Mean Classification Research Articles

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73–81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network.

For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.

Tensor Factorization and Attention-Based CNN-LSTM Deep-Learning Architecture for Improved Classification of Missing Physiological Sensors Data

One of the essential issues for efficient control of prosthesis is the accurate classification of target movements hidden in electroencephalography (EEG) and electromyography (EMG) signals. However, in the presence of missing data in acquired signals, the classification accuracy degrades significantly as the amount of missing data increases, reducing the control performance of the prosthesis. This research proposes a framework based on tensor (multidimensional array) factorization and attention-based convolutional neural network (CNN)-long short-term memory (LSTM) deep learning (DL) for recovering missing data and performing classification of target movements, respectively. To recover missing data in tensor factorization, Canonical/Polyadic Weighted OPTimization (CP-WOPT) is employed, and its performance is compared to state-of-the-art factorization methods, whereas the performance of CNN-LSTM-attention layer (Attn) is compared to state-of-the-art machine learning and DL classifiers. Results show that CNN-LSTM-Attn obtained the mean classification accuracy of 98%, 83%, and 90% on complete (0% missing data), partially complete (10% to 50% missing data), and tensor-recovered real-world EEG and EMG data, respectively, demonstrating the applicability of the proposed framework.

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.

Study of relic classification based on neural network and K mean classification

Over time, its own chemical elements will change in response to environmental changes, commonly known as weathering, which will lead to changes in the chemical composition ratios and affect the judgment of the class. This paper focuses on the chemical composition of different types of glass. By comparing the chemical indicators of each type, an identification model of the cultural relic category is established to predict the chemical content of the weathering process. To solve this, the neural network, chi-square test, and Spearman correlation analysis are used and the calculation results are given. According to the definition of sensitivity, after analysis, the accuracy was taken as the evaluation index, and the type of chemical composition was taken as the factor. K-value cluster analysis was performed for the type of chemical composition of the two glass categories in turn, and the accuracy was compared, and the accuracy of high potassium glass was close to 83%, and the accuracy of lead-barium glass was close to 69%, concluding that the type of chemical composition, which has little effect on the accuracy, has little sensitivity. In different categories, the variation of independent variables has a large impact on the accuracy and a large sensitivity. Finally, the advantages and disadvantages of the model are analyzed, and the model is extended to a larger domain.

Means of evaluation in environmental media discourse

The purpose of the article is to describe and classify the linguistic means expressing evaluative meaning in the environmental media discourse. The objectives of the study include the identification of different means expressing evaluation in media texts devoted to environmental issues, identifying lexical items and stylistic devices for the formation of an evaluation strategy in modern media texts devoted to environmental issues, creating a typology of evaluative linguistic means in media environmental discourse. The method of component analysis of the lexical meaning of a word was used in the work, it enabled full understanding and characterization of lexical units with an evaluative component in their semantics. The method of content analysis of media texts made it possible to identify the pragmatic potential of axiological vocabulary used in Russian media texts dealing with environmental issues. The article considers the category of evaluation, which is universal for media discourse, and proposes a classification of linguistic means of expressing evaluative meaning in environmental media discourse. It is supposed to divide all linguistic means of expression of evaluative meaning into two main groups according to their positive or negative meaning. The authors carried out a detailed part-of-speech analysis of the lexical items of both groups, proposed their classification and analysed stylistic devices as a way of expressing a positive or negative connotation. The analysis showed that the most frequent among all the lexical items under consideration are adjectives with different meanings, which are widely used as a means of influence in the environmental media discourse and are capable of expressing ethical, aesthetic and rationalistic (utilitarian) assessment.

Means of the Expressing Sentiment in the Conditions of Modern Business Internet Correspondence in Russian, English and Chinese Languages

This research is devoted to the analysis of the means of expressing sentiment in business internet correspondence. The purpose of the study: review and study of the means of expressing tonality in an official business style; determination of the tone of business texts using various means on the material of business internet correspondence in Russian, English, Chinese. The objective of the study: to supplement the classification of means in three languages (taking into account their specifics) to determine the tone of the text and to distinguish the means of expressing the tone for business internet correspondence.

Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance during Motor Training.

As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (~10% and ~18% improvement respectively), reaching ~89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data.

Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa. A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis. Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains. The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.

