Miracle-dz/UniFAR-FTUs huggingface dataset:
Reformas do Estado e políticas públicas: trajetórias de democratização e privatização em educação. Brasil e Portugal, um diálogo entre pesquisas

On the basis of concrete empirical research trajectories, the article addresses how the State relates to the private sector regarding education, and what the implications to democratization and the right to education in Brazil and Portugal are. The ways and the meanings in which the State takes on new roles are analysed, keeping public funding in some cases, whereas in others the funding is private; in some situations public power remains in control and in others private actors take the direction and control of the policies. In this sense, the growing centrality of education in public policies seems to correspond, among other phenomena, to an expansion of educational responses and of the populations concerned, accompanied by the multiplication of the modalities of privatization and commodification of education. It is concluded that the market logic has actively participated in the direction and execution of educational policies, with serious consequences for the process of democratization.Keywords: Redefinition of the role of the State; educational policies; marketization of education; the right to education; managerialism
Design and Analysis in Software Engineering Part 1 : The Language of Case Studies and Formal Experiments

Software engineers have many questions to answer. The test team wants to know which technique is best for finding faults in code. Maintainers seek the best tool to support configuration management. Project managers try to determine what types of experience make the best programmers or designers, while designers look for models that are good at predicting reliability. To answer these and other questions, we often rely on the advice and experience of others, which is not always based on careful, scientific reseaxch [Fenton et al. 1994]. As a software practitioner, it is important to make key decisions or assessments in an objective and scientific way. So we need to know two things: what assessment techniques are available to us, and which should we use in a given situation? Will Tracs has asked me to write an on-going column in SIGSOFT Notes to address these issues. In this first article, I will explain some of the terminology to be used in subsequent articles. In future axticles, I and my invited colleagues will try to point out some of the key items affecting decisions about how to do research and how to evaluate the research of others. In the course of doing this, we will also show how softwaxe engineering reseaxch sometimes differs from research in other fields. For instance, in medicine, it is usually easy to test a new drug by giving a placebo to the control group, But in software engineering we cannot do that we cannot ask a group not to use a design technique; the group has to use some technique if it is to complete the design, and we end up losing control rather than having a carefully-controlled comparison. As always, your ~comments axe welcome, preferably through electronic mail; I will try to address in future columns the major points you may raise in your queries.
The Effect of EFT (Emotional Freedom Techniques) on Athletic Performance: A Randomized Controlled Blind Trial

The present study investigates the effect of a psychophysiological intervention, Emotional Freedom Techniques (EFT), on athletic performance. It evaluates whether a single EFT treatment can produce an improvement in high- performance men's and women's PAC-10 college basketball team members (n = 26). The treatment group received a 15 minute EFT session while a performance-matched attention control group received a placebo intervention of similar dura- tion. Performance was measured on free throws and vertical jump height. The time frame of data collection and treatment simulated an actual basketball game. A statistically significant difference between the two groups was found for free throws (p<.03). On post-test, players who received the EFT intervention improved an average of 20.8%, while the atten- tion control group decreased an average of 16.6%. There was no difference between treatment groups in jump height. When performance was analyzed separately by gender, trends toward significance were found for the women's team on both performance measures with better results for the EFT intervention group. This indicates that EFT performed as an intervention during the course of an athletic event may improve free throw performance.
Investigating the Masturbatory Behaviours of Canadian Midlife Adults

INVESTIGATING THE MASTURBATORY BEHAVIOURS OF CANADIAN ADULTS IN MIDLIFE Katarina Kovacevic Advisors: Dr. Robin R. Milhausen University of Guelph, 2016 Dr. John Beaton & Dr. Tuuli Kukkonen Previous research indicates that masturbation is a common and pleasurable activity related to sexual satisfaction and sexual health outcomes (Herbenick, Reece, Sanders, Dodge, Ghassemi & Fortenberry, 2009; Phillippsohn & Hartmann, 2009; Hulburt & Whittaker, 1991; Pinkerton, Bogart, Cecil & Abramson, 2003; Shulman & Horne, 2003). This study examined masturbatory frequency, pleasure and approaches and potential impact for sexual health and satisfaction in a national sample of Canadian men (N= 1111) and women (N= 1010) aged 40-59. Gender differences in masturbation frequency and pleasure were robust: men were almost 7 times more likely than women to report masturbating 4+ times per week and women almost 2.5 times more likely than men to report no masturbation in the last year. Further, women were more likely than men to report vibrator use and report that masturbation was very pleasurable. Men who reported more frequent masturbation reported lower levels of sexual satisfaction in their relationship, whereas women’s masturbation frequency was not related to their sexual satisfaction in relationships. Men and women’s masturbation frequency was not related to sexual health. The limitations and implications of the results are discussed.
Multi-modal target detection for autonomous wide area search and surveillance

