Miracle-dz/UniFAR-FTUs huggingface dataset:
cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis

Graphic processing units (GPUs) are rapidly gaining maturity as powerful general parallel computing devices. A key feature in the development of modern GPUs has been the advancement of the programming model and programming tools. Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on Nvidia many-core GPUs. In functional magnetic resonance imaging (fMRI), the volume of the data to be processed, and the type of statistical analysis to perform call for high-performance computing strategies. In this work, we present the main features of the R-CUDA package cudaBayesreg which implements in CUDA the core of a Bayesian multilevel model for the analysis of brain fMRI data. The statistical model implements a Gibbs sampler for multilevel/hierarchical linear models with a normal prior. The main contribution for the increased performance comes from the use of separate threads for fitting the linear regression model at each voxel in parallel. The R-CUDA implementation of the Bayesian model proposed here has been able to reduce significantly the run-time processing of Markov chain Monte Carlo (MCMC) simulations used in Bayesian fMRI data analyses. Presently, cudaBayesreg is only configured for Linux systems with Nvidia CUDA support.
Influência do grau de ativação celular de linfócitos T no prognóstico de pacientes com Leishmaniose visceral coinfectados pelo HIV-1 acompanhados prospectivamente

Chronic immune activation and lymphocytic depletion are striking features in HIV-1 and Leishmania infatum infection. Such activation has been associated with the exhaustion of the immune system in HIV+ patients and can contribute to frequent recurrences of visceral leishmaniasis (VL) in coinfected patients. Therefore, it is important to investigate whether maintenance of anti-Leishmania treatment combined with antiretroviral therapy (ART) is able to decrease the levels of cellular activation and hence reduce the number of VL relapses. In this ways, the aim of this study was to evaluate the cellular activation degree and its impact on the immune impairment in VL/HIV patients, as well as to determine their influence on prognosis (relapse or clinical remission) after anti-Leishmania treatment. The patients were divided into non-recurrent (NR; n=6) and recurrent (R; n=11). Both groups were followed by active phase up to 12 months post-treatment (mpt). They were on ART and use of secondary prophylaxis with amphotericin B (50mg/fortnightly) from the end of the VL treatment. CD4+ T cell counts, activation degree (CD38+/HLA-DR+), immunosenescence (CD57+/CD27-), soluble CD14 levels and anti-Leishmania antibodies were evaluated in the patients and healthy controls. During the active phase of VL, both groups had similar levels of all parameters However, at 6 and 12 mpt, the NR group showed a significant gain in CD4+T cells, as well as a decrease in the percentage of activated CD4 and CD8 T lymphocytes. Unlikely, R group kept a low immune reconstitution, a high degree of activation of both CD4 and CD8 subpopulations (p <0.0001 and p <0.01, respectively), and high levels of sCD14 (p <0.05), suggesting a persistent level of immune activation in R coinfected patients. Viral and parasite load remained low or undetectable with no correlation with activation. However, this activation degree was negatively correlated with impaired immune reconstitution of patients. Despite these differences, the percentages of senescent T lymphocytes were similarly high between R and NR groups. The smaller capacity of the R group in reducing cell activation levels compared to NR, may be related to some functional impairment of effector response that has been declining every recurrence episode. In addition, secondary prophylaxis seems to modify the natural history of co-infected patients who are presenting the first episode of the disease, as opposed to those who already have several recurrences
Acquisition semi-automatique de collocations à partir de corpus monolingues et multilingues comparables

Cet article présente une méthode d’acquisition semi-automatique de collocations. Notre extraction monolingue estime pour chaque co-occurrence sa capacité à être une collocation, d’après une mesure statistique modélisant une caractéristique essentielle (le fait qu’une collocation se produit plus souvent que par hasard), effectue ensuite un filtrage automatique (en utilisant les vecteurs conceptuels) pour ne retenir que des collocations d’un certain type sémantique, puis effectue enfin un nouveau filtrage à partir de données entrées manuellement. Notre extraction bilingue est effectuée à partir de corpus comparables, et a pour but d’extraire des collocations qui ne soient pas forcément traductions mot à mot l’une de l’autre. Notre évaluation démontre l’intérêt de mêler extraction automatique et intervention manuelle pour acquérir des collocations et ainsi permettre de compléter les bases lexicales multilingues.
Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11

The number and size of social networks have grown significantly as years have passed. With its 1.5 billion active users, Facebook is by far the most popular social networks on the planet. From kindergarten kids to grandparents to teenagers, Facebook attracts users of all ages, religions, personalities and social status. Facebook users are sharing their personal information, their lifestyle, their precious moments and their feelings online. In this paper, we download a set of comments from the page ‘Opposing Views’ from Facebook. These were then categorised into either a positive comment or a negative comment using the auto code feature in NVivo 11. Comments where no positive or negative sentiments are found are considered to be neutral. Out of 626 comments, 29.6% were found to contain positive sentiments while 62.0% were found to contain negative sentiments. The outcome of this work can be used by businesses to assess public reviews about their products. This will help them understand what is working and what is not. Thus, they can improve their products and respond to customer demands sufficiently quickly.
TRAPDROID: Bare-Metal Android Malware Behavior Analysis Framework

