Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging islands of specialized code using macros and reified types

Parametric polymorphism in Scala suffers from the usual drawback of erasure on the Java Virtual Machine: primitive values are boxed, leading to indirect access, wasteful use of heap memory and lack of cache locality. For performance-critical parts of the code, the Scala compiler introduces specialization, a transformation that duplicates and adapts the bodies of classes and methods for primitive types. Specializing code can speed up execution by an order of magnitude, but only if the code is called from monomorphic sites or from other specialized code. Still, if these "islands" of specialized code are called from generic code, their performance becomes similar to that of generic code, losing optimality. To address this, our project builds high performance "bridges" between "islands" of specialized code, removing the requirement that full traces need to be specialized: We use macros to delimit performance-critical "gaps" between specialized code, which we also specialize. We then use reified types to dispatch the correct specialized variant, thus recovering performance across the "islands". Our transformation obtains speedups up to 30x and around 12x in average compared to generic only code, by enabling specialization to completely remove boxing and reach its full potential.
Ti Reactive Sintering of Electrically Conductive Al2O3–TiN Composite: Influence of Ti Particle Size and Morphology on Electrical and Mechanical Properties

In the current study, Al2O3–TiN composites were successfully fabricated with various particle sizes (10, 20, 30, and 50 μm) and concentrations (5, 10, 15, and 20 vol %) via a novel ball milling + Ti reactive sintering process. By applying the reactive sintering, Ti powders will transform into TiN particles, which act as mechanical reinforcements and electrical conductors in the Al2O3 matrix. The ball milling process alters the Ti powder morphology from a low-aspect-ratio sphere into a high-aspect-ratio disc, which reduces the electrical percolation threshold value from 29% to 15% in the current setup. However, such a threshold value is insensitive to the particle size. Meanwhile, the Ti particle size has a significant influence on the material’s mechanical properties. A small particle size results in less porosity and hence higher flexural strength of the composite.
Social network collaborative filtering framework and online trust factors: A case study on Facebook

Recommender systems have been proposed to exploit the potential of social network by filtering the information and offer recommendations to a user that he is predicted to like. Collaborative Filtering (CF) is believed to be a suitable underlying technique for recommender systems on social network, because CF gathers tastes of similar users; and social network provides such a collaborative social environment. One inherent challenge however for running CF on social network is quantitative estimation of trust between friends. Although many researchers investigated trust metrics and models in social network, none so far has efficiently integrated them in a CF algorithm. The contribution of this paper is a framework of collaborative filtering on social network, and a novel approach in measuring trust factors by data-mining over a survey dataset provided by The Facebook Project.1 The quantitatively estimated trust factors can be used as input parameters in the CF algorithm. Facebook is taken as a case study here to illustrate the concepts.
ADVANCED PROCESS CONTROL

CONTENTS SUMMARY 1. WHAT IS ADVANCED CONTROL? 2. PROCESS MODELS 2.1. Mechanistic Models 2.2. Black Box Models 2.3. Qualitative Models 2.4. Statistical Models 3. MODEL BASED (MODERN) AUTOMATIC CONTROL 3.1. PID Control 3.2. Predictive Constrained Control 3.3. Multivariable Control 3.4. Robust Control and the Internal Model Principle 3.5. Globally Linearising Control 4. STATISTICAL PROCESS CONTROL 4.1. Conventional SPC 4.2. Algorithmic SPC 4.3. Active SPC 5. DEALING WITH DATA PROBLEMS 5.1. Inferential Estimation 5.2. Data Conditioning and Validation 5.3. Data Analysis 6. HIGHER LEVEL OPERATIONS 6.1. Process Optimisation 6.2. Process Monitoring, Fault Detection, SUMMARY
Symmetric multi-view stereo reconstruction from planar camera arrays

