Reviewerly/exHarmony huggingface dataset:
Users’ Perspectives on Improving English Skills Through Twitter and Telegram

The need to use English in communication places English as an important language to master, especially in this modern era of the Internet. The Internet provides a bunch of information with fast distribution and a wide networking reach. The Internet can also provide material to learn English, which can be accessed for free. The two platforms that provide this access are Twitter and Telegram. Based on this phenomenon, this research aims to dig deeply into users’ perspectives related to their experience using Twitter and Telegram to improve their English skills. The method used in this research is descriptivequalitative. The data sources were collected from open-ended structured interviews, follow-up interviews, and library research. The subjects of the interview are Twitter and Telegram users who use the application to learn English outside of their formal classroom. The analysis of the interviews was done by taking notes and recording the interview results. In addition, the interview results were interpreted and transcribed into English using formal academic language. The journal analysis was conducted by summarizing and synthesizing the sources to record the title, author, year of publication, research background, research questions, and research findings and discussion. The findings show that users have positive responses and experience using Twitter and Telegram to improve their English skills.
Rancang Bangun Aplikasi Audit Internal Fakultas (Studi Kasus: FMIPA Universitas Lampung)

Evaluation of faculty performance is an assessment of the quality of a faculty at the University. This Accreditation Assessment is carried out internally as well as externally by the National Accreditation Board for Higher Education (BAN PT). The Faculty of Mathematics and Natural Sciences is one of the faculties at the University of Lampung, where faculty accreditation is the most important educational support used as an assessment criterion, namely vision, mission, objectives, student archives, and others. The amount of data is one of the problems faced by faculties in storing accreditation data. Referring to the above problems, it is appropriate to develop an "Faculty Performance Evaluation Information System and the University of Lampung Study Program Implementation Unit using the Laravel Framework" which can facilitate the process of searching for the archives and data needed. This information system was built using the prototype method with three stages, namely Listen To Customer (at this stage the system requirements analysis process is carried out based on customer requirements), Build/Revise Mock Up (at this stage the design and coding process is carried out according to customer requests), and Test Drives Mock Up (at this stage testing the system that has been made to the user is carried out). The coding for the development of this system uses the PHP programming language and to simplify the coding process for the Laravel framework design. The testing method used on this system uses the Black Box Testing method with the Equivalence Partitioning type. The result of this study is a Web-Based Faculty Performance Evaluation Information System that can facilitate staff in inputting the required data.
APPROACHES TO AUTOMATIC WORD SENSE DISAMBIGUATION BASED ON UNEVEN DISTRIBUTION OF WORD SENSES IN CORPUS

Word Sense Disambiguation (WSD) is a key task of automatic semantic analysis that affects other upstream tasks. Nevertheless, the selection of appropriate sense of ambiguous word in context is a complicated task even for human native speakers. It is even more relevant for automatic disambiguation models. That is why we need any observations and heuristics able to make the WSD task simpler or performance higher. Researchers have noticed that the distribution of ambiguous word senses follow certain laws. In the paper we discuss three hypotheses about word senses distribution in corpus: 1) Most Frequent Sense, MFS; 2) One Sense per Discourse and 3) One Sense per Collocation. The following results were obtained on the material of a corpus of Russian texts. Most Frequent Sense based algorithm demonstrates relatively high precision on both training and test set (85.7% and 71.1% respectively). The One Sense per Discourse hypothesis has been confirmed in 93% of texts. The One Sense per Collocation hypothesis has been confirmed in 84.46% word pairs from texts. The exceptions are related to difficulties and errors by manual word sense labeling. Heuristics based on uneven distribution of ambiguous words in corpus allow to make WSD task simpler and can be used by collecting training data sets for WSD models.
Utilising QR-Code of Covid-19 Vaccine for Attendance Using Extreme Programming Method

The digital attendance system enables more efficient data processing, real-time time monitoring, and reduces human errors in the recording process. One of the schools that has developed this technology is SMK Negeri 4 Kota Sorong. This is related to the last two years, where almost everyone has been required to receive the COVID-19 vaccine for prevention and monitoring of student attendance at school. To support the monitoring of student attendance at school, a web-based COVID-19 Vaccine QR-Code utilization system is needed at SMK Negeri 4 Sorong, Southwest Papua. This student attendance system has three access levels, which are divided into admin, teachers, and parents. This research uses PHP programming language and MySQL database. The development of this system is carried out using the Extreme Programming method, which includes steps such as problem identification, data collection, applying the Extreme Programming method, and implementation. The school attendance system, which has been built with its features, has been proven to work perfectly through feature testing using black-box testing with a 100% success rate. From the results of this testing, the utilization of the COVID-19 Vaccine QR-Code can be used for attendance and departure monitoring.
Real Time Traffic Sign Recognition Model Using Convolutional Neural Network

