Reviewerly/exHarmony huggingface dataset:
Research on the Intelligent Construction and Optimized Development Path of College Stadiums

The research on the optimal development path of intelligent construction of college stadiums can improve the level of intelligent service of college stadiums at home and abroad, enhance the utilization rate of college stadiums, effectively crack the problem of unbalanced and insufficient allocation of college stadium resources, meet the growing and diversified exercise needs of students and teachers to the maximum extent, and promote the development of higher quality of national fitness, which has very important theoretical guidance significance and it is of great theoretical guidance and practical application value. As an important place in colleges and universities, college stadiums must keep up with the trend of the times and make full use of intelligent technology to provide better service and intelligent experience. The construction of college intelligent stadiums is imperative. General Secretary Xi Jinping emphasized that "the concept of health-first education should be established, and physical education classes should be offered in full, so as to help students have fun, enhance their physical fitness, improve their personality and refine their will in physical exercise. In this paper, by investigating the wisdom status of sports venues in the most representative 15 colleges and universities in Ningbo, a random sample of research was conducted, using literature, mathematical statistics, fieldwork, Delphi method and questionnaire survey. In the process of wisdom construction, the special characteristics of different venues need to be considered, and it is important not to copy the model of popular venues, but must adopt the path suitable for universities. On the other hand, using the Internet of Things, big data, VR technology, cloud computing, virtual reality and other emerging information technology, we can solve the problems of "low utilization rate, poor service quality, slow service level, old facilities and equipment" and other problems in the construction of university sports venues, and provide important feasible ideas for further development.
An Evaluation of the Instructions in English Textbook for the Tenth-Grade Students of Senior High School

The research aims to (1) explain whether the students can understand the English textbook instructions (2) explain the component of the English textbook instructions that affect the students’ unsuccess in understanding the textbook instructions (3) evaluate whether the English textbook instructions for the tenth grade of senior high school meet the criteria of Ur (1996) and Harmer (2007) theories. This research uses a descriptive qualitative research design, then the data collection techniques are observation, interview, and document analysis, the analysis begins with reading and studying all the data. Then continues by observing and interviewing the students and the English teacher. The result of this study showed that 38% of the students found themselves capable of understanding they were able to understand instructions in the textbook. The rest of the students still struggled in understanding the textbook instruction. Unfamiliar words are the most affecting the students’ unsuccess in understanding textbook instructions. The textbook instructions that exist in the textbook entitled “Bahasa Inggris untuk kelas X” is categorized as “very clear” and “very unambiguous” based on Ur (1996) and Harmer (2007) theories. The results of the analysis showed that students need teacher assistance in understanding textbook instructions even though the instructions are categorized as clear instructions. Keywords: Textbook Analysis, Textbook Instructions, English Textbook
A Path to Solving Robotic Differential Equations Using Quantum Computing

Abstract Quantum computing and quantum information science is a burgeoning engineering field at the cusp of solving challenging robotic applications. This paper introduces a hybrid (gate-based) quantum computing and classical computing architecture to solve the motion propagation problem for a robotic system. This paper presents the quantum-classical architecture for linear differential equations defined by two types of linear operators: unitary and non-unitary system matrices, thereby solving any linear ordinary differential equations. The ability to encode information using bits or qubits—is essential in any computation problem. The results in this paper also introduce two novel approaches to encoding any arbitrary state vector or any arbitrary linear operator using qubits. Unlike other algorithms that solve ordinary differential equations (ODEs) using purely quantum or classical architectures, the ODE solver presented in this paper leverages the best of quantum and classical computing paradigms.
Printed Ottoman text recognition using synthetic data and data augmentation

The Ottoman script, which was in use for over five centuries, is an Arabic alphabet-based writing system. It became obsolete after the change of alphabet in Turkey. There are plenty of Ottoman documents, overwhelmingly printed in Naskh style. This work presents a DL-based character recognition system for the printed Ottoman script. We first generate a synthetic text image dataset from a text corpus and then augment it using some image processing methods. We develop a hybrid convolutional neural network-bidirectional long short-term memory recognizer and train it with the original and the augmented datasets. Finally, we apply a transfer learning procedure for adapting the system to real image data. The proposed system obtains 0.11 CER on synthetic data and 0.16 CER on real data comprising of line images from a printed historical Ottoman book.
Blockchain: a promising technology for judicial translation services in cases with foreign elements

