Reviewerly/exHarmony huggingface dataset:
Incremental Machine Learning for Text Classification in Comment Moderation Systems

Over the last decade, researchers presented (semi-)automated comment moderation systems (CMS) based on machine learning (ML) and natural language processing (NLP) techniques to support the identification of hateful and offensive comments in online discussion forums. A common challenge in providing and operating comment moderation systems is the dynamic nature of language. As language evolves over time, continuous performance evaluations and resource-inefficient model retraining are applied to ensure high-quality identification of hate speech in the long-term use of comment moderation systems. To study the potentials of adaptable machine learning models embedded in comment moderation systems, we present an incremental machine learning approach for semi-automated comment moderation systems. This study shows a comparison of incrementally-trained ML models and batch-trained ML models used in comment moderation systems.
The Professional Teachers’ Training as an Important Factor of Quality Education

Purpose: The purpose of the chapter is to analyze approaches to defining the essence of the phenomenon of “quality of education” and to determine the means of its improvement. Design/methodology/approach: The authors used a method of observation to identify the features of the current state of the education system and to determine the general level of professional preparedness of teachers. Schoolchildren, their parents, and the teachers themselves took part in the assessment of the important components of the teacher’s professionalism through oral questioning. The simulation method is used to identify the objects of monitoring. Findings: It showed that model for monitoring the quality of education formed, and it proved that the quality of the education system largely depends on the quality of the teacher’s professional training. Originality/value: A prospective method of solving the determined problem is the usage of the effective monitoring system for evaluating the quality of teacher education at a local level. The compiled model reflects conditions at which the objective model of monitoring should be conducted based on an integral approach.
A Brave New Algorithm to Maintain the Exploration/Exploitation Balance

At the beginning of this year one of the authors read “A brave new world”, a novel by Aldous Huxley. This book describes a dystopia, which anticipates the development of world-scale breeding technology, and how this technology creates the optimal human race. Taking into account that when talking about genetic algorithms our goal is to achieve the optimum solution of a problem, and this book kind of describes the process for making the “perfect human”, or rather the “perfect human population”, we will try to work on this parallelism in this paper, trying to find what is the key to the evolution processes described in the book. The goal is to develop a genetic algorithm based on the fecundation process of the book and compare it to other algorithms to see how it behaves, by investigating how the division in castes affects the diversity in the poblation. In this paper we describe the implementation of such algorithm in the programming language Julia, and how design and implementation decisions impact algorithmic and runtime performance.
Quality of Service in Quantum Networks

In the coming years, quantum networks will allow quantum applications to thrive thanks to the new opportunities offered by end-to-end entanglement of qubits on remote hosts via quantum repeaters. On a geographical scale, this will lead to the dawn of the quantum internet. While a fullblown deployment is yet to come, the research community is already working on a variety of individual enabling technologies and solutions. In this article, with the guidance of extensive simulations, we take a broader view and investigate the problems of quality of service (QoS) and provisioning in the context of quantum networks, which are very different from their counterparts in classical data networks due to some of their fundamental properties. Our work leads the way toward a new class of studies that will allow the research community to better understand the challenges of quantum networks and their potential commercial exploitation.
A Study of Signal Modulation Recognition Based on Wavelet Analysis and Random Forest

Nowadays, the recognition of signal modulation mode has received tremendous attention for its indispensable role in both military and civilian fields. With several typical types of modulation signals generated, a signal recognition method based on wavelet analysis and the random forest is proposed. Afterward, to reduce the time and space complexity of the algorithm, redundant features were eliminated by correlation analysis. By adopting a decision tree and random forest algorithm, the modulation recognition accuracies of the signals are calculated and analyzed. Finally, under different signal-noise ratios, a further study is carried out about the effects that wavelet autocorrelation denoising and wavelet soft-threshold denoising have on the recognition accuracy of Random Forest.
Codewords-Expanded Enhanced Residual Vector Quantization for Approximate Nearest Neighbor Search

A codewords-expanded enhanced residual vector quantization (CERVQ) is proposed to improve the accuracy of approximate nearest neighbor (ANN) search for feature vectors. It combines enhanced residual vector quantization (ERVQ) with the method of calculating mean-equisection vector to reduce training error and improves the quantization accuracy. Firstly, the mean-equisection vector is used to compute residual vector as the input to next layer in the training stage, except for the first layer. Based on this, an iterative method for optimizing codebooks is designed to reduce overall quantization error. Secondly, the codebook of each layer is expanded with the mean-equisection vectors to generate new codewords in quantization stage, which are used to quantize input feature vectors to improve quantization accuracy. Finally, a method of calculating asymmetric Euclidean distance is implemented for ANN search. The CERVQ is compared with 5 typical methods on two public SIFT and GIST datasets. Experiment results show that the training error reduces by 10%~24% and the recall rate of ANN search is increased by 1%~44%. In addition, the scale of codebook in the CERVQ can reduce by 50% under the condition of reaching the same recall rate.
Genetic algorithms: an evolutionary-based global random search

