Miracle-dz/UniFAR-FTUs huggingface dataset:
ardnerella vaginalis-associated bacterial vaginosis in ulgarian women

Background: Bacterial vaginosis (BV) is the most common cause of vaginal discharge in women of reproductive age. The purpose of this study was to determine the frequency of BV in Bulgarian pregnant and nonpregnant women from several age ranges and to compare three different laboratory methods for Gardnerella vaginalis detection in patents suffering from BV. Methods: Between September 2011 and June 2012, 809 women of 16–40 years of age separated in two major groups: nonpregnant – 469 (355 with and 114 without symptoms) and pregnant – 340 (213 and 127 respectively) were enrolled for the study. The women underwent three different laboratory tests simultaneously: scoring of Gram staining of vaginal smear, culture, and polymerase chain reaction (PCR) assay for G. vaginalis. Results: The microscopic method detected high frequency of BV in symptomatic (57%) whereas only a minority of asymptomatic subjects (14%) were detected. G. vaginalisassociated BV was diagnosed in approximately equal proportions when evaluated with PCR and microscopic method for both pregnant and nonpregnant women. The comparative analysis of microscopic evaluation, culture and PCR assays demonstrated greater concurrence (about 90%) between Gram staining and PCR detection for BV, than both methods compared to culture. The combination of microscopy and PCR turned out to be very reliable and repeatable for detecting G. vaginalis-associated BV. Conclusions: This is the first comparative investigation on the epidemiology of G. vaginalisassociated BV in Bulgaria. The established highest frequency in the young Bulgarian women (21–30 years) is alarming and should be considered in prophylaxis and reproductive programmes. © 2013 Elsevier Editora Ltda. All rights reserved. urally occurring microflora. Any change in the resident flora ntroduction acterial vaginosis (BV) is the most common cause of npleasant vaginal odor and discharge in women of ∗ Corresponding author at: Department of Medical Microbiology, Medica E-mail address: renigergova@mail.bg (R.T. Gergova). 413-8670/$ – see front matter © 2013 Elsevier Editora Ltda. All rights r ttp://dx.doi. org/10.1016/j.bjid. 2012.10. 026 reproductive age.1,2 It is induced by an imbalance in natl University of Sofia, 2 Zdrave Street, 1431 Sofia, Bulgaria. including reduction of lactobacilli allows for different anaerobic bacteria to gain a foothold and multiply.3–7 Nevertheless the process is multifactorial and the initial mechanism of
The prescribing of potentially dangerous drug combinations involving antipsychotics within US physician offices

Objective: Antipsychotic use has increased rapidly over the past decades. Because of the way in which most antipsychotics are metabolized, simultaneous use of antipsychotics with other medications that may interact negatively with the antipsychotic have the potential to cause serious harm. Using seven years of U.S. outpatient data for potentially dangerous prescribing patterns, this study updates and expands earlier research of nine combinations of potentially dangerous drug combinations. Methods: 107 million physician office visits, where an antipsychotic was prescribed or continued, were analyzed from the 2006-2012 National Ambulatory Medical Care Survey (NAMCS) and used to calculate dangerous prescribing patterns and visit rates. Building on a prior list of potentially dangerous drug combinations involving nine antipsychotics: clozapine, risperidone, olanzapine, quetiapine, ziprasidone, aripiprazole, haloperidol, perphenazine, and chlorpromazine, these drugs were classified into major, moderate, and major/ moderate combinations of prescribing patterns based on their capability to cause permanent damage or harm. Results: 10 percent of all U.S. physician office visits involving antipsychotics resulted in the patient being exposed to potentially dangerous drug combinations. The most prevalent combination involved risperidone and sertraline. Greater risks of exposure were associated with being between 15 and 40 years of age, being male, being covered by Medicaid or Medicare, and having schizophrenia or depression. Conclusions: These findings point to the continued need for research and interventions that aim to identify appropriate drug dosage levels for potentially dangerous drug combinations and pragmatically inform physicians.
Infrared Face Recognition: A Review of the State of the Art

In this paper a review of the state of the art has been presented on Infrared face recognition. A comprehensive review of the state of the art has been already done in [1] in 2008. Our review is a complement of the mentioned work [1] with more emphasis given to more recent or more important publications. Initially, we will review the basic important works on Infrared Face Recognition before 2008. Afterwards, we will focus on the recent works which are not reviewed in [1]. Finally, we will draw the conclusions
Brain Tissue Classification Based on Spectroscopy Data