Purpose Technical advantages embraced by blockchain, such as distributed ledger, P2P networks, consensus mechanisms and smart contracts, are highly compatible with addressing the security issues of transferring and storing judicial documents and obtaining the feedback and evaluation of judicial translation services in cases with foreign elements. Therefore, based on this, a consortium blockchain-based model for supervising the overall process of judicial translation services in cases with foreign elements is proposed. Design/methodology/approach Some judicial documents are required to be translated when there are language barriers in cases with foreign elements. The purpose of this paper is expected to address security issues, which is ignored, in the process of translating judicial documents. Findings The experimental results show that the model constructed in this paper can effectively guarantee the security and privacy of transferring and storing translated judicial documents in cases with foreign elements, and realize the credibility and traceability of feedbacks and evaluations of judicial translation services. In addition, the underlying network communications is stable and the speed for processing data can meet the requirements of practical application. Originality/value The research in this paper provides an innovative scheme for judicial translation services in cases with foreign elements. The model constructed is conducive to protecting the security of the transfer and storage of judicial documents and improving the efficiency and modernization ability of hearing cases with foreign elements.
Cybersecurity Workforce Landscape, Education, and Industry Growth Prospects in Southeast Asia

Given the chronic lack of qualified professionals in the cybersecurity industry, the present paper seeks to evaluate the current interest in cybersecurity across Southeast Asia nations and then compare it with the available educational offerings of related degrees in each country to identify eventual gaps in the market. The goal is to assess whether there is a need for additional degree programs in cybersecurity and to evaluate the potential for future growth in the industry by providing a solid educational foundation for aspiring professionals. To estimate current interest from prospective students and profesionals in cybersecurity across each country, leaderboards from the popular TryHackMe gamified cybersecurity training platform are referenced. We further discuss issues by considering the related formal education programmes offered by the top universities in each country, identified by their presence in the QS world university rankings. The data are then used to propose two new metrics: the ‘Cybersecurity Education Prospects Index’ (CEPI) and the ‘Cybersecurity Industry Prospects Index’ (CIPI), which show how most of the eleven countries in Southeast Asia do have an unmet demand for cybersecurity education and only a few of them have already developed an educational infrastructure that is ready to support the growing needs of the local and international industry.
An application of optical character recognition for document digitization at the Banking Academy

Digital transformation of education and training institutions is becoming an urgent task and Banking Academy is not an exception. In order to facilitate the digital transformation process, digitization tasks must always be promoted. In this paper, the author conducts a research about digitization technology and proposes solutions for digitizing text documents in Banking Academy. Specifically, this article introduces core technologies in document digitization such as Optical Character Recognition, Intelligent Text Processing, investigates typical solutions on the Vietnamese Digitization market to choose the appropriate one and conducts an experiment based on FPT.AI Reader with manually-collected datasets from several departments in the Academy. The experimental results are impressive, with 27% word error rate and only 16% error in the text containing title, department name, document type. This solution could be improved to apply to the digitization process at the Banking Academy in the future.
FGSS: Federated global self-supervised framework for large-scale unlabeled data

Due to the unique advantages of collaborative learning on isolated yet unlabeled data, federated self-supervised learning has received increasing attention from both academic and industrial researchers. Most of the existing federated self-supervised approaches concentrate on the classical scenario, i.e., a large amount of unlabeled data is stored on the clients. However, in many real-world applications, partial labels may be available to the client user, while a large amount of unlabeled data remains on the server side. The existing federated self-supervised methods may usually have difficulty in addressing this scenario. In this paper, we propose a creative federated global self-supervised framework (FGSS) for large-scale unlabeled data that innovatively uses self-supervised learning on the server side, and during every round of communication, we use a small amount of labeled data from the client to facilitate the performance of self-supervised learning. To address the heterogeneity of local data from different clients, we designed an aggregation approach that can adjust the weight of each local model based on the frequency of participation in the communication and the size of its dataset. Experimental results show that our framework outperforms the most existing state-of-the-art methods in both IID and non-IID settings under certain conditions.
A Survey of Fuzzy Pattern Tree Classification Algorithms

