Supervised Identification of Writer\u27s Native Language Based on Their English Word Usage

In this paper, we investigate the possibility of constructing an automated tool for the writer\u27s first language detection based on a~document written in their second language. Since English is the contemporary lingua franca, commonly used by non-native speakers, we have chosen it to be the second language to study. In this paper, we examine English texts from computer science, a field related to mathematics. More generally, we wanted to study texts from a domain that operates with formal rules. We were able to achieve a high classification rate, about~90\%, using a relatively simple model (n-grams with logistic regression). We trained the model to distinguish twelve nationality groups/first languages based on our dataset. The classification mechanism was implemented using logistic regression with L1~regularisation, which performed well with sparse document-term data table. The experiment proved that we can use vocabulary alone to detect the first language with high accuracy

Time Series Classification Using Images

This work is a contribution to the field of time series classification. We propose a novel method that transforms time series into multi-channel images, which are then classified using Convolutional Neural Networks as an at-hand classifier. We present different variants of the proposed method. Time series with different characteristics are studied in this paper: univariate, multivariate, and varying lengths. Several selected methods of time-series-to-image transformation are considered, taking into account the original series values, value changes (first differentials), and changes in value changes (second differentials). In the paper, we present an empirical study demonstrating the quality of time series classification using the proposed approach

Online learning of windmill time series using Long Short-term Cognitive Networks

Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, update the model with the new information is often very expensive to perform using traditional Recurrent Neural Networks (RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. These recently introduced neural systems consist of chained Short-term Cognitive Network blocks, each processing a temporal data chunk. The learning algorithm of these blocks is based on a very fast, deterministic learning rule that makes LSTCNs suitable for online learning tasks. The numerical simulations using a case study with four windmills showed that our approach reported the lowest forecasting errors with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit, and a Hidden Markov Model. What is perhaps more important is that the LSTCN approach is significantly faster than these state-of-the-art models

Perioperative lung ultrasound pattern changes in patients undergoing gynecological procedures — a prospective observational study

Objectives: General anesthesia and positive pressure ventilation are associated with perioperative pulmonary complications.Lung ultrasound (LUS) is a method used to evaluate lung parenchyma. The purpose of this study was to evaluate LUSpatterns in a cohort of women undergoing gynecological surgery with uncomplicated general anesthesia.Material and methods: Patients were assessed according to the 8-zone LUS assessment protocol used to detect lungsliding, A-lines, B-lines, interstitial syndrome and lung consolidation. Each patient was screened at specific time intervals:before induction of anesthesia, at induction, 30 and 60 minutes after induction and within two hours after recovery.Results: A total of 99 patients undergoing gynecological surgery with uneventful anesthesia from November 2017 to November2018 were included in this study. A total of 426 LUS records were retained for further analysis. Overall, no significantchanges to patients’ A-line appearance were detected, regardless of the time of assessment. There was, however, an increasein the number of B-lines at the screening times of 30 and 60 minutes after induction, as compared to initial assessments(p = 0.011 and p < 0.001 respectively), and an increase in the number of positive regions (≥ 3 B-lines) at 30 and 60 minutesafter induction and after recovery, as compared to initial assessment (p < 0.001; p < 0.001 and p = 0.001 respectively).Conclusions: An uneventful anesthesia may predispose to abnormal LUS findings and should be considered while interpretingof LUS results in cases with perioperative pulmonary complications

Pregnancy related and postpartum admissions to intensive care unit in the obstetric tertiary care center — an 8-year retrospective study

Objectives: The purpose of the study was to analyze the incidence of maternal morbidity and mortality of pregnant and postpartum women admitted to the intensive care unit (ICU).Material and methods: Retrospective analysis of all pregnant and postpartum patients admitted to ICU of the obstetric tertiary care center between January 1, 2007 and December 31, 2014.Results: A total of 266 patients with pregnancy and postpartum related morbidity were admitted to ICU (12.56 per 1000 deliveries). It accounted for 21.08% of all adult admissions of the unit. Mean age was 30.2 ± 5.6 years, mean gestational age was 30.8 ± 7.6 weeks. Two hundred forty patients (90.23%) were primiparous, 17 (6.4%) were twin pregnancy. Main reasons of admission included hypertensive disorders of pregnancy n = 99 (37.22%; 4.68 per 1000 deliveries), hemorrhage n = 46 (17.29%; 2.17 per 1000 deliveries) and sepsis/infection n = 46 (17.29%; 2.17 per 1000 deliveries). Median length of stay was five days (IQR 4–7). Artificial ventilation was required in 91 patients (34.21%), 147 (55.26%) required vasoactive drugs, 33 (12.41%) had metabolic disturbances, 21 (7.89%) required total parenteral nutrition and 4 (1.50%) renal replacement therapy. We report four maternal deaths (1.5%; 0.19 per 1000 deliveries).Conclusions: There are three main reasons of obstetric ICU admissions: hypertensive disorders of pregnancy, obstetric hemorrhage and sepsis/infection. The majority of obstetric patients admitted to ICU did not require multi-organ supportive therapy. Availability of intermediate care facility could reduce unnecessary admission to ICU

