Reviewerly/exHarmony huggingface dataset:
Investigating EFL learners' writing anxiety in English class

Writing is a productive-skill that several students struggle with writing in EFL. A proper piece of writing needs appropriate writing skills. Writing skills are crucial for developing the students' academic writing. Thus, good writing is regarded as a measuring tool the performance of students' academic. Anxiety impedes students' performance in writing due to several reasons. Therefore, the prime goal of the current study is to explore the levels, the dominant type of WA and the sources of anxiety in writing English as foreign language. For this purpose, 365 university students participated. The data were collected through implementing two questionnaires. Firstly, Cheng's (2004) SLWAI was adapted to investigate the levels as well as the types of WA. Then, the CSLWAI questionnaire was adapted which were promoted by (Rezaei and Jafari, 2014) to find the causes of WA at Soran university. The findings showed that the individuals had moderate to severe degrees of WA. The findings also showed that the most provoking form of WA was avoidance behavior anxiety. In addition, the results of the CSLWAI demonstrated that low self-confidence, time limitations, linguistic incompetence, fear of tests, stress of doing a perfect work, insufficient and in effective feedback were main causes of writing apprehension.
Mr-TEE

Industry 4.0 is increasingly using commodity hardware and software in place of dedicated control systems to lower costs and increase flexibility. However, this means that critical control code must compete for resources with an increasingly complex software stack that exposes a new attack surface. The Mixed Criticality Trusted Execution Environment (Mr-TEE) tackles this problem at its root, by delivering availability for safety-critical control code, while running untrusted applications in a minimally modified Linux stack. This is achieved by providing a real-time scheduler and novel peripheral sharing system in the Secure World. Mr-TEE hence provides the best of both worlds for Industry 4.0 developers, ensuring the trusted execution of time-sensitive control applications, while minimizing design effort and restrictions for untrusted applications. Evaluation on an Arm TrustZone-enabled Cortex MCU demonstrates a worst-case overhead of 1.88% to support real-time scheduling in the TEE.
A Dataset of Vietnamese Documents for Text Detection

Document analysis and recognition is a crucial technique for automating the input process of forms, receipts, documents at banks, governments, companies. With demands in both research and industry, there are available datasets for Document Analysis and Recognition in English, Chinese, Arabic, and Indic. However, there is no publicly datasets for Vietnamese Document Analysis and Recognition. In this paper, we introduce a new dataset for Vietnamese Document analysis named VNDoc, which aims to set up a standard dataset for researching and developing Vietnamese Document Analysis Systems. The dataset contains 226 documents scanned from mobile phones and scan machines. The documents are collected from diverse categories such as legal and administrations, invoices, resumes, handwriting forms, and so on, which target various applications. At the first stage, we provide ground truth for text lines, which allow performing research in text detection and layout analysis. Moreover, we describe a statistical analysis of text length and bounding box in the dataset and initial experiments for the existing methods for text detection. We are going to provide text transcriptions and available for research communities.
DeepGram: Combining Language Transformer and N-Gram based ML Models for YouTube Spam Comment Detection

Spam comments on YouTube videos are a persistent issue that can negatively impact the user experience and content creator's reputation. In this paper, we propose an algorithm called "DeepGram" for detecting YouTube spam comments using a combination of deep learning-based language transformer models and N-gram based machine learning models. The algorithm leverages the power of language transformers, which have shown significant success in various natural language processing tasks, along with N-gram based models that capture local context and patterns in the text data. The proposed algorithm goes through several stages, including data collection, text preprocessing, feature extraction, and model training. The collected YouTube comments are preprocessed by removing special characters, punctuation, and HTML tags, and converting them to lowercase. Common stop words are also removed, and stemming or lemmatization is applied to reduce dimensionality. The algorithm then extracts features from the preprocessed comments using a combination of language transformer models and N-gram based features. Finally, the features are fed into machine learning models for training and evaluation. Experimental results on a large dataset of YouTube comments show that the DeepGram algorithm achieves high accuracy and robust performance in detecting spam comments. The proposed algorithm can be potentially employed as an effective tool for YouTube content creators and platform moderators to combat spam comments and improve the quality of user interactions on YouTube videos.
Analyze the Novel Apriori Algorithm for Frequent Pattern Generation and Improving the Performance by Generating Powersets for Unique Records