Prototypical Quadruplet for Few-Shot Class Incremental Learning

The scarcity of data and incremental learning of new tasks pose two major bottlenecks for many modern computer vision algorithms. The phenomenon of catastrophic forgetting, i.e., the model's inability to classify previously learned data after training with new batches of data, is a major challenge. Conventional methods address catastrophic forgetting while compromising the current session's training. Generative replay-based approaches, such as generative adversarial networks (GANs), have been proposed to mitigate catastrophic forgetting, but training GANs with few samples may lead to instability. To address these challenges, we propose a novel method that improves classification robustness by identifying a better embedding space using an improved contrasting loss. Our approach retains previously acquired knowledge in the embedding space, even when trained with new classes, by updating previous session class prototypes to represent the true class mean, which is crucial for our nearest class mean classification strategy. We demonstrate the effectiveness of our method by showing that the embedding space remains intact after training the model with new classes and outperforms existing state-of-the-art algorithms in terms of accuracy across different sessions.

Load Monitoring Based on Monte Carlo Simulation for the Identification of Fast Charging Stations

In this paper, four EV fast chargers are assumed to be located at the parking lot of a commercial facility, namely a supermarket. The supermarket consists of different centers that provide different services at different hours of operation. The Monte Carlo Simulation method is utilized to estimate the power profile of the commercial load. Both the commercial load and the fast charging station (FCS) are connected to the main grid at the point of common coupling (PCC). The aim of this paper is to apply load disaggregation using a single point sensing at the PCC to manage the system's energy by proposing a machine learning-based method for detecting and classifying the four EV fast chargers rated 50 kW, 90 kW, 150 kW, and 350 kW, respectively. The commercial load can be considered as a background load disturbing the disaggregation process at the PCC. The voltage and current signals are monitored at the PCC and utilized by Fourier transform to determine three features: the change of the active, reactive, and apparent power. Two machine learning classifiers, i.e., Kernel Naive Bayes (KNB) and k-Nearest Neighbors (KNN), are introduced in order to develop the appropriate prediction models. The results reveal that the KNB outperforms the KNN based on the mean classification accuracy as well as the F-measure index.

『字類註釋』의 同字異釋형 중출 표제한자의 새김과 분류 - 한자 새김 자료와 비교를 중심으로 -

본고에서는 19세기의 문헌 『자류주석』의 특징적 새김 양상인 동자이석(同字異釋)형 중출 표제한자의 새김과 수록 방식을 살펴보았다. 한자 새김 자료에서 표제한자의 중복 출현은 매우 드물다. 본고에서 분석한 『자류주석』의 동자이석 중출 표제한자는 48개이다. 대다수가 한자초학서에는 나오지 않는데, 20세기의 자서류 『자전석요』, 『신자전』의 풀이와 비교해 보면, 『자전석요』와 『신자전』에는 모두 단일 표제한자로 수록되어 복수의 새김이 제시되어 있다. 『자류주석』과 『신자전』의 새김 방식이 유사하고, 한문주석이 공통적인 부분이 많다. 『자류주석』은 이전 한자초학서의 정보를 단순 답습하지 않고 여러 전거 문헌을 면밀히 살펴 개별 한자의 새김을 정리하고 이를 의미 부류 분류를 정밀히 하여 표제한자로 중복 제시하였다. 이로써 분류어휘집의 정체성을 더욱 분명히 하는 동시에 개별 한자에 대한 이해를 심층화하였다.

Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms

The objective of this study was to investigate the potential of creating a pipeline to classify the marbling score obtained from ribeye area (REA) images using computer vision and machine learning methods. Our database consisted of images and measurements (N = 2,446) from the transversal cut between the 12th and 13th ribs of the Longissimus dorsi muscle from carcasses of a beef cattle population (Bos taurus). Each sample was previously labeled by the industry using a low, medium or high marbling score. The prediction accuracies of two tree-based Machine Learning (ML) algorithms (Decision Tree - DT and Random Forest - RF) were compared, as well as different measures for extracting features from the REA images, which were used as input for the ML algorithms. In order to extract features based on detectable color patterns and textures contained in smaller parts of the grayscale image, we proposed the application of the local binary pattern (LBP) method prior to the adoption of ML methods. Mean classification accuracies for the test set ranged from 45.78% to 91.25% for different test scenarios. The results were mostly impacted by the feature extraction metrics, ML methods, potential subjectivity during the classification process by the industry, and the number of classes evaluated together. The best prediction accuracy results were achieved after performing the cross-validation (20% in each balanced group, 5 folds, and 10 repetitions), considering solely the extreme groups (low and high marbling scores) and pre-selecting from each group the 400 most visually representative samples. The RF algorithm outperformed the DT for most scenarios. After increasing the number of images to 580 samples for the same two groups, the highest testing accuracies were reduced to 83.05% for RF and 75.58% for DT. Such a decrease in the classification accuracies may be associated with the addition of erroneously classified images, due to the subjective nature of the industry evaluation. In conclusion, our preliminary studies showed the LBP method as a powerful feature extraction strategy considering a scenario where the labels were well defined. Our results revealed high accuracies for the classification of marbling extremes, but there is an evident need to improve the understanding of the biological and visual aspects that led to a sharp drop in classification accuracy after the insertion of the intermediate groups of marbling. In addition, the authors highlight the importance of an accurate labeling process for achieving better classification accuracy when applying supervised classification methods.

