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Investigating process monitoring techniques is required to reduce the loss of property and life caused by industrial processes accidents. Fault diagnosis, which attempts to determine the fault type, is a vital step in process monitoring because it can help operators respond to abnormal situations appropriately. Adequate data labels to train supervised fault diagnosis models are difficult to acquire in practice; however, semi-supervised methods, which are attracting increasing attention, can use unlabeled data. Self-labeled algorithms are an effective paradigm of semi-supervised methods, but their applications in industrial process fault diagnosis do not meet expectations, because they are prone to performance deterioration when handling industrial process data. To address this issue, a self-training algorithm with a modified confidence measure is proposed. The confidence measure is temporal-spatial with temporal identities of data introduced to its definition and calculation, which makes the algorithm adaptable to industrial processes. The proposed algorithm is also self-adaptive to avoid time-consuming hyper-parameter tuning processes. The benchmark Tennessee Eastman process data were used to evaluate the proposed algorithm, and the experiment results demonstrate its superiority compared to competing semi-supervised methods.

Objective To determine the effects of the training duration and frequency on lip-seal strength (LSS) in older people. Background Lip-seal is important for speaking, eating and swallowing. LSS decreases after training ends; therefore, continuous training is essential. Materials and methods Participants underwent the resistance training of LSS. Regarding training duration, eight women aged ≥65 years participated in a crossover study with trainings A (direction: 1, duration: 50 seconds) and B (directions: 3, duration: 3 minutes), daily for 4 weeks. Regarding training frequency, 40 women aged ≥65 years were divided into four groups based on frequency (everyday, every-other-day, once-a-week and control groups), and all groups excluding the control group performed training B for 4 weeks. LSS was measured at weeks 0, 2 and 4 using a digital strain gauge. Friedman's test was used, followed by Steel-Dwass test (α = 0.05). Results Regarding the effects of the training duration, significant differences in LSS were noted between weeks 0 and 4 for training B, but no difference was noted for training A. Regarding training frequency, significant differences were observed between weeks 0 and 2 or 4 in the everyday and once-a-week groups. Significant differences were observed in the every-other-day group between weeks 0 and 4 and no difference in the control group. For all groups, median LSS was higher in week 2 or 4 than that in week 0. Conclusion Lip-seal training for 3 minutes per session everyday, every-other-day or once-a-week for 4 weeks increased LSS of older people.

PurposeTwitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.Design/methodology/approachIn the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.FindingsThe proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.Originality/valueThis paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

Recently, graph convolutional networks (GCNs) have achieved significant success in many graph-based learning tasks, especially for node classification, due to its excellent ability in representation learning. Nevertheless, it remains challenging for GCN models to obtain satisfying predictions on graphs where only few nodes are with known labels. In this paper, we propose a novel entropy-aware self-training algorithm to boost semi-supervised node classification on graphs with little supervised information. Firstly, an entropy-aggregation layer is developed to strengthen the reasoning ability of GCN models. To the best of our knowledge, this is the first work to combine the entropy-based random walk theory with GCN design. Furthermore, we propose an ingenious checking part to add new nodes as supervision after each training round to enhance node prediction. In particular, the checking part is designed based on aggregated features, which is demonstrated more effective than previous methods and boosts node classification significantly. The proposed algorithm is validated on six public benchmarks in comparison with several state-of-the-art baseline algorithms, and the results illustrate its excellent performance.

Improving Offensive and Hate Speech (OHS) classifiers’ performances requires a large, confidently labeled textual training dataset. Our study devises a semi-supervised classification approach with ...

This dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework. WikiOIE is based on UDPipe and the Universal Dependencies project for text processing. It easily allows customizing the information extraction (IE) approach to automatically extract triples (subject, predicate, object). This dataset contains relations extracted by a supervised approach based on self-training. The extraction process is provided in JSON format. Version 2 of the dataset was extracted using an improved version of the learning algorithm. The files of version 2 are identified by the suffix "_reg" in the file name. More information and the Java code are available here: https://github.com/pippokill/WikiOIE Self-training approach: Lucia Siciliani, Pierluigi Cassotti, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro2021. Extracting Relations from Italian Wikipedia using Self-Training. In Proceedings of the Eighth Italian Conference on Computational Linguistics (CLiC-it2021). CEUR-WS. WikiOIE framework: Pierluigi Cassotti, Lucia Siciliani, Pierpaolo Basile, Marco de Gemmis, and Pasquale Lops. 2021. Extracting relations from Italian Wikipedia using unsupervised information extraction. In Proceedings of the 11th Italian Information Retrieval Workshop 2021 (IIR 2021). CEUR-WS.