This paper aims to summarise the development of a robust and accurate system for detecting traffic signs in real-time using convolutional neural networks (CNN) and the Python programming language. By enabling autonomous vehicles to reliably identify and interpret traffic signs, the goal is to increase road safety. The proposed method seeks to handle difficulties brought on by real-world settings, such as fluctuating lighting, weather, and obstructions, by developing an efficient pre-processing method and using a CNN-based model for feature extraction and classification. The goal is to achieve high accuracy in traffic sign detection while maintaining real-time performance, making it a feasible solution for autonomous driving systems.
APPLICATION OF INTERACTIVE TEACHING METHODS IN THE EDUCATIONAL PROCESS

Abstract. The article reveals the problem of using an online platform as a basis for implementing pedagogical interaction on the Internet using various telecommunication channels with on-line or off-line modes, mass communications, social network services to enhance the cognitive activity of students. The concept of “educational online platforms” is revealed, on which innovative teacher tools for organizing an interactive educational process are located, which are in demand when implementing programs for the study and application of interactive technologies in education, since on their basis it is possible not only to provide theoretical and practical educational materials, but also to implement the very interactive interaction of participants in the educational process. The article describes innovative learning technologies, including types of online platforms and ways of using them in the educational process. Thus, the word interactive - the term interaction refers to the tendency to talk or communicate with someone (a person) or with someone (such as a computer) in an interview manner. Innovative educational work is formulated through the rational introduction of new technologies that adapt the educational process to productive learning. These include modern technologies such as cable and satellite television, video conferencing, teleforums, telethons, portfolios, personal achievements in education and training, and life skills training, including social learning, among which various achievements are mentioned. In the article, I focused on various online platforms that I use in the educational process. For example: Quizzlett, Wordwall, Canva, Padlet, Wooclap, Bouncy Balls, etc. Key words. Online learning; educational online platforms; network technologies; interactivity; interactive learning technologies; Internet services
ASSESSMENT TECHNOLOGY AND METHODOLOGY OF APPLICATION IN PREPARATION FOR THE UNIFIED NATIONAL TESTING

Abstract. The use of assessment technology in preparation for the Unified National Testing is a necessary condition for the formation of mental qualities of a student's personality. The article describes the assessment technology and the methodology of its application in the preparation of the UNT. It includes the improvement of teaching methods of disciplines, the training of competent personnel for these disciplines, an objective assessment of students' knowledge. The system of preparation for the Unified National Testing educational work, the introduction of an interactive way of learning that adapts the educational process to productive education. It was said that interactive learning is, first of all, learning in which teachers and students interact and conduct interviews. About the main goals of interactive learning: the establishment of free and open creative communication between students, the orientation of students to independently search for knowledge, the development of necessary skills and abilities in students. We want to share our experience in improving the results of the Unified National Testing. We are developing an annual plan for students. The annual plan is based on the materials presented to each class. Exam materials cover various topics: historical (foreign) problems, proverbs, idioms and phrasal verbs, etc. It is important that the preparation for the "Unified National Testing" takes place in three stages: step-by-step preparation of goals, the development of knowledge on the subject of choice for deepening knowledge, identifying the student's capabilities and assistance in finding the right solution, working with each student individually and replenishing their knowledge, etc. The importance of the criterion assessment is emphasized-the type of assessment of students, since it is important to know the subject, depending on it, the issues of obtaining more points, as a result of the UNT. Important - communication skills, consistency, discipline, economical use of time, concentration, complete assimilation of materials, competence of students participating in the UNT, Unified National Testing, all this motivates to a good result. We believe that the methods used will help students to effectively apply their knowledge, get positive and good results. Interactive methods are also of great importance.
An Integrated Two-Factor Authentication Scheme for Smart Communications and Control Systems

Fast and reliable authentication is a crucial requirement of communications networks and has various research challenges in an Internet of Things (IoT) environment. In IoT-based applications, as fast and user-friendly access and high security are required simultaneously, biometric identification of the user, such as the face, iris, or fingerprint, is broadly employed as an authentication approach. Moreover, a so-called multi-factor authentication that combines user identification with other identification information, including token information and device identity, is usedto enhance the authentication security level. This paper proposes a novel twofactor authentication scheme for intelligent communication and control systems by utilizing the watermarking technique to incorporate the mobile device authentication component into the user’s facial recognition image. Our proposed scheme offers user-friendliness while improving user security and privacy and reducing authentication information exchange procedures to provide a secure and lightweight schema in real applications. The proposed scheme’s security advantages are validated using the widely accepted Burrows–Abadi–Needham (BAN) logic and experimentally assessed using the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator tool. Finally, our experimental results show that the proposed authentication scheme is an innovative solution for a smarthome control system, such as a smart lock door operation.
ENGLISH IN THE INTERNET