Knowledge discovery is the course of absorbing facts and relations from immense amounts of data. Algorithms like Apriori let an experimenter locate the hidden pattern within a dataset. However, numerous applications do not utilize the Apriori technique because it takes a prolonged time to discover the frequent itemset. If the largest frequent itemset with length \(k\) exists, the algorithm accomplishes a \(k\) scan to deal with the time-consuming difficulty caused by the \(k\) number of scans. Rajeswari and Vaithiyanathan (A novel method for frequent pattern mining, International Journal of Engineering and Technology 5(3) (2013), 2150 - 2154) devised a Novel algorithm for frequent pattern mining that uses a single scan to discover a frequent itemset of length \(k\) by constructing a subset of transactions. In this work, the Novel algorithm's performance is embellished by generating powersets for unique records. Powerset generation is a costly operation that takes exponential time to compute. So, avoidance of unnecessary computation results in performance enhancement. To accomplish used two mechanisms to improve the performance of the Novel method viz Transaction Subset Plus Cache (TSPC) and Transaction Subset Without Duplicate Record (TSWDR). Finally, performance analysis is done between four algorithms Apriori for frequent pattern generation, Novel method for frequent pattern generation, TSPC and TSWDR.
Creative Processes

In this chapter, we look at creative processes that involve computing and data as material. We intentionally put this chapter in the last part of this book, also as a reflective perspective on what we wrote earlier. We emphasized making over thinking in the first two parts; now it’s finally time to explore the framing of this book. We start with the ideation and look at two different approaches before proceeding with abstraction layers and ways to shift technical perspectives within our work.
Practice of “Precision Teaching + Independent Learning” on “Moshuiping” Platform

Wuhan No. 3 Boarding Middle School is located on Jiangdi Middle Road, Hanyang District, Wuhan. Under the guidance of the concept of quality education, the school actively carries out the curriculum reform of “precision teaching + independent learning” based on the smart education platform. The school starts by changing the traditional teaching mode and relies on Tianyu’s smart education platform (Moshuiping) to implement the “precision teaching + independent learning” quality classroom. The school aims to comprehensively improve students’ core literacy and lay the foundation for every student’s quality life.
Research on the Design of Systematic Model of Hybrid Learning and the Research on Effective Model

The study constructed a “stepwise deepening” blended teaching model (GMSDBL), and carried out a 2-month quasi-experimental study in a school in Foshan, mainly solving “how to design blended learning” and “ How to determine which blended learning mode is more effective”, the study shows that there are significant differences in deep learning capabilities in multiple blended learning modes.
Building a Regional Efficient Operation Management Platform

Shanxue Online is an intelligent application service complex, providing customized technology research and development services and resource content expansion services according to the individual needs of regions and schools.
Flow Fields and Particle Storms with p5.js

After taking you through the four steps and the larger example MOUNTROTHKO, it is now time to look at Processing’s younger sibling that plays the web: p5.js. In this and the next chapter, we will work on a set of new examples using p5.js and explore how we can create generative art and make use of more complex touch and sensor input for interactivity.
Cloud School: Practice and Thinking of the New Pattern of Smart Education

The main educational objects of No.5 Junior Middle School of SND are migrant children. The migrant children refer to those who follow their parents (The parents are non-resident migrant workers.) to live or study in another place or the children of non-registered employed persons.
Multi-Microcontroller Sensor Data Acquisition to Enhance UAV Attitude Control, Predictive Maintenance, and Healthcare Solutions

This paper studies multi-microcontroller data acquisition systems in various industries, such as UAV attitude control, predictive maintenance, and healthcare solutions, specifically human-fall detection. We use state-of-the-art microcontrollers, L475 and L485I, from the STM32 family to capture sensor data. We propose a five-layered data acquisition architecture, which includes the development of a graphical user interface to make data collection more efficient. We then pre-process the collected data before feeding it to machine learning models and deep neural networks. We use a decision tree classifier for UAV stability control, a gradient boosting classifier for human fall detection, and a multi-layer perceptron classifier for predictive maintenance. The results show the robustness and reliability of the proposed architecture, and it offers promising implications for optimizing maintenance practices, providing safer options, and enhancing patient care.
Secure Data Provenance for Internet of Vehicles with Verifiable Credentials

In this paper, a novel secure data provenance proto-col is proposed for Internet of Vehicles (IoVs) based on verifiable credentials (VCs). Our protocol leverages VC technology to achieve provable security properties in terms of source identity, location authenticity, and data integrity, these are the fundamental properties for achieving secure data provenance. We present a detailed protocol description, security analysis and verification to validate the claims of the protocol. Our protocol is decentralised and suited to the dynamic and multi-hopping environment of the IoVs.
Area-optimized Serial Architecture of PRINT Cipher for Resource-constrained Devices

