Reviewerly/exHarmony huggingface dataset:
A Real-Time Dynamic Gesture Variability Recognition Method Based on Convolutional Neural Networks

Among the many problems in machine learning, the most critical ones involve improving the categorical response prediction rate based on extracted features. In spite of this, it is noted that most of the time from the entire cycle of multi-class machine modeling for sign language recognition tasks is spent on data preparation, including collection, filtering, analysis, and visualization of data. To find the optimal solution for the above-mentioned problem, this paper proposes a methodology for automatically collecting the spatiotemporal features of gestures by calculating the coordinates of the found area of the pose and hand, normalizing them, and constructing an optimal multilayer perceptron for multiclass classification. By extracting and analyzing spatiotemporal data, the proposed method makes it possible to identify not only static features, but also the spatial (for gestures that touch the face and head) and dynamic features of gestures, which leads to an increase in the accuracy of gesture recognition. This classification was also carried out according to the form of the gesture demonstration to optimally extract the characteristics of gestures (display ability of all connection points), which also led to an increase in the accuracy of gesture recognition for certain classes to the value of 0.96. This method was tested using the well-known Ankara University Turkish Sign Language Dataset and the Dataset for Argentinian Sign Language to validate the experiment, which proved effective with a recognition accuracy of 0.98.
IDBNet: Improved differentiable binarisation network for natural scene text detection

Abstract The text in the natural scene can express rich semantic information, which helps people understand and analyse daily things. This paper focuses on the problems of discrete text spatial distribution and variable text geometric size in natural scenes with complex backgrounds and proposes an end‐to‐end natural scene text detection method based on DBNet. The authors first use IResNet as the backbone network, which does not increase network parameters while retaining more text features. Furthermore, a module with Transformer is introduced in the feature extraction stage to strengthen the correlation between high‐level feature pixels. Then, the authors add a spatial pyramid pooling structure in the end of feature extraction, which realises the combination of local and global features, enriches the expressive ability of feature maps, and alleviates the detection limitations caused by the geometric size of features. Finally, to better integrate the features of each level, a dual attention module is embedded after multi‐scale feature fusion. Extensive experiments on the MSRA‐TD500, CTW1500, ICDAR2015, and MLT2017 data set are conducted. The results showed that IDBNet can improve the average precision, recall, and F‐measure of a text compared with the state of art text detection methods and has higher predictive ability and practicability.
Optimized blockchain network model for <scp>6G</scp> cellular vehicle‐to‐everything communication

Abstract Road safety of electric cars has required large and fast data flow. The six‐generation (6G) networks that provide hyper‐speed, ultra‐reliable, and minimal delay meet the requirements. Data reliability can be supported with popular blockchain technology, which is still widely accepted as a decentralized data management system. This article used an optimum path with the highest signal‐to‐noise constraints for the proposed algorithms. The essential requirement of error‐free data transmission for high‐speed and large data has been successfully implemented. Three‐stage implementations are suggested. The edge computers at the partners are used as storage to resolve the first level. A C‐V2X network solution is used to resolve the second level of economic activity. A two‐stage optimization strategy is used to consider the last level of high network transaction levels. The results of the newly introduced optimization algorithms are simulated and contrasted with those of a traditional, time‐consuming step‐by‐step calculation method. The introduced algorithms tested with real‐time experimental signal‐to‐noise ratio (SNR) and bit error rate (BER) values, giving successful results.
A semi-supervised method to generate a persian dataset for suggestion classification

Suggestion mining has become a popular subject in the field of natural language processing (NLP) that is useful in areas like a service/product improvement. The purpose of this study is to provide an automated machine learning (ML) based approach to extract suggestions from Persian text. In this research, first, a novel two-step semi-supervised method has been proposed to generate a Persian dataset called ParsSugg, which is then used in the automatic classification of the user’s suggestions. The first step is manual labeling of data based on a proposed guideline, followed by a data augmentation phase. In the second step, using pre-trained Persian Bidirectional Encoder Representations from Transformers (ParsBERT) as a classifier and the data from the previous step, more data were labeled. The performance of various ML models, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and the ParsBERT language model has been examined on the generated dataset. The F-score value of 97.27 for ParsBERT and about 94.5 for SVM and CNN classifiers were obtained for the suggestion class which is a promising result as the first research on suggestion classification on Persian texts. Also, the proposed guideline can be used for other NLP tasks, and the generated dataset can be used in other suggestion classification tasks.
A semantically enhanced text retrieval framework with abstractive summarization