We present a novel stereo algorithm which performs surface reconstruction from planar camera arrays. It incorporates the merits of both generic camera arrays and rectified binocular setups, recovering large surfaces like the former and performing efficient computations like the latter. First, we introduce a rectification algorithm which gives freedom in the design of camera arrays and simplifies photometric and geometric computations. We then define a novel set of data-fusion functions over 4-neighborhoods of cameras, which treat all cameras symmetrically and enable standard binocular stereo algorithms to handle arrays with arbitrary number of cameras. In particular, we introduce a photometric fusion function which handles partial visibility and extracts depth information along both horizontal and vertical baselines. Finally, we show that layered depth images and sprites with depth can be efficiently extracted from the rectified 3D space. Experimental results on real images confirm the effectiveness of the proposed method, which reconstructs dense surfaces larger by 20% on Tsukuba.
TENSOR CLASSIFICATION FOR P 300-BASED BRAIN COMPUTER INTERFACE

Classification methods have been widely applied in most brain computer interfaces (BCIs) that control devices for better quality of life. Most existing classification methods for P300-based BCIs extract features based on temporal structure related to P300 components of event-related potentials (ERPs). Some others exploit the spatial distribution of ERPs optimally selected by recursive channel elimination. However, none of them employed multilinear structures which exploit hidden features in P300-based BCI data. In this paper, we propose a new feature extraction method based on tensor decomposition for ERP-based BCIs. The method seeks an optimal feature subspace simultaneously spanned by temporal and spatial bases, and additional bases which indicate a variant of ERPs obtained by different degrees of polynomial fittings. The proposed method has been evaluated by both the BCI competition III data set II and the affective face driven paradigm data set, and achieved 92% and 95% classification accuracies respectively, which were better than those of most existing P300-based BCI algorithms.
A Review on Prediction of Multiple Diseases and Performance Analysis using Data Mining and Visualization Techniques

In the field of medical science a tremendous amount of data is generated, doctors need to test the patient physically to find out the injuries and disease of the patient. This paper outlines the idea of predicting a particular disease by performing operations on the digital data generated in the medical diagnosis. In this project an efficient genetic algorithm hybrid with the techniques like back propagation and Naive Bayes approach for disease prediction is proposed. Bad clinical decisions would cause death of a patient which cannot be aff orded by any hospital. To achieve a correct and cost eff ective treatment, computer technology Systems can be developed to make good decision. There is a lot of medical information unexplored, which gives rise to an important query of how to make useful information out of the data. The purpose of this project is to construct a basic prototype model which can determine and extract unknown knowledge (patterns, concepts and relations) related with multiple disease from a past database records of specified multiple diseases. It can solve complicated queries for detecting a particular disease and thus assist medical practitioners to make intelligent clinical decisions which traditional decision support systems were not able to. By providing efficient treatments, it can help to reduce costs of treatment. The medical organizations are ”rich in data” but their ”knowledge utilization is poor ”. There is a lack of sufficiency of improved analysis techniques to find relations, concepts and patterns in the medical data. Data mining is science and engineering study of extracting previously undiscovered patterns from a huge set of data. In this paper, data mining methods namely, Decision tree, Naïve Bayes, and Back-Propagation(ANN) algorithms are implemented on medical data sets . The medical datasets will be represented graphically(graphs , charts , shapes )using diff erent visualization techniques. The algorithms are compared and evaluated on basis of their accuracy and time consumption factors. The algorithm which gives out high accuracy and less duration to give the output is analysed and implemented.
Constructive Use of MP/NLOS bias of GNSS Pseudoranges: Performance Analysis by Type of Environment