Generalised wide are search and surveillance is a common-place tasking for multi-sensory equipped autonomous systems. Here we present on a key supporting topic to this task - the automatic interpretation, fusion and detected target reporting from multi-modal sensor information received from multiple autonomous platforms deployed for wide-area environment search. We detail the realization of a real-time methodology for the automated detection of people and vehicles using combined visible-band (EO), thermal-band (IR) and radar sensing from a deployed network of multiple autonomous platforms (ground and aerial). This facilities real-time target detection, reported with varying levels of confidence, using information from both multiple sensors and multiple sensor platforms to provide environment-wide situational awareness. A range of automatic classification approaches are proposed, driven by underlying machine learning techniques, that facilitate the automatic detection of either target type with cross-modal target confirmation. Extended results are presented that show both the detection of people and vehicles under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance evaluation is presented at an episodic level with individual classifiers optimized for maximal each object of interest (vehicle/person) detection over a given search path/pattern of the environment, across all sensors and modalities, rather than on a per sensor sample basis. Episodic target detection, evaluated over a number of wide-area environment search and reporting tasks, generally exceeds 90%+ for the targets considered here.
Compressive Sensing in Holography

Compressive sensing provides a new framework for simultaneous sampling and compression of signals. According to compressive sensing theory one can recover compressible signals and images from far fewer samples or measurements that traditional methods use. Applying compressive sensing theory for holography comes natural since three-dimensional (3D) data is typically very redundant, thus it is also very compressible. Holographic imaging itself is inherently a compressive process that encodes 3D data in 2D images. The application of compressive sensing for holography may reduce the acquisition effort in terms of acquisition time and hardware; it reduces the overall captured data thus reducing storage requirements and transmission bandwidth. We present a survey of compressive sensing techniques proposed for coherent and incoherent holography. We also present theoretical results that provide applicative practical rules for using of compressive sensing theory in holography. Compressive Sensing, Holography
Modeling the routing of an autonomous system with C-BGP

Today, the complexity of ISPs' networks make it difficult to investigate the implications of internal or external changes on the distribution of traffic across their network. In this article we explain the complexity of building models of large ISPs' networks. We describe the various aspects important to understanding the routing inside an AS. We present an open source routing solver, C-BGP, that eases the investigation of changes in the routing or topology of large networks. We illustrate how to build a model of an ISP on a real transit network and apply the model on two "what-if" scenarios. The first scenario studies the impact of chances in the Internet connectivity of a transit network. The second investigates the impact of failures in its internal topology.
Truth-Telling by Third-Party Auditors and the Response of Polluting Firms: Experimental Evidence from India

In many regulated markets, private, third-party auditors are chosen and paid by the firms that they audit, potentially creating a conflict of interest. This paper reports on a two-year field experiment in the Indian state of Gujarat that sought to curb such a conflict by altering the market structure for environmental audits of industrial plants to incentivize accurate reporting. There are three main results. First, the status quo system was largely corrupted, with auditors systematically reporting plant emissions just below the standard, although true emissions were typically higher. Second, the treatment caused auditors to report more truthfully and very significantly lowered the fraction of plants that were falsely reported as compliant with pollution standards. Third, treatment plants, in turn, reduced their pollution emissions. The results suggest reformed incentives for third-party auditors can improve their reporting and make regulation more effective.
Object-oriented program behavior analysis based on control patterns

Code-patterns are statically recurring structures specific to a programming language. It can be parallel to aid in designing software systems for solving particular problems. Contrary to compilation assistance of code-patterns, control-patterns are dynamically recurring structures invoked during program execution time. It can be used for benchmarking OO-programs and underlying architecture like Java and Java-VM. We propose a run-time profiler based on control patterns and show that all run-time trace can be represented by a directed graph, a constrained output and a Boolean function. A transformed graph can be fed into a data-mining analyzer to find out the recurring structure representing run-time behaviors.
COSTA: Adaptive Indexing for Terms in a Large-scale Distributed System

