Reviewerly/exHarmony huggingface dataset:
Design System Using Matlab to Recognize Patterns as Face Detection

This paper presents the design and implementation of a face detection system using MATLAB. The system utilizes various techniques, including preprocessing, feature extraction, and pattern recognition algorithms, to accurately detect faces in images. The methodology section provides a detailed explanation of the system's workflow, highlighting the preprocessing techniques for enhancing image quality, the feature extraction methods for capturing discriminative facial characteristics, and the utilization of SVM-based algorithms for pattern recognition. The system's performance is evaluated using established metrics such as accuracy, precision, recall, and F1 score, and comparisons with existing approaches are made. The experimental setup, including data partitioning, parameter optimization, and computational efficiency analysis, is described to ensure reproducibility and reliability. The results demonstrate the system's effectiveness in accurately detecting faces in various scenarios. Future work can focus on further enhancing the system's performance and expanding its applications in face recognition and facial expression analysis.
Application of Event Semantic Model Analysis in Machine Translation

The application of event semantic model analysis in machine translation is a process of using semantic based methods to improve the quality of machine translation. The main idea is that the human brain can automatically understand and interpret a sentence, but it cannot do so for the text. Therefore, we need to find out how to translate words into concepts (semantic features) that are similar or related to each other, and then use these concepts as the input of translated sentences.
Design and Implementation of Intelligent Tourism System Based on Dijkstra Algorithm

This paper studies the design and implementation of Intelligent Tourism System Based on Dijkstra algorithm. Intelligent Tourism System (ITS) is a system that can manage complex systems such as tourism and transportation. Its consists of various components: management system (MS), which provides users with information about destinations; Decision support system to enable managers to make decisions based on information provided by the Management Committee; Information management system (IMS) for managing data collected from different sources.
A Case Study of Cluster Analysis of the Whole Slide Images

Machine learning has achieved great success in image recognition and has been widely used in many fields, especially the data mining of massive image features in the era of big data. Clustering analysis is an important task in machine learning. This paper takes clustering in pathological section image recognition as an application case and discusses the application effects of different clustering models. Firstly, the relevant algorithms used are introduced, including K-means and Gaussian mixture model, hierarchical clustering algorithm and Louvain algorithm. Secondly, it is very important to evaluate the effectiveness of clustering results. In this paper, Silhouette score and V-measure are selected as indicators to evaluate the clustering algorithm, that is, the clustering methods determined by different data structures are not suitable for various data models. The cluster analysis of 5000 colorectal cancer tissue patches was carried out by python programming. Through the analysis and discussion of clustering results, the goodness of fit and clustering results of different models are compared and analyzed.
Rural Internet Celebrity Culture Online Communication System Based on Data Mining Algorithm

In the online communication Big data age system, how to attract target customers to the products pushed by the platform has become a difficult problem for many communication systems. This paper studies the online communication system of rural internet celebrity culture based on the analysis of association rules in data mining algorithm. Data mining can be used in many fields such as information management, query optimization, decision support and so on, Using the information in the database to calculate the frequency, so as to achieve the effect of accurate push, is the most important problem in the study of rural internet celebrity Culture online communication system.
Design of Rural Internet Celebrity Culture Information Management System Based on Back Propagation

BP -Back Propagation is a structure with strong nonlinear mapping capability and flexibility. It is composed of forward calculation and reverse calculation, which effectively reduces the error caused by calculation. The factors considered in the construction of the information management database of rural internet celebrity culture are more the benefits that internet celebrity culture can bring. Therefore, this paper mainly designs the rural internet celebrity culture information management system based on the influence and other factors in the internet celebrity information management system, Reduce the error by calculating the mean square deviation and error coefficient, and realize the function of making reasonable planning for the development of rural internet celebrity Culture in the system.
Service Quality Evaluation System of Cross-Border B2B E-Commerce Websites

Compared with the Transnational E-commerce of independent product supply channels, the platform Transnational E-commerce has always been at a disadvantage in product quality control and anti-counterfeiting. In terms of customer service, it is difficult for platform-based Transnational E-commerce to monitor the customer service quality of retailers, and consumers are also dissatisfied with the service attitude of merchants. Due to the nonstandard operation of transnational retailers on the platform, it is difficult to implement unreasonable 7-day return rules such as Transnational E-commerce. On this basis, we will establish a service quality evaluation system for Transnational E-commerce retailers, guide the Transnational E-commerce retailers of the platform to carry out transnational e-commerce business, and promote the Transnational E-commerce service enterprises to continuously improve the service quality. Establish a scientific and rigorous service management system to further guide the standardized development of Transnational E-commerce.
Recovering hierarchies in terms of content similarity