We present methods for classification of brain tissue as tumorous or healthy in this paper with an additional emphasis on distinguishing between astrocytoma and glioblastoma in case of tumorous tissue, which is an important decision as it has large impact on the following therapy and prognosis for the patient. We compared three different approaches to this task, namely linear discriminant analysis (LDA), support vector machines (SVM), and artificial neural networks (ANN). The classification was performed based on the relative concentrations of the most important metabolites within the respective tissue. These concentrations were gathered using proton magnetic resonance spectroscopy with short echo times. We received very satisfying results with almost 100% correct decisions between tumorous and healthy tissue and about 90% correct decisions between astrocytoma and glioblastoma.
A Self-Adaptation Framework of Microservice Systems

Microservice has been more and more applied to build software systems in industry field and research in academic field. And software systems are increasingly expected to dynamically self-adapt to accommodate resource variability, changing user needs, and system faults. Compared with traditional software systems, microservice systems have some characteristics, such as the highly self-contained components and the dynamic running instance, which pose challenges to traditional self-adaptation methods. Therefore, it needs to propose corresponding techniques and methods to cope with the characteristics of microservice systems. This paper analyzes the special selfadaptive requirements of microservice systems, proposes a microservice reference model, which describes basic elements and their relationships of microservice systems. Then we present a microservice system self-adaptation framework MSSAF to support the self-adaptation of microservice systems. We illustrate the feasibility and effectiveness of our approach in the context of a microservice system case. Keywords-self-adaptation; microservice; reference model;
Fe b 20 03 Black Hole Thermodynamics and Riemann Surfaces ∗

We use the analytic continuation procedure proposed in our earlier works to study the thermodynamics of black holes in 2+1 dimensions. A general black hole in 2+1 dimensions has g handles hidden behind h horizons. The result of the analytic continuation is a hyperbolic 3-manifold having the topology of a handlebody. The boundary of this handlebody is a compact Riemann surface of genus G = 2g + h− 1. Conformal moduli of this surface encode in a simple way the physical characteristics of the black hole. The moduli space of black holes of a given type (g, h) is then the Schottky space at genus G. The (logarithm of the) thermodynamic partition function of the hole is the Kähler potential for the Weil-Peterson metric on the Schottky space. Bekenstein bound on the black hole entropy leads us to conjecture a new strong bound on this Kähler potential. Expanded version of a talk given in the Math Department, SUNY, Stony Brook and in the Physics Department, University of Maryland. † krasnov@aei.mpg. de
Title Assessing How Students Learn in Team-Based Learning : Validation of the Knowledge ReConsolidation Inventory

Purpose: The purpose of the present study was to establish the construct validity of a new instrument to measure psychological learning processes associated with Team-based learning (TBL), the Knowledge Re-Consolidation Inventory (KRCI). The instrument was designed to measure six factors: (1) self-guided preparation, (2) knowledge consolidation, (3) retrieval practice, (4) peer elaboration, (5) feedback, and (6) transfer of knowledge. Method: Two samples were taken, consisting in total of 197 firstand second-year medical students from Singapore. To establish the construct validity of the KRCI, two confirmatory factor analyses were conducted (CFA). First, an exploration sample (N1⁄490) was taken from the second-year medical students to conduct a preliminary CFA, and it resulted in elimination of items with poor psychometric properties. A confirmatory sample (N1⁄4107) was then taken from the first-year medical students to conduct a second CFA to cross-validate the KRCI with reduced items. Results: From the original 38 items, 16 remained. The resulting model fitted the data well. The second CFA with the crossvalidation sample replicated the findings of the first analysis and supported the factorial structure of the hypothesised six-factor model. Tests of factorial invariance demonstrate that the factorial structure of the KRCI was stable across measurements. Discussion: The results of the study suggest that the KRCI is a valid and reliable instrument capable of measuring the six psychological mechanisms underlying TBL. & 2017 King Saud bin Abdulaziz University for Health Sciences. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Digital Words: Moving Forward with Measuring the Readability of Online Texts

The readability of a digital text can influence people’s information acquisition (Wikipedia articles), online security (how-to articles), and even health (WebMD). Readability metrics can also alter search rankings and are used to evaluate AI system performance. However, prior work on measuring readability has significant gaps, especially for HCI applications. Prior work has (a) focused on grade-school texts, (b) ignored domain-specific, jargon-heavy texts (e.g., health advice), and (c) failed to compare metrics, especially in the context of scaling to use with online corpora. This paper addresses these shortcomings by comparing well-known readabilitymeasures and a novel domain-specific approach across four different corpora: crowd-worker generated stories, Wikipedia articles, security and privacy advice, and health information. We evaluate the convergent, discriminant, and content validity of each measure and detail tradeoffs in domain-specificity and participant burden. These results provide a foundation for more accurate readability measurements in HCI. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Empirical studies in HCI ;
MODEL-BASED CONTROL VALVE STICTION COMPENSATION