Abstract Recently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence‐to‐sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these models can improve ranking effectiveness by enhancing input semantics. This article introduces SE‐BERT, a semantically enhanced bidirectional encoder representation from transformers (BERT) based ranking framework that captures more semantic information by modifying the input text. SE‐BERT utilizes a pretrained generative language model to summarize both sides of the candidate passage and concatenate them into a new input sequence, allowing BERT to acquire more semantic information within the constraints of the input sequence's length. Experimental results from two Text Retrieval Conference datasets demonstrate that our approach's effectiveness increasing as the length of the input text increases.
Quantitative Approximation by Multiple Sigmoids Kantorovich-Choquet Quasi-interpolation Neural Network Operators

In this chapter we present univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity.
Neural Networks Approximation of Time Separating Stochastic Processes

Here we study the univariate quantitative approximation of time separating stochastic process over a compact interval or all the real line by quasi-interpolation neural network operators.
Parametrized Gudermannian Activation Function Based Banach Space Valued Neural Network Multivariate Approximation

Here we give multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types.
Banach Space Valued Multivariate Multi Layer Neural Network Approximation Based on q-Deformed and $$\lambda $$-Parametrized A-Generalized Logistic Function

Here we research the multivariate quantitative approximation of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N}$$ , $$N\in \mathbb {N},$$ by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We investigate also the case of approximation by iterated multilayer neural network operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its partial derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a q-deformed and $$\lambda $$ -parametrized A-generalized logistic function, which is a sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network are with one or multi hidden layers.
Voronovskaya Type Asymptotic Expansions for General Sigmoid Functions Induced Quasi-Interpolation Neural Network Operators

Here we reexamine further the quasi-interpolation general sigmoid function based neural network operators of one hidden layer. Based on fractional calculus theory we derive fractional Voronovskaya type asymptotic expansions for the approximation of these operators to the unit operator, as we are studying the univariate case
Multiple General Sigmoids Activated Banach Space Valued Neural Network Multivariate Approximation

Here we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density functions induced by several different among themselves general sigmoid functions.
Degree of Approximation by Multiple Sigmoids Kantorovich-Shilkret Quasi-interpolation Neural Network Operators

In this chapter we derive multivariate quantitative approximation by Kantorovich-Shilkret type quasi-interpolation neural network operators with respect to supremum and $$L_{p}$$ norms. This is done with rates using the multivariate modulus of continuity. We approximate continuous and bounded functions on $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ .
Banach Space Valued Neural Network Multivariate Approximation Based on Hyperbolic Tangent Like Activation Function

Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types.
General Sigmoid Induced Banach Space Valued Neural Network Multivariate Approximation

Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a general sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. It follows [13].
Sentence-level heuristic tree search for long text generation

Abstract In this study, we primarily aim to address the exposure bias issue in long text generation intrinsic to statistical language models. We propose a sentence-level heuristic tree search algorithm, specially tailored for long text generation, to mitigate the problem by managing generated texts in a tree structure and curbing the compounding of biases. Our algorithm utilizes two pre-trained language models, an auto-regressive model for generating new sentences and an auto-encoder model for evaluating sentence quality. These models work in tandem to perform four critical operations: expanding the text tree with new sentences, evaluating the quality of the additions, sampling potential unfinished text fragments for further generation, and pruning leaf nodes deemed unpromising. This iterative process continues until a pre-defined number of [EOS] tokens are produced, at which point we select the highest-scoring completed text as our final output. Moreover, we pioneer two novel token-level decoding techniques—nucleus sampling with temperature and diverse beam search with sampling. These methods, integrated with our sentence-level search algorithm, aim to improve the consistency and diversity of text generation. Experimental results, both automated measures (including Jaccard similarity, Word2vec similarity, and unique word ratio) and human evaluations (assessing consistency, fluency, and rhetorical skills), conclusively demonstrate that our approach considerably enhances the quality of machine-generated long-form text. Through this research, we aim to inspire further innovations in sentence-level search-based text generation algorithms.
New bag of features using reinforcement Aquila optimization and weighted Bayesian Gaussian mixture modelling for dental images