Abstract Random process are used as tools for random search in minimization algorithm, as an alternative to gradient-based searching algorithm used for instance in least square optimization. Genetic algorithms are presented in the Chapter 9, with application to non linear fitting, autoregressive moving average models. As an example of improved optimization with respect to other approaches, the travelling salesman problem is here solved with genetic algorithms.
Influence of Antenna Pattern Sidelobes on the Performance of Scanning Radar Angular Super-Resolution Algorithm

In order to improve the angular resolution of scanning radar, a lot of super-resolution algorithms have been developed in recent years. However, the super-resolution performance is affected by many factors due to the ill-conditioned nature of inverse problem. In this paper, from the perspective of numerical simulation, we illustrated the influence of the antenna pattern sidelobes on the condition number of the convolution matrix, and then analyzed the influence on the performance of the super-resolution algorithm.
Neg/pos-Normalized Accuracy Measures for Software Defect Prediction

In evaluating the performance of software defect prediction models, accuracy measures such as precision and recall are commonly used. However, most of these measures are affected by neg/pos ratio of the data set being predicted, where neg is the number of negative cases (defect-free modules) and pos is the number of positive cases (defective modules). Thus, it is not fair to compare such values across different data sets with different neg/pos ratios and it may even lead to misleading or contradicting conclusions. The objective of this study is to address the class imbalance issue in assessing performance of defect prediction models. The proposed method relies on computation of expected values of accuracy measures based solely on the value of the neg and pos values of the data set. Based on the expected values, we derive the neg/pos-normalized accuracy measures, which are defined as their divergence from the expected value divided by the standard deviation of all possible prediction outcomes. The proposed measures enable us to provide a ranking of predictions across different data sets, which can distinguish between successful predictions and unsuccessful predictions. Our results derived from a case study of defect prediction based on 19 defect data sets indicate that ranking of predictions is significantly different than the ranking of conventional accuracy measures such as precision and recall as well as composite measures F1-value, AUC of ROC, MCC, G-mean and Balance. In addition, we conclude that MCC attains a better defect prediction accuracy than F1-value, AUC of ROC, G-mean and Balance.
Unknown-Oriented Learning for Open Set Domain Adaptation

Open set domain adaptation (OSDA) aims to tackle the distribution shift of partially shared categories between the source and target domains, meanwhile identifying target samples non-appeared in source domain. The key issue behind this problem is to classify these various unseen samples as unknown category with the absent of relevant knowledge from the source domain. Though impressing performance, existing works neglect the complex semantic information and huge intra-category variation of unknown category, incapable of representing the complicated distribution. To overcome this, we propose a novel Unknown-Oriented Learning (UOL) framework for OSDA, and it is composed of three stages: true unknown excavation, false unknown suppression and known alignment. Specifically, to excavate the diverse semantic information in unknown category, the multi-unknown detector (MUD) equipped with weight discrepancy constraint is proposed in true unknown excavation. During false unknown suppression, Source-to-Target grAdient Graph (S2TAG) is constructed to select reliable target samples with the proposed super confidence criteria. Then, Target-to-Target grAdient Graph (T2TAG) exploits the geometric structure in gradient manifold to obtain confident pseudo labels for target data. At the last stage, known alignment, the known samples in the target domain are aligned with the source domain to alleviate the domain gap. Extensive experiments demonstrate the superiority of our method compared with state-of-the-art methods on three benchmarks.
Derzhavin University initiatives in system development of continuing pedagogical education in the Tambov Region