The article shows the role of the English language and its dominance on the Internet. Both ad-vantages and disadvantages of the widespread use of English on the Internet are considered
Consideration of Data Security and Privacy Using Machine Learning Techniques

As artificial intelligence becomes more and more prevalent, machine learning algorithms are being used in a wider range of domains. Big data and processing power, which are typically gathered via crowdsourcing and acquired online, are essential for the effectiveness of machine learning. Sensitive and private data, such as ID numbers, personal mobile phone numbers, and medical records, are frequently included in the data acquired for machine learning training. A significant issue is how to effectively and cheaply protect sensitive private data. With this type of issue in mind, this article first discusses the privacy dilemma in machine learning and how it might be exploited before summarizing the features and techniques for protecting privacy in machine learning algorithms. Next, the combination of a network of convolutional neural networks and a different secure privacy approach is suggested to improve the accuracy of classification of the various algorithms that employ noise to safeguard privacy. This approach can acquire each layer's privacy budget of a neural network and completely incorporates the properties of Gaussian distribution and difference. Lastly, the Gaussian noise scale is set, and the sensitive information in the data is preserved by using the gradient value of a stochastic gradient descent technique. The experimental results showed that a balance of better accuracy of 99.05% between the accessibility and privacy protection of the training data set could be achieved by modifying the depth differential privacy model's parameters depending on variations in private information in the data.
AUTOMATED COUNTING OF SQUAMOUS EPITHELIAL CELLS IN LIQUID-BASED CYTOLOGY SAMPLES

Cytologic screening is only effective with adequate material. The Bethesda System (TBS), an international system for standardizing the results of cervical cytology, establishes criteria for adequacy. According to the TBS, a sample prepared by liquid-based cytology (LBC) can be classified as adequate if it contains not less than 5,000 squamous epithelial cells. There are methods of manual assessment of the cellularity of cytological samples. However, the uniformity of cellular material distribution of LBC samples makes possible the application of automatic tools for assessing the adequacy of the material based on the cellularity criterion. This paper compares the results of digital LBC samples adequacy assessment using the automated artificial neural network method Vision Cyto Pap and the manual assessment method. Analysis of the results of counting squamous epithelial cells in 506 LBC samples showed a significant correlation (r = 0.93) between the automated and manual method. The high diagnostic parameters (sensitivity = 93.8 % and specificity = 99.2 %) of the automated Vision Cyto Pap method allow to conclude that the algorithms of such a tool can be used to determine the adequacy of the cytological material by the number of squamous epithelium cells with sufficient reliability.
NFTs AS THE NEXT STEP IN DIGITAL ECONOMY

Non-fungible tokens (NFTs) are a type of digital asset that represents ownership or authenticity proof of a unique item using blockchain technology. Each NFT has a distinct ID, and its ownership and transaction history are recorded using blockchain technology. In other words, NFT is a way for blockchain technology to certify ownership and tradeable rights to digital assets. Unlike cryptocurrency units, they cannot be exchanged like for like. They are unique for each item and, therefore, create a new way of authenticating [1]. Text, picture, video, audio, and game items can all be copied, but the token itself cannot be copied. Tokens are stored cryptographically on a blockchain, which is an immutable ledger that facilitates the process of recording transactions and tracking assets. An immutable ledger is permanent and immune to data corruption, as once the information is recorded, it cannot be altered. These concepts are combined with the benefits of smart contracts. A smart contract is a precondition written into the code on a blockchain. If predetermined conditions are met and verified, computers execute actions like releasing funds to the appropriate parties, issuing tickets, sending notifications, etc. They are commonly used to automatically carry out an agreement, ensuring that all involved parties promptly know the result without the need for an intermediary or delays. The blockchain ledger is subsequently updated upon the culmination of the transaction [2]. NFTs enable the generation and exchange of distinctive digital assets and have the potential to create new economic opportunities and value streams. They have already shown significant potential for generating substantial returns for their holders, thus generating tangible value for both buyers and sellers.
Research and analysis of problem-solving strategies of conic section in high school mathematics