Abstract Dentistry diseases are worldwide concern with the fact that 5% of medical budget is spent on it. Dental diagnosis requires frequent visits to clinic and multiple personal check‐ups by an expert. This delays the process of diagnosis as well as introduces the danger of oral infection spread. Automation is boon to speed up the process of diagnosis, reduce expert's involvement and help in handling volume of patients. In situations like pandemic as seen in this decade, computer based automatic systems in health have proven their importance and necessity. For dental diagnosis, images are useful tool for better anatomical views and accurate decision of treatment. However, manual dental analysis increases load on dentists for initial check‐up that can be easily performed with automated systems or self‐kits. Oral cavity is a dental illness, if not identified and treated on time may lead to other serious ailments. This work presents a framework that performs tooth image classification for cavity and non‐cavity with new bag of features (NBoF) method. NBoF method is an attempt to improve the performance of bag of features (BoF) using the proposed reinforcement Aquila optimization (RAO) and weighted Bayesian Gaussian mixture modelling (WBGMM). An analysis of the performance of the NBoF using the proposed RAO and WBGMM is conducted using standard metrics. The comparative study of results proves that the proposed NBoF method outperforms the existing state‐of‐the‐art algorithms.
Value Distributions of Perfect Nonlinear Functions

Abstract In this paper, we study the value distributions of perfect nonlinear functions, i.e., we investigate the sizes of image and preimage sets. Using purely combinatorial tools, we develop a framework that deals with perfect nonlinear functions in the most general setting, generalizing several results that were achieved under specific constraints. For the particularly interesting elementary abelian case, we derive several new strong conditions and classification results on the value distributions. Moreover, we show that most of the classical constructions of perfect nonlinear functions have very specific value distributions, in the sense that they are almost balanced. Consequently, we completely determine the possible value distributions of vectorial Boolean bent functions with output dimension at most 4. Finally, using the discrete Fourier transform, we show that in some cases value distributions can be used to determine whether a given function is perfect nonlinear, or to decide whether given perfect nonlinear functions are equivalent.
The long journey of resistance toward acceptance: Understanding digital forensic accreditation in England and Wales from a social science perspective

Abstract Drawing on topical academic and practitioner accounts, our contribution examines the challenges of accreditation in digital forensics (DF), specifically, the adoption of ISO/IEC 17025 standard for forensic science support providers in England and Wales. We built on the debate generated by the Forensic Science Regulator's introduction of this standard in DF to reflect on the position and status of DF community of practice. We argue that a social science approach can help illuminate the organizational and professional tensions generated by this development and to understand better the implications for the role of DF specialists and their position in the wider forensic science community. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation Crime Scene Investigation &gt; Education and Formation
Beyond the Microscope: A Technological Overture for Cervical Cancer Detection

Cervical cancer is a common and preventable disease that poses a significant threat to women's health and well-being. It is the fourth most prevalent cancer among women worldwide, with approximately 604,000 new cases and 342,000 deaths in 2020, according to the World Health Organization. Early detection and diagnosis of cervical cancer are crucial for reducing mortality and morbidity rates. The Papanicolaou smear test is a widely used screening method that involves the examination of cervical cells under a microscope to identify any abnormalities. However, this method is time-consuming, labor-intensive, subjective, and prone to human errors. Artificial intelligence techniques have emerged as a promising alternative to improve the accuracy and efficiency of Papanicolaou smear diagnosis. Artificial intelligence techniques can automatically analyze Papanicolaou smear images and classify them into normal or abnormal categories, as well as detect the severity and type of lesions. This paper provides a comprehensive review of the recent advances in artificial intelligence diagnostics of the Papanicolaou smear, focusing on the methods, datasets, performance metrics, and challenges. The paper also discusses the potential applications and future directions of artificial intelligence diagnostics of the Papanicolaou smear.
Conditions for the formation of creative individuality among future teachers in the educational space of a pedagogical university

The study aims to determine the potential of pedagogical education to promote the formation of creative individuality among future teachers. The paper analyses the modern challenges for higher pedagogical education caused by the digitalisation of the educational environment of the university and, consequently, the changing role of a teacher. The specifics of the process of creative individuality formation among modern teachers are clarified. The essence of the process of formation of pedagogical artistry as an integrative quality of a professional is determined. The psychological and pedagogical conditions for the organisation of professional pedagogical education contributing to creative individuality formation among beginning teachers are analysed. The scientific novelty of the study lies in determining the pedagogical conditions for creative individuality formation among future teachers at the university. The researchers propose using elements of theatrical pedagogy as a pedagogical tool through the introduction of a training course aimed at the development of pedagogical artistry as a component of future teachers’ creative individuality. As a result, the possibility of forming the creative individuality of a future teacher in modern conditions of education digitalisation based on pedagogical artistry development is substantiated.
The Effects of the Flipping Classroom and Peer Instructional Models on Learning Mathematics

This study examines the impact of the flipped classroom and peer instruction teaching models on students' mathematics performance. More than one hundred students participate in a four-month study, and they are divided into three groups. A control group is being taught according to the traditional model. The first experimental group is being taught according to
A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.