determinaron asociaciones entre variables (Ji-cuadrado de Pearson o test exacto de Fisher, valores esperados <5), (mann-whitney.y un modelo de regresión logística incondicional (p<0.05)). Resultados: Se evaluaron 162 trabajadores, los síntomas de SFC/EM con mayor prevalencia fueron sueño no reparador (38,3%) y dolor muscular (30,2%). Se encontró asociación estadísticamente significativa entre fatiga severa y crónica por al menos 6 meses con alteración en sistema nervioso (p=0,016) y consumo de medicamentos (p=0,043), así mismo entre el sueño no reparador con el número de horas de sueño de 5 a 7 horas (p=0,002). Conclusión: En los vigilantes el síntoma de SFC/EM más prevalente fue sueño no reparador y este se asoció con el número de horas de sueño de 5 a 7 horas. Con el estudio se pudieron determinar los casos probables de SFC/EM los cuales se beneficiarían de una valoración médica integral para un diagnóstico oportuno.

Criação de uma framework para desenvolvimento de sistemas de comunicação aumentativa e alternativa

Nesta dissertação descreve-se uma proposta de implementação de uma plataforma de desenvolvimento de Sistemas de Comunicação Aumentativa e Alternativa para programadores, com o objectivo de melhorar a produtividade e diminuir os tempos despendidos na implementação deste tipo de soluções. Esta proposta assenta numa estrutura composta por widgets configuráveis por código e integráveis em novas aplicações, numa filosofia de reaproveitamento de objectos e funcionalidades, permitindo ainda a uniformização da estrutura do código no desenvolvimento de softwares deste tipo. Esta plataforma pretende ainda dar flexibilidade aos programadores, através da possibilidade de introdução de novas funcionalidades e widgets, permitindo também que se testem novas abordagens ao software durante a investigação. A implementação em tecnologias open source independentes da plataforma, permitirá ainda utilizar os objectos deste toolkit em vários sistemas operativos. ABSTRACT: ln this master thesis we describe an implementation proposal for an Augmentative and Alternative Communication Framework for developers, with the objective of improves the productivity and reduces the implementation times for these types of solutions. This proposal is based on a customized widgets structure that can be integrated in new applications, with the purpose of reuse common features of these applications, also allowing standardize the code structure in this kind of software development. This framework intends to provide flexibility to programmers giving them the possibility of introduce new functionalities and widgets, allowing them to test new approaches during research. The implementation based on open-source technologies, platform independent, allows the use of this toolkit in several different operating systems.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

How to explain: from theory to practice

Today we live in an age where the internet and artificial intelligence allow us to search for information through impressive amounts of data, opening up revolutionary new ways to make sense of reality and understand our world. However, it is still an area of improvement to exploit the full potential of large amounts of explainable information by distilling it automatically in an intuitive and user-centred explanation. For instance, different people (or artificial agents) may search for and request different types of information in a different order, so it is unlikely that a short explanation can suffice for all needs in the most generic case. Moreover, dumping a large portion of explainable information in a one-size-fits-all representation may also be sub-optimal, as the needed information may be scarce and dispersed across hundreds of pages. The aim of this work is to investigate how to automatically generate (user-centred) explanations from heterogeneous and large collections of data, with a focus on the concept of explanation in a broad sense, as a critical artefact for intelligence, regardless of whether it is human or robotic. Our approach builds on and extends Achinstein’s philosophical theory of explanations, where explaining is an illocutionary (i.e., broad but relevant) act of usefully answering questions. Specifically, we provide the theoretical foundations of Explanatory Artificial Intelligence (YAI), formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. We present empirical results in support of our theory, showcasing the implementation of YAI tools and strategies for assessing explainability. To justify and evaluate the proposed theories and models, we considered case studies at the intersection of artificial intelligence and law, particularly European legislation. Our tools helped produce better explanations of software documentation and legal texts for humans and complex regulations for reinforcement learning agents.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Systematic Management of Variability in UML-based Software Product Lines

This paper presents SMarty, a variability management approach for UML-based software product lines (PL). SMarty is supported by a UML profile, the SMartyProfile, and a process for managing variabilities, the SMartyProcess. SMartyProfile aims at representing variabilities, variation points, and variants in UML models by applying a set of stereotypes. SMartyProcess consists of a set of activities that is systematically executed to trace, identify, and control variabilities in a PL based on SMarty. It also identifies variability implementation mechanisms and analyzes specific product configurations. In addition, a more comprehensive application of SMarty is presented using SEI's Arcade Game Maker PL. An evaluation of SMarty and related work are discussed.