Special means in police activity: a new concept

The article is devoted to a theoretical study of the problems of legal regulation of the use of special means by police officers during the performance of official duties. The analysis was carried out and the general characteristics of the existing classification of special means, the interpretation of the normative-legal definition of the methods of their implementation, and the lack of a unified view of these elements of official activity were noted.
 It is noted about the peculiarities during the introduction of martial law of the service and service-combat activities of the police, which consist in the possibility for its employees to use any improvised means in a special capacity and to ignore the general rules (order and conditions) of application in cases of the need to repel an attack or detain a person and elimination of danger in a state of extreme necessity regarding a certain category of offenders: persons participating in armed aggression against Ukraine. It is emphasized that an official interpretation of such a right is required in the field of application of special means.
 It was determined that special means are used mainly within the scope of police powers, either preventive (inadmissibility of injuries) or coercive (maximum lethal consequences, disabling of personnel and military equipment). But some of the provided means have a combined scope of application, and during the period of martial law they can change their purpose depending on the situation of use, which requires a review of the affiliation of certain special means to coercive ones. The problems of the ambiguity of enlisting service animals as coercive police measures, as well as the lack of certainty among special means that allow the exercise of official powers, such as those belonging to mandatory tactical medical equipment, are highlighted. This will make it possible to harmonize the terminology used in the relevant law ("medical", "pre-medical", "non-medical" assistance).
 The validity of a conceptually and fundamentally new approach to the existing system of special means, which are used or used for the performance of police powers, is determined, and its improvement is a promising direction of law-making.

ДО ПИТАННЯ АНАЛІЗУ ГЕОМЕТРИЧНИХ МОДЕЛЕЙ СУЧАСНИХ ГРУНТООБРОБНИХ ЗНАРЯДЬ

Agricultural production plays a very important role in the national economy of Ukraine. Its effectiveness largely depends on the technical means which are used. Therefore, the further improvement of the latter is an actual scientific and applied problem. At present, one of the progressive directions of its successful solution is the wide application of computer information technologies, in particular computer-aided design, for the development of the specified technical objects. One of the generally recognized foundations for this is geometric modeling, without which it is difficult to imagine most modern computer-aided design systems. It is important that geometric models combine other models of processed industrial products into a single whole, for example, their strength, manufacturing technology, operation, etc. This ensures the implementation of a comprehensive, i.e. multi-aspect, optimization of the created product for its entire life cycle. Effective use of computer shaping tools for certain groups of technical objects is possible by adapting the first to the second. A necessary condition for this process is the classification of existing geometric means and reproducible products in the appropriate aspects. This contributes to obtaining high-quality results of computer-aided design. If necessary, the existing shaping methodology is supplemented with new methods, techniques, algorithms, models, etc. In this way, the mutual improvement of the theoretical and practical aspects of geometric modeling and agricultural production is carried out. The main purpose of this publication is to present the basics of the methodology proposed on the foundation of the structural-parametric approach for carrying out an integrated, i.e. combined, classification of agricultural machines and means of automated geometric modeling. As examples, some existing tillage tools were analyzed. For other technical products, the outlined concept is applied in a similar way. The article also defines the perspectives for conducting further scientific research in the considered direction. Key words: computer-aided design, geometric models, tillage tools, agricultural production, structural-parametric shaping.