In the realm of mobile devices, malicious applications pose considerable threats to individuals, companies and governments. Cyber security researchers are in a constant race against malware developers and analyze their new methods to exploit them for better detection. In this paper, we present TRAPDROID, a dynamic malware analysis framework mostly focused on capturing unified behavior profiles of applications by analyzing them on physical devices in real-time. Our framework processes events, which are collected from system calls, binder communications, process stats, and hardware performance counters and combines them into a simple, yet meaningful behavior format. We evaluated our framework’s detection rate and performance by analyzing an up-to-date malware dataset, which also contains specially crafted applications with malicious intent. The framework is easy to use, fast and providing high accuracy in malware detection with relatively low overhead.
Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control

This paper first offers a transversal view through microfluidics theory and applications, starting from a brief overview on microfluidic systems and related theoretical issues, covering different kinds of phenomena, from continuous to multiphase flow. Multidimensional models, from lumped parameters to numerical models and computational solutions, are then considered as preliminary tools for the characterization of spatio-temporal dynamics in microfluidic flows. Following these, experimental approaches through original monitoring opto-electronic interfaces and systems are discussed. Finally, a vision of two phase microfluidic phenomena is given through nonlinear analyses applied to experimental time series.
Informative quality estimation of machine translation output

Despite the recent advances in the field of machine translation (MT), today, MT systems cannot guarantee that the sentences they produce will be fluent and coherent in both syntax and semantics. With post-editing of MT output becoming a common practice in fast-paced Computer-Assisted Translation (CAT) workflows, research on Quality Estimation (QE) has thrived in recent years. Despite the link between MT errors and the cognitive effort involved in correcting them, current QE studies often focus on finding informative features that capture the monolingual and bilingual properties of given source/MT output pairs and estimate overall post-editing effort at word, sentence or document level without making a distinction between MT error types. In this thesis, we present a comprehensive approach to automatic error detection as a basis for understanding the relationship between different types of MT errors and the corresponding post-editing effort and take a first step towards informative quality estimation systems of machine translation, which are able to justify the basis for estimated quality. In order to study the relationship between MT errors and post-editing effort on a large scale, we developed an error taxonomy and a corpus of MT errors originating from statistical (SMT), rule-based and neural machine translation systems for English-Dutch and obtained post-edited versions of the MT output of this corpus. The error taxonomy is grounded in translation quality assessment literature and allows for an MT-specific, fine-grained error annotation based on the main distinction between accuracy and fluency errors. Moreover, the hier-
Strategies for Random Contract-Based Testing

Testing plays a central role in software quality assurance. Although this is widely agreed upon, many software projects in industry suffer from improper testing. Inadequate testing tools and underestimation of the costs of thorough testing are among the reasons. The research community has been trying to address some of the challenges of testing through automated solutions, which aim at taking most of the burden of devising, running, and evaluating tests off the shoulders of programmers and testers. This thesis is part of this effort: it proposes several strategies for automatically testing contracted object-oriented software, that is software built according to the principles of Design by Contract. One of these strategies is based on the notion of “object distance”: a measure of how different two objects are, taking into account the objects’ types, their direct values, and a recursive application of the distance on their common attributes. Using this notion, the proposed strategy (called ARTOO) selects test inputs with the greatest distance between them and thus maximizes the diversity of the inputs. Proposing a new testing strategy must be accompanied by a thorough evaluation of the performance of this strategy, both in absolute terms and compared to other testing strategies. This thesis hence also presents the results of such evaluations, for the newly introduced algorithms and for existing ones. These results offer insights into the performance of random testing for object-oriented software on such issues as the evolution of the number of found faults over time, the predictability of random testing, and the types of faults found through random testing, through manual testing and by users of the software. Experimental evaluation indicates that the newly introduced ARTOO strategy requires on average 5 times less tests to uncover faults than a random strategy combined with boundary-value testing. The proposed and examined testing strategies are based on the presence of executable specification in the form of contracts in the software
Metal nanocavity light sources integrated with passive waveguide components

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Automatic extraction of causal knowledge from natural language texts