Abstract Several real-world and abstract structures and systems are characterized by marked hierarchy to the point of being expressed as trees. Since the study of these entities often involves sampling (or discovering) the tree nodes in a specific order that may not correspond to the original shape of the tree, reconstruction errors can be obtained. The present work addresses this important problem based on two main resources: (i) the adoption of a simple model of trees, involving a single parameter; and (ii) the use of the coincidence similarity as the means to quantify the errors by comparing the original and reconstructed structures considering the effects of hierarchical structure, nodes content, and uncertainty. Several interesting results are described and discussed, including that the accuracy of hierarchical reconstructions is highly dependent on the values of the uncertainty parameter as well as on the types of trees and that changes in the value of the content parameter can affect the accuracy of reconstructing hierarchies.
Contingency Planning of Visual Contamination for Wheeled Mobile Robots with Chameleon-Inspired Visual System

To enable mobile robots to effectively deal with the emergency of visual contamination, contingency planning based on case-based reasoning (CBR) was performed in this paper. First, for a wheeled mobile robot (WMR) equipped with a chameleon-inspired visual system, a target search model in chameleon-inspired binocular negative correlation movement (CIBNCM) mode was established. Second, a CBR-based contingency planning model of visual contamination for WMRs was established, where the reasoning process using CBR for visual contamination was analyzed in detail. Third, through the analysis of environment perception when visual contamination occurs, a perception model in chameleon-inspired visual contamination for WMRs was built. Finally, to validate the proposed approach, a contingency planning experiment scheme for visual contamination was designed based on the robot’s general planning of target tracking, and the experimental result is discussed. The proposed CBR-based contingency planning approach for visual contamination can reason out effective solutions corresponding to the contamination situations. The rationality of the approach was verified by experiments with satisfactory results. Moreover, compared with the contingency planning method based on rule-based reasoning, the accuracy of target retracking after the robot visual system is contaminated is significantly higher for the CBR-based contingent planning method used in this paper.
An Analysis of the Evaluation of the Translation Quality of Neural Machine Translation Application Systems

Neural machine translation (NMT) is applied to generate a more reliable and accurate translation practice as the most cutting-edge technology. In recent years, NMT has achieved gratifying results. However, the main obstacle for market-oriented NMT application systems appears to suffer from weak translation quality that fails to meet users’ needs. This paper focuses on the machine translation of political documents and implements six dominant NMT application systems in the market to evaluate their translation quality. The evaluation process further employs both BLEU and NIST technical evaluation algorithms and re-verifies the results with the manual evaluation method called the “Score Ranking System” to compare the performances of the six NMTs in Chinese-English translations of political documents. Through diagnosis and evaluation of the problems and errors in NMTs, the paper eventually proposes the “Cue Lexicon+” model to remedy prominent problems. Besides, the “NMT+ Lexicon Intelligent Translation Assistant” soft is developed and the “Cue Lexicon+” is integrated into the NMT application systems to further improve the translation quality, providing a reference and research basis to increase the performance and update the NMT application systems.
SUTORI FOR ONLINE LANGUAGE EDUCATION

