Miracle-dz/UniFAR-FTUs huggingface dataset:
O CONHECIMENTO DO CONTEXTO NA CONSTRUÇÃO DO CONHECIMENTO PEDAGÓGICO DO CONTEÚDO DOS FUTUROS PROFESSORES DE EDUCAÇÃO FÍSICA

O objetivo foi investigar como o conhecimento do contexto emerge nas praticas pedagogicas de estudantes-professores em diferentes etapas da formacao inicial em Educacao Fisica. Os participantes deste estudo de caso qualitativo foram quatro duplas de estudantes que representavam diferentes etapas de um curso de Licenciatura. A coleta de dados ocorreu por meio de entrevistas e de questionarios. Os resultados mostram que houve preocupacao maior dos estudantes investigados com o microcontexto de aula. Especificamente, a gestao de infraestrutura de aula, logisticos e recursos disponiveis e, em menor proporcao a gestao dos conteudos. Conclui-se que a falta de reflexao e utilizacao de conhecimentos de contextos mais amplos comprometeu a qualidade da intervencao e formacao pedagogica dos estudantes-professores.
Bridging the gap: SE technology transfer into practice: study design and preliminary results

Background: Particularly during and after research projects, technology transfer into practice plays an important role for academia to get technologies into use and for industry to improve their development. Objective: Our goal was to gain more and current knowledge about how technology transfer from software engineering (SE) research into industrial practice is accomplished best and how to measure the effectiveness of this transfer. Method: We conducted a study in the context of two German research projects, covering many different organizations from industry and academia. Results: This paper presents the design of the study and the survey performed. After introducing the concept of technology transfer we used and adapted, we present preliminary results. Conclusions: We observed that traditional means such as meetings or workshops are still the most widely used mediums for technology transfer in SE. We also discovered that, even though the duration of transfer depends on the object being transferred, the average duration is three years, which is far less than previously published (~18 years).
Mental health service use among South Africans for mood, anxiety and substance use disorders.

BACKGROUND Europe and North America have low rates of mental health service use despite high rates of mental disorder. Little is known about mental health service use among South Africans. DESIGN A nationally representative survey of 4351 adults. Twelve-month DSM-IV (Diagnostic and Statistical Manual, 4th edition) diagnoses, severity, and service utilisation were determined using the World Health Organization Composite International Diagnostic Interview (CIDI). Twelve-month treatment was categorised by sector and province. South Africans in households and hostel quarters were interviewed between 2002 and 2004 in all nine provinces. OUTCOME MEASURES 4 317 respondents 18 years and older were analysed. Bivariate logistic regression models predicted (i) 12-month treatment use of service sectors by gender, and (ii) 12-month treatment use by race by gender. RESULTS Of respondents with a mental disorder, 25.2% had sought treatment within the previous 12 months; 5.7% had used any formal mental health service. Mental health service use was highest for adults with mood and anxiety disorders, and among those with a mental disorder it varied by province, from 11.4% (Western Cape) to 2.2% (Mpumalanga). More women received treatment, and this was largely attributable to higher rates of treatment in women with mood disorders. Age, income, education and marital status were not significantly associated with mental health service use. Race was associated with the treatment sector accessed in those with a mental disorder. CONCLUSIONS There is a substantial burden of untreated mental disorders in the South African population, across all provinces and even in those with substantial impairment. Greater allocation of resources to mental health services and more community awareness initiatives are needed to address the unmet need.
Personalized Ranking in Signed Networks Using Signed Random Walk with Restart

How can we rank users in signed social networks? Relationships between nodes in a signed network are represented as positive (trust) or negative (distrust) edges. Many social networks have adopted signed networks to express trust between users. Consequently, ranking friends or enemies in signed networks has received much attention from the data mining community. The ranking problem, however, is challenging because it is difficult to interpret negative edges. Traditional random walk based methods such as PageRank and Random Walk with Restart cannot provide effective rankings in signed networks since they assume only positive edges. Although several methods have been proposed by modifying traditional ranking models, they also fail to account for proper rankings due to the lack of ability to consider complex edge relations. In this paper, we propose Signed Random Walk with Restart, a novel model for personalized ranking in signed networks. We introduce a signed random surfer so that she considers negative edges by changing her sign for walking. Our model provides proper rankings reflecting signed edges based on the signed surfer. Through extensive experiments, we demonstrate that SRWR achieves the best accuracy (up to 87%) for sign prediction, and predicts trolls 4× more accurately than other ranking models.
New algorithms and empirical findings on minimum weight triangulation heuristics (extended abstract)

