Reviewerly/exHarmony huggingface dataset:
Speech Recognition of Accented Mandarin Based on Improved Conformer

The convolution module in Conformer is capable of providing translationally invariant convolution in time and space. This is often used in Mandarin recognition tasks to address the diversity of speech signals by treating the time-frequency maps of speech signals as images. However, convolutional networks are more effective in local feature modeling, while dialect recognition tasks require the extraction of a long sequence of contextual information features; therefore, the SE-Conformer-TCN is proposed in this paper. By embedding the squeeze-excitation block into the Conformer, the interdependence between the features of channels can be explicitly modeled to enhance the model's ability to select interrelated channels, thus increasing the weight of effective speech spectrogram features and decreasing the weight of ineffective or less effective feature maps. The multi-head self-attention and temporal convolutional network is built in parallel, in which the dilated causal convolutions module can cover the input time series by increasing the expansion factor and convolutional kernel to capture the location information implied between the sequences and enhance the model's access to location information. Experiments on four public datasets demonstrate that the proposed model has a higher performance for the recognition of Mandarin with an accent, and the sentence error rate is reduced by 2.1% compared to the Conformer, with only 4.9% character error rate.
A Framework for Distributed Feature Selection

Many current multivariate filter feature selection approaches consider redundancy and relevance between features and class vectors simultaneously. However, these multivariate filter algorithms calculate the suitability of features by only the intrinsic characteristics of the data. In this paper, we suggest a new distributed framework to offset the multivariate feature selection problem. We propose the interaction with classifiers in multivariate filter feature selection. Our proposed framework calculates the relevance of each feature to class labels by embedded algorithms. Then, this technique examines redundancy among features through multivariate filter algorithms. In addition, in the proposed framework, we use horizontal distribution of data instead of using all them once. This approach reduces the runtime of the process in datasets with many samples and environments without centralized data. The results of the evaluation show that the proposed framework can improve classification accuracy compared with the methods just based on multivariate filters. In addition, the experimental results demonstrate that our algorithm outperforms compared approaches in precision, recall, and runtime.
Internet slang in virtual communication as a linguistic phenomenon

The aim of the study is to identify trends in the development of Internet slang caused by transformations in the field of politics, economics and culture. The paper discusses the features of Internet slang functioning in the communication of Internet users. The formation of Internet slang testifies to the functional dynamics of the modern Russian language, therefore it is of particular interest to linguists. The scientific novelty of the study lies in developing the word-formation and functional-semantic classifications of Russian Internet slang. The paper presents the analysis of 120 lexical units related to Internet slang. These units are considered in three aspects: meaning, word-formation types and intentions. As a result, it has been proved that Russian Internet slang as an ambivalent phenomenon, on the one hand, contributes to an increase in the expressiveness and emotionality of virtual communication and, on the other hand, it can negatively affect the literacy level of native speakers of the Russian language.
Semantic field “Anti-politeness” and its manifestation in online communication

The aim of the research is to describe anti-politeness as a lexico-semantic category with an extended semantic field. The research is conducted using the material of online comments. The paper gives examples illustrating the manifestation of anti-politeness in the Internet discourse, analyses lexico-stylistic characteristics of online comments. The research is novel in that it is the first to distinguish anti-politeness as a separate lexico-semantic category and investigate the relationship between the notions of “anti-politeness”, “rudeness” and “impoliteness”. As a result, the following anti-politeness strategies have been identified: rudeness, obscenity, intrusiveness, boorishness, disrespect, tactlessness, hostility, contempt, arrogance, indifference, mockery, irony and sarcasm. These strategies are implemented in speech with the help of certain linguistic means, including invectives, negative vocabulary, comparisons and metaphors with pejorative components, hyperboles, rhetorical questions, expressive constructions etc. They can be accompanied by non-verbal elements accepted in the Internet environment, such as emoticons, emoji and punctuation marks in the emotive function.
Semantic and functional features of diminutive word formation in women’s speech

The aim of the study is to identify the semantic and functional features of derivatives-diminutives in women’s speech using the material of Internet communication in forums. The paper considers the characteristics of the use of derivatives-diminutives in the speech of the participants communicating in forums. The conditions for the concrete realisation of certain types of meanings of diminutive suffixes in a gender-specific context are also revealed. In the course of the study, the connection between socially organised pragmatic meanings and the functioning of diminutive suffixes has been determined for the first time in order to identify a possible interpretation of women’s tendency to use diminutive forms and their role in gender construction, which accounts for the scientific novelty of this study. As a result of the study, it has been found that in women’s speech, the semantic and functional features of diminutive word formation are closely related to the need to construct women’s social role. In women’s speech, these features most often serve as a way of expressing a positive assessment of the speaker, they convey a positive attitude towards the addressee or referent and are used for marking, forming a personal sphere.
Artistic originality of M. Ravenhill’s monodrama “The Experiment”

