Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network Structure Optimization based on Network Pruning

Recently, the number of layers of neural network model is deeper and deeper, the number of parameters is more and more, and the calculation scale is also larger and larger. This improves the use conditions of some excellent models, which is not conducive to the wide application of deep learning methods in more fields. In view of this trend of increasing the size of neural network models, in this paper, we optimize the structure of a convolutional neural network model for image super-resolution, which reduces the size of the model. The model structure optimization method we use is network pruning, which simplifies the number of layers and parameters of the model, improves the effect of the model and reduces the computational consumption of the model. The key insight of network pruning is to remove the relatively redundant and unimportant parts of the network to make the original network sparser and more streamlined. And the simplified model can keep the original performance. The original model used a cascade structure for multiple sampling, resulting in the increase of the scale of the neural network. By removing the redundant sampling structure and retaining only one sampling process, the number of layers of the model is reduced to 1/3 of the original. Under the same data set (BSD300) training, the PSNR (evaluation index of model effect) of the model is improved from 24.471 db to 24.490 db, and the training time is reduced by 13.8% of the original.
NBA Game Analysis based on Big Data

As an important sport, basketball has a history of more than 100 years. With the rapid development of basketball, data analysis technology has also been widely used. In basketball games, the data that people see is a set of systematic data formed by professional workers through division of labor and close cooperation, classification and statistical analysis. In addition, based on information on the field, spectators get a general idea of how athletes are performing during the game and predict where the data will go. Based on the above background, this paper discusses the significance and application of NBA game data analysis methods, and shows that in NBA games, data analysis can effectively promote the development of basketball.
Review of Integrated Diffractive Deep Neural Networks

An integrated photonic diffractive deep neural network ( ID^2 NN) is one of the most exciting cross-discipline fields of artificial intelligence and optical computing, combining deep learning with the power of light-speed processing on an integrated platform. We know that neural network in a digital computer is based on transistors, which have significant challenges in keeping pace with Moore's law and limited real-time processing applications due to the increased computational costs associated with them. However, with remarkable progress and advancement in silicon photonic integrated circuits over the last few decades, ID^2 NN hold the promise of on-chip miniaturisation and high-speed performance with low power consumption. This paper covers the essential theoretical background for constructing the ID^2 NN and reviews the research status of optical diffractive neural networks in the field of neuromorphic computing. Problems of narrowing down current ID^2 NN applications are also included in this review. Finally, future research directions for ID^2 NN are discussed, and conclusions are delivered.
Pronunciation Tutor for Deaf Children based on ASR

ASR, whose full name is Automated Speech Recognition, is a technology that converts human speech into text. Speech recognition, a multidisciplinary field, is closely related to acoustics, phonetics, linguistics, digital signal processing theory, information theory, computer science and other disciplines. ASR has been applied in educational technology such as deaf children's education in this day and age. This paper makes a preview of a project in which a computer-aided tutor for deaf children instruction based on the speech recognition technology. This tutor utilizes three effective models and is combined with data mining technology. Two evaluation approaches and overview of embedded experiment are also detailed in this paper.
Designing A Korean French-Learners’ Speech Corpus (KFLSC) for Spoken Language Assessment

This paper presents how to design a French speech corpus produced by Korean learners (Korean French-learners’ Speech Corpus: KFLSC), which aims to provide an AI training database for spoken language assessment within the Korean AI Hub Project. The corpus is required to include audio recording, their corresponding transcription, metadata, assessment labeling, and error annotations. Both reading speech and spontaneous speech will be recorded by using our scripts and instruction prompts. The transcription of audio files will be performed both automatically and manually. Detailed speech assessment and scoring information are provided along with proposed pronunciation and spoken language assessment rubrics. We also suggest tagging guidance on pronunciation and spoken language errors. This study will contribute to building a large-scale French learner corpus for French spoken language assessment and the education of French as a foreign language, and to the development of automatic speech assessment and computer-aided language learning technologies.
End-to-end named entity recognition for Vietnamese speech

One of the first steps in comprehending natural or spoken language is named-entity recognition. It plays a critical role in natural language processing applications such as text clustering, subject detection, topic detection, and text summarization. The most common techniques in information extraction using named-entity recognition can be applied to building automatic question-answering systems, semantic web technology, automatic translation machines, and so on. For input text with correct formatting, studies on the named entity recognition (NER) task have achieved excellent and nearly human-equal results. However, for the output documents of the speech recognition system (ASR), the normative signs of the text, such as punctuation and uppercase, no longer exist, causing difficulties for the researchers. In this paper, we present the process of building a Vietnamese speech dataset for the NER task and propose a new end-to-end approach for the NER of Vietnamese speech. In addition, we combine the multi-task learning model with the punctuation and uppercase (CaPu) recovery model and demonstrate the combination’s effectiveness when documenting the approximately 5% improvement in the F1 score.
Predictive Analysis for Text Classification: Discrete Units in Company Registration Discourse