number of edges points examined. The weight of a triangulation is the sum of the lengths of all the edges in the triangulation. A Mnimum Weight ! f%iangtdation (MWT) of a point set S is a triangulation that minimizes weight over all possible triangulations. Wang and Aggarwal [19] and Baraquet and Sharir [1] use the MWT of simple polygons to reconstruct three dimensional objects from two dimensional contours (or slices). The MWT also arises in numerical analysis in a method suggested by Yoeli [20] for numerical approximation of bivariate data. Though it has been shown how to compute the MWT in time 0(n3) for the special case of n-vertex simple polygons [9], there are no known efficiently computable algorithms for the MWT of general point sets. We therefore seek efficiently computable approximations to the MWT, of which there are a number of heuristics. We report on the practical efficiency and effective nees of various of these heuristics including multiple algorithms for the greedy triangulation, other approaches to locally minimal triangulations, and some new algorithms that perform comparatively well. Our empirical findings come from a number of different tests. We report on triangulations of general points sets over random distributiona, as well as of simple polygons taken from an application to medical image proceaaingl. In addition to a comparison of run times and weights, we also analyze certain primitive operations such as the
Robust multi-human tracking by detection update using reliable temporal information

In this paper, we present a multiple human tracking approach that takes the single frame human detection results as input, and associates them hierarchically to form trajectories while improving the original detection results by making use of reliable temporal information. It works by first forming tracklets, from which reliable temporal information can be extracted, and then refining the detection responses inside the tracklets. After that, local conservative tracklets association is performed and reliable temporal information is propagated across tracklets. The global tracklet association is done lastly to resolve association ambiguities. Comparison with two state-of-the-art approaches demonstrates the effectiveness of the proposed approach.
Using grid computing based components in on demand environmental data delivery

The grid computing technology is evolving from the emergence to the stable and production status. In the computational science field the grid computing approach for storage and computing element resource sharing is a common way thanks to middleware as the Globus Toolkit and grid environments as the Condor project. In many domain applications, as environmental science, bioinformatics and interactive multimedia delivery, the shared resource is composed by tagged data and contents instead of hardware components. We provide a grid aware component leveraging on our Resource Broker Service implementing a wrap over the world-wide spread OpenDAP scientific data access protocol, ensuring effective and efficient grid based content distribution, and contributing to the "grand challenge" concerning the development of grid-aware software infrastructure for data intensive environmental applications.
Cancers in Togo from 1984 to 2008: Epidemiological and Pathological Aspects of 5251 Cases

Objective. To describe the epidemiological and histological aspects of cancers in Togo. Materials and Methods. We made a retrospective review of the epidemiological and pathological features of cancers observed from 1984 to 2008 at the laboratory of pathology of CHU-TOKOIN in Lomé, Togo. Results. During our study period, we found 5251 cases of cancers with an annual average frequency of 210 cases. The sex ratio, male/female, was 0.9 and the average age of occurring was 45.3 years. This average age was 46.9 years for men and 43.8 years for women. The most frequent cancers for men were prostate cancer (12.9%), nonmelanoma skin cancer (10.4%), and gastric cancer (10.3%). For women it was breast cancer (27.1%), cervix cancer (11.2%) and non-Hodgkin lymphoma (6.3%). Histologically, it was carcinomas in 68.1% of the cases, sarcomas in 11% of the cases and non-Hodgkin lymphomas in 12.6% of the cases. Children cancers were primarily Burkitt lymphoma (27.9% of cases) and retinoblastoma (8.5% of cases). Conclusion. This study shows that cancers are frequent in Togo and emphasizes on the necessity of having a cancer register for the prevention and the control of this disease in Togo.
Tool wear states recognition based on genetic algorithm and back propagation neural network model

The recognition model of tool wear states based on genetic algorithm and back propagation (BP) network is proposed. There are some disadvantages in BP algorithm, such as low rate of convergence, easily falling into local minimum point and weak global search capability. In order to settle these problems, genetic algorithm is used to optimize BP neural network. At first, the genetic algorithm is used to optimize the weights and threshold values of BP neural network when its topology is determined. Then the stable weights and threshold values can be obtained after a number of generations' crossover and mutation. Assign them to the BP neural network as the initial value and retrain the network. The global optimal solution of the network parameters can be obtained in this way, and the performance of condition recognition network can be improved. The experimental results show that the neural network optimized by genetic algorithm greatly improves the efficiency and accuracy of the tool wear states recognition system.
Are reality monitoring differences between truthful and deceptive autobiographical accounts affected by standardisation for word-count and the presence of others?