A global analysis of Y-chromosomal haplotype diversity for 23 STR loci

In a worldwide collaborative effort, 19,630 Y-chromosomes were sampled from 129 different populations in 51 countries. These chromosomes were typed for 23 short-tandem repeat (STR) loci (DYS19, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS385ab, DYS437, DYS438, DYS439, DYS448, DYS456, DYS458, DYS635, GATAH4, DYS481, DYS533, DYS549, DYS570, DYS576, and DYS643) and using the PowerPlex Y23 System (PPY23, Promega Corporation, Madison, WI). Locus-specific allelic spectra of these markers were determined and a consistently high level of allelic diversity was observed. A considerable number of null, duplicate and off-ladder alleles were revealed. Standard single-locus and haplotype-based parameters were calculated and compared between subsets of Y-STR markers established for forensic casework. The PPY23 marker set provides substantially stronger discriminatory power than other available kits but at the same time reveals the same general patterns of population structure as other marker sets. A strong correlation was observed between the number of Y-STRs included in a marker set and some of the forensic parameters under study. Interestingly a weak but consistent trend toward smaller genetic distances resulting from larger numbers of markers became apparent.Peer reviewe

Pattern Classification with Rejection Using Cellular Automata-Based Filtering

Part 1: AlgorithmsInternational audienceIn this article we address the problem of contaminated data in pattern recognition tasks, where apart from native patterns we may have foreign ones that do not belong to any native class. We present a novel approach to image classification with foreign pattern rejection based on cellular automata. The method is based only on native patterns, so no knowledge about characteristics of foreign patterns is required at the stage of model construction. The proposed approach is evaluated in a study of handwritten digits recognition. As foreign patterns we use distorted digits. Experiments show that the proposed model classifies native patterns with a high success rate and rejects foreign patterns as well

Pattern classification with Evolving Long-term Cognitive Networks

This paper presents an interpretable neural system-termed Evolving Long-term Cognitive Network-for pattern classification. The proposed model was inspired by Fuzzy Cognitive Maps, which are interpretable recurrent neural networks for modeling and simulation. The network architecture is comprised of two neural blocks: a recurrent input layer and an output layer. The input layer is a Long-term Cognitive Network that gets unfolded in the same way as other recurrent neural networks, thus producing a sort of abstract hidden layers. In our model, we can attach meaningful linguistic labels to each neuron since the input neurons correspond to features in a given classification problem and the output neurons correspond to class labels. Moreover, we propose a variant of the backpropagation learning algorithm to compute the required parameters. This algorithm includes two new regularization components that are aimed at obtaining more interpretable knowledge representations. The numerical simulations using 58 datasets show that our model achieves higher prediction rates when compared with traditional white boxes while remaining competitive with the black boxes. Finally, we elaborate on the interpretability of our neural system using a proof of concept. (c) 2020 The Authors. Published by Elsevier Inc

Modelling Human Cognitive Processes

Part 5: Modelling and OptimizationInternational audienceThe article presents an application of fuzzy sets with triangular norms and balanced fuzzy sets with balanced norms to decision making modelling. We elaborate on a vector-based method for decision problem representation, where each element of a vector corresponds to an argument analysed by a decision maker. Vectors gather information that influence given decision making task. Decision is an outcome of aggregation of information gathered in such vectors. We have capitalized on an inherent ability of balanced norms to aggregate positive and negative premises of different intensity. We have contrasted properties of a bipolar model with a unipolar model based on triangular norms and fuzzy sets. Secondly, we have proposed several aggregation schemes that illustrate different real-life decision making situations. We have shown suitability of the proposed model to represent complex and biased decision making cases