The several progress of the free-accessible global navigation satellites system (GNSS) is not without major hurdles and challenges in it course of application in urban setting. Several error sources in these environments such as multipath and non-line-of-sight (NLOS) reception, signal masking and poor constellation geometry hinder the required positioning accuracy by GNSS signals. Facing this pressing need for performance enhancement in NLOS conditions, a new trend of approaches seek a constructive use of these degraded signals by correcting ranging measurements, using a 3D GNSS simulator for instance. However, this approach may engender a great risk of deteriorating PR measurements instead of correcting them if the compensation term is not accurate enough. Therefore, we propose in this paper to address the influence of PR bias estimation on the performances of this positioning method based on the correction of PR measurement. This original study permits us defining the maximum level of inaccuracy on bias estimation that any 3D GNSS simulator, or other tools, mustn’t exceed. A detailed study on this most acceptable level of inaccuracy on the PR bias estimation is performed using real GNSS data in Toulouse and encompass analysis by type of environment (Urban, Peri-urban and rural environments) and by type of GNSS signals.
Enhanced Trustworthy and High-Quality Information Retrieval System for Web Search Engines

The WWW is the most important source of information. But, there is no guarantee for information correctness and lots of conflicting information is retrieved by the search engines and the quality of provided information also varies from low quality to high quality. We provide enhanced trustworthiness in both specific (entity) and broad (content) queries in web searching. The filtering of trustworthiness is based on 5 factors – Provenance, Authority, Age, Popularity, and Related Links. The trustworthiness is calculated based on these 5 factors and it is stored thereby increasing the performance in retrieving trustworthy websites. The calculated trustworthiness is stored only for static websites. Quality is provided based on policies selected by the user. Quality based ranking of retrieved trusted information is provided using WIQA (Web Information Quality Assessment) Framework.
Fast algorithm for multiple-objects min-/spl epsiv/ problem

Fast algorithm for joint near-optimal approximation of multiple polygonal curves is proposed. It is based on iterative reduced-search dynamic programming introduced earlier for the min-/spl epsiv/ problem of a single polygonal curve. The proposed algorithm jointly optimizes the number of line segments allocated to the different individual curves, and the approximation of the curves by the given number of segments. Trade-off between time and optimality is controlled by the breadth of (the search, and by the numbers of iterations applied.
Interleukin 10 genotypes in irritable bowel syndrome: evidence for an inflammatory component?

Background and aims: Inflammation may play a role in the pathogenesis of irritable bowel syndrome in some individuals, such as in those who develop symptoms following a dysenteric illness. Persisting inflammation, resulting from an imbalance of cytokines regulating the inflammatory response, is one possible mechanism. As the elaboration of cytokines is under genetic control, this study was designed to establish whether there might be a genetic predisposition to an altered pattern of anti-inflammatory cytokine production in patients with irritable bowel syndrome. Subjects: A total of 230 unselected patients with irritable bowel syndrome and 450 healthy, ethnically matched controls were studied. Methods: DNA was extracted from peripheral blood leucocytes of subjects. Allele and genotype frequencies were determined for the anti-inflammatory cytokine interleukin 10 at the site (−1082) concerned with production in lymphocytes. Transforming growth factor β1 (codons 10 and 25) genotypes were also examined in a smaller group of subjects. Results: Patients with irritable bowel syndrome had significantly reduced frequencies of the high producer genotype for interleukin 10 than controls (21% v 32%; p=0.003). There was no apparent relationship with any particular bowel habit subtype. Genotypes for transforming growth factor β1 were not altered. Conclusions: These preliminary results suggest that at least some patients with irritable bowel syndrome may be genetically predisposed to produce lower amounts of the anti-inflammatory cytokine interleukin 10. This lends some support to the hypothesis that there may be an inflammatory or genetic component in some cases of this condition and that further studies in specific irritable bowel syndrome subgroups are justified.
A new generation for intelligent anti-internet worm early system detection