We introduce COSTA, for content-based search using term aggregation. Besides advantages shared with other P2P-based information retrieval systems, the system has several characteristics that distinguish itself from other systems: First, an adaptive indexing scheme which can dynamically identify important terms is used. Important terms are indexed in a chord-like ring, while other terms are aggregated in a balanced tree. We argue that this architecture is more flexible and suitable for term indexing than DHT-based methods. Furthermore, this structure allows to eliminate the requirement of maintaining global knowledge, and hence we can avoid the difficulty in maintaining such knowledge. Term aggregation is useful not only for performance enhancement, but also for improving the quality of search, by using of the term statistics information obtained via the aggregation. Traditional IR techniques such as query expansion can be utilized based on the information. Therefore, COSTA finely integrates distributed indexing with information retrieval. Advanced techniques, such as node clustering, caching and workload balance, are employed. We show that more existing optimization techniques can be adopted for further improvement of the system's performance.
Performance improvement of a power line communication system using OFDM under the effect of fading and impulsive noise with diversity reception

Performance of a power line communication system is severely deteriorated by fading and multipath effect. Transmitted signal propagate along a shortest path between the transmitter and the receiver, but additional path (echoes) should also be considered. This will result in a multipath scenario with frequency selective effect. The impulsive noise is considered time variant, has short duration, random occurrence with a high power spectral density (PSD). It causes bit error in the signal. Using orthogonal frequency division multiplexing (OFDM) technique, the effect of impulsive noise and fading can be improved greatly. In this paper, an analytical approach using diversity reception is carried out to examine the performance improvement of a power line channel in fading and impulsive noise. The system bit error rate (BER) is compared numerically for both binary phase shift keying (BPSK) and OFDM system. The BER results show that there is significant improvement in OFDM. Also the performance is remarkably upgraded using diversity reception. The BER performance is investigated using multiple antennas and also calculated analytically.
2 Methodology : Question Answering Topic Model

Community Question Answering (CQA) services contain large archives of previously asked questions and their answers. We present a statistical topic model for modeling Question-Answering archives. The model explicitly captures relationships between questions and their answers by modeling topical dependencies. We show that the model achieves improved performance in retrieving the correct answer for a query question compared to the LDA model. Our model can also be used for automatic tagging of questions and answers. This is useful for providing topical browsing capabilities for legacy Q&A archives.
Web 2.0 Vision for the Blind

In today’s world, access to the Web has turned into a basic need. The Web eases daily life signicantly and has turned into an essential - and in some scenarios even exclusive channel for information, communication and transactions. However, the Web does not simplify life for everyone. To guarantee the benet of the Web for the entire human pop
Economic Impact of Tissue Testing and Treatments of Metastatic NSCLC in the Era of Personalized Medicine

A paradigm-shift in the management of non-small cell lung cancer (NSCLC) has resulted in many new therapies becoming available for patients with advanced disease. Stratification of treatment by histologic and molecular subtype is recommended to obtain the greatest clinical benefit for patients while minimizing adverse effects of treatment. However, these advances in diagnosis and treatment of NSCLC have come at a financial cost. This review highlights the economic impact of screening for molecular abnormalities and targeted treatment for advanced NSCLC. Major determinants of cost are drug acquisition and molecular testing. As technologies advance, molecular testing costs may reduce. However, we must collaborate with payers and manufacturers to ensure that high drug costs do not limit patient accessibility to potentially beneficial treatment.
A Novel Color Transfer Algorithm for Image Sequences

This paper presents a novel image sequence color transfer algorithm (ISCT). It is able to render an image sequence with color characteristics borrowed from three user-given target images. The input of this algorithm consists of a single input image (I1) and three target images (T1, T2, T3). The output of the algorithm is an image sequence {Si} with N images that embeds itself with color mood variations. A user selects necessary parameters, such as N and a color variation curve (CVC), through a user friendly interface we have developed. The algorithm completes the task in three steps. First, it executes a color transfer algorithm using the input image and three target images, producing three basis images (B1, B2, B3). Their color features are thus borrowed from the three target images (T1, T2, T3). Given the user-defined CVCs and three basis images generated in the first step, the ISCT algorithm then interpolates the mean and variance values for N images. Finally, referring to the input image and values interpolated in the second step, the algorithm automatically generates an image sequence. This is achieved in several seconds by applying the color transfer algorithm individually for every interpolated value. In addition, we have developed a user interface which makes it possible to view the rendered image sequence. Experimental results show that our algorithm swiftly produces an image sequence containing color variation with little user intervention. Given only three target images, the novel ISCT algorithm can demonstrate its ability to produce an image sequence with color mood variation as well as visually plausible effects. This algorithm is automatic, effective, and expeditious, and is appropriate for many applications.
Beneficial Effect of Conversion to Belatacept in Kidney-Transplant Patients with a Low Glomerular-Filtration Rate