The aim of the study is to identify the distinctive features of the monodrama “The Experiment” (2009) by the English writer M. Ravenhill. The paper examines the theoretical aspects of the monodrama, as well as carries out a comprehensive analysis of the text of the play from the perspective of its artistic originality. The analysis of the text made it possible to identify the artistic originality of the play, as well as some common, typological features of the dramatic compositions of a “plunge into the soliloquy” type (P. Pavis). The scientific novelty of the paper lies in the fact that M. Ravenhill’s monodrama has not previously been analysed from the perspective of its artistic specificity in both Russian and foreign science: this study is the first to consider in detail Mark Ravenhill’s play “The Experiment” and its artistic originality. As a result, we come to the conclusion that M. Ravenhill uses a special artistic strategy of the “theatre of cruelty”, combining the features of the classical monodrama with his own author’s modifications: the protagonist’s inner dialogue; change of the narrator; narration in the form of a stream of consciousness with violation of syntactic and logical norms; the reader’s/viewer’s identification with the image of an unreliable narrator; introduction of reminiscence as a leading visual and expressive technique.
Blockchain Technology Adoption Effect Onknowledge Management State of the Art Survey

The International Journal of Managing Value and Supply Chains ( IJMVSC ) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of value and supply chain management. The journal provides a platform to disseminate new ideas and new research, advance theories, and propagate best practices in the management of value and supply chain management, looking across both product and service-based businesses. This will include works based in service management, logistics and distribution, operations management, process management, flow control, and customer service. The journal offers a forum in which academics, consultants, and practitioners in a variety of fields can exchange ideas to further research and improve practices in all areas of business. The International Journal of Managing Value and Supply Chains ( IJMVSC ) seek to establish new collaborations, new best practices, and new theories in the management of both product and service-based organizations around the world.
State and Action Abstraction for Search and Reinforcement Learning Algorithms

Decision-making in large and dynamic environments has always been a challenge for AI agents. Given the multitude of available sensors in robotics and the rising complexity of simulated environments, agents have access to plenty of data but need to carefully focus their attention if they want to be successful. While action abstractions reduce the complexity by concentrating on a feasible subset of actions, state abstractions enable the agent to better transfer its knowledge from similar situations. In this article, we want to identify the different techniques for learning and using state and action abstractions and compare their effect on an agent's training and its resulting behavior.
On the Use of Quasi-Sigmoids in Function Approximation Problems with Neural Networks

The paper aims to introduce and illustrate a novel kind of nonlinearity to be used as an activation function for the neurons of a Neural Network (NN). It is shown how the proposed function can be regarded as a generalization of the standard sigmoid. For this reason, it is referred to as quasi-sigmoid. The features of a backpropagation algorithm based on quasi-sigmoids are illustrated and commented. The properties of quasi-sigmoidal networks are compared to basic sigmoidal models on benchmark test cases as well as on sample function approximation problems in electromagnetics. The performance of quasi-sigmoidal networks as function approximators are shown to be generally superior to sigmoidal one by reason of the flexibility introduced and/or in terms of degrees of freedom of the model. As a by-product, the proposed activation function allows to carry out data-driven detections of nonlinearity in the data.
Fuzzy or Neural, Type-1 or Type-2—When Each Is Better: First-Approximation Analysis

In many practical situations, we need to determine the dependence between different quantities based on the empirical data. Several methods exist for solving this problem, including neural techniques and different versions of fuzzy techniques: type-1, type-2, etc. In some cases, some of these techniques work better, in other cases, other methods work better. Usually, practitioners try several techniques and select the one that works best for their problem. This trying often requires a lot of efforts. It would be more efficient if we could have a priori recommendations about which technique is better. In this paper, we use the first-approximation model of this situation to provide such a recommendation.
Uncertainty in Computer and Decision-Making Sciences: A Bibliometric Overview