Worm requires host computer with an address on the Internet and any of several vulnerabilities to create a big threat environment.We propose intelligent early system detection mechanism for detecting internet worm.The mechanism is combined of three techniques: Failure Connection Detection (FCD) which concerns with detecting the internet worm and stealthy worm in which computer infected by the worm by using Artificial Immune System; and the Traffic Signature Detection (TSD) which responsible for detecting traffic signature for the worm; and the DNA Filtering Detection (DNAFD) which converts traffic signature to DNA signature and sending it to all computer that connected with the router to create a firewall for new worms.Our proposed algorithm can detect difficult stealthy internet worm in addition to detecting unknown internet worm.
Characterization of Viral Agents Causing Acute Respiratory Infection in a San Francisco University Medical Center Clinic during the Influenza Season

Abstract Background. With use of polymerase chain reaction (PCR) and a centrifugation-enhanced viral culture method, we characterized the viruses causing acute respiratory infection in adults during an influenza season. Methods. During January-March 2002, nasopharyngeal wash specimens from previously healthy adults presenting with respiratory symptoms were evaluated for viral pathogens with centrifugation-enhanced viral culture and PCR. Results The diagnoses in 266 cases included unspecified upper respiratory infection (in 142 [54%] of the cases), acute bronchitis (42 [16%]), sinusitis (23 [9%]), pharyngitis (22 [8%]), and pneumonia (17 [6%]). The use of a shell vial assay and PCR identified a pathogen in 103 (39%) of the patients, including influenza A or B in 54, picornavirus in 28 (including rhinovirus in 24), respiratory syncytial virus (RSV) in 12, human metapneumovirus in 4, human coronavirus OC43 in 2, adenovirus in 2, parainfluenza virus type 1 in 1, and coinfection with influenza and parainfluenza virus type 1 in 2. Conclusion. Our findings demonstrate that, even during the influenza season, rhinovirus and RSV are prevalent and must be considered in the differential diagnosis of adult acute respiratory infection before prescribing antiviral medication. Human coronavirus and human metapneumovirus did not play a substantial role. PCR was an especially useful tool in the identification of influenza and other viral pathogens not easily detected by traditional testing methods.
Heartworm Disease (Dirofilaria immitis) and Their Vectors in Europe – New Distribution Trends

Cardiopulmonary dirofilariasis is a cosmopolitan disease caused by Dirofilaria immitis, which affects mainly canids and felids. Moreover, it causes zoonotic infections, producing pulmonary dirofilariasis in humans. Heartworm disease is a vector-borne transmitted disease, thus transmission depends on the presence of competent mosquito species, which is directly related to favorable climate conditions for its development and survival. Cardiopulmonary dirofilariasis is mainly located in countries with temperate and tropical climates. Europe is one of the continents where animal dirofilariasis has been studied more extensively. In this article we review the current prevalence of canine and feline cardiopulmonary dirofilariasis in the European continent, the transmission vectors, the current changes in the distribution and the possible causes, though the analysis of the epidemiological studies carried out until 2001 and between 2002 and 2011. The highest prevalences have been observed in the southern European countries, which are considered historically endemic/hyperendemic countries. Studies carried out in the last 10 years suggest an expansion of cardiopulmonary dirofilariasis in dogs toward central and northern Europe. Several factors can exert an influence on the spreading of the disease, such as movement of infected animals, the introduction of new species of mosquitoes able to act as vectors, the climate change caused by the global warming, and development of human activity in new areas. Veterinary controls to prevent the spreading of this disease, programs of control of vectors, and adequate protocols of prevention of dirofilariasis in the susceptible species should be carried out.
Les services Web