This thesis studies the automatic recognition of implicit causal relations between clauses. Previous work, although more accurate than a random baseline, does not achieve sufficient accuracy for practical use. First we show, using annotation experiments, that recognising implicit causation is a subjective task, in spite of its association with several observable features. We then propose an evaluation protocol that takes the subjectivity of the task into account. Previous linguistics work uses the notion of world knowledge. We show that the most likely feature for representing this knowledge -verb pairs- is not predictive of causation in practice. We then show that the current state of the art is not sufficient to allow us to represent nor to acquire the world knowledge that is necessary for this task. We conclude that the field cannot make important progress without first solving the problem of abstract eventuality representation and clustering.
Deep Learning Approaches for Human Activity Recognition in Video Surveillance - A Survey

Recognition of the human activities in videos has gathered numerous demands in various applications of computer vision such as Ambient Assisted Living, intelligent surveillance, Human Computer interaction. One of the most pioneering technique for Human Activity Recognition is based upon deep learning and this paper focuses on various approaches based on that. Convolution Neural Network and Recurrent Neural Networks are mostly used in deep learning architectures. Deep Learning have the capacity of automatic learning of the features from the input modality. Analysis based on Methodology, Accuracy, classifier and datasets is presented in this survey paper.
THE EFFICIENT IMAGE SEARCHING BY USING DYNAMIC MULTI-VIEW HASHING METHOD

Image processing is a method which takes an image as an input and show the image or highlights of the image as a output. It includes investigation and control to enhance the nature of the image. Image retrieval framework is perusing, looking and retrieval of the image in light of the given inquiry. The question might be a content or image. It recover the image in view of the shading, shape, surface and some different qualities of the image. Highlight extraction is to depict the highlights of the image with precision. Hashing is a method to change over the unpredictable data into file data, in light of the record data it can locate the required data effortlessly. Multi-see arrangement hashing technique is utilized to change over the image into parallel code, it depends on bit non-negative grid factorization. Non-negative lattice factorization is to study and discover the non-negative parts of the image data. Portion is to locate the moment parts that might be edges, bends, dabs in the images. KNMF dismisses the non-negative esteems in the piece images. In this paper, we recover the images utilizing various component extractors to get the related image from the given inquiry image look, the element extractors utilized as a part of this undertaking are GIST, HOG(Histogram Of Gradient), LBP(Local Binary Pattern), SURF(Speeded Up Robust Feature) and COLORHIST(Color histogram), it is assessed in caltech256 dataset and their outcomes demonstrate that utilizing different element extraction we can get the proficient image.
Doping evolution of antiferromagnetism and transport properties in nonsuperconducting BaFe2−2xNixCrxAs2

We report elastic neutron scattering and transport measurements on the Ni and Cr equivalently doped iron pnictide BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$. Compared with the electron-doped BaFe$_{2-x}$Ni$_{x}$As$_{2}$, the long-range antiferromagnetic (AF) order in BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$ is gradually suppressed with vanishing ordered moment and N\'{e}el temperature near $x= 0.20$ without the appearance of superconductivity. A detailed analysis on the transport properties of BaFe$_{2-x}$Ni$_{x}$As and BaFe$_{2-2x}$Ni$_{x}$Cr$_{x}$As$_{2}$ suggests that the non-Fermi-liquid behavior associated with the linear resistivity as a function of temperature may not correspond to the disappearance of the static AF order. From the temperature dependence of the resistivity in overdoped compounds without static AF order, we find that the transport properties are actually affected by Cr impurity scattering, which may induce a metal-to-insulator crossover in highly doped BaFe$_{1.7-y}$Ni$_{0.3}$Cr$_{y}$As$_{2}$.
Signal Processing using CMOS-MEMS Integrated Resonators

A central part of the ubiquitous computing world of today is Wireless Sensor Networks (WSN), consisting of node-based components in a mesh that detects the environment around it. The sensing and radio part of the WSN node are typically off-chip components which are needed in order to realize the required performance. This thesis sets forth to investigate how to use MicroElectroMechanical Systems (MEMS) resonators as signal processing units, directly integrated in Complementary Metal-Oxide Semiconductor (CMOS) technology. By integrating MEMS resonators directly with CMOS, one can omit some of the typical off-chip devices and thus enable more compact and cost-efficient WSN nodes. CMOS-MEMS resonator structures have been made by etching CMOS dies after being processed, thus defining structures from the metal layers offered in the CMOS process. Post-processing of CMOS dies was possible through a service known as Application Specific MEMS Process Service (ASIMPS) where Carnegie Mellon University (CMU) has etched and released the MEMS structures. This post-CMOS process was further developed by making MEMS out of two different 90 nm CMOS processes. Five different CMOS runs were performed, three at UiO and two at CMU in 0.35 μm, 0.25 μm and 90 nm CMOS processes from both Taiwan Semiconductor Manufacturing Company (TSMC) and ST Microelectronics (STM). Different resonator topologies have been modeled, simulated and measured. A set of basic resonators were combined in order to make more advanced multiport MEMS resonators, enabling down-mixing of high-frequency signals to an intermediate-frequency. Composite resonators have been connected at selected nodal points in order to obtain higher order filtering characteristics. Higher-order MEMS filters were made in different ways and compared. Soft frequency tunable MEMS resonators and multi-mode features of composite MEMS resonators were investigated. Composite MEMS resonators and CMOS amplifiers have been combined to convert the resonator current to a voltage, enabling voltage-to-voltage filters and mixer-filters. High gain, low-noise Trans-Impedance Amplifiers (TIAs) was made and different Trans-Impedance Amplifier (TIA) topologies were evaluated. The various combinations of MEMS and CMOS resulted in unique filtering capabilities with an increased Q-factor and low-noise performance.
A highest-label preflow algorithm for the minimum flow problem

