Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Thrombophilic Gene Mutation on Recurrence of Venous Thromboembolism : A Retrospective Cross-Sectional Study

The aim of this study is to evaluate the influence of the risk factors from the point of thrombophilic gene mutations (TGM) with type and inter-cooperation’s on recurrence in venous thromboembolism (VTE) patients.This retrospectively and crosssectionally designed study was conducted between 2008–2009. The VTE patients, who were evaluated for TGM were elected. Among a total of 109 patients, the mean age at first VTE event was 42.6±14.1 years. Fifty-nine (54.1%) patients were male. While 33 (30.3%) patients had primary VTE, 46 (42.2%) patients had recurrent VTE (rVTE). In the univariate analysis, the significant variables associated with the increased rate of rVTE were age ≥40 years, first event at in-hospital, malignancy, internal medical disease, TGM, factor V Leiden, prothrombin G20210A. The analyses of different mutation count on rVTE pointed that, there were significant differences in recurrence rates, except groups with no mutation and one mutation. Additionally, the increasing number of clinical risk factors and TGM per case were considerably associated with rVTE in both univariate and multivariate analysis. Regarding the risk of rVTE, the TGMs were significant but do not appear to play a vital role per se. However, simultaneous existence of clinical risk factors, including TGM seem to be more important for the prediction of rVTE.
Quantitative phytochemical analysis and Anti-tuberculosis activity of some selected medicinal plants in some Northern parts of Bauchi state , Nigeria

Plants based remedies have been playing a vital role in health care delivery in spite of the influence of orthodox medicines. Tuberculosis remained one of the oldest diseases in the world that caused serious health concern globally. But Tuberculosis and other respiratory diseases are among the common ailments claimed to be treated using some local plants. This research was designed to investigate Anti-tuberculosis activity of Nine (9) selected medicinal plants collected in some local communities in the Northern parts of Bauchi state, Nigeria. The quantitative phytochemical analysis was also carried out. The result indicated the highest saponins content (83.89%) in Butyrospermum paradoxum and the least saponins content (30.79%) was observed in Striga hermonthica. The arial parts of S. hermonthica have 33.60% steroids content. Butyrospermum paradoxum also recorded the highest tannin content (67%) and steroids (51.43%) while the least tannin content was found in Pilostigma reticulatum. But the least steroids content was observed in root extracts of C. mucronata. The highest percentage of phenols (70.20%) was observed in Euphorbia hirta but with lowest percentage of flavonoids. Pilostigma reticulatum has the highest percentage of alkaloids (28.31%) followed by Cissampelos mucronata with 23.67%. Waltheria indica has 65.31% saponins, 43.26% steroids and 36.30% of phenols. X. americana has 50.86% saponins, 28.41% steroids and 42.29% tannins. T. Indica has 14.40% alkaloids and 20.84% steroids. E. Senegalensis has 52.11% flavonoids, 34.84% saponins, 18.62% steroids and 27.14% tannins. The quantitative phytochemical analysis of the crude methanol extracts revealed that the plants contained significant quantity of bioactive agents in varied amounts. The phytochemicals were responsible for the utilization of these plants in traditional medicine. Micro broth dilution method was employed to investigate the anti tuberculosis activity of these plants. The result revealed that only four (4) plants out of the nine (9) tested exhibited anti tuberculosis activity with Minimum Inhibitory Concentration (MIC) range of 1.25 to 5.0mg/ml. S. hermonthica has MIC of 1.25mg/ml, E. senegalensis has MIC of 2.5mg/ml, C. mucronata and E. hirta have same MIC of 5.0mg/ml. No activity was observed for the Extracts of the following five (5) plants; W. indica, X. americana, P. reticulatum, B. paradoxum and T. indica. The active plants have shown significant anti TB activity since these MIC values are for the crude extracts of the plants. The present study indicated that the plants could be useful in anti tuberculosis drug discovery. Therefore the most active plants will be considered for further research to isolate and characterize the bioactive agents responsible for the observed ant-iTB activity.
Towards unsupervised extraction of linguistic typological features from language descriptions