L’accès aux systèmes d’information s’appuie aujourd’hui de plus en plus sur des technologies Internet. Les efforts de standardisation dans ce contexte ont accentué l’engouement des personnes et des organisations (aussi bien académiques, qu’industrielles, commerciales, ou institutionnelles) pour l’utilisation de l’Internet et ont permis l’émergence des services Web comme support de développement des applications accessibles par Internet. Ainsi, les technologies associées aux services Web sont devenues incontournables pour le développement d’applications interagissant les unes avec les autres par le biais de l’Internet. Nous proposons dans ce chapitre de faire un point sur le sujet des services Web. L’objectif de la discussion est de traiter des aspects conceptuels de la modélisation des services aussi bien que des aspects liés à leur implantation. La section 4.1 commence par donner quelques définitions et principes nécessaires à la compréhension de la suite. Puis la section 4.2 discute de la modélisation des interactions supportées par un service Web en abordant les deux points de vue, structurel et comportemental. La section 4.3 donne un éclairage sur les principaux standards pour la description de services Web en les distinguant selon leur capacité à traiter des aspects structurels, comportementaux et non-fonctionnels des services. Dans la section 4.4 nous décrivons quelques outils existants pour la mise en œuvre de services Web. Finalement la section 4.5 donne, en forme de conclusion, quelques perspectives faisant actuellement l’objet de recherches.
SAPPHIRE: Stochastically acquired photoplethysmogram for heart rate inference in realistic environments

A novel method, Stochastically Acquired Photoplethysmo-gram for Heart rate Inference in Realistic Environments (SAPPHIRE), is proposed for robust remote heart rate measurement through broadband video. A set of stochastically sampled points from the cheek region is tracked and used to construct corresponding time series observations via skin erythema transforms. From these observations, a photo-plethysmogram (PPG) waveform is estimated via Bayesian minimization, with the required posterior probability inferred using a Monte Carlo approach. To mitigate the effects of noise, the contribution of each observation is weighted based on the observation's likelihood to contain relevant data. A bandpass filter is applied to the estimated PPG waveform to omit implausible heart rate frequencies, and the heart rate is estimated through frequency domain analysis. Experimental results acquired from a set of thirty videos indicate significantly improved performance in comparison to state-of-the-art methods.
Identity Avoidance and Rendaku

One important observation that is made in the past phonological research is that constraints on output structures can both block and trigger phonological processes (i.e., conspiracy: Kisseberth 1970). This paper reports an experiment which shows that an Identity Avoidance constraint (a.k.a. the OCP) both blocks and triggers one phonological process in the same language, namely rendaku in Japanese. Our wug- experiment shows that rendaku is more likely to apply when the two CV moras across a morpheme boundary are identical; i.e. an Identity Avoidance constraint triggers rendaku. The experiment also shows that rendaku is less likely to apply when it would result in two adjacent identical CV moras across a morpheme boundary; i.e. the Identity Avoidance constraint blocks rendaku. These blocking and triggering effects of the general Identity Avoidance constraint on rendaku are a new discovery in Japanese phonology, despite the fact that rendaku has been studied extensively in the previous literature, suggesting the importance of experimentation in phonological research. Moreover, our case study offers experimental confirmation of the OCP conspiracy in natural languages.
A Review of Methods for Correction of Intensity Inhomogeneity in MRI

Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI), are still prominent and can adversely affect quantitative image analysis. In this paper, numerous methods that have been developed to reduce or eliminate intensity inhomogeneities in MRI are reviewed. First, the methods are classified according to the inhomogeneity correction strategy. Next, different qualitative and quantitative evaluation approaches are reviewed. Third, 60 relevant publications are categorized according to several features and analyzed so as to reveal major trends, popularity, evaluation strategies and applications. Finally, key evaluation issues and future development of the inhomogeneity correction field, supported by the results of the analysis, are discussed
Acoustic and perceptual effects of vocal training in amateur male singing

This paper reports our investigation of the acoustic effects of vocal training for amateur singers and of the contribution of those effects to perceived vocal quality. Recording singing voices before and after vocal training and then analyzing changes in acoustic parameters with a focus on features unique to singing voices, we found that two different F0 fluctuations (vibrato and overshoot) and singing formant were improved by the training. The results of psychoacoustic experiments showed that perceived voice quality was influenced more by the changes of F0 characteristics than by the changes of spectral characteristics and that acoustic features unique to singing voices contribute to perceived voice quality in the following order: vibrato, singing formant, overshoot, and preparation.