ABSTRACT Although a number of methods have been proposed to control for word-count differences between truthful and deceptive accounts, there is no uniformity amongst researchers using the Reality Monitoring (RM) criteria as to when, why or how to standardise for word-count differences. Another factor that also has received little attention in the literature is whether the number of others present when a person is providing an account alters the lexical profile of accounts such that RM scores are affected. To investigate these issues, 62 autobiographical statements, 31 truthful and 31 deceptive, were generated under 3 conditions, no person present, 1 and 2 persons present, and were analysed before and after standardisation for word-count and duration. Results showed that the criteria successfully discriminated between truthful and deceptive accounts when no attempt to control for word-count was made and, to a lesser extent, when accounts were standardised for duration; however, they failed to discriminate after accounts had been standardised for length. The presence of others did not affect the ability to distinguish between truthful and deceptive accounts. The results highlight the difficulties involved in developing normative standardisation criteria which could be used in the field to classify individual or small numbers of cases.
Constructive theory refinement in knowledge based neural networks

Knowledge based artificial neural networks offer an approach for connectionist theory refinement. We present an algorithm for refining and extending the domain theory incorporated in a knowledge based neural network using constructive neural network learning algorithms. The initial domain theory comprising propositional rules is translated into a knowledge based network of threshold logic units (threshold neuron). The domain theory is modified by dynamically adding neurons to the existing network. A constructive neural network learning algorithm is used to add and train these additional neurons using a sequence of labeled examples. We propose a novel hybrid constructive learning algorithm based on the tiling and pyramid constructive learning algorithms that allows a knowledge based neural network to handle patterns with continuous valued attributes. Results of experiments on two non-trivial tasks (the ribosome binding site prediction and the financial advisor) show that our algorithm compares favorably with other algorithms for connectionist theory refinement both in terms of generalization accuracy and network size.
Data Preparation Process for Construction Knowledge Generation through Knowledge Discovery in Databases

As the construction industry is adapting to new computer technologies in terms of hardware and software, computerized construction data are becoming increasingly available. The explosive growth of many business, government, and scientific databases has begun to far outpace our ability to interpret and digest the data. Such volumes of data clearly overwhelm the traditional methods of data analysis such as spreadsheets and ad-hoc queries. The traditional methods can create informative reports from data, but cannot analyze the contents of those reports. A significant need exists for a new generation of techniques and tools with the ability to automatically assist humans in analyzing the mountains of data for useful knowledge. Knowledge discovery in databases (KDD) and data mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so that the construction manager may analyze the large amount of construction project data. These technologies combine techniques from machin.
Sparse matrix-vector multiplication on network-on-chip

Abstract. In this paper, we present an idea for performing matrix-vector multiplication by using Network-on-Chip (NoC) architecture. In traditional IC design on-chip communications have been designed with dedicated point-to-point interconnections. Therefore, regular local data transfer is the major concept of many parallel implementations. However, when dealing with the parallel implementation of sparse matrix-vector multiplication (SMVM), which is the main step of all iterative algorithms for solving systems of linear equation, the required data transfers depend on the sparsity structure of the matrix and can be extremely irregular. Using the NoC architecture makes it possible to deal with arbitrary structure of the data transfers; i.e. with the irregular structure of the sparse matrices. So far, we have already implemented the proposed SMVM-NoC architecture with the size 4×4 and 5×5 in IEEE 754 single float point precision using FPGA.
Changing Role of Teachers in the Present Society

The young today are facing the world in which communication and information revolution has led to changes in all spheres: scientific, technological, political, economic, social, and cultural. To be able to prepare our young people to face the future with confidence purpose and responsibility, the crucial role of teachers cannot be overemphasized. Given these multidimensional demands, Role of teachers also has to change. In the past, teachers used to be a major source of knowledge, the leader, and educator of their students school life. The changes that took place in education have initiated to change the role of teachers. In this article, we will examine how the role of teachers in the present society has to change.
Climate Change and UV-B Impacts on Arctic Tundra and Polar Desert Ecosystems