The conic section is a crucial part of high school mathematics and usually plays the role of tough problem in examinations. Hence, this dissertation will use the literature analysis method to introduce the mathematical background of the conic section to enable students to know about the conic section more clearly and explain the basic concept of the conic section such as the first and second definitions of conic section, then introduce some second level conclusions such as focus length formula and average property of parabola. After that, this dissertation will provide some conic section problems in past exam papers and corresponding problem-solving processes to show how to use the basic concept and these common second-level conclusions of the conic section, then summarize the train of thought of the conic section. For choice questions and fill-in-the-blank questions, combining the condition given by questions with the first definition and second common level is important, and for questions that need to write the complete process, setting parameters and Vieta theorem is essential, and for extremely complicated problems, special methods such as homogenization can be considered to reduce the calculation.
The application of fractal and chaos theory in photo processing

Nowadays, more and more people are looking for a faster and safer way of encryption. Fractal coding and chaos encryption fit those two points. This article discussed the usage of Fractal and Chaos Theory in photo processing. For example, Fractals and chaos theory are usually used in picture encrypting and picture processing to make the object in the target picture much more clearly. And currently, the method become advanced. The current method can provide great defense from hacking. Meanwhile, in terms of compression performance, it achieves a higher compression ratio than other solutions while meeting the reconstruction visual quality. In this article, the writer analyses the future of Fractal coding by talking about the advantages and disadvantages of this method which came from different articles. Furthermore, in this paper, the author discusses the definition of Fractal and the properties of Fractal. The Iterated Function System (IFS) is also a key point in photo processing.
Sequential recommendation based on graph transformer

Sequential Recommendation (SR) is an important scenario in recommendation tasks. Sequential recommendations model the sequential pattern between item-item or user-item based on a user's recent activity in a time series to predict their next preference. However, existing methods are based only on the conventional Graph Neural Networks (GNN) as a model architecture for adaptive fine-tuning of specific SR tasks. To get better recommendation results, more advanced GNNs can be used as the network architecture of the SR method. This paper introduces graph transformer, a combination of GNN and a good sequential task processing model. Then a cross-sectional comparison is made with the current SR method model and its suitability for application in SR tasks is discussed. The comparison shows that the graph transformer is similar in principle and structure to the current SR models, and requires the addition of some adaptive components to be applied in SR tasks. The superior performance after application can be demonstrated from the results data of the Benchmarking-GNNs and Long-Range Graph Benchmark on the models.
RESEARCH ON THE FRAMEWORK OF MODERN DISTANCE EDUCATION KNOWLEDGE MANAGEMENT SYSTEM BASED ON KNOWLEDGE CHAIN

Knowledge management is applied to modern distance education and has become an indispensable management model for the healthy operation of its education system. Currently, knowledge management research presents The situation is complicated and the theoretical systems and views are also different. In order to overcome the shortcomings of too broad research on knowledge management, people have put forward the concept of knowledge chain. concepts, and attribute the core of knowledge management to the management of knowledge chains. This article uses the knowledge chain model as a framework to study knowledge management in distance education. Based on the understanding of the original knowledge chain, a new knowledge chain model was established and the current situation of knowledge management in modern distance education was analyzed. On this basis, proposed the modern far Programming education knowledge management system framework.
Aspect-level sentiment classification with fused local and global context

Abstract Sentiment analysis aims to determine the sentiment orientation of a text piece (sentence or document), but many practical applications require more in-depth analysis, which makes finer-grained sentiment classification the ideal solution. Aspect-level Sentiment Classification (ALSC) is a task that identifies the emotional polarity for aspect terms in a sentence. As the mainstream Transformer framework in sentiment classification, BERT-based models apply self-attention mechanism that extracts global semantic information for a given aspect, while a certain proportion of local information is missing in the process. Although recent ALSC models have achieved good performance, they suffer from robustness issues. In addition, uneven distribution of samples greatly hurts model performance. To address these issues, we present the PConvBERT (Prompt-ConvBERT) and PConvRoBERTa (Prompt-ConvRoBERTa) models, in which local context features learned by a Local Semantic Feature Extractor (LSFE) are fused with the BERT/RoBERTa global features. To deal with the robustness problem of many deep learning models, adversarial training is applied to increase model stability. Additionally, Focal Loss is applied to alleviate the impact of unbalanced sample distribution. To fully explore the ability of the pre-training model itself, we also propose natural language prompt approaches that better solve the ALSC problem. We utilize masked vector outputs of templates for sentiment classification. Extensive experiments on public datasets demonstrate the effectiveness of our model.