Evaluation of three brands of drug interaction software for use in intensive care units

Objective To evaluate drug interaction software programs and determine their accuracy in identifying drug-drug interactions that may occur in intensive care units. Setting The study was developed in Brazil. Method Drug interaction software programs were identified through a bibliographic search in PUBMED and in LILACS (database related to the health sciences published in Latin American and Caribbean countries). The programs` sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting drug-drug interactions. The accuracy of the software programs identified was determined using 100 clinically important interactions and 100 clinically unimportant ones. Stockley`s Drug Interactions 8th edition was employed as the gold standard in the identification of drug-drug interaction. Main outcome Sensitivity, specificity, positive and negative predictive values. Results The programs studied were: Drug Interaction Checker (DIC), Drug-Reax (DR), and Lexi-Interact (LI). DR displayed the highest sensitivity (0.88) and DIC showed the lowest (0.69). A close similarity was observed among the programs regarding specificity (0.88-0.92) and positive predictive values (0.88-0.89). The DIC had the lowest negative predictive value (0.75) and DR the highest (0.91). Conclusion The DR and LI programs displayed appropriate sensitivity and specificity for identifying drug-drug interactions of interest in intensive care units. Drug interaction software programs help pharmacists and health care teams in the prevention and recognition of drug-drug interactions and optimize safety and quality of care delivered in intensive care units.

A Referential Model for Small Companies of Development Software

This paper presents the proposal for a reference model for developing software aimed at small companies. Despite the importance of that represent the small software companies in Latin America, the fact of not having its own standards, and able to meet their specific, has created serious difficulties in improving their process and also in quality certification. In this sense and as a contribution to better understanding of the subject they propose a reference model and as a means to validate the proposal, presents a report of its application in a small Brazilian company, committed to certification of the quality model MPS.BR.

XSophe-Sophe-XeprView (R). A computer simulation software suite (v. 1.1.3) for the analysis of continuous wave EPR spectra

The XSophe-Sophe-XeprView((R)) computer simulation software suite enables scientists to easily determine spin Hamiltonian parameters from isotropic, randomly oriented and single crystal continuous wave electron paramagnetic resonance (CW EPR) spectra from radicals and isolated paramagnetic metal ion centers or clusters found in metalloproteins, chemical systems and materials science. XSophe provides an X-windows graphical user interface to the Sophe programme and allows: creation of multiple input files, local and remote execution of Sophe, the display of sophelog (output from Sophe) and input parameters/files. Sophe is a sophisticated computer simulation software programme employing a number of innovative technologies including; the Sydney OPera HousE (SOPHE) partition and interpolation schemes, a field segmentation algorithm, the mosaic misorientation linewidth model, parallelization and spectral optimisation. In conjunction with the SOPHE partition scheme and the field segmentation algorithm, the SOPHE interpolation scheme and the mosaic misorientation linewidth model greatly increase the speed of simulations for most spin systems. Employing brute force matrix diagonalization in the simulation of an EPR spectrum from a high spin Cr(III) complex with the spin Hamiltonian parameters g(e) = 2.00, D = 0.10 cm(-1), E/D = 0.25, A(x) = 120.0, A(y) = 120.0, A(z) = 240.0 x 10(-4) cm(-1) requires a SOPHE grid size of N = 400 (to produce a good signal to noise ratio) and takes 229.47 s. In contrast the use of either the SOPHE interpolation scheme or the mosaic misorientation linewidth model requires a SOPHE grid size of only N = 18 and takes 44.08 and 0.79 s, respectively. Results from Sophe are transferred via the Common Object Request Broker Architecture (CORBA) to XSophe and subsequently to XeprView((R)) where the simulated CW EPR spectra (1D and 2D) can be compared to the experimental spectra. Energy level diagrams, transition roadmaps and transition surfaces aid the interpretation of complicated randomly oriented CW EPR spectra and can be viewed with a web browser and an OpenInventor scene graph viewer.

Computational chemistry on Fujitsu vector-parallel processors: Development and performance of applications software

In this and a preceding paper, we provide an introduction to the Fujitsu VPP range of vector-parallel supercomputers and to some of the computational chemistry software available for the VPP. Here, we consider the implementation and performance of seven popular chemistry application packages. The codes discussed range from classical molecular dynamics to semiempirical and ab initio quantum chemistry. All have evolved from sequential codes, and have typically been parallelised using a replicated data approach. As such they are well suited to the large-memory/fast-processor architecture of the VPP. For one code, CASTEP, a distributed-memory data-driven parallelisation scheme is presented. (C) 2000 Published by Elsevier Science B.V. All rights reserved.