TESOL JournalEarly View e733 MEDIA REVIEW SUTORI FOR ONLINE LANGUAGE EDUCATION Bing Mei, Corresponding Author Bing Mei [email protected] orcid.org/0000-0001-9185-7509 School of Foreign Languages, Henan University Correspondence Email: [email protected]Search for more papers by this authorXiaojuan Wang, Xiaojuan Wang Zhengzhou Erqi District Experimental Middle SchoolSearch for more papers by this authorWenting Lv, Wenting Lv Boai No.1 Middle SchoolSearch for more papers by this author Bing Mei, Corresponding Author Bing Mei [email protected] orcid.org/0000-0001-9185-7509 School of Foreign Languages, Henan University Correspondence Email: [email protected]Search for more papers by this authorXiaojuan Wang, Xiaojuan Wang Zhengzhou Erqi District Experimental Middle SchoolSearch for more papers by this authorWenting Lv, Wenting Lv Boai No.1 Middle SchoolSearch for more papers by this author First published: 23 May 2023 https://doi.org/10.1002/tesj.733 https://www.sutori.com Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL REFERENCES Lepp, M., Luik, P., & Tark, T. M. (2022). How can web lessons be taught to reduce screen fatigue, motivational, and concentration problems in different disciplines? Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.871770 Moorhouse, B. L. , & Kohnke, L. (2021). Responses of the English-language-teaching community to the COVID-19 Pandemic. RELC Journal, 52, 359– 378. https://doi.org/10.1177/00336882211053052 Richards, C. (2005). The design of effective ICT-supported learning activities: Exemplary models, changing requirements, and new possibilities. Language Learning and Technology, 9(1), 60– 79. https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/2e0dc2f7-9c8c-4e15-b3d4-28b856f76161/content Sun, S. Y. H. (2020). Using patterns-based learning design for CALL tasks. Computer Assisted Language Learning, 34, 1061– 1084. https://doi.org/10.1080/09588221.2019.1657902 Yamashita, T. (2021). Corrective feedback in computer-mediated collaborative writing and revision contributions. Language Learning and Technology, 25(2), 75– 93. http://hdl.handle.net/10125/73434 Early ViewOnline Version of Record before inclusion in an issuee733 ReferencesRelatedInformation
Multi‐disease classification model using deep neural network and Strassen's rectilinear fine‐tune bouncing training algorithm

Abstract A deep neural network (DNN) is being used in the healthcare industry to improve care delivery at a lower cost and in less time. A DNN is well‐known for its diagnostic applications. However, it is also increasingly being utilized to guide healthcare management decisions. DNNs have been applied to real‐life disease classification problems. The performances of DNN are improved by reducing the training time, performing fast classification, and improving the parameters such as accuracy and error rate, and so forth. We have proposed a hybrid DNN named Strassen's rectilinear fine‐tune bouncing training (SRFBT) Algorithm by combining Strassen's theorem and bouncing training algorithm. The simulation result shows that the SRFBT algorithm outperforms both the DNN, support vector machine, radial basis function network and deep belief network algorithms in terms of network training, testing time and accuracy on various UCI machine learning healthcare datasets. The SRFBT has improved performance with a minimum average training time of 43.6249 and a maximum accuracy of 96.86.
A novel multimodal online news popularity prediction model based on ensemble learning

Abstract The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this article, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this article, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection and effective modelling to create a robust news popularity Prediction Model. Three kinds of features—meta‐features, text features and image features are used to design an influential and robust model. The relative error performance measure Root Mean Squared logarithmic error (RMSLE) is used to quantify the popularity prediction error. Further, the RMSLE outcome shows 0.351 which is the lowest error value given by the proposed model. Further, the most important features are also sought out to show the dependence of the best‐fit model on text and image features.
Applying multisensor in‐car situations to detect violence

Abstract Violence recognition is challenging because it can be presented in very different forms. For example, it can be present in an image by a person hitting another person or present in audio by a person being rude to another. Thus, audio and video are essential features to be analysed. In the audio approach, speech processing, music, and ambient sound are some of the main points of this problem since finding similarities and differences between these domains is necessary. Human activity can be classified into four different categories in the video approach, depending on the complexity and the number of body parts involved in the action. Examples of Human activity categories are considered: gestures, actions, interactions and activities. Recognizing human actions in the video becomes a challenge with this varied set of human activities. Furthermore, in the last years, the growth of deep learning techniques applied to this area has been enormous, and the reason is that their results surpass traditional signal processing on a large scale. This article is based on audio and video signals inside a vehicle to detect violence. Furthermore, the architecture used was ResNet model with Mel‐spectrogram methodology for audio signals. The proposed method for video signal representation was RGB, which applied four different models: C2D, I3D, X3D, and Flow‐Gated. Finally, multimodal fusion was applied at the end of the process.
Photonic ternary quantum random number generators

We construct a class of three-dimensional photonic quantum random number generators (QRNGs) and prove that each of them generates maximally unpredictable digits via measurements that are robust to errors. This shows that every sequence generated is strongly incomputable; hence its quality is provably better than that of every pseudo-random sequence. These results suggest that incomputability in physics is real and practically applicable. Finally, we present photonic implementations of three-dimensional QRNGs and discuss device independence.
Cybersecurity Technology Foresight: 2040 Scenarios for Turkey