Belatacept has been found to be efficient at preserving good kidney function in maintenance kidney-transplant patients. Herein, we report on the use of belatacept as a rescue therapy for two kidney-transplant patients presenting with severe adverse events after treatment with calcineurin inhibitors (CNIs) and mammalian target-of-rapamycin (mTOR) inhibitors. Two kidney-transplant patients developed severely impaired kidney function after receiving CNIs. The use of everolimus was associated with severe angioedema. Belatacept was then successfully used to improve kidney function in both cases, even though estimated glomerular-filtration rate before conversion was <20 mL/min. These case reports show that belatacept can be used as a rescue therapy, even if kidney function is very low in kidney-transplant patients who cannot tolerate CNIs and/or mTOR inhibitors.
Supreme Court Clerkships and “Feeder” Judges

Because law clerks are integral to the work of the Supreme Court, the selection of clerks is important. Observers of the Court have referred to “feeder judges,” by which we mean court of appeals judges from whom justices draw large numbers of clerks. This article analyzes the feeder-judge phenomenon in the 1976–1985 and 1995–2004 terms of the Court. It verifies that justices do rely heavily on certain court of appeals judges as sources of clerks and that justices differ considerably in the sets of feeder judges from whom they draw clerks. It also shows that there is a strong ideological element to the use of feeder judges by individual justices and that this element has strengthened over time.
Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. Typically, sentiment classification has been modeled as the problem of training a binary classifier using reviews annotated for positive or negative sentiment. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance because words that occur in the train (source) domain might not appear in the test (target) domain. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods on a benchmark data set containing Amazon user reviews for different types of products. We conduct an extensive empirical analysis of the proposed method on single- and multisource domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus. Moreover, our comparisons against the SentiWordNet, a lexical resource for word polarity, show that the created sentiment-sensitive thesaurus accurately captures words that express similar sentiments.
A staircase detection method for 3D point clouds

Staircase detection in an important ability required by indoor robots, allowing for multi-floor exploration in 3D environments. We present an algorithm for stair-case detection from point-cloud data based on a new minimal 3D map representation and the estimation of step-like features that are grouped based on adjacency in order to emerge dominant staircase structures. Experiments performed using noisy RGB-D sensor data from robot exploration trials showed a reliable detection performance under varying conditions.
High-resolution structure of the photosynthetic Mn4Ca catalyst from X-ray spectroscopy

The application of high-resolution X-ray spectroscopy methods to study the photosynthetic water oxidizing complex, which contains a unique hetero-nuclear catalytic Mn4Ca cluster, is described. Issues of X-ray damage, especially at the metal sites in the Mn4Ca cluster, are discussed. The structure of the Mn4Ca catalyst at high resolution, which has so far eluded attempts of determination by X-ray diffraction, X-ray absorption fine structure (EXAFS) and other spectroscopic techniques, has been addressed using polarized EXAFS techniques applied to oriented photosystem II (PSII) membrane preparations and PSII single crystals. A review of how the resolution of traditional EXAFS techniques can be improved, using methods such as range-extended EXAFS, is presented, and the changes that occur in the structure of the cluster as it advances through the catalytic cycle are described. X-ray absorption and emission techniques (XANES and Kβ emission) have been used earlier to determine the oxidation states of the Mn4Ca cluster, and in this report we review the use of X-ray resonant Raman spectroscopy to understand the electronic structure of the Mn4Ca cluster as it cycles through the intermediate S-states.
An optimized message aggregation method to resolve funneling effect in mobility management

In infrastructure-based mobile ad hoc networks, every mobile node has to send registration message periodically to register with an Internet Gateway (IG) or update its presence. Since registration messages are delivered to an IG, the mobile nodes near an IG can be congested, thus experiencing a lot of collisions in message transmission. This phenomenon is called “funneling effect. ” In this paper, we employ message aggregation technique to resolve the funneling effect and use a skewed time synchronization to maximize the message aggregation. We show by resorting to simulation that the message aggregation technique can alleviate the funneling effect greatly.