The study of lightweight symmetric cipher has gained attention because of the rising interest for security administrations in constrained computing environments, like in the Internet of ThingsInternet of Things and other resource-constrained devices. Most of the IoT devices are connected to the real world and it has a wide scope of utilization in different domains. They collect data and transfer personal information over the network, and the amount of data shared by IoT is growing at an extraordinary scale. These devices have a physical constraint in area, memory, and power. Conventional cryptographyCryptography algorithms such as Data Encryption Standard (DES) and Advanced Encryption Standard (AES) provide high security to the devices and are capable of handling extensive input data. However, area required and power consumption of AES and DES ciphers are high for resource-constraint devices. AES and DES are best suited for devices where area and power are not the factor of concern. PRINT cipherPRINT cipher is a lightweight block cipherBlock Cipher which is a well-known encryption algorithm that consumes less hardware area due to the straightforward encryption operation. This paper discusses the FPGA implementation of the 6-bit data path serial architectureSerial Architecture of the PRINT cipherPRINT cipher. It is one of the block ciphersBlock Cipher requiring minimum resources and supports block sizes of 48 and 96 bits with the key sizes of 80 and 160 bits, respectively. The proposed design is implemented in Verilog on different FPGA families. This paper presents the serial hardware architecture of PRINT cipherPRINT cipher that supports a 48-bit data path with an 80-bit key size length. The slice count of 39 is obtained on Virtex-5, which is 44.2859% less than the slice consumed by the PRESENT architecture, and the proposed architecture is best suited for devices that require less hardware area. Hardware implementation results on different FPGA families such as Spartan-7 and vertex-5 have been discussed.
Efficient Quantum Private Comparison without Sharing a Key

Quantum private comparison (QPC) allows at least two users to compare the equality of their secret information, for which the security is based on the properties of quantum mechanics. To improve the use of quantum resources and the efficiency of private comparison, a new QPC protocol based on GHZ-like states is proposed. The protocol adopts unitary operations to encode the secret information instead of performing quantum key distribution (QKD), which can reduce the amount of computation required to perform QKD and improve the utilization of quantum resources. The decoy photon technique used to detect channel eavesdropping ensures that the protocol is resistant to external attacks. The quantum efficiency of the protocol reaches 66%. Compared with many previous QPC schemes, the proposed protocol does not need to share a key and has advantages in quantum efficiency and quantum resources.
Blended Teaching System for College English with Multi-source Data Fusion in Edge Computing

The modern education system involves the online education system with the traditional method of studies. This increases the education system's versatility and helps increase the quality of studies. The use of modern digital technology also helps the students to gather and accumulate information about their study topics. The use of edge computation helps the education system to access all the information in comparatively less time. This increases the performance of the students and teachers in analyzing the different things in the study. Multi-source data fusion is important for comparing synchronized data from different sources. This fusion system helps to allocate the data through the database and helps the students to use data from a distinctive database. This system has saved the time of students to search different contents. They are getting ready-made content from the database as per their requirements. This type of technology is essential to increase students' performance. These systems collectively develop the education system more easily and will enhance students' interest. The students could learn English using different related content and practice sheets. Digital education helps students to get appropriate sources of study materials for developing quality.
Hubungan konformitas teman sebaya dengan motivasi belajar siswa SMP Muhammadiyah

&lt;p&gt;This study aims to see and determine the relationship between peer conformity and learning motivation.The study was conducted at SMP Muhammadiyah 1 Mlati Sleman in the academic year 2021/2022. This research was a primary research with a proportional random sampling design and a sample of 129 class VIII students was obtained. The measurement uses primary data, which is distributing questionnaires to the sampel. The analytical technique used to prove the hypothesis is to test the classical assumptions and test the pearson product moment correlation. The results showed that peer conformity has a very strong influence on student learning motivation. This is evidenced by the Pearson Correlation value of -0.011 and a significance value of 0.899.&lt;/p&gt;
Absolute phase retrieval based on fringe amplitude encoding without any additional auxiliary pattern