In the context of the main trends of pedagogical education such as “digital pedagogy: e-learning and electronic educational environment”, “practice-oriented training” we describe the initiatives of the Pedagogical Institute of Derzhavin University in the development of the system of continuing pedagogical education of the Tambov Region. The most significant initiatives described in the article include the formation of an association of continuing pedagogical education with the aim of developing a regional system of continuing pedagogical education through the implementation of variable educational and professional tracks for the formation of young professionals; adjustment of curricula for testing the core of pedagogical training; clarification of the nomenclature of programs of training areas “Pedagogical Education” with two profiles; the opening of a Pedagogical Technopark as a center for the organization of teaching students methods and technologies of teaching academic subjects, including for preparation for pedagogical activity on the basis of a cluster of modern natural science laboratories, a research and training center in biotechnology, a cluster of high-tech laboratories specializing in education, research and development in information technology and robotics, a studio complex for creative activity of schoolchildren; creation of a center for scientific and methodological support of teachers; conducting fundamental and applied research in education in priority areas of development of science and technology of the Tambov Region and integrating the results of scientific research into the educational activities of the system of continuing pedagogical education of the Tambov Region – preschool education, pre-professional pedagogical training, higher education; postgraduate support of young teachers.
Deep Intra Prediction by Jointly Exploiting Local and Non-Local Similarities

Intra prediction, which aims to remove the redundancies within a frame, has shown promising performance by simply projecting and interpolating samples along multiple angular directions. Recently, with numerous approaches devoted to learning nonlinear predictors with deep neural networks (DNN) based on local correlations, much less work has been dedicated to exploring non-local self-similarities in intra prediction. In this paper, we propose a unified prediction model that exploits both local and non-local correlations for intra prediction. The proposed model not only supports the nonlinear prediction using local reference samples as input, but also aggregates useful non-local information from a large reconstructed region with a Patch-level Non-local Attention Network (PNA-Net). More specifically, PNA-Net incorporates template matching with attention mechanism in feature domain to obtain the responses of all non-local features to the content to be predicted, leading to the prediction produced with weighted non-local patches. Finally, the predictions in the local and non-local manners are blended adaptively with a trainable network, ensuring the capability to handle a variety of contents. Experimental results on Versatile Video Coding (VVC) software VTM-11.0 show that the proposed model achieves on average 4.69% bit rate savings for natural scene sequences, and 4.24% bit rate savings for screen content sequences under the all intra configuration.
A screen of slide detection method using deep learning-based segmentation and Hough transform

Recently, COVID-19 has accelerated the non-contact culture. Many presentations, such as workshops and conferences, are conducted in an online and offline hybrid mode in a conference room. In presentations, a screen of the slide is particularly important. Therefore, we propose an algorithm that detects the screen in an image. Firstly, a screen region is extracted using a deep learning-based instance segmentation method. However, this extracted region has a noisy boundary. We designed an image processing algorithm composed of 7 main steps to solve this noise and detect the screen. To validate the proposed method, a real dataset was qualitatively evaluated, and the result images show that only meaningful screen regions in the test image can be extracted.
Unexp-DIN: Unexpected Deep Interest Network for Recommendation

Studies on recommender systems persuaded accuracy of predictions, leading to the filter bubble problem which narrowed the user's interest areas. Unexpected recommendation (UR) is one of the solutions in handling the filter bubble. However, existing research on UR still faces two shortcomings. On the one hand, it is difficult to measure the unexpectedness of items to users accurately. On the other hand, there is a decrease in accuracy when making unexpected item recommendations. In this paper, we propose an Unexpected Deep Interest Network (Unexp-DIN) model to solve these problems. We use mean-shift for multiple clustering and Cluster-attention to maximize the unexpectedness. Then we use the constraint function and the hybrid utility function to simultaneously improve accuracy and unexpectedness. Experiments on three publicly available benchmark datasets show that our model has different degrees of improvement in both accuracy and unexpectedness.
Face Forgery Detection of Deepfake Based on Multiple Convolutional Neural Networks

One technology called “Deepfake” has appeared on the internet at the end of 2017, while the most common application is generation and manipulation of faces in videos. As it develops, more and more researchers pay attention to face forgery detection originated from deepfake. In this paper we will demonstrate one detection method based on multiple convolution neural networks including EfficientNet, Xception and 3D convolutional neural networks (abbreviated as 3D CNN). Above networks will be combined according to certain rules so that optimal effect is likely to be attained. Concretely speaking, a pre-filter would be conducted based on confidential interval of known videos. After that, a weight is given to each network according to their performance of different networks, integrating the advantages of each model. Result of above algorithm is going to be illustrated with detailed analysis, which indicates an obvious enhancement of accuracy comparing to single network. Finally, further improvement of above algorithm will be discussed.
Semantic Segmentation Based on Spatial Pyramid Pooling and Multilayer Feature Fusion