General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? An assessment of impacts on Arctic terrestrial ecosystems has emphasized geographical variability in responses of species and ecosystems to environmental change. This variability is usually associated with north-south gradients in climate, biodiversity, vegetation zones, and ecosystem structure and function. It is clear, however, that significant east-west variability in environment, ecosystem structure and function, environmental history, and recent climate variability is also important. Some areas have cooled while others have become warmer. Also, east-west differences between geographical barriers of oceans, archipelagos and mountains have contributed significantly in the past to the ability of species and vegetation zones to relocate in response to climate changes, and they have created the isolation necessary for genetic differentiation of populations and biodiversity hot-spots to occur. These barriers will also affect the ability of species to relocate during projected future warming. To include this east-west variability and also to strike a balance between overgeneralization and overspecialization, the ACIA identified four major sub regions based on large-scale differences in weather and climate-shaping factors. Drawing on information, mostly model output that can be related to the four ACIA subre-gions, it is evident that geographical barriers to species re-location, particularly the distribution of landmasses and separation by seas, will affect the northwards shift in vegetation zones. The geographical constraints—or facili-tation—of northward movement of vegetation zones will affect the future storage and release of carbon, and the exchange of energy and water between biosphere and atmosphere. In addition, differences in the ability of vegetation zones to relocate will affect the biodiversity associated with each zone while the number of species threatened by climate change varies greatly between subregions with a significant hot-spot in Beringia. Overall, the subregional synthesis demonstrates the difficulty of generalizing projections of responses of ecosystem structure and function, species loss, and biospheric feedbacks to the climate …
Selecting Attributes for Sport Forecasting using Formal Concept Analysis

In order to address complex systems, apply pattern recongnition on their evolution could play an key role to understand their dynamics. Global patterns are required to detect emergent concepts and trends, some of them with qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). Implications and association rules are usefull to reasoning on previously selected attributes, providing a formal foundation for logical reasoning. In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge-Based system for confidence reasoning.
The use of CASE tools

1. rnSTRA@T Computer-Aided Software Engineering (CASE) technologies are tools that provide automated assistance for software development [3]. The goal of introducing CASE tools is the reduction of the time and cost of software development and the enhancement of the quality of the systems developed [3], [2Q This paper explores the use of CASE tools. We ask several questions. Are CASE tools being used? If yes, what features within the tool are being used? Next, we explore PWQ potential reasons for the expected low use. Do CASE tools change the job of the Systems developer in an Unattractive way? And are the people who are expected to use CASE took motivated to use them? 233 systems developers were surveyed to answer these questions. We found that CASE tooh are being used but IPQt in many CQmptiC! S. Within the companies that have adopted CASE ~QQ, few people are acfuahy us-mg tbe took. The systems developers who use CASE tOQk are using formal m&bQdQ~Qgk InQre Often than SyStemS developers who do not use CASE took Systems developers allocate their time differently depending on whether they are Norman L. Chervany University of Minnesota Information and Decision Sciences, 271 19 th Avenue South Minneapolis, MN 55455 (612) 624-l 520
Specifying E-Alliance contract dynamics through the MOISE+ reorganisation process

When enterprises join to form virtual dynamic enterprises, they need to state their contracts. In this paper we propose to use the MOISE+ organisational model to describe constraints and obligations entitled in a contract. The model is suitable for this application since it was developed to deal with dynamic environments where the organisation, and thus the contracts, may change in a kind of reorganisation.
Extracting Emotional Semantics from Color Image using Analytical Hierarchy Process

In order to understand the emotional information of the color image, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the 'semantic gap' between the visual features and the richness of human perception. In this paper we develop an emotional-based mapping scheme using analytical hierarchy process (AHP) on the basis of HSI color space. The AHP is used to provide a systematical way to evaluate the fitness of emotional labels for interpreting the emotional messages of a color image. Experimental results are given and discussed showing the excellent performance of the proposed method.
Variational Bayesian EM for SLAM

Designing accurate, robust and cost-effective systems is an important aspect of the research on self-driving vehicles. Radar is a common part of many existing automotive solutions and it is robust to adverse weather and lighting conditions, as such it can play an important role in the design of a self-driving vehicle. In this paper, a radar-based simultaneous localization and mapping (SLAM) algorithm using variational Bayesian expectation maximization (VBEM) is presented. The VBEM translates the inference problem to an optimization one. It provides an efficient and powerful method to estimate the unknown data association variables as well as the map of the environment as perceived by a radar and the unknown trajectory of the vehicle.