In hyperspectral remote sensing, the classification of hyperspectral imagery is an important issue of concern. However, obtaining sufficient labelled samples for the classification is hard work and...

Traffic congestion has become a significant obstacle to the development of mega cities in China. Although local governments have used many resources in constructing road infrastructure, it is still insufficient for the increasing traffic demands. As a first step toward optimizing real-time traffic control, this study uses Shanghai Expressways as a case study to predict incident-related congestions. Our study proposes a graph convolutional network-based model to identify correlations in multi-dimensional sensor-detected data, while simultaneously taking into account environmental, spatiotemporal, and network features in predicting traffic conditions immediately after a traffic incident. The average accuracy, average AUC, and average F-1 score of the predictive model are 92.78%, 95.98%, and 88.78%, respectively, on small-scale ground-truth data. Furthermore, we improve the predictive model’s performance using semi-supervised learning by including more unlabeled data instances. As a result, the accuracy, AUC, and F-1 score of the model increase by 2.69%, 1.25%, and 4.72%, respectively. The findings of this article have important implications that can be used to improve the management and development of Expressways in Shanghai, as well as other metropolitan areas in China.

Aim The Ministry of Health, Labour, and Welfare of Japan proposed a regulation of overtime work as a reform in work style. However, the regulation may deteriorate the quality of medical services due to the reduction in training time. Thus, the study aimed to reveal perceptions in terms of generation gaps in views on self-training and overtime work, among members of the Japan Society of Obstetrics and Gynecology (JSOG). Methods A web-based, self-administered questionnaire survey was conducted among members of the JSOG. In total, 1256 respondents were included in the analysis. Data were collected on age, sex, experience as a medical doctor, location of workplace, work style, the type of main workplace, and number of full-time doctors in the main workplace. The study examined the attitudes of the respondents toward overtime work and self-training. The respondents were categorized based on experience as a medical doctor. Results According to years of experience, 112 (8.9%), 226 (18.0%), 383 (30.5%), 535 (42.6%) doctors have been working for ≤5, 6-10, 11-19, and ≥ 20 years, respectively. Although 54.5% of doctors with ≤5 years of experience expected the regulation on working hours to improve the quality of medical services, those with ≥20 years of experience expressed potential deterioration. After adjusting for covariates, more years of experience were significantly related with the expectation of deterioration in the quality of medical services. Conclusions The study revealed a generation gap in the views about self-training and overtime work among obstetricians and gynecologists in Japan.

Authors and collaborators have continued clinical practice and research on rehabilitation and self-training, in which various problems were found. Protocol: The author himself tried home self-training exercise of push-up for 2 months, which was successfully achieved. Results: Positive changes were 94 to 96.5cm in chest circumference, and 45 to 100 times in continuous push-up, respectively. Discussion: From the viewpoint of sport psychology, close relationship among motivation, self-efficacy and performance has been observed. Self-efficacy can influence one’s beliefs concerning accomplishing and continuing the task, activities and effort. This report will hopefully become the reference for future practice and research development.

In recent years, tumor classification based on the gene expression omnibus has become a continuous attention field in the area of bioinformatics. Integration machine learning techniques are an efficient methods to solve these problems. Generally, in order to obtain good performance in the supervised learning tasks, a large number of labelled samples will be required. However, in many cases, only a few labelled samples and abundant unlabelled samples exist in the training database. The process of labelling these unlabelled samples manually is difficult and expensive. Therefore, semi-supervised learning approaches have been proposed to utilize unlabelled samples to improve the performance of a model. However, noisy samples decrease the robustness of model in semi-supervised learning. We wish training style that samples can be implemented to train by from high- to low-confidence, self-training can meet this requirement, and the deep forest approach with the hyper-parameter settings used in this work can obtain good accuracy. Therefore, in this paper, we present a novel semi-supervised learning approach with a deep forest model to increase the performance of tumor classification, which employs unlabelled samples and minimizes the cost; that is, a updated unlabelled sample mechanism is investigated to expand the number of high-confidence pseudo-labelled samples. Multiple real-world experiments indicate that our proposed approach can obtain results up 0.96 accuracy and F1-Score, and 0.9798 AUCs.