In recent years, significant progress has been made in semantic segmentation methods. Traditional semantic segmentation methods based on convolutional neural network (CNN) are prone to lose spatial information in the feature extraction stage, and pay less attention to global context information, especially, in some lightweight real-time semantic segmentation networks. This is a huge challenge for semantic segmentation tasks. In addition, although some methods have improved this problem to a certain extent, they are often embedded in specific networks and cannot be applied to other network models. Aiming at these problems, a semantic segmentation method based on multilayer feature fusion is proposed. The flexible and lightweight squeeze–excitation module is used to improve the spatial pyramid pooling (SPP) network, and the accuracy of the semantic segmentation method is further improved by extracting network feature information at different levels. To verify the efficiency and commonality of our methodology, we selected ERFNet and Deeplabv3 networks to experiment on Cityscapes and COCO data sets. Experiments show that our best method can improve 3.1% mIoU and 3.2% mAcc on the Cityscapes data set relative to ERFNet, and at the same time, our method can achieve 61.93 FPS on 1024 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 512 resolution images and the best improvement of 0.9% mIoU 1.4% mAcc was achieved on the Deeplabv3 network. The experimental results show that the improved multilayer feature fusion structure can improve the accuracy of the semantic segmentation network.
Named Entity Recognition of Benefit Enterprise Policy Based on RoBERTa_wwm_ext-BiLSTM-CRF

To promote the development and innovation of enterprises, government departments at all levels have issued numerous enterprise benefiting policies. Named entity recognition technology can be used to extract key domain information in policy texts to help companies quickly find matching policies. Due to the problems of blurred entity boundary and polysemy of words, named entity recognition in the policy text of benefiting enterprises. In this paper, the deep learning model RoBERTa_wwm_ext-BiLSTM-CRF is applied, and the pre-training model RoBERTa_wwm_ext is applied to the field of enterprise benefiting policy for the first time. The semantic information of policy words is obtained through the RoBERTa_wwm_ext model, BiLSTM is used to extract the semantic features of the context, and finally, use CRF to sequence optimize output. The current mainstream entity recognition models are compared through experiments, and the results show that the model is better than the comparison model in terms of accuracy, recall and F1, and the F1 value reaches 86.4%. Problems such as word polysemy can be well represented.
Technical college educational environment - a space of immersion into real professionally oriented ecological activity

The article highlights the importance of the educational environment of the technical college in the formation of ecological culture of technical education and identifies the nature and quality of the relationship between man and the social environment, which is manifested in the system of values that motivate environmental behavior in all human activities. Ecologically cultural personality of the technical education seeker has a high level of ecological knowledge, developed ecological thinking, in his behavior is guided by the priority of ecological values, explores his profession as an effective means of improving the environment and solving environmental problems. The publication considers the approaches to the formation of ecological culture during classroom and extracurricular activities and on their basis reveals the role, significance and directions of the formation of ecological knowledge of students of technical college. It was found that the use of interactive technologies allows to intensify the assimilation and creative use of theoretical material; allows the model of technical education to model vital ecological situations; to involve students of technical college in the decision of ecological problems, the decision by them of nature-responsible problems, acquisition of practice of ecological activity in college.
Are You Referring to Me? - Giving Virtual Objects Awareness

This work introduces an interaction technique to determine the user's non-verbal deixis in Virtual Reality (VR) applications. We tailored it for multimodal speech & gesture interfaces (MMIs). Here, nonverbal deixis is often determined by the use of ray-casting due to its simplicity and intuitiveness. However, ray-casting's rigidness and dichotomous nature pose limitations concerning the MMI's flexibility and efficiency. In contrast, our technique considers a more comprehensive set of directional cues to determine non-verbal deixis and provides probabilistic output to tackle these limitations. We present a machine-learning-based reference implementation of our technique in VR and the results of a first performance benchmark. Future work includes an in-depth user study evaluating our technique's user experience in an MMI.
Fusion of Digital Mammography with High-Resolution Breast PET: An Application to Breast Imaging

One of the strategies to reduce mortality from breast cancer is based on screening using digital mammography as an initial evaluation. The detection and diagnosis of breast carcinomas are achieved by interpreting images of different modalities including digital mammograms, magnetic resonance imaging, ultrasound, and thermography; however, the literature shows that multimodal image fusion is highly accurate in representing breast carcinomas. Due to the human complexity in the diagnosis and detection of breast cancer and the implication of using historical patient imaging records, it is important to use processing tools that allow the analysis of breast images for possible improvements of the diagnosis. This chapter proposes the use of diversified data sets composed from different modalities to support the breast cancer diagnosis process and demonstrates that by applying various processing techniques it is possible to support the interpretation of the findings and that they can improve the precision in detecting breast cancer.