Foresight is a methodical and comprehensive approach that prioritizes investment and research in order to shape the future and implement future strategies. Cybersecurity, one of the most important elements of Industry 4.0, is to protect information and communication systems against cyber attacks and to ensure the availability, confi-dentiality and integrity of these systems and the data therein. This study aims to make a theoretical and practical contribution to foresight and cybersecurity studies by summarizing the updated version of the methods and outputs used in the doctoral thesis titled “Technology Foresight and Modeling: Turkish Cybersecurity Foresight 2040”, con-ducted at Middle East Technical University (METU) in 2019. As a result of the study, Turkey's national cybersecurity technology foresight was given with different scenarios; Turkey's strengths, weaknesses, opportunities and threats in terms of cybersecurity were determined; a cybersecurity technology taxonomy which is officially accepted by the Presidency of Defense Industries was created; the cybersecurity status of the universities and companies in Turkey has been determined; and concrete policy recommendations based on foresight outputs have been put forward. The study is the most comprehensive cybersecurity foresight in Turkey in terms of topics covered and participants, which comprises nearly 150 experts from government, military, academia, and the private sector. Overall, the study provides important insights into the current state of cybersecurity in Turkey and makes recommendations for the future. Its wide range of stakeholders and comprehensive analysis make it a valuable resource for understanding the complex landscape of cybersecurity in the country.
Application Research on Feature Extraction of Liver Image Based on Neural Network

Feature extraction is the key link in the process of image description, classification and analysis. Based on cognitive theory and perceptual experience, given a texture image, no matter how the texture image is transformed, rotated, scaled, affine, or even distorted, the image obtained should be the same texture from the perspective of human observation. Correspondingly, the extracted texture features should also be unchanged. Therefore, invariant texture feature extraction is an indispensable part in practical application and theoretical analysis. From the perspective of texture feature extraction, this paper studies the feature extraction and application of texture image by using the characteristics of PCNN. The icmnn model is improved by adding the connection intensity factor to couple the image structure information and weaken the gray information, so as to extract the image structure information and make the scrambling evaluation result consistent with the visual evaluation. Because the improved algorithm extracts the structure information of the image indirectly, in order to further extract the structure information directly, the image is processed by bit decomposition method, and the direct structure information is extracted by icmnn.
Film Recommendation System with Reinforcement Learning

The main purpose of the project is to develop a recommendation system based on deep learning, which can be used in the film industry. The main components of the system are: deep learning modeling and training, deep learning classification and prediction, and deep learning for collaborative filtering. The basic idea behind deep learning is to train neural networks by using a large amount of data (such as images) as input. Neural network consists of many layers. Each layer obtains input from the previous layer and generates output from the next layer. The first few layers are called convolution graphs or feature maps because they contain movie information about specific features, such as Co.
Communication-Efficient Federated Learning with Model Pruning

In this work, we propose a new model pruning method for federated learning to mitigate the local computation and communication bottlenecks of connected devices, while maintaining a similar accuracy as the original model. The main purpose of this work is to design a communication-efficient federated learning to accelerate learning by reducing communication overhead and latency. In the proposed method, the model pruning method presented by the convergence rate and loss function of federated learning is mathematically analyzed. The experimental results show that the proposed method can improve the communication efficiency of the federated learning by reducing a large amount of model parameters transmitted while maintaining the accuracy via jointly optimizing the pruning ratio and device selection.
Path Based Subgraph Searching in Distributed Environments

In this paper, we propose a distributed processing method for efficiently performing subgraph search in an environment that these graph data are distributed and stored. We use statistics of the data graph to determine the search order. The query graph is divided into triplets through the determined search sequence. The di-vided queries are searched in parallel on the distributed graph of each slave ac-cording to the search order, and the local search results searched in each slave are joined and returned. The performance evaluation verifies the superiority of the proposed method through performance evaluation with the existing method.
The Application of Computer Technology in English Writing Teaching

With the rapid development of China’s economy, people pay more attention to students’ English ability, prompting the school to constantly innovate English writing teaching methods, using computer technology can effectively improve the problems in English teaching, and make students’ oral English ability significantly improved. Therefore, this article briefly explains the importance of using computer technology, the shortcomings of traditional English writing teaching methods, the principles that should be followed, and the application of this technology in English writing teaching, such as creating a real learning situation, optimizing the way of practice and review, etc., and hope to be helpful to school English teaching in China.