Publications on uncertainty and decision-making systems have grown exponentially. This chapter aims to apply bibliometric tools and techniques to analyze the evolution in the last decades of research documents focused on uncertainty and decision-making systems in computer sciences. The analysis is divided into key sections including journals, institutions, key scholars, countries, institutions, collaborations, and word frequency. This chapter utilizes the SCOPUS scientific database, identifying 11,122 papers with an average of 19.94 citations per document and an average of 7.2 years from publication between 1964 and 2021. Results show the fundamental metrics for the evolution and growth of academic research in this field.
Evolving Stacking Neuro-Fuzzy Probabilistic Networks and Their Combined Learning in Online Pattern Recognition Tasks

The patterns (images) classification-recognition problem under conditions of overlapping classes (fuzzy) and a small training dataset is considered. The proposed method is based on a modification of the classical probabilistic neural network by D. Specht, improved with combined learning, which includes classical supervised learning, “lazy learning” based on the concept of “Neurons at data points”, self-learning based on the rule “Winner takes all” and evolving learning system architecture. During the learning process, simultaneously both activation functions and the architecture itself are tuned. Fuzzy modifications of the probabilistic neural network are introduced, where one-dimensional membership functions are used instead of multidimensional activation functions, which reduce the number of adjustable parameters. Stack architectures are proposed, where a probabilistic neural network is used as an autoencoder, and the actual recognition process is implemented using a modified radial-basis function network operating in recognition mode. A distinctive feature of the proposed stack systems is that information can be processed both in the traditional vector form and in the matrix form inherent in images. The proposed systems are characterized by a high learning speed, which allows them to solve problems that arise within the general problem of Data Stream Mining.
Using Fuzzy Set Approaches for Linguistic Data Summaries

The basics of linguistic summary approaches using fuzzy set techniques are described. Following we describe the quantification of the information contained in a linguistic summary and finally we consider the formulation of summaries involving rich concepts.
Neural and Granular Fuzzy Adaptive Modeling

The chapter addresses adaptive nonlinear systems modeling focusing on the uninorm-based evolving neuro-fuzzy network and the granular evolving min-max fuzzy approaches. Neuro-fuzzy networks have shown to be highly competitive with multilayer neural networks, but their performance have not been yet contrasted with contemporary adaptive granular modeling methods such as evolving min-max modeling. Neuro-fuzzy network and granular evolving min-max modeling are benchmarked against state of the art evolving modeling methods using the Box and Jenkins gas furnace, and Mackey-Glass chaotic time series data. It is shown that evolving granular min-max modeling is a powerful modeling approach because, in addition to accuracy, it benefits from the transparency conveyed by the developed models.
Human-Centric Question-Answering System with Linguistic Terms

Current Question-Answering Systems can automatically answer questions posed by humans in a natural language. However, most of them can only answer questions that do not contain imprecise concepts and lead to short answers. This paper introduces a Human-Centric Question-Answering system capable of answering questions containing user-defined, personalized linguistic terms. The system works with information represented in the form of knowledge graphs. We describe the system and present its main components, emphasizing a few extensions that make the system distinctive. We illustrate the execution of the system with a few examples.
Enabling Surrogate-Assisted Evolutionary Reinforcement Learning via Policy Embedding

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE.
Continual Learning with a Memory of Non-similar Samples

The replay method is an effective strategy to address the problem of catastrophic forgetting, a key challenge for continuous learning. However, the sample sets obtained by replay-based methods generally suffer from local data information deficiencies. This problem leads to an imbalance in the plasticity-stability of the model on older tasks. This paper proposes a novel method, called non-similar sample storage (NSS). Non-similar refers to the Euclidean distance for feature vectors of different samples being far. NSS extracts the feature vectors of the samples and then calculates the similarity of the feature vectors for each sample after the current task training. Samples that contribute less to the model classification effect among similar samples are iteratively deleted, and the subset of low-similarity samples is retained. Moreover, NSS reserves 30% of the storage space for saving samples near the center of the sample set. Low-similarity samples stored by NSS get larger losses during the replay process, leading to lower training effectiveness of the current task. This paper introduces a knowledge distillation strategy to solve this problem. A variable parameter was used to balance the classification loss of the new task with the distillation loss of the old task (NSS-D). Experimental results in CIFAR10 and imbalanced CIFAR10 show that NSS maximizes the data's global information and can retain the model's ability to recognize old tasks better. Compared with classical algorithms, NSS-D performs better on CIFAR100 (48.8%) and ImageNet-200 (36.7%).
Local Path Planning Algorithm Designed for Unmanned Surface Vessel Based on Improved Genetic Algorithm