In this paper, we describe the highest-label preflow algorithm for minimum flow. This algorithm is a special implementation of the generic preflow algorithm developed by Ciurea and Ciupala in [7]. It examines always an active node with the highest distance label and runs in O(n2√m) time.
Prevalence of malocclusion characteristics and chief motivational factor for treatment in orthodontic patients from Maharashtra, India

Objective: To determine the incidence of malocclusion characteristics and chief motivational factor for treatment in orthodontic patients from Maharashtra, India. Materials and Methods: The present study was based on the examination of dental casts of pretreatment records of 560 orthodontic patients (319 females and 241 males). The relationship of the first upper and lower molars according to the Angle's classification was examined. Crowding, spacing, crossbite, overjet, overbite were recorded. The chief complaint of the patients was recorded from their history proformas. Results: The study demonstrated that Class I malocclusion was found in 275, Class II Division 1 in 195, Class II Division 2 in 58 and Class III malocclusion in 32 subjects. Crossbite was observed in 62 (11.07%), an increased overjet in 346 (61.7%) and spacing was detected in 82 (14.6%) patients. Esthetics was found to be the ruling chief complaint of patients seeking orthodontic treatment.
An atmospheric modelling system for Lebanon

Viene qui descritto lo sviluppo e l’applicazione di un sistema modellistico atmosferico messo a punto per il territorio del Libano (AMS-Libano) nell’ambito dell’assistenza tecnica fornita da ENEA al Ministero dell’Ambiente Libanese tra il 2013 ed il 2014. AMS-Libano rappresenta uno strumento modellistico di riferimento per le politiche di qualita dell’aria in Libano, permettendo di approfondire ipotesi di gestione della qualita dell’aria in maniera efficace ed economica. Il sistema modellistico nasce dall’esperienza di ENEA nel progetto MINNI in supporto al Ministero Italiano dell’Ambiente e della Tutela del Territorio e del Mare sulle politiche nazionali di qualita dell’aria e nell’ambito di esperienze di ricerca su confronti di modelli a livello Europeo. La catena modellistica unisce un modello meteorologico di mesoscala (RAMS), un elaboratore di inventari di emissioni (EMMA) e un modello di trasporto e chimica in atmosfera (FARM). Le concentrazioni tridimensionali di inquinanti atmosferici (NO2, O3, PM10, PM2.5, SO2) sono calcolate considerando le dinamiche dell’atmosfera e le reazioni chimiche fra gas e la speciazione del particolato con una risoluzione spaziale di 5 Km sul Libano. L’inventario atmosferico delle emissioni di sorgenti antropogeniche per il Libano, compilato dal CEREA (Francia) e dall’Universita Saint Joseph (Libano), e stato adattato per AMS-Libano e integrato con nuove informazioni su grandi sorgenti puntuali e simulazioni specifiche di emissioni biogeniche. La mappe delle concentrazioni medie annuali di NO2, O3, PM10, PM2.5 e SO2 mostrano la distribuzione dell’inquinamento atmosferico su tutto il dominio di studio ed evidenziano situazioni di superamento dei limiti in vigore nell’Unione Europea, considerati come riferimento. Il sistema e stato testato su un caso di base rappresentato dalle emissioni del 2010, e su due scenari emissivi: nuovi limiti ai valori emissivi per i cementifici e nuove configurazioni funzionali per le centrali elettriche situate a Zouk e Jyeh. La messa a punto di un sistema modellistico atmosferico per il Libano ha fornito un quadro completo delle aree piu inquinanti della regione dove le misure di mitigazione appaiono piu urgenti, i piani di sviluppo devono essere rivisti o devono essere applicati valori limite delle emissioni piu stringenti. I valori di concentrazione forniscono anche un caso base per gli studi di valutazione di impatto ambientale e per la proposta di nuove attivita con impatto in atmosfera. Inoltre il sistema modellistico e in grado di valutare l’efficacia di nuovi scenari di emissione, dati per esempio dal cambiamento di valori limite di emissioni delle diverse sorgenti, dalla trasformazioni pianificate di alcune centrali o da future attivita estrattive off-shore.