Gene expression classifiers are gaining increasing popularity for stratifying tumors into subgroups with distinct biological features. A fundamental limitation shared by current classifiers is the requirement for comparable training and testing datasets. Here, we describe a self-training implementation of our probability ratio-based classification prediction score method (PRPS-ST), which facilitates the porting of existing classification models to other gene expression datasets. In comparison with gold standards, we demonstrate favorable performance of PRPS-ST in gene expression–based classification of diffuse large B-cell lymphoma (DLBCL) and B-lineage acute lymphoblastic leukemia (B-ALL) using a diverse variety of gene expression data types and preprocessing methods, including in classifications with a high degree of class imbalance. Tumors classified by our method were significantly enriched for prototypical genetic features of their respective subgroups. Interestingly, this included cases that were unclassifiable by established methods, implying the potential enhanced sensitivity of PRPS-ST. Significance: The adoption of binary classifiers such as cell of origin (COO) has been thwarted, in part, by the challenges imposed by batch effects and continual evolution of gene expression technologies. PRPS-ST resolves this by enabling classifiers to be ported across platforms while retaining high accuracy. This article is highlighted in the In This Issue feature, p. 215

This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification. The proposed approach firstly identifies meaningful prototypes from labelled samples at multiple levels of granularity and, then, self-organizes a highly transparent, multi-layered recognition model by arranging them in a form of pyramidal hierarchies. After this, the learning model continues to self-evolve its structure and self-expand its knowledge base to incorporate new patterns recognized from unlabelled samples by exploiting the pseudo-label technique. Thanks to its prototype-based nature, the overall computational process of the proposed approach is highly explainable and traceable. Experimental studies with various benchmark image recognition problems demonstrate the state-of-the-art performance of the proposed approach, showing its strong capability to mine key information from unlabelled data for classification.

With the exponential growth of biological data, labeling this kind of data becomes difficult and costly. Although unlabeled data are comparatively more plentiful than labeled ones, most supervised learning methods are not designed to use unlabeled data. Semi-supervised learning methods are motivated by the availability of large unlabeled datasets rather than a small amount of labeled examples. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. This paper introduces an approach based on a model of semi-supervised learning, which is the self-training with a deep learning algorithm to predict missing classes from labeled and unlabeled data. In order to assess the performance of the proposed approach, two datasets are used with four performance measures: precision, recall, F-measure, and area under the ROC curve (AUC).

To proactively assess the losses caused by the deterioration of metro tunnels during the operational period, a new method, the cloud model‐based random forests (CRFs), is proposed to discu...

The semi-supervised self-training method is one of the successful methodologies of semi-supervised classification and can train a classifier by exploiting both labeled data and unlabeled data. However, most of the self-training methods are limited by the distribution of initial labeled data, heavily rely on parameters and have the poor ability of prediction in the self-training process. To solve these problems, a novel self-training method based on density peaks and natural neighbors (STDPNaN) is proposed. In STDPNaN, an improved parameter-free density peaks clustering (DPCNaN) is firstly presented by introducing natural neighbors. The DPCNaN can reveal the real structure and distribution of data without any parameter, and then helps STDPNaN restore the real data space with the spherical or non-spherical distribution. Also, an ensemble classifier is employed to improve the predictive ability of STDPNaN in the self-training process. Intensive experiments show that (a) STDPNaN outperforms state-of-the-art methods in improving classification accuracy of k nearest neighbor, support vector machine and classification and regression tree; (b) STDPNaN also outperforms comparison methods without any restriction on the number of labeled data; (c) the running time of STDPNaN is acceptable.

The semi-supervised self-training method is one of the successful methodologies of semi-supervised classification. The mislabeling is the most challenging issue in self-training methods and the ensemble learning is one of the common techniques for dealing with the mislabeling. Specifically, the ensemble learning can solve or alleviate the mislabeling by constructing an ensemble classifier to improve prediction accuracy in the self-training process. However, most ensemble learning methods may not perform well in self-training methods because it is difficult for ensemble learning methods to train an effective ensemble classifier with a small number of labeled data. Inspired by the successful boosting methods, we introduce a new boosting self-training framework based on instance generation with natural neighbors (BoostSTIG) in this paper. BoostSTIG is compatible with most boosting methods and self-training methods. It can use most boosting methods to solve or alleviate the mislabeling of existing self-training methods by improving the prediction accuracy in the self-training process. Besides, an instance generation with natural neighbors is proposed to enlarge initial labeled data in BoostSTIG, which makes boosting methods more suitable for self-training methods. In experiments, we apply the BoostSTIG framework to 2 self-training methods and 4 boosting methods, and then validate BoostSTIG by comparing some state-of-the-art technologies on real data sets. Intensive experiments show that BoostSTIG can improve the performance of tested self-training methods and train an effective k nearest neighbor.