Abstract Legal discourse shows variation most commonly in terms of contrasts between languages, textual genres, communicative settings (professional vs. lay communication), translation methods and categories of authors, the last constituting a testing ground for the text-prediction task presented in this article. The research project involves quantitative analysis of selected discrete units and their statistical processing with the R tool for the purpose of generating random forest and decision tree models. It is hypothesised that it is possible to effectively predict text authorship based on the grammatical profile of the texts. The prediction model proposed here covers two authorship categories, institutional name and professional title, and these encapsulate authorship sub-categories related to institutional and work position background. The prediction accuracy parameters for the authorship-based text classification in both cases prove to be statistically satisfactory. More specific findings show that the text classification models for some authorship sub-categories are more effective than for others. Further, some discrete units have distinctively high discriminative power for the texts. The analysis is conducted on a custom-designed corpus, composed of English texts processed in company registration proceedings. The corpus is homogenous in terms of the function and the communicative context of the texts, which assures reliability of the findings and at the same time captures the variationist aspect of legal communication by taking the varied authorship factor into account.
Radio, mobile communications, and women’s empowerment: Experiences in Mathare, Nairobi

This article presents findings from a study of young women’s empowerment through the use of information and communication technology (ICT), specifically the use of radio and mobile communications, in the Mathare informal settlements of Nairobi. Data was collected through focus group discussions and in-depth interviews with young women. The findings were extrapolated through the lens of Longwe’s five- stage women’s empowerment framework (Longwe, 1991). Longwe posits that the empowerment of women can be viewed through a prism of ascension, the initial stage being welfare, followed by access, conscientisation, participation, and then control, the highest level of empowerment. The study finds that the use of radio and mobile communications has empowered many young women to engage in conversations on issues affecting them, and with useful information on how to improve their means of livelihood. At the same time, mobile communications have enabled many young women to achieve financial inclusion by engaging in income-generating activities. In addition, mobile communications have empowered young women by providing them with access to financial services and the ability to manage their finances. Such empowerment, when viewed through the Longwe (1991) framework, is a remarkable progression on empowerment by young women through the initial stages up to the highest levels of participation and control. At the same time, the study finds that the empowerment of young women through ICTs is held back to some extent by socioeconomic and cultural factors that are the result of patriarchal traditions and mindsets.
INFECÇÃO POR COVID-19 EM PACIENTES COM DIABETES MELLITUS TIPO 2: UMA REVISÃO DE LITERATURA

CASE REPORTSa middle-aged man with acute intestinal obstruction due to an ileo-ileal intussusception of inverted Meckel's diverticulum with a lipoma that was managed by laparoscopy.Worthy of note is the very scarce number of case reports on this exceeding uncommon association.
Nanophotonic reservoir computing for COVID-19 pandemic forecasting

The coronavirus disease 2019 (COVID-19) has spread worldwide in unprecedented speed, and diverse negative impacts have seriously endangered human society. Accurately forecasting the number of COVID-19 cases can help governments and public health organizations develop the right prevention strategies in advance to contain outbreaks. In this work, a long-term 6-month COVID-19 pandemic forecast in second half of 2021 and a short-term 30-day daily ahead COVID-19 forecast in December 2021 are successfully implemented via a novel nanophotonic reservoir computing based on silicon optomechanical oscillators with photonic crystal cavities, benefitting from its simpler learning algorithm, abundant nonlinear characteristics, and some unique advantages such as CMOS compatibility, fabrication cost, and monolithic integration. In essence, the nonlinear time series related to COVID-19 are mapped to the high-dimensional nonlinear space by the optical nonlinear properties of nanophotonic reservoir computing. The testing-dataset forecast results of new cases, new deaths, cumulative cases, and cumulative deaths for six countries demonstrate that the forecasted blue curves are awfully close to the real red curves with exceedingly small forecast errors. Moreover, the forecast results commendably reflect the variations of the actual case data, revealing the different epidemic transmission laws in developed and developing countries. More importantly, the daily ahead forecast results during December 2021 of four kinds of cases for six countries illustrate that the daily forecasted values are highly coincident with the real values, while the relevant forecast errors are tiny enough to verify the good forecasting competence of COVID-19 pandemic dominated by Omicron strain. Therefore, the implemented nanophotonic reservoir computing can provide some foreknowledge on prevention strategy and healthcare management for COVID-19 pandemic.
New Design of Secure Communication System Based on Dynamic Linear Receiver

