Miracle-dz/UniFAR-FTUs huggingface dataset:
Grammatical inference with grammar-based classifier system

This paper takes up the topic of a task of training Grammar-based Classifier System (GCS) to learn grammar from data. GCS is a new model of Learning Classifier Systems in which the population of classifiers has a form of a context-free grammar rule set in a Chomsky Normal Form. GCS has been proposed to address both the natural language grammar induction and also learning formal grammar for DNA sequence. In both cases near-optimal solutions or better than reported in the literature were obtained. Key-Words: Machine Learning, Grammatical Inference, Learning Classifier Systems, Natural Language Processing, Promoter Recognition
Online Classification with Partially Labelled Texts

In this investigation, we propose a novel approach to document stream classification using both online topic model and partially labelled documents. Although we may have several features for the classification, it seems natural that these features may vary dynamically depending upon the contents of stream. This is because they depend heavily on each theme within one class while we should follow dynamic mixture of them. Especially in the stream of news articles, word frequency changes dramatically because of bursts of the themes. Here we propose a dynamically learning method based on topic models assuming prior distribution of probabilities over classes adjusted by partially labelled documents in stream.
Two steps towards practical compact routing

The stress on today's inter-domain routing system is constantly and sharply increasing. The mere size of the Internet, operational practices and a number of limitations of the routing protocol itself worsen the scaling behavior of this fundamental service at an alarming rate. This paper introduces 2SIDR, a two step inter-domain routing approach that aims at significantly reducing the state requirements of routers while minimizing the incurred path stretch penalty. This fundamental trade-off has already been analyzed on a graph theoretical basis in the domain of compact routing. In contrast to those theoretical approaches, 2SIDR aims at what we call practical compactness. It deliberately gives up mathematical bounds while only relying on data that is available in practice and adhering to existing business relationships between operators in the Internet.
Schedulability analysis of applications with stochastic task execution times

In the past decade, the limitations of models considering fixed (worst-case) task execution times have been acknowledged for large application classes within soft real-time systems. A more realistic model considers the tasks having varying execution times with given probability distributions. Considering such a model with specified task execution time probability distribution functions, an important performance indicator of the system is the expected deadline miss ratio of the tasks and of the task graphs. This article presents an approach for obtaining this indicator in an analytic way. Our goal is to keep the analysis cost low, in terms of required analysis time and memory, while considering as general classes of target application models as possible. The following main assumptions have been made on the applications that are modeled as sets of task graphs: the tasks are periodic, the task execution times have given generalized probability distribution functions, the task execution deadlines are given and arbitrary, the scheduling policy can belong to practically any class of non-preemptive scheduling policies, and a designer supplied maximum number of concurrent instantiations of the same task graph is tolerated in the system. Experiments show the efficiency of the proposed technique for monoprocessor systems.
A Note on Time Measurements in LAMMPS

We examine the issue of assessing the efficiency of components of a parallel program at the example of the MD package LAMMPS. In particular, we look at how LAMMPS deals with the issue and explain why the approach adopted might lead to inaccurate conclusions. The misleading nature of this approach is subsequently verified experimentally with a case study. Afterwards, we demonstrate how one should correctly determine the efficiency of the components and show what changes to the code base of LAMMPS are necessary in order to get the correct behavior.
APAC: Augmented PAttern Classification with Neural Networks

Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recently invented stochastic regularization techniques on the CIFAR-10 dataset.
Fast Integral MeanShift: Application to Color Segmentation of Document Images

Global Mean Shift algorithm is an unsupervised clustering technique already applied for color document image segmentation. Nevertheless, its important computational cost limits its application for document images. The complexity of the global approach is explained by the intensive search of colors samples in the Parzen window to compute the vector oriented toward the mean. For making it more flexible, several attempts have tried to decrease the algorithm complexity mainly by adding spatial information or by reducing the number of colors to shift or even by selecting a reduced number of colors to estimate the means of density function. This paper presents a fast optimized Mean Shift with a much reduced computational cost. This algorithm uses both the discretisation of the shift and the integral image which allow the computation of means into the Parzen windows with a reduced and fixed number of operations. With the discretisation of the color space, the fast optimised MeanShift also memorizes all existing paths to avoid shifting again colors along similar path. Despite the square shape of the Parzen windows and the uniform kernel used, the results are very similar to those obtained by the global Mean Shift algorithm. The proposed algorithm is compared to the different existing implementation of similar algorithms found in the literature.
Distributed Alamouti full-duplex relaying scheme with direct link