Data classification algorithm is the core content of big data mining. Its main function is to extract valuable knowledge and information,analyze the characteristics of all kinds of information, and provide data basis for further research. With the wide application of data mining technology, data classification algorithms continue to emerge and gradually improve. The classic classification algorithms include decision tree classification algorithm, naive Bayes algorithm, support vector machine classification algorithm, artificial neural network classification algorithm, fuzzy pattern tree (FPT) and so on. This paper summarizes several common algorithms in data classification algorithms, and analyzes their characteristics to understand their algorithm principles and application scenarios.
Invoice Detection and Classification based on Improved YOLOv5s

For the complex problems of invoice occlusion, invoice deformation, dark environment, excessive noise and so on in invoice detection, this paper proposes an improved YOLOv5s invoice detection and classification method. In order to improve the generalization ability of the model, the attention mechanism is introduced to improve the feature extraction ability of the network. By adding cavity convolution to the YOLOv5S backbone network and the neck network, and adding context transformation network to the backbone network, the robustness of the model is improved. For model output, flexible non-maximum suppression is used to replace non-maximum suppression to improve the detection effect. Comparative experiments show that the accuracy, recall and average accuracy of the proposed method are greatly improved.
Challenges and Future Research Directions for Stream Clustering

Since the incoming data is unlabeled, streams are more closely tied to clustering. Researchers have focused on stream analysis since streams are produced by a variety of stream applications, including cell phones, GPS, autos, security sensors, televisions, social media, and more. An extensive survey is conducted on the problems relating to data stream clustering in this study. This study discusses various forms of drift, windowing techniques, drift detection technique and clustering algorithm that have been recently employed in the literature to quickly handle incoming data, and an open research questions are conducted and potential future research areas for big data stream clustering.
Twitter Sentiment Analysis using Collaborative Multi Layer Perceptron(MLP) Classifier

In the last decade, Twitter is facing a major challenge in identifying the sentiments of the tweets. These tweets are useful in understanding the opinion of different end users about a variety of topics. Sentiment Analysis is more important to analyse the text data in the dataset. But the tweets consist of non-useful characters which make sentiment analysis difficult. With multi-layer perceptron classifier, classification for new tweets as either positive or negative is done. This can be done by using a range of features like tokenisation, lemmatisation, stemming on the words used in the tweets, the sentiment of the words, and the user’s profile. Feature extraction is made to clean the words/phrases containing non-useful characters like URL, Numeric alphabets, punctuations etc. It improves the accuracy for the large amount of data with high efficiency.
A Medical Assisting System Using Augured Reality For Critical Analysis

Experts frequently monitor new developments to enhance their working environment. They frequently are the first to implement advances that allow their industry to provide a more attentive and patient experience. As the mindful working environment has continued to grow in the computer age, a number of developments have emerged as expectedly challenging developments. Augured reality (AR) is swiftly becoming accessible, affordable, and sensible; as a result, it will undoubtedly be applied to medical services to enhance clinical information utilization. Applications are currently being explored for their position in the specialists, whether it has to do with life structures, intraoperative medical procedure, or post-employable recuperation. The expansion of false data to at least one of the faculties that enables the customer to conduct tasks more effectively is what is meant by AR. We suggest a framework in which crucial information for experts is displayed on foggy glasses recollected for an AR headset and thus integrated with this current reality view. In this project, the ongoing patient data from medical clinics collected by sensors connected to patients receive treatment once the sensor has assessed their characteristics, at this point it is managed, sent off by doctors using expanded reality glasses, and an alarm is set off in case an unusual condition develops. Given the patient’s present medical condition, the specialist can take the appropriate action.
Text Recognition Based On Encoder And Decoder Framework

Now-a-days we all are using digital technologies in all sections. Handwriting textbook recognition is an active and utmost exploration areas in the field of image processing and pattern recognition but, still we’re using Handwriting clones converted into electronic clones to communicate and store electronically.Through the textbook, we reuse the supplied image, rooting features, and feting it. The training of the system to fete and classify objects takes place, as well as the creation of a bracket schema. The system is trained using this system. Handwriting textbook recognition refers to detecting the computer digital comprehensible. Handwriting textbook input for Handwriting sources similar as photos, paper documents, and other sources. Occasionally it’s complex to understand the mortal hand jotting as cursive handwriting, Poor quality document/ image, different individualities have different handwriting styles and other coffers.The main end of this design is to develop a Handwriting textbook recognition system which is used to read scholars and lectures handwritten notes, croakers conventions, Research and Development labs etc. A handwriting recognition system handles formatting, performs correct segmentation into characters, and find correct presumptive of words. The use of neural networks for feting handwriting textbook is more effective and robust. The end is to ameliorate the effectiveness of neural networks for Handwriting textbook recognition. Keywords - Presumptive, Pattern, Neural
A Visionary Approach to Detect Spoofing Website using Machine Learning Algorithms