However, there is a lack of publications describing the application of NLP techniques to extract typological features directly from language descriptions contained in grammar books, dissertations, and linguistics articles. Collections of such descriptive sources are accumulating as PDFs (including many from scans) that have subsequently been OCR’ed. In this paper, we describe our first attempt at building an NLP pipeline that extracts typological features from OCR’ed linguistic descriptions.
Spatio-temporal feature extraction and classification of Event-Related Potentials

Brain-Computer Interfaces (BCI) translate variations in the Electroencephalogram (EEG) into a set of particular commands, in order to control a real world machine. For this purpose, it is necessary to classify reliably EEG signals. Classifying EEG activities is a challenging task since EEG recordings exhibit distinct and individualized spatial and temporal characteristics correlated with noise and various physical and mental activities. To increase classification accuracy, it is thus crucial to enhance the Signal to Noise Ratio (SNR) and to identify relevant spatio-temporal features. This paper presents a method for denoising Event-Related Potential (ERP) data and for identifying discriminant spatio-temporal characteristics. First, a Blind Source Separation (BSS) strategy is used to denoise data and enhance SNR. Second, a resampling procedure based on Global Field Power (GFP) automatically selects temporal windows. Third, a spatially weighted SVM (sw-SVM) learns a spatial filter optimizing the classification performance for each temporal feature. Finally, the so obtained ensemble of sw-SVM classifiers are combined using a weighted combination of all sw-SVM outputs. Results indicate that denoising and identification of spatio-temporal features of ERP enhance the classification accuracy, yield a better understanding of the underlying physiology and provide useful insight about the spatio-temporal characteristics of the ERP.
THE PERCEPTION OF SPEAKER CERTAINTY IN SALERNO ITALIAN INTONATION

One of the main uses of intonation form is to signal pragmatic information. Recent research on intonational meaning has shown that epistemic information about propositional content of utterances within a discourse can also be tonally encoded. The purpose of this study is to investigate whether speaker certainty about the answer to a polar question can be recovered by listeners of Salerno Italian and whether identification is affected by tune type. In an online perception survey, three similarly frequent polar question tunes were rated according to degree of perceived speaker certainty relative to the question response. Results support the hypothesis that tune type affects degree of certainty perception. The study also supports the hypothesis that additional factors – both sociophonetic (i.e., living abroad) and idiosyncratic – might affect variability in intonation perception within a language community.
Feature Extraction of Moving Object over a Belt Conveyor Using Background Subtraction Technique

Many Industrial Robots are becoming an autonomous system with the help of vision based Artificial Intelligence. Computer vision predominantly played a vital role in developing an autonomous robot, which provides more reliable information about the dynamic environment. The Industrial robots are widely used for objects pick and place operations. The capability of extracting moving object features in the video sequence is a fundamental and crucial problem in many vision systems. The objective of the present work is to extract the features of moving objects such as location, color and centroid over the belt conveyor using image subtraction technique and color based segmentation. The image subtraction methodology is developed based on Gaussian Mixture Model (GMM) which separates the moving objects from its background based on the intensity difference of respective pixel in the reference frame to current frame. The subtracted frame is further processed using RGB color based segmentation to separate the objects based on color. The centroid of the separated color objects is extracted using region properties. Feature extraction routine is developed using digital Image processing techniques in MATLAB.
Intervention of Meso-Organizations in the Integration to the Market of Rural Producers