Microseismic monitoring is an indispensable technique in characterizing the physical processes that are caused by extraction or injection of fluids during the hydraulic fracturing process. Microseismic data, however, are often contaminated with strong random noise and have a low signal-to-noise ratio (S/N). The low S/N in most microseismic data severely affects the accuracy and reliability of the source localization and source-mechanism inversion results. We have developed a new denoising framework to enhance the quality of microseismic data. We use the method of adaptive sparse dictionaries to learn the waveform features of the microseismic data by iteratively updating the dictionary atoms and sparse coefficients in an unsupervised way. Unlike most existing dictionary learning applications in the seismic community, we learn the features from 1D microseismic data, thereby to learn 1D features of the waveforms. We develop a sparse dictionary learning framework and then prepare the training patches and implement the algorithm to obtain favorable denoising performance. We use extensive numerical examples and real microseismic data examples to demonstrate the validity of our method. Results show that the features of microseismic waveforms can be learned to distinguish signal patches and noise patches even from a single channel of microseismic data. However, more training data can make the learned features smoother and better at representing useful signal components.

Self-training method can train an effective classifier by exploiting labeled instances and unlabeled instances. In the process of self-training method, the high confidence instances are usually selected iteratively and added to the training set for learning. Unfortunately, the structure information of high confidence instances is so similar that it leads to local over-fitting during the iterations. In order to avoid the over-fitting phenomenon, and improve the classification effect of self-training methods, a novel divide-and-conquer ensemble self-training framework based on probability difference is proposed. Firstly, the probability difference of instances is calculated by the category probability of each classifier, the low-fuzzy and high-fuzzy instances of each classifier are divided through the probability difference. Then, a divide-and-conquer strategy is adopted. That is, the low-fuzzy instances determined by all the classifiers are directly labeled and high-fuzzy instances are manually labeled. Finally, the labeled instances are added to the training set for iteration self-training. This method expands the training set by selecting low-fuzzy instances with accurate structure information and high-fuzzy instances with more comprehensive structure information, and it improves the generalization performance of the method effectively. The method is more suitable for noise data sets and it can obtain structure information even in a few labeled instances. The effectiveness of the proposed method is verified by comparative experiments on the University of California Irvine (UCI).

Inthis article, wefocus on the classification problem to semi-supervised learning in non-stationary environment. Semi-supervised learning is a learning task from both labeled and unlabeled data points. There are several approaches to semi-supervised learning in stationary environment which are not applicable directly for data streams. We propose a novel semi-supervised learning algorithm, named STDS. The proposed approach uses labeled and unlabeled data and employs an approach to handle the concept drift in data streams. The main challenge in semi-supervised self-training for data streams is to find a proper selection metric in order to find a set of high-confidence predictions and a proper underlying base learner. We therefore propose an ensemble approach to find a set of high-confidence predictions based on clustering algorithms and classifier predictions. We then employ the Kullback-Leibler (KL) divergence approach to measure the distribution differences between sequential chunks in order to detect the concept drift. When drift is detected, a new classifier is updated from the new set of labeled data in the current chunk; otherwise, a percentage of high-confidence newly labeled data in the current chunk is added to the labeled data in the next chunk for updating the incremental classifier based on the proposed selection metric. The results of our experiments on a number of classification benchmark datasets show that STDS outperforms the supervised and the most of other semi-supervised learning methods.

Machine-learning techniques allow geoscientists to extract meaningful information from data in an automated fashion, and they are also an efficient alternative to traditional manual interpretation methods. Many geophysical problems have an abundance of unlabeled data and a paucity of labeled data, and the lithology classification of wireline data reflects this situation. Training supervised algorithms on small labeled data sets can lead to overtraining, and subsequent predictions for the numerous unlabeled data may be unstable. However, semisupervised algorithms are designed for classification problems with limited amounts of labeled data, and they are theoretically able to achieve better accuracies than supervised algorithms in these situations. We explore this hypothesis by applying two semisupervised techniques, label propagation (LP) and self-training, to a well-log data set and compare their performance to three popular supervised algorithms. LP is an established method, but our self-training method is a unique adaptation of existing implementations. The well-log data were made public through an SEG competition held in 2016. We simulate a semisupervised scenario with these data by assuming that only one of the 10 wells has labels (i.e., core samples), and our objective is to predict the labels for the remaining nine wells. We generate results from these data in two stages. The first stage is applying all the algorithms in question to the data as is (i.e., the global data), and the results from this motivate the second stage, which is applying all algorithms to the data when they are decomposed into two separate data sets. Overall, our findings suggest that LP does not outperform the supervised methods, but our self-training method coupled with LP can outperform the supervised methods by a notable margin if the assumptions of LP are met.