Today, everyone is addicted to the internet. Web searching has become important for everyone despite their age, location or occupation. However, as the use of the internet is increasing, the spoofing attacks through malicious websites are also increasing. All conducted online purchases and online pursuits being online amount transaction, e-reservation, recharge online and moreonline. As the number of Internet users grows, automatic URL detection is becoming increasingly more important for protected users. The proposed research will compare the contrasting classification models such as Random Forest, Decision Tree and Logistic Regression which have the highest accuracy of differentiation between legitimate sites from phishing sites. The intention of the research is to find the attacking sites and best ML algorithm which has the highest accuracy.
Comparison of Cryptography algorithms in Blockchain

Various algorithms have been employed to enhance the performance of blockchain. Successful algorithms have been developed with the potential to use blockchain technology. In this study, we analyze and explain a list of significant blockchain-based algorithms, including Rivest Shamir Adleman, Advanced Encryption Standard, Data Encryption Standard, Digital Signature, and Elliptic Curve Cryptography. In this article, we examine the Quantum Teleportation security algorithm for Blockchain Technology. This technique is based on encryption to provide well security.Quantum teleportation compares favorably to RSA, DES, AES, and ECC. This survey document serves as a valuable guide for upcoming research issues in Blockchain Technology.
Detection Of Cyberbullying based On Online Social Networks

Online bullying is affected by cyber bullying, which has grave repercussions. It is text-based and occurs in several forms. Most social media networks’ format. The majority of the recent studies have focused on this topic with Most of the models developed in these studies and common machine learning techniques are scalable at once within a single social network. Deep learning-based models claim to be able to overcome the limitations of conventional models and improve the execution of discovery by developing methods for the identification of instances of online harassment. Despite the fact that there are many antiquated methods available to control the occurrence, there is still a lack of demand to successfully control the pestering. To effectively use machine learning and language preparation to filter the pestering in the virtual environment and to prevent the barbaric result A framework is suggested to provide two distinct descriptions of cyber bullying. Our method makes use of CNN’s creative idea for content analysis, however the Current tactics employ a dishonest method to handle provide the arrangement with less accuracy. A current dataset is used for testing, and when our system is compared to other available approaches, it is discovered to provide greater precision and grouping.
Analyses of Machine Learning Techniques for Sign Language to Text conversion for Speech Impaired

Human computer interaction is the research of how individuals and computers interact. When someone does not understand what we are saying, especially when they do not, hand gestures are an excellent way to communicate. It is also a fundamental part of human-computer interaction. It’s essential to comprehend hand signals in order to make sure that everyone in the group understands what the person is trying to say and also that the computer understands what we will be saying. This project’s primary objective is to experiment with various methods for hand gesture recognition. In this project, we use a camera sensor to identify nonverbal communication. Because most individuals do not really know sign language because there are not many interpreters, we first tried to create hand gesture recognition. Then, we built a real-time method for American Sign Language based on deep neural network finger typing, backed again by an approach with media Pipe. We offer a deep cognitive network (CNN) method for identifying human hand gestures in photographs taken with a camera. The objective is to separate camera images from hand motions made during human activity. The training and test data for the CNN were created using skin model, hand location, and orientation information. The filter is the first thing the hand goes through before it is classified according to the sort of hand motion it will make. To build this model, we used computer vision, deep learning, and machine learning. Our Media Pipe model does a good job of detecting multiple gestures
Traffic Vehicles Detection Using Deep Learning Techniques

The idea behind this research idea is to develop a deep learning model for traffic vehicle detection, which predicts types of vehicles (Truck, Car, Bike) using frame difference through OpenCV library. The aim of this research work is to help CCTV surveillance capture the weight of in and out traffic in highways(national and express), which can be used by engineers to analyze the traffic. Many feature extraction techniques has been applied such as Frame Difference, Contour mapping to draw out insightful features for model to predict on.