In this paper, the design issues of the chaotic secure communication system will be scientifically explored. Based on time-domain analysis, a new secure communication system with dynamic linear receivers will be constructed. This secure communication system can not only make the error signal close to zero, but also can correctly calculate its exponential convergence rate. Finally, several numerical simulation results are proposed to illustrate the practicability and correctness of the main results.
Intelligent Algorithm for Judging Basketball Training based on Multi-Angle Attitude Data Image Acquisition and Morphological Analysis

This paper mainly reviews the development and improvement of pixel-based morphometric algorithms (VBM), focusing on the DART -EL algorithm developed in recent years. Realize the automatic acquisition of multi-pose images; at the same time, build a modularized posture acquisition training detection model program for posture training and recognition. Taking the construction of the special physical fitness evaluation index system of college men's basketball players as the research object, the results of the special physical fitness evaluation are analyzed and studied, and the theoretical analysis of the special physical fitness characteristics of college men's basketball players is carried out.
Research on Component Incremental Integration Testing Technology

The research on component incremental integration testing technology, firstly, the component incremental integration testing design is carried out, and the component incremental integration process is described; then, according to the integration testing design, a simulation test is proposed for each component integration test in the integration testing process. Combined with physical testing, the integration testing method analyzes the interaction between the components under test and the unintegrated components in the system, and establishes the component integration interaction model; then according to the component integration interaction model, the test cases are described by XML; finally, the C++ based on the XSLT style sheet is used. The script framework converts test cases into test scripts, designs a component integration test framework, drives binary integration test script programs in this framework, and tests the components under test.
Intelligent Recording System Coupled with Ex-Situ Poverty Alleviation, Relocation and Rural Revitalization based on a Comparative Analysis of Telemetry Databases

On the premise that the comprehensive region matching algorithm is used as the image similarity measure, this paper proposes an urban region retrieval method in remote sensing image database based on the descending order of the average high-frequency signal intensity. This paper attempts to put forward the concept of “targeted governance”, and points out that the governance method of precise top-level design, precise execution process, refined supervision mechanism, and refined feedback mechanism can solve the problem of community governance of relocated poverty alleviation and relocation in the “post-relocation era”. An index system was built to comprehensively evaluate the benefits of relocation for poverty alleviation and relocation from five dimensions of rural revitalization, including industrial prosperity, ecological livability, rural civilization, effective governance, and affluent life.
On the emergence of inflection classes

Laz has a rich verb classification system. While the language presents robust morphological correlates of argument structure, it also exhibits finer morphological distinctions that have been argued to encode less canonical semantic features. This paper argues that these distinctions merely constitute inflection classes in the synchronic grammar of Laz but could be a residue of a grammar where these semantic features were active in exponent selection.
RANCANGAN SISTEM INFORMASI KOST PUTRI MALIKA BERBASIS WEBSITE MENGGUNAKAN FRAMEWORK LARAVEL DAN MYSQL

This scientific writing discusses the creation of a website for the registration and reservation process at Putri Malika's Kost. With this website, it is hoped that it can assist the patient registration and reservation process, so that prospective boarding house occupants no longer need to register or make reservations manually. Apart from that, this website is also expected to help the process of recording user data that makes reservations. The research method used is the System Development Life Cycle (SDLC). The stages in the SDLC method are planning, analysis, design, implementation and testing of the system. Making this website uses the Laravel framework and MySQL as the database. This website can be accessed via the address https://www.teamstart.my.id/. Based on the results of trials conducted in this study, it can be concluded that the registration and reservation website for prospective residents of the Putri Malika Boarding House was successfully created and can be used by prospective residents and boarding house residents to register or make reservations and receive notifications of billing and payment history, as well as assisting the data collection process. boarders and registrars
Research on Path Planning of Mobile Robot Based on Improved Theta* Algorithm