The study of meso-organizations (Helmsing, 2001; North, 1990) has focused on rural or poor communities which are expected to increase their level of development. These secondlevel organizations help communities facing weak institutional capital to reach their economic goals by extending bridges between them and the necessary resources that are out of their scope. Integration to the market, the ability to use the links of productive and commercial chains to capture value, is one mechanism by which these economic goals are achieved. Using the neo-institutional perspective, this study seeks to understand the relationship between the resources or services that meso-organizations provide and the level of integration to the market (IM) that rural producers in a post-conflict context, such as Colombia, are able to reach. Findings suggest that those resource bridges contributing to their integration are quite specific. Likewise, this study shows that the rural producers’ relational capital is an important bastion for their IM. The final section discusses the policy and strategy implications of these findings. * The authors acknowledge the support of Universidad de los Andes School of Management and the National Planning Department (DNP) of Colombia. This paper is derived from a research project that in its first phase was carried out with Eduardo Wills and Manuel Moscoso. We are grateful to them for the promotion of the research project in both organizations, and for their valuable contribution to the initial design of the research, participation in building the database and preparation of a report cited below. Liliana Gomez, David Aguilar, Victor Baldrich and Leonardo Riano provided outstanding research assistantship. Alejandra Palacios and Adriana Valenzuela participated in the organization of focus groups in the regions. We thank the support of local leaders, authorities and participants in the survey and in focus groups, where preliminary results of the global project were devolved and discussed. The ideas presented in this paper are nonetheless the sole responsibility of the authors.
F0 Variability Measures Based on Glottal Closure Instants

The periodicity of a voiced-sound signal can reﬂect physiological conditions such as identity, age, and voice disorder. One way to look into this periodicity is to measure the temporal variability of vocal fundamental frequency (F0). This paper proposes 2 measures of F0 variability based on glottal closure instant (GCI). GCI is essential to the detection of F0 when the signal waveform varies substantially between adjacent cycles, e.g., in breathy voice. Frequency-selective variability measurements are taken from an interpolated sequence of fundamental-period values based on GCIs, including certain spectral-shape parameters which constitute a multi-variate measure. The utility of these measures was demonstrated in two experiments designed for inter-and intra-speaker comparisons, respectively.
Implementation of Pipelined Architecture in Humming Bird for Reducing Area and High Throughput

Hummingbird is a new ultra light weight cryptography algorithm target for resources constrained devices like RFID tag, smart card and wireless sensor. In this project Pipelined Architecture is designed and implemented in humming bird cryptography algorithm to get reduced area and improved throughput by adding hash function. Humming bird is to meet stringent response time and power requirements which can provide the design security with a small block size. This algorithm resists to the most common attacks like birthday attack, algebraic attack, linear and differential cryptanalysis. The humming bird algorithm is integrating into a privacy preserving and authentication protocol. In this algorithm to reduce the clock cycle for encrypt and decrypt the message. The result shows that this algorithm has higher security and throughput with improved area. Keywords— Light weight Cryptography, Mutual Authentication protocol, Security Analysis.
EFFECT OF GIBBERELLIC ACID AND POTASSIUM FOLIAR SPRAYS ON PRODUCTIVITY AND PHYSIOLOGICAL AND BIOCHEMICAL PARAMETERS OF PARTHENOCARPIC CUCUMBER CV . ‘ SEVEN STAR F 1 ’

A study was carried out to evaluate the potential of exogenously applied potassium nitrate [1.0 g·dm (K1), 2.5 g·dm (K2) and 5 g·dm (K3)] plus gibberellic acid (GA3) [0.005 g·dm (G1), 0.01 g·dm (G2) and 0.015 g·dm (G3)] on the growth and development of the cucumber (Cucumis sativus L.) ‘Seven Star F1’ during various growth stages (40, 55 and 70 days after sowing). Treatment of plants with K plus GA3 stimulated growth shortened the fruit maturation period and increased quality compared with nonsprayed control. The combination G2K2 resulted in maximum total yield, plant height, and total chlorophyll, phosphorus and nitrogen content in the leaves compared with other treatments. Fruit matured significantly earlier in plants sprayed with G2K2. Total yield and fruit quality dry matter, total suspended solids and antioxidant activity percentages were significantly higher in sprayed plants compared with controls. Potassium content in leaves and fruits reached the maximum level with G3K3 treatment. The results clearly showed that the foliar application of G2K2 proved to be the best choice for growing cucumber in terms of quantity and quality of yield.
IDENTITY, SOCIALIZATION AND ENVIRONMENT IN TRANSGENDER SPEAKERS: SOCIOPHONETIC VARIATION IN CREAK AND /S/