In full duplex relaying, the direct link and the decode and forward processing delay are not always negligible. The signal transmitted by the source thus interferes, at the destination, with the delayed signal retransmitted by the relay. This paper presents a novel full duplex transmission scheme based on distributed Alamouti encoding (denoted by FDAE) that eliminates the interference problem and combines efficiently each transmitted signal and its delayed copy at the destination for decode and forward relaying. The performances of FDAE are compared to the full duplex system with interference at the destination (denoted by FDI) and to the conventional half duplex relaying. The simulation results show the harmful effect of the interference problem on the end-to-end achievable data rate and on the bit error rate. They also show that our proposed scheme provides a highest end-to-end achievable data rate and lower bit error rate than FDI due to its ability to take advantage of full duplexing while eliminating interference.
Analytical support for MANET internetworks from war game data

There have been many studies in recent years reporting the performance and survivability of mobile ad-hoc networks (MANETs). These studies invariably consider the network as covering a relatively small geographic area. However, in many crises, such as may occur during disaster relief and military operations, a wide geographic area may require network coverage. In these circumstances, the infrastructure free mobile wireless communications features of MANETs may be critical in forming a satisfactory network. Given transmission range limitations within current MANET proposals, it is likely that providing network coverage to a wide geographic area would be problematic. This paper presents the results of a study on clustering in the spatial distribution of communication nodes. A realistic environment (e.g. topography and organisational limitations) and operating procedures are introduced through the use of the results of war games. The spatial distribution of nodes is used as the basis for MANET internetwork topology generation.
Sharp boundary inversion in crosswell travel-time tomography

The reconstruction of seismic images of the medium from crosswell travel-time data is a typical example of the ill-posed inverse problem. In order to obtain a stable solution and to replace an ill-posed problem by a well-posed one, a stabilizing functional (stabilizer) has to be introduced. The role of this functional is to select the desired stable solution from a class of solutions with specific physical and/or geometrical properties. One of these properties is the existence of sharp boundaries separating rocks with different petrophysical parameters, e.g., oil- and water-saturated reservoirs. In this paper, we develop a new tomographic method based on application of a minimum support stabilizer to the crosswell travel-time inverse problem. This stabilizer makes it possible to produce clear and focused images of geological targets with sharp boundaries. We demonstrate that the minimum support stabilizer allows a correct recovery of not only the shape but also the velocity value of the target. We also point out that this stabilizer provides good results even with a low ray density, when the traditional minimum norm stabilizer fails.
The Role of 18F-FDG PET/CT in Large-Vessel Vasculitis: Appropriateness of Current Classification Criteria?

Patients with clinical suspicion of large-vessel vasculitis (LVV) may present with nonspecific signs and symptoms and increased inflammatory parameters and may remain without diagnosis after routine diagnostic procedures. Both the nonspecificity of the radiopharmaceutical 18F-FDG and the synergy of integrating functional and anatomical images with PET/CT offer substantial benefit in the diagnostic work-up of patients with clinical suspicion for LVV. A negative temporal artery biopsy, an ultrasonography without an arterial halo, or a MRI without aortic wall thickening or oedema do not exclude the presence of LVV and should therefore not exclude the use of 18F-FDG PET/CT when LVV is clinically suspected. This overview further discusses the notion that there is substantial underdiagnosis of LVV. Late diagnosis of LVV may lead to surgery or angioplasty in occlusive forms and is often accompanied by serious aortic complications and a fatal outcome. In contrast to the American College of Rheumatology 1990 criteria for vasculitis, based on late LVV effects like arterial stenosis and/or occlusion, 18F-FDG PET/CT sheds new light on the classification of giant cell arteritis (GCA) and Takayasu arteritis (TA). The combination of these observations makes the role of 18F-FDG PET/CT in the assessment of patients suspected for having LVV promising.
Optimal design of geometric parameters of a four-bar based manipulator for an underwater robotic platform

In this paper, we propose a starfish capturing four-bar based manipulator for an underwater robotic platform. The manipulator is composed of six links, two torsion springs, and one motor. The model is parameterized to link lengths and spring constants, and we solve kinematics and dynamics to get the trajectory of the end-effector of the manipulator. Capturing stroke, the distance while the end-effector keeps in contact to the ground, is used for the objective function to increase capturing capacity. Six lengths of the links and two torsion spring constants are used for design variables. The reaction force, the moment, and some geometric conditions are considered as inequality constraints in the optimization process. Through the optimization, the capturing stroke of the manipulator is increased by 56% comparing to the initial stroke.
Combination ledipasvir-sofosbuvir for the treatment of chronic hepatitis C virus infection: a review and clinical perspective