Diarization partitions an audio stream into segments based on the voices of the speakers. Real-time diarization systems that include an enrollment step should limit enrollment training samples to reduce user interaction time. Although training on a small number of samples yields poor performance, we show that the accuracy can be improved dramatically using a chronological self-training approach. We studied the tradeoff between training time and classification performance and found that 1 second is sufficient to reach over 95% accuracy. We evaluated on 700 audio conversation files of about 10 minutes each from 6 different languages and demonstrated average diarization error rates as low as 10%., Comment: 5 pages, 5 figures, ICASSP 2021

The purpose of the study was the presence of a theoretical foundation of knowledge in sports and pedagogical disciplines among cadets of the Ministry of Internal Affairs Universities, which determines their success in further professional activities. Theory not only motivates, programs, and regulates, but also controls the practical activities of the future police officer. The successful acquisition of certain knowledge and skills serves as criteria for their entry into the general cultural baggage of a specialist, expanding the opportunities for the development of his professional activity

Extracting event temporal relations is an important task for natural language understanding. Many works have been proposed for supervised event temporal relation extraction, which typically requires a large amount of human-annotated data for model training. However, the data annotation for this task is very time-consuming and challenging. To this end, we study the problem of semi-supervised event temporal relation extraction. Self-training as a widely used semi-supervised learning method can be utilized for this problem. However, it suffers from the noisy pseudo-labeling problem. In this paper, we propose the use of uncertainty-aware self-training framework (UAST) to quantify the model uncertainty for coping with pseudo-labeling errors. Specifically, UAST utilizes (1) Uncertainty Estimation module to compute the model uncertainty for pseudo-labeling unlabeled data; (2) Sample Selection with Exploration module to select informative samples based on uncertainty estimates; and (3) Uncertainty-Aware Learning module to explicitly incorporate the model uncertainty into the self-training process. Experimental results indicate that our approach significantly outperforms previous state-of-the-art methods.

Traditional supervised time series classification (TSC) tasks assume that all training data are labeled. However, in practice, manually labelling all unlabeled data could be very time-consuming and often requires the participation of skilled domain experts. In this paper, we concern with the positive unlabeled time series classification problem (PUTSC), which refers to automatically labelling the large unlabeled set U based on a small positive labeled set PL. The self-training (ST) is the most widely used method for solving the PUTSC problem and has attracted increased attention due to its simplicity and effectiveness. The existing ST methods simply employ the one-nearest-neighbor (1NN) formula to determine which unlabeled time-series should be labeled. Nevertheless, we note that the 1NN formula might not be optimal for PUTSC tasks because it may be sensitive to the initial labeled data located near the boundary between the positive and negative classes. To overcome this issue, in this paper we propose an exploratory methodology called ST-average. Unlike conventional ST-based approaches, ST-average utilizes the average sequence calculated by DTW barycenter averaging technique to label the data. Compared with any individuals in PL set, the average sequence is more representative. Our proposal is insensitive to the initial labeled data and is more reliable than existing ST-based methods. Besides, we demonstrate that ST-average can naturally be implemented along with many existing techniques used in original ST. Experimental results on public datasets show that ST-average performs better than related popular methods.

Head-up tilt (HUT) testing supports the diagnosis of syncope by detecting abnormalities in heart rate and blood pressure changes. Home-based self-training can be of benefit to neurocardiogenic patients if during clinical HUT, heart rate decreases in the early stage of being in an upright position. However, HUT testing is not always possible in the hospital as it is inconvenient and sometimes even risky for patients with cardiac abnormalities as it may trigger a loss of consciousness and arrhythmia. To address this, the current paper introduces a personalized HUT simulation to determine the efficacy of at-home training. To develop the model, Holter ECG recordings were obtained from 28 syncope patients and the simulated output was compared to clinical findings. The model aims to predict heart rate changes associated with the simulated HUT that can indicate efficacy of an at-home program. Heart rate represents a variable of velocity in the model measured in liters per second against gravity. The results show that a decrease in heart rate in early simulated HUT as determined by the model shows a greater than 84% efficiency for syncope patients to benefit from at-home training and allows physicians to recommend home training during an online or telemedicine consultation. Keywords— head-up tilt test, syncope, blood flow, heart rate prediction Clinical Relevance— The cardiovascular model predicts the patient-specific efficacy of at home tilt-training for patients diagnosed with syncope.

Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, that is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser's output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods., Comment: Accepted to TACL in September 2019

We study the slab pre-marshalling problem to re-position slabs in a way that the slabs are stored in the least number of stacks and each stack contains only the slabs of the same group, which can b...