This study investigates sociophonetic variation in creak and /s/ in 17 transgender speakers in the UK, comparing 12 transmasculine (TM) speakers and 5 transfeminine (TF) speakers. Assuming that transgender speakers would attempt to distance themselves from the norms of their assigned sex and recruit or avoid recruiting the perceptually low pitch of creak, it was predicted that TM speakers would have a lower centre of gravity for /s/ and use more creak than TF speakers. However, TM speakers were found to have a significantly higher centre of gravity for /s/ than TF speakers and did not differ significantly from TF speakers in creak usage. Taken together with qualitative data, this suggests that speaker identities, socialization experiences and environment all contribute to variation in /s/ and creak in these speakers.
FROM ZERO TO SIXTY : CALIBRATING REAL-TIME

Recent advances in data recording technology have given researchers new ways of collecting online and continuous data for analyzing input–output systems. For example, continuous response digital interfaces are increasingly used in psychophysics. The statistical problem related to these input–output systems reduces to linking time-varying covariates to a continuous response variate. Using real-time data obtained from an experiment in psychoacoustics, we showcase new statistical tools that incorporate dynamical elements of an input–output system. We employ functional data analysis (FDA) methods and a simple differential equation to analyze and model the continuous responses. Furthermore, we outline the issues involved in analyzing input–output systems when the exact form of the underlying mathematical model is not known. Finally, we develop a calibration method to facilitate inter-subject and intra-subject comparisons.
Face Recognition Based Student Attendance System with OpenCV C

In this paper we introduce a system for student attendance in Classes and any Lecture hall by using the openCV(Open source Computer Vision) with some face detection and face recognition algorithms. The aim is to take the number of people present in the class and take attendance to each of them using face detection algorithms and face recognition algorithms to determine the actual identification of persons which of them are present. This approach can use in different situations like security purpose, Authentication etc. But here in this paper it is implemented to take attendance to the students in a class room or in a Seminar hall. It may be also used for the prevention of unauthorized persons in a system and in security issues for security or in any illegal activities.
Cross-Lingual Transfer Learning for Affective Spoken Dialogue Systems

This paper presents a case study of cross-lingual transfer learning applied for affective computing in the domain of spoken dialogue systems. Prosodic features of correction dialog acts are modeled on a group of languages and compared with languages excluded from the analysis. Speech from different languages was recorded in carefully staged Wizard-of-Oz experiments, however, without the possibility to ensure balanced distribution of speakers per language. In order to assess the possibility of cross-lingual transfer learning and to ensure reliable classification of corrections independently of language, we employed different machine learning approaches along with relevant acoustic-prosodic features sets. The results of the experiments with mono-lingual corpora (trained and tested on a single language) and cross-lingual (trained on several languages and tested on the rest) were analyzed and compared in the terms of accuracy and F1 score.
Parallel vs. Non-Parallel Voice Conversion for Esophageal Speech

State of the art systems for voice conversion have been shown to generate highly natural sounding converted speech. Voice conversion techniques have also been applied to alaryngeal speech, with the aim of improving its quality or its intelligibility. In this paper, we present an attempt to apply a voice conversion strategy based on phonetic posteriorgrams (PPGs), which produces very high quality converted speech, to improve the characteristics of esophageal speech. The main advantage of this PPG based architecture lies in the fact that it is able to convert speech from any source, without the need to previously train the system with a parallel corpus. However, our results show that the PPG approach degrades the intelligibility of the converted speech considerably, especially when the input speech is already poorly intelligible. In this paper two systems are compared, an LSTM based one-to-one conversion system, which is referred to as the baseline, and the new system using phonetic posteriorgrams. Both spectral parameters and f0 are converted using DNN (Deep Neural Network) based architectures. Results from both objective and subjective evaluations are presented, showing that although ASR (Automated Speech Recognition) errors are reduced, original esophageal speech is still preferred by subjects.
The 5 th International Conference on Emerging Ubiquitous Systems and Pervasive Networks ( EUSPN-2014 ) Towards Delay-Sensitive Routing in Underwater Wireless Sensor Networks