The Theta* algorithm is a path planning algorithm based on graph search, which gives the optimal path with more flexibility than A* algorithm in terms of routes. The traditional Theta* algorithm is difficult to take into account with the global and details in path planning and traverses more nodes, which leads to a large amount of computation and is not suitable for path planning in large scenarios directly by the Theta* algorithm. To address this problem, this paper proposes an improved Theta* algorithm, namely the W-Theta* algorithm. The heuristic function of Theta* is improved by introducing a weighting strategy, while the default Euclidean distance calculation formula of Theta* is changed to a diagonal distance calculation formula, which finally achieves a reduction in computation time while ensuring a shorter global path; the trajectory optimization is achieved by curve fitting of the generated path points to make the motion trajectory of the mobile robot smoother. Simulation results show that the improved algorithm can quickly plan paths in large scenarios. Compared with other path planning algorithms, the algorithm has better performance in terms of time and computational cost. In different scenarios, the W-Theta* algorithm reduces the computation time of path planning by 81.65% compared with the Theta* algorithm and 79.59% compared with the A* algorithm; the W-Theta* algorithm reduces the memory occupation during computation by 44.31% compared with the Theta* algorithm and 29.33% compared with the A* algorithm.
Reconstruction of the Indigenous Sources of Gold of the Glu-Kharinsky Placer (Kolyma River Basin, Magadan Region, Russia)

A comparative analysis of typomorphic features of gold placers and indigenous sources of gold was carried out for the Glukharinsky ore-placer node (the Nadezhda deposit and Glukharinskoye, Tyomnoye, and Tyi-Jurye ore occurrences).A correlation between fineness of placer native gold and composition of relic minerals preserved inside gold particles was obtained for the first time.This made it possible to establish mineral parageneses of native gold from potential indigenous sources.The study of placer gold fineness in combination with com-position of mineral inclusions allowed us to identify seven types of gold paragenesis: petzite-hessite with a fineness of 855‰, pyrite-polybasite-galena (a fineness of 670‰), bismuth telluride (900‰), cosalite-vikingite (790‰), galena (870‰), glaucodot (950‰), and hydrohematite (840‰).It was also shown that the placer contains the material from ore occurrences of different formation: gold-quartz-porphyry occurrences in granitoids (Au-Te-Bi type) and gold-silver occurrences.The method developed by us allows one to estimate the percentage of each ore source in placer formation.
Trajectory Planning for A Massive Number of UAVs in the Environment with Static and Dynamic Obstacles: A Mean Field Game Approach

Trajectory planning of massive unmanned aerial vehicles (UAVs) is very difficult in an environment with static and dynamic obstacles. This is mainly due to the huge number of UAVs, which pose challenges to their interaction and collision avoidance with companions and obstacles. In this paper, we propose a trajectory planning algorithm for a massive number of UAVs based on the mean field game (MFG). First, a differential game of N UAVs in a 3D environment is constructed, and the collision avoidance with static and dynamic obstacles is considered in the cost functional of each UAV. Then, when the number of UAVs is very large, the above differential game is transformed into a MFG using the mean field approximation. The existence and uniqueness of the equilibrium solution are proved. Finally, we derive the variational primal-dual formulation of the proposed MFG model and solve it with APAC-Net. The performance of the proposed algorithm is validated in an environment with multiple static obstacles and two different types of dynamic obstacles.
Research on the Application of Computer Virtual Reality Technology in the Appreciation of Holographic Projection of Literary Works

The organic combination of holographic projection digital technology and the analysis of literary works has a positive role in improving the humanistic quality of contemporary students and reforming and innovating education and teaching methods. The deep integration of holographic projection digital technology and literature analysis will have a “revolutionary” impact. This paper proposes an interactive holographic projection method based on Leap Motion. Using Leap Motion's local gesture skeleton recognition technology, it can recognize human gestures to control holographic images, and realize real-time interaction between users and three-dimensional images of literary works. The experimental results show that the method has good real-time performance, strong stability and high user-friendliness.
Research on the Application System of Course Test Questions and Examinations under Computer Big Data Technology

The open online college English computer teaching management system is an open teaching system that integrates classroom teaching and tutoring, courseware production and management. The development and use of the English online examination system has fundamentally changed the traditional examination method and truly separated teaching and examination. The examination system designed by the use of network and computer information technology makes the examination procedures and test propositions of students reach an unprecedented standard, standardizes the examination procedures, and provides a feasible scheme for the reform of English examination methods. The article designs a “Network-based College English Examination System”. The system adopts the C/S and B/S mixed mode of three-tier structure, selects Delphi programming language, and uses SQLServer2000 database as the system development tool. This paper analyzes the composition of the functional modules of the system, studies the technology of test question extraction, and proposes a translation question scoring strategy.