A model-based stiction compensation algorithm has been developed based on the H. Zabiri et al. Mixed Integer Quadratic Programming (MIQP) model predictive controller (MPC) algorithm which uses optimization to compensate for backlash in actuators. MIQP-based MPC shows promising result for stiction compensation. However, the backlash compensation formulation alone can remove oscillation caused by stiction dead-band but fails to reduce the offset caused by stiction slip-jump. Several modifications are proposed to solve the offset issue. The MIQP optimization problem constrains were loosened to give more flexibility to the optimizer. Simulation studies were conducted using a 2x2 distillation column model. With loosened constraints, MIQP based MPC reduced the offset at the expense of introducing oscillation into the system.
A fifteen year record of CO emissions constrained by MOPITT CO 1 observations

A fifteen year record of CO emissions constrained by MOPITT CO 1 observations 2 3 Zhe Jiang, John R. Worden, Helen Worden, Merritt Deeter, Dylan B. A. Jones, Avelino F. 4 Arellano, Daven K. Henze 5 6 7 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA 8 National Center for Atmospheric Research, Boulder, CO, USA 9 Department of Physics, University of Toronto, Toronto, ON, Canada 10 Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA 11 Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA 12 13 14 15
Flattening simulations of 3 D thick sheets made of fi ber composite materials

Recently, fiber composite materials have been attracting attention from industry because of their remarkable material characteristics, including light weight and high stiffness. However, the costs of products composed of fiber materials remain high because of the lack of effective manufacturing and designing technologies. To improve the relevant design technology, this paper proposes a novel simulation method for deforming fiber materials. Specifically, given a 3D model with constant thickness and known fiber orientation, the proposed method simulates the deformation of a model made of thick fiber-material. The method separates a 3D sheet model into two surfaces and then flattens these surfaces into two dimensional planes by a parameterization method with involves cross vector fields. The cross vector fields are generated by propagating the given fiber orientations specified at several important points on the 3D model. Integration of the cross vector fields gives parameterization with low-stretch and low-distortion. & 2015 Society of CAD/CAM Engineers. Production and hosting by Elsevier. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
P ARTITIONING FOR I MAGE S EGMENTATION USING I SOPERIMETRIC A PPROACH : A R EVIEW

Graph cut is fast method performing a binary segmentation. Graph cuts proved to be a useful multidimensional optimization tool which can enforce piecewise smoothness while preserving relevant sharp discontinuities. This paper is mainly intended as an application of isoperimetric algorithm of graph theory for image segmentation and analysis of different parameters used in the algorithm like generating weights, regulates the execution, Connectivity Parameter, cutoff, number of recursions. We present some basic background information on graph cuts and discuss major theoretical results, which helped to reveal both strengths and limitations of this surprisingly versatile combinatorial algorithm. KEYWORDSGraph cut method, isoperimetric algorithm, and connectivity parameter
A general closed-form spread option pricing formula

We propose a new accurate method for pricing European spread options by extending the lower bound approximation of Bjerksund and Stensland (2011) beyond the classical Black–Scholes framework. This is possible via a procedure requiring a univariate Fourier inversion. In addition, we are also able to obtain a new tight upper bound. Our method provides also an exact closed form solution via Fourier inversion of the exchange option price, generalizing the Margrabe (1978) formula. The method is applicable to models in which the joint characteristic function of the underlying assets forming the spread is known analytically. We test the performance of these new pricing algorithms performing numerical experiments on different stochastic dynamic models.1
Deep Learning Model for Paraphrases Identification of Tweets

Paraphrase identification is the task of identifying automatically whether a pair of sentences carries the same meaning. In this paper, we propose a deep learning system for paraphrase identification of tweets. The proposed system integrates the state-of-the-art features and Gated Recurrent Units to extract high level features of two tweets and identify whether they are identical. The effectiveness of the proposed system has been evaluated by using it in the supervised task of paraphrase in Twitter that presented in SemEval 2015, obtaining results which show its superiority over the state-of-the-art systems.
Towards A Skills Taxonomy

When evaluating job applications, recruiters and employers try to determine whether the information that is provided by a job seeker is accurate and whether it describes an individual that possesses sufficient skills. These questions are related to the hidden skills and skills resolution problems. In this paper we argue that using a skills taxonomy to identify and resolve unknown relationships between text that describe an applicant and job descriptions is the best way for addressing these problems. Unfortunately, no comprehensive, publicly available taxonomy exists. To this end, this work proposes an automated process for creating a skills taxonomy. Effective and efficient methods for bootstrapping a taxonomy are critical to any process that names and characterizes the properties and interrelationships of entities. To this end, we present three potential methods for bootstrapping and extending our skills taxonomy. We propose a hybrid scheme that combines the beneficial features of those methods. Our hybrid approach seeds the bootstrapping process with publicly available resources and identifies new skill terms and corresponding entity relationships. In this paper, we focus specifically on using Wikipedia as our corpus and exploiting its structure to populate the taxonomy. We begin by constructing a relationship graph of possible skill terms from Wikipedia. We then use a data mining methodology to identify skill terms. Our results are promising, and we are able to achieve a 98% classification rate.
Secure VANETs: Trusted Communication Scheme Between Vehicles and Infrastructure Based on Fog Computing