In Underwater Acoustic Sensor Networks (UASNs), fundamental difference between operational methodologies of routing schemes arises due to the requirement of time-critical applications therefore, there is a need for the design of delay-sensitive techniques. In this paper, Delay-Sensitive Depth-Based Routing (DSDBR), Delay-Sensitive Energy Efficient Depth-Based Routing (DSEEDBR) and Delay-Sensitive Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (DSAMCTD) protocols are proposed to empower the depth-based routing schemes. The proposed approaches formulate delay-efficient Priority Factors (PF) and Delay-Sensitive Holding time (DS HT ) to minimize end-to-end delay with a small decrease in network throughput. These schemes also employ an optimal weight function WF for the computation of transmission loss and speed of received signal. Furthermore, solution for delay lies in efficient data forwarding, minimal relative transmissions in low-depth region and better forwarder selection. Simulations are performed to assess the proposed protocols and the results indicate that the three schemes largely minimize end-to-end delay of network. c © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshuki.
Air Pollution, Behavioral Bias, and the Disposition Effect in China

Abstract Inspired by the recent health science findings that air pollution affects mental health and cognition, we examine whether air pollution can intensify the cognitive bias observed in the financial markets. Based on a proprietary data set obtained from a large Chinese mutual fund family consisting of complete trading information for more than 773,198 accounts in 247 cities, we find that air pollution significantly increases investors’ disposition effects. Analysis based on two plausible exogenous variations in air quality (the vast dissipation of air pollution caused by strong winds and the Huai River policy) supports a causal interpretation. Mood regulation provides a potential mechanism.
Traditional knowledge on medicinal plants used for the treatment of domestic cattle in Dhikura village of Arghakhanchi district, Nepal

Present study was carried out to document ethno-veterinay information for the treatment of livestock diseases in Dhikura village of Arghakhanchi district. Information on the use of plants was collected from October to November, 2014 by interviewing key informants using a semi-structured open-ended questionnaire. The ethno-veterinary surveys of the study area revealed veterinary uses of 54 angiosperms (herbs-22, shrubs-7, trees-20 & climbers-5) used for the treatment of different diseases of domestic cattle. Among life forms studied, herbs was found to be highly medicinal followed by trees; and most medicinal plants of study area was represented by the families- Leguminosae and Moraceae. Besides, it was also found that leaf to be highly medicinal followed by stem/bark for livestock diseases.
SPIRE-fluent: A Self-Learning App for Tutoring Oral Fluency to Second Language English Learners

Second language (L2) learners often achieve oral fluency by correct pronunciation of words with appropriate pauses. It has been shown that the L2 learners improve their language skills using mobile apps in a self-learning manner. Effective learning is possible with apps that provide detailed feedback. However, apps that train oral fluency in an automatic way are not available. In this work, we present SPIRE-fluent app, which provides an automatic feedback with scores representing learner’s pronunciation quality, for each word in a sentence and for the entire sentence. The word specific scores are computed based on the correctness of pronunciation with respect to the expert’s audio. Further, the app displays the syllables uttered and a set of two types of pauses produced by the learners and the expert while speaking the sentence. Considering this as a feedback, the learner can correct their mistakes based on the mismatches between those utterances. In addition, it also estimates any pause made by the learners within a word and highlights the syllable containing the phoneme preceding the pause.
Improved Disentanglement through Aggregated Convolutional Feature Maps

We present a simple image preprocessing method for training VAEs leading to improved disentanglement compared to directly using the images. In particular, we propose to use regionally aggregated feature maps extracted from CNNs pretrained on ImageNet. Our method achieves the first rank on 3 of 5 metrics on the challenge’s public leaderboard.