Chronic hepatitis C treatment has continued to evolve, and interferon-free, oral treatment with direct-acting antiviral agents is the current standard of care. Recently, a new treatment, which is a combination of two direct-acting antiviral agents, ledipasvir 90 mg (anti-NS5A) and sofosbuvir 400 mg (anti-NS5B), has been approved in the US and the European Union for the treatment of chronic hepatitis C viral infection. In Phase III trials among chronic hepatitis C virus genotype 1 monoinfected (treatment-naïve, treatment-experienced, and with advanced liver disease or posttransplant) patients and HIV–hepatitis C virus coinfected patients, the ledipasvir-sofosbuvir fixed-dose combination is associated with a higher rate of sustained virologic response at 12 weeks after therapy has ceased. According to preliminary data, the ledipasvir-sofosbuvir combination also may be effective against hepatitis C genotype 4 virus infection. The ledipasvir-sofosbuvir combination taken orally is generally well-tolerated. Moreover, the combination treatment may suppress the effect of predictive factors of chronic hepatitis C that have historically been known to be associated with treatment failure. Thus, the fixed-dose single-tablet combination of ledipasvir-sofosbuvir offers a new era for the effective treatment of a variety of patients suffering from chronic hepatitis C virus infection.
Changes in sprint, change of direction and jump performance during a competitive season in male lacrosse players

Changes in Sprint, Change of Direction, and Jump Performance during a Competitive Season in Male Lacrosse Players This study aimed to examine performance changes in lacrosse players during a 24-week competitive season (including 22 weeks of periodized resistance training (RT)). Eleven male lacrosse players (age 24.5 ± 4.3 years, height 180.4 ± 5.6 cm, body mass 80.5 ± 5.7 kg, 1 repetition maximum (1RM) back squat (BS) 113.3 ± 17.8 kg, 1RM power clean (PC) 67.2 ± 19.7 kg) participated in this study. Testing included 5-, 10-, 20-m sprints, change of direction (COD), countermovement jump (CMJ), and squat jump (SJ). Testing sessions occurred during week one (T-1), week six (T-2), week 15 (T-3), and week 24 (T-4) of a domestic season. Significant improvements in 20-m sprint performance occurred from T-1 to T-4 (3.04 ± 0.07 seconds, 2.98 ± 0.08 seconds, p = 0.018). Left leg COD tests significantly improved from T-1 to T-2 (3.20 ± 0.15 seconds, 3.10 ± 0.13 seconds, p = 0.005), and T-1 to T-4 (3.20 ± 0.15 seconds, 3.07 ± 0.12 seconds, p = 0.014). Right leg COD testssignificantly improved from T-1 to T-4 (3.20 ± 0.15 seconds, 3.08 ± 0.12 seconds, p = 0.002), and T-3 to T-4 (3.20 ± 0.15 seconds, 3.08 ± 0.12 seconds, p = 0.001). SJ height significantly decrease from T-1 to T-2 (38.2 ± 2.63 centimeters, 36.7 ± 2.57 centimeters, p = 0.008).
Attributions About Morally Unreliable Characters: Relationship Closeness Affects Moral Judgments

ABSTRACT Across 6 studies, relationship closeness strongly influenced moral attribution. First, we found that moral attributions were stronger when one interacted with an intimate compared to less intimate other. Second, we examined attributions about those who treat some people well and others badly. Moral character attributions were worse when an actor harmed an intimate relationship partner compared to a less close relationship partner. Further, the effect of relationship partner on moral attributions was influenced by perceivers’ beliefs about one’s true character. Perceivers were apt to believe that one’s true character is revealed when interacting with close others.
Performance Evaluation of Face Recognition in the Wavelet Domain

In recent years, many different image features have been used for face recognition. The Gabor wavelet feature is the most widely used image feature in face recognition systems because its recognition rate is superior to that of Eigenface systems. However, it remains unclear as to whether Gabor wavelet features are indeed the best wavelet features for face recognition. In this paper, we extract image features of facial images from various wavelet transforms (e.g., Haar, French hat, Mexican hat, Daubechies, Coiflet, Symlet, and O-spline) and evaluate their face recognition performance. We also compare the recognition performance of fixed- and adaptive-scale wavelet features. The results demonstrate that the performance of the wavelets assessed here is similar to that of the Gabor wavelet, and that the performance of adaptive-scale wavelet features is superior to that of fixed-scale features
Investigating hybrid SSD FTL schemes for Hadoop workloads