In the Vehicular Ad-hoc Networks (VANETs), a vehicle or the vehicle driver could be recognized and tracked by eavesdropping its queries (e.g., beacons) by an adversary as these contains personal information like location, speed, and communication of the vehicles. This attack leads to threats to the vehicle`s location and leakage of personal information. The current solution, is to use the anonymizer as a third trusted party between the vehicles and the LBS. In this paper we refer to the use of a Fog server with Fog anonymizer to secure the communication among the vehicles and LBS. Our scheme consists of four phases. In first phase, the vehicle driver initiates the communication process and generate the encrypted messages. These messages may contain the sub-messages. In phase 2, the Fog server received the messages via different roots. The Fs combined the messages and decrypt the messages based on the PK received by the vehicle. All the Fog server perform the same task for encryption and decryption. If any of the Fog server was compromised, we still had the link for communication. In Phase 3, the Fog anonymizer receive the messages from the Fog node, anonymize them based on the anonymization process. Thereafter, the Fog anonymizer send these messages to LBS to achieve desired goals. The Fog anonymizer perform the same job for anonymization and de-anonymization, while sending and receiving the messages from the LBS. In the last phase, the LBS received the messages from the Fog anonymizer, understand the communication messages, compile the desired results, and sent them back to the Fog anonymizer. Our analysis shows that the proposed scheme preserved the location privacy based on the queries at low communication and computational cost.
` A Review On Various Techniques To Recognize Gesture Based Facial Expressions

Nowadays, the Facial Expression Recognition (FER) system is a very important research topic in the fields of pattern recognition, Human-Computer Interaction (HCI) and Artificial Intelligence. The most essential steps in FER system are facial feature extraction and classification of expressions which improves the system performance for human computer interaction. This article reviews the feature extraction approaches such as Local Binary Pattern (LBP) (Divide the examined window into cells (e.g. 16x16 pixels for each cell). For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience). Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector. Optionally normalize the histogram. Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window. ), Spatio-Temporal feature using Local Zernike moment and Local Directional Position Pattern (LDPP). Also, the various combinations of Convolutional Neural Network (CNN) and Deep Neural Network (DNN) classifiers are reviewed. In addition, this paper makes a comparative study of various techniques used for analysing gesture based facial expressions and their corresponding performance. KeywordsFacial Expression Recognition (FER), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Local binary pattern (LBP), feature extraction.
Decentralizing the role of root authority in CP-ABE by the help of blockchain

Attribute-based encryption (ABE) is classified as an extended public key cryptosystem which replaces the public key of a user with his/her attributes in order to reduce the management overhead of their public keys. Another advantage of ABE is to handle monotonic access control with attributes. Nevertheless, ABE has some disadvantages like a fully trustness on the central component. In this paper, we propose a novel way of CiphertextPolicy Attribute-based encryption (CP-ABE) by applying features of the decentralized ledger in the blockchain to overcome the current disadvantages of CP-ABE. We provide the role distribution of the central component to achieve a more reliable system and prevent a single point of failure from strong adversaries.
Information extraction from user-generated content in the classical music domain

The applications of Information Extraction (IE) on User-generated Content (UGC) have widely benefited from the emergence of microblogging services in the last decade. In particular, ​Twitter has been at the center of attention of many studies because of its widespread use and easy accessibility. Among the several fields which have benefited from this source, in particular Named Entity Recognition (NER) has demonstrated how challenging can be obtaining useful information from the noisy space of ​tweets​. From another perspective, recently in the field of Music Information Retrieval (MIR) researches have shown how NLP techniques such as IE and NER can be an important resource to improve accuracy and precision in tasks like Music Recommendation,​ Artist​ ​Similarity​ ​or​ ​Genre​ ​Classification​. The objective of this thesis is to investigate methods to extract information from user-generated content in a specific channel related to Classical Music, BBC Radio 3​, through the use of NER techniques. We investigate​ how ​state-of-the-art​ ​methods​ ​in​ NER can​ ​be​ applied​ to​ detect entities​ ​in​ the​ music​ ​domain, and how​ contextual​ information​ ​can contribute​ with​ ​NER​​ in​ ​this​​ particular​ ​case.