An absolute phase retrieval method based on fringe amplitude encoding is proposed. Different from the conventional intensity coding methods which are based on time division multiplexing with multiple additional auxiliary patterns, the proposed fringe order encoding strategy is codeword overlapping interaction based on space division multiplexing. It just directly encodes different fringe amplitudes for different periods in corresponding sinusoidal phase-shifting patterns to generate space division multiplexing composite sinusoidal phase-shifting patterns and quantifies the fringe amplitudes into four levels as encoding strategy, so it can retrieve absolute phase without any additional auxiliary patterns. To improve the anti-interference capability of the proposed method, a codeword extraction method based on image morphological processing is proposed to segment the grayscale. Consequently, both the phase-shifting sinusoidal deformed patterns and the single frame space division multiplexing four gray-level codewords for fringe order recognition can be extracted respectively from the captured composite deformed patterns. Then, a half-period single-connected domain correction method is also proposed to correct the codewords. Moreover, in order to suppress the effect of jump errors, the phase zero points are constructed to segment the positive and negative ranges of the phase, making the phase unwrapping process segmented. The experimental results demonstrate the feasibility and effectivity of the proposed method.
Chaotic Particle Swarm Optimization with Spiral-shaped Update Mechanism

Particle swarm optimization (PSO), as one of the metaheuristic optimization methods, has the merits of fast convergence, few adjustable parameters and simple searching process, but it also has the disadvantages of easily falling into local optimal and low convergence precision. Hence, a chaotic PSO with spiral-shaped update strategy (CPSOS) is presented in this work. First, the chaotic mapping method is used to generate the initial swarm to enhance the quality of initial particles. Second, a method based on time-varying acceleration coefficients is employed to trade off the exploration and exploitation through the analysis of velocity and position changes for particle. In addition, an adaptive position updating strategy based on spiral-shaped mechanism is adopted in different evolutionary states to get better global convergence. Finally, simulation experiment of the CPSOS algorithm is carried out based on 12 classical benchmark functions, and the convergence analysis under 2-dimensional environment is also detailed. It can be concluded from the convergence figures that the CPSOS has much better robustness and stability by improving the shortcomings of standard particle swarm optimization.
APPLYING COLLABORATIVE FILTERING METHOD FOR DOCUMENT RECOMMENDER SYSTEM

The recommender system helps recommend relevant information items to the user. In recommender systems, collaborative filtering is commonly used to gauge users' interest in new products. Collaborative filtering systems often rely on data about the similarity of users or products in the system in the past to predict preferences or new products for specific users. In this article, we apply the collaborative filtering technique with the k-nearest neighbor to recommend documents for the English center. The implementation process includes the following steps: Firstly, we build a system to collect and store data in the database; Secondly, we implement a recommendation algorithm with three cases, including Case 1 for new users, Case 2 for users who have seen the most document items, and Case 3 for centers' members. The results make it easier for users to find documents.
APPLICATION OF MACHINE LEARNING IN DETECTING LOAN DELINQUENCY: CASE STUDY OF MICROFINANCE INSTITUTION IN UZBEKISTAN

The rise of the internet has revolutionized the way we live, work, and communicate. Alongside this digital revolution, a new phenomenon has emerged - big data
Paper Invoice Dilemma and Electronic Invoice Solution

This paper will start from the definition and purpose of paper invoice, analyze the shortcomings and problems in the use of paper invoice, and introduce the definition, advantages and purpose of electronic invoice, in order to solve the dilemma faced by the use of paper invoice. As a traditional form of bill, paper invoice plays an important role in commercial transactions, but there are some problems in its use. First of all, paper invoices are prone to damage, loss or tampering, increasing the risk and uncertainty of transactions. Secondly, the preservation and management of paper invoices requires a lot of time, space and labor costs, which brings no small burden to enterprises. In addition, because the paper invoice cannot realize real-time information sharing and tracking, resulting in the information process is not smooth and inefficient. In order to solve these problems of paper invoice, electronic invoice came into being. Electronic invoice is a new form of bill based on electronic data exchange and digital signature technology. It has the following advantages: First, electronic invoice does not need paper carrier, reduces the cost of use and management, and is not easy to be damaged, lost or tampered with, improving the reliability and security of transactions. Secondly, electronic invoices can realize real-time information sharing and tracking, which improves the efficiency and convenience of transactions. In addition, the electronic invoice can also be seamlessly connected with the enterprise's financial management system, to achieve automated accounting processing, reducing manual operations and errors. In short, there are a series of shortcomings and problems in the use of paper invoices, such as easy loss, difficult management, not easy to share information and so on. As a solution, electronic invoice has been more and more widely used and promoted by virtue of its advantages of eliminating paper carrier, improving transaction security and reliability, and realizing real-time information sharing and tracking. In the future, with the continuous development and improvement of technology, electronic invoices will become the mainstream form of business transactions, and promote the process of paper invoices to electronic transformation.