A lightweight deep learning approach to mouth segmentation in color images

PurposeMouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.Design/methodology/approachThis paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.FindingsExtensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.Originality/valueThe modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

How to optimize the 2D/3D urban thermal environment: Insights derived from UAV LiDAR/multispectral data and multi-source remote sensing data

The systematical exploration of how two-dimensional (2D) and three-dimensional (3D) features of urban landscapes influence land surface temperature (LST) is still limited. Therefore, we investigated the influence of three main urban landscapes—urban green space, impervious land, and water bodies on LST, with a particular focus on the 3D vegetation metrics of green volume (GV) and leaf area index (LAI). We used Yinchuan City, China, as a case study. We quantified the impacts of various 2D/3D metrics of the three landscape types on LST using a random forest analysis with multiple sources, including Unmanned Aerial Vehicle (UAV) and remote sensing images. We then generated a Bayesian Network (BN) model to identify the optimal configurations for each landscape type. We found that using 11 of the 31 metrics considered, our model could explain 81.8% of the observed variance in LST of Yinchuan City. Among those, water body metrics were the most important, followed by vegetation abundance, impervious land metrics, and landscape pattern of urban green space. The mean classification error of the BN model was only 22.9%. We suggest that this makes the BN model a promising support tool for urban planning with a view to urban heat island mitigation. Our findings also stress the importance of considering both 2D and 3D features when considering urban cooling strategies.

A convolutional neural network-based framework for analysis and assessment of non-linguistic sound classification and enhancement for normal hearing and cochlear implant listeners.

Naturalistic sounds encode salient acoustic content that provides situational context or subject/system properties essential for acoustic awareness, autonomy, safety, and improved quality of life for individuals with sensorineural hearing loss. Cochlear implants (CIs) are an assistive hearing device that restores auditory function in hearing impaired individuals. Most CI research advancements have focused on improving speech recognition in noisy, reverberant, or time-varying diverse environments. Relatively few studies have explored non-linguistic sound (NLS) perception among CIs, and those that have carried out such studies generally reported poor perception, suggesting a clear deficit in current CI sound processing systems. In this study, a convolutional neural network (CNN)-based NLS classification model is used as a framework to compare unprocessed and CI-simulated NLS classification and evaluate NLS perception targeted algorithms among CI listeners. Additionally, a NLS enhancement algorithm that focuses on improving identifiability and perception among CI listeners is proposed. The proposed NLS enhancement algorithm is evaluated based on identifiability performance using the CI-simulated NLS classification model. The proposed NLS classification framework was able to achieve near human-level performance with no significant effect of classification modality (model vs human subject) and achieved mean classification scores of 85.86% for NH (p = 0.3758) and 65.25% for CI (p = 0.1725). Among the four different feature-based methods of the proposed NLS enhancement algorithm, the "harmonicity"-based one achieved highest mean classification accuracy of 63.75%, when compared to baseline, and demonstrated significant improvement in performance (p = 0.0403). The resulting proposed comparative NLS classification framework contributes toward (i) advancement of NLS recognition studies, (ii) mitigation of CI user recruitment constraints and listener evaluation with NH listeners, (iii) development of a community shared testbed for comparative NLS studies, and (iv) advancement of NLS enhancement studies (identifiability and perceptual factors) among CI listeners.

Features of Nonverbal Means of Communication as a Way to Express Social Status in a Literary Text (on the Example of A.P. Chekhov's Short Story "Thick and Thin")

In his early works, A.P. Chekhov often draws the reader's attention to the forms of behavior of the characters. In a literary text, nonverbal means of communication used to describe communication between actors acquire special significance. The article is devoted to the problem of typology, role and effectiveness of nonverbal linguistic means of communication in the aspect of their use in the author's literary text. The relevance of the research lies in the insufficient knowledge of the place and role of paralinguistic means of communication in the early works of A.P. Chekhov. The subject of the study is the linguistic means of describing nonverbal behavior in a literary text. The object of the study was the ways of expressing nonverbal communication of the characters of the work. In the course of the work, descriptive and analytical research methods were used. The research material was the story of A.P. Chekhov "Thick and Thin" (1883). The practical significance of the work consists in the application of the research results in the courses of the Russian language, communication theory, philological analysis of text, stylistics. The aim of the study is to identify and analyze, based on the classification of nonverbal means of linguistic units existing in linguistics, with the help of which A.P. Chekhov in his early stories describes the nonverbal behavior of the characters, their composition and functions.