The Flash Translation Layer (FTL) is the core engine for Solid State Disks (SSD). It is responsible for managing the virtual to physical address mappings and emulating the functionality of a normal block-level device. SSD performance is highly dependent on the design of the FTL. For the last few years, several FTL schemes have been proposed. Hybrid FTL schemes have gained more popularity since they try to combine the benefits of both page-level mapping and block-level mapping schemes. Examples include BAST, FAST, LAST, etc. To provide high performance, FTL designers face several cross cutting issues: the right balance between coarse and fine grain address mapping, the asymmetric nature of reads and writes, the write amplification property of Flash memory, and the wear-out behavior of flash. The MapReduce paradigm has become a very popular paradigm for performing parallel and distributed computations on large data. Hadoop, an open-source implementation of MapReduce, has accelerated MapReduce adoption. Flash SSD is increasingly being used as a storage solution in Hadoop deployments for faster processing and better energy utilization. Little work has been done to understand the endurance implications of SSD on Hadoop-based workloads. In this paper, using a highly flexible and reconfigurable kernel-level simulation infrastructure, we investigate the internal characteristics of various hybrid FTL schemes using a representative set of Hadoop workloads. Our investigation brings out the wear-out behavior of SSD for Hadoop-based workloads including wear-leveling details, garbage collection, translation and block/page mappings, and advocates the need for dynamic tuning of FTL parameters for these workloads.
Context Recognition for visitors of Exhibitions

For development of more convenient and useful applications, context-awareness has long been studied. This paper introduces an Android app that detects the current location, moving status, and watching status of the visitor. Our app investigates accelerometer values in order to determine moving status. With the accelerometer and orientation values our app determines the current location and watching status. Our design of the system is discussed in this paper.
Evaluation of the Mtb72F Polyprotein Vaccine in a Rabbit Model of Tuberculous Meningitis

ABSTRACT Using a rabbit model of tuberculous meningitis, we evaluated the protective efficacy of vaccination with the recombinant polyprotein Mtb72F, which is formulated in two alternative adjuvants, AS02A and AS01B, and compared this to vaccination with Mycobacterium bovis bacillus Calmette-Guérin (BCG) alone or as a BCG prime/Mtb72F-boost regimen. Vaccination with Mtb72F formulated in AS02A (Mtb72F+AS02A) or Mtb72F formulated in AS01B (Mtb72F+AS01B) was protective against central nervous system (CNS) challenge with Mycobacterium tuberculosis H37Rv to an extent comparable to that of vaccination with BCG. Similar accelerated clearances of bacilli from the cerebrospinal fluid, reduced leukocytosis, and less pathology of the brain and lungs were noted. Weight loss of infected rabbits was less extensive for Mtb72F+AS02A-vaccinated rabbits. In addition, protection against M. tuberculosis H37Rv CNS infection afforded by BCG/Mtb72F in a prime-boost strategy was similar to that by BCG alone. Interestingly, Mtb72F+AS01B induced better protection against leukocytosis and weight loss, suggesting that the polyprotein in this adjuvant may boost immunity without exacerbating inflammation in previously BCG-vaccinated individuals.
Analysis on Predicting Spousal Physical Violence in the Philippines Using Binary Logistic Regression

This study was conducted for the purpose of identifying the factors that contribute to spousal physical violence in the Philippines using binary logistic regression. The variables are the demographic characteristics, history of violence and the marital union profile of the couple. This paper used National Demographic and Health Survey 2013 as a research instrument. This consists of 7,182 ever-married women (weighted). Two separate regression analyses were constructed to model spousal physical violence against men and against women. Of the seventeen variables, only four variables for spousal physical violence against women are found to be significant at 0.01 level of significance with 0.6578 goodness-of-fit. The significant variables are age of the woman at first cohabitation, parental violence, frequency of husband’s drunkenness and the length of cohabitation of the couple. For spousal physical violence against men, five variables are significant with 0.8673 goodness-of-fit, namely; age of the woman, if the woman ever experienced physical violence from her husband/partner, parental violence, if her father ever beaten her mother and her husband’s frequency of drunkenness.
Marriage choices and social reproduction: The interrelationship between partner selection and intergenerational socioeconomic mobility in 19th-century Sweden

This article studies the relationship between partner selection and socioeconomic status (SES) attainment and mobility in five rural parishes in southern Sweden, 1815-1894. Three different aspects of partner selection are considered: age, social origin, and geographical origin. We use an individual-level database containing information on the SES origin (parental land holding and occupation), age difference, and place of birth of the married couple. The results show a powerful association between partner selection and SES attainment and mobility. Social heterogamy was particularly important, but age heterogamy and geographic exogamy was also clearly related to both SES attainment and mobility.
Combining LBP and Adaboost for facial expression recognition

A novel approach to facial expression recognition based on the combination of local binary pattern (LBP) and Adaboost is proposed. Firstly, facial expression images are processed with LBP operator, which can eliminate the effect of environment lighting in a certain extent and has the powerful capability of texture feature description. And then facial expression features are presented with LBP histograms of expression image which is divided into several blocks. The features with powerful discriminability are selected by a modified Adaboost so as to predigest the design of classifier and shorten the cost time. Finally, the support vector machine (SVM) classifier is used for expression classification. The algorithm is implemented with Matlab and experimented on Japanese female facial expression database(JAFFE database). A facial expression recognition rate of 65.71% for person-independent is obtained and shows the effectiveness of the proposed algorithm.