At present, the algorithms used in local path planning of unmanned ships mainly include simulated annealing algorithm, artificial potential field method and genetic obstacle is above the globally planned algorithm. Among them, genetic algorithm has strong spatial search ability and strong adaptive ability. However, due to the low efficiency of the traditional genetic algorithm, it cannot meet the needs of the real-time path planning of unmanned ships. To solve this problem, this paper designs an improved genetic algorithm based on dynamic fitness function to make up for the shortcomings of the traditional genetic algorithm. This method can improve genetic manipulation by guiding the direction of population evolution optimization and meet the needs of unmanned ship obstacle avoidance. Aiming at the complex dynamic environment in the local path planning of unmanned ship, a simulation experiment combining static environment and dynamic environment is designed. Simulation results show that the improved algorithm has a better obstacle avoidance effect in dynamic environment.
Yolov5 Outdoor Dynamic Object Detection Based on Multi-scale Feature Fusion

Outdoor object target detection is a very popular research task. 7 categories of data that fit the outdoor scenario were selected from the VOC2012 dataset as the current dataset for this study. In order to improve the detection accuracy of outdoor objects while keeping the network in good real-time, we propose a new yolov5 outdoor object detection network structure based on multi-scale feature fusion, which fuses shallow and deep features at different scales so that the fused feature layer has rich semantic information while enhancing the positioning information. The MAP of its proposed network on the VOC2012 dataset improved by 1.5% compared to Yolov5, and the FPS reached 61.51, which is better than the original Yolov5 method.
Research on Improved Image Dehazing Algorithm Based on Dark Channel Prior

The dark channel prior dehazing algorithm can clear the foggy images to different degrees. However, the algorithm still has some deficiencies, such as the halo phenomenon at the edge of the image in the area of sudden change in depth of field; inaccurate estimation of transmittance, resulting in color shift in the image; inaccurate estimation of atmospheric light value, resulting in darker images after defogging, etc. Therefore, it is necessary to improve the dark channel prior algorithm. This paper improves the dehazing algorithm on the basis of in-depth study of the dark channel prior algorithm, and proposes a new dehazing algorithm based on the atmospheric scattering model. The simulation results show that the improved algorithm in this paper can effectively suppress the halo and color distortion in the abrupt depth of field area, and the obtained defogged image has rich detail information, clearer image, and moderate brightness. The algorithm has improved in objective parameters such as average gradient, structural similarity, peak signal-to-noise ratio and information entropy.
Research on Target Detection Algorithm of Unmanned Surface Vehicle Based on Deep Learning

The typical YOLO algorithm, SSD algorithm and RCNN model all have the problem of low detection accuracy or slow detection speed in surface target detection, and cannot be applied to surface unmanned ships that require high real-time and precision. In response to the above problems, this paper designs a maritime target detection model based on multi-scale features, uses four basic modules to build a backbone network for feature extraction, optimizes the YOLOv3 model, and designs a maritime target detection model Net-Y; Based on the analysis of the application scenario requirements and characteristics of unmanned vehicle target detection, a special data set for maritime target detection was constructed; Net-Y and Net-S were respectively trained by using a loss function based on the intersection and union ratio. Experimental results show that the proposed model has improved performance compared with other sea surface object detection algorithms.
Local Path Planning Combined with the Motion State of Dynamic Obstacles

Aiming at the problem that the current local path planning algorithm treats moving obstacles as transient static ones when avoiding obstacles, lacking initiative and security, a local path planning algorithm combining dynamic obstacle motion state is proposed. Firstly, the position information of all obstacles is obtained by a single scan LIDAR. Then, segmenting and linearly fitting all laser point clouds, removing the known obstacles in the map; and marking possible emerging dynamic obstacles with circles of appropriate size; then detecting the dynamic obstacles through the change of the coordinates of the center of the circle, obtaining the center coordinates of the dynamic obstacles continuously; thus, the equation of motion is solved by using the least squares. Finally, the cost map of dynamic obstacles is expanded in the direction of velocity, by combining DWA dynamic window method to realize avoiding dynamic obstacles safely. In this paper, through the simulation experiments and real environment experiments of robot meeting scenes from multiple angles, the results show that expanding the cost map of the motion direction of the dynamic obstacles provides more sufficient response space for the robot to avoid the dynamic obstacles. Compared with the original DWA, the algorithm in this paper can avoid dynamic obstacles more safely, which proves the security and feasibility of this algorithm.