Reviewerly/exHarmony huggingface dataset:
Analysis on connection and translation of natural language to traditional computer language

From Siri to ChatGPT, natural language processing has been applied to human-computer conversations with remarkable success. Natural language processing (NLP) is an intimate connection between humans and computers, allowing machine learning to directly translate human language into computer language. They appear to be able to communicate directly with humans. Using a literature review methodology, this paper examines the connection and translation of natural language to traditional computer language. In addition to describing the fundamental principles, primary tasks, and implementation steps of NLP, it analyzes the problems that may arise during the development process in the future. In addition to comparing, citing, and classifying human language and computer language, the paper employs appropriate computer language processing tools. Through a side-by-side comparison of natural language and traditional computer language, the fundamental principles of natural language processing are explained and analyzed concisely, and some helpful suggestions for the detailed processing of natural language are provided.
Structural analysis of U-Net and its variants in the field of medical image segmentation

Medical image segmentation can provide valuable information for doctors, it has important research value in the medical field. Meanwhile, U-Net, as the fundamental networks for such tasks, brings a substantial improvement in the segmentation performance of traditional medical images. With the increasingly widespread use of U-Net, researchers have designed various U-Net variants according to different task requirements. However, most of the current summaries of U-Net variants are divided according to the direction of network applications, and the structural relationship between the variant networks and U-Net is not elaborated. Therefore, this paper classifies U-Net variants according to their network framework by elaborating the principles of U-Net structure. According to the U-Net network structure, it is divided into three main categories: backbone improvement, module addition and cross-network fusion. Further, the characteristics, advantages and disadvantages of different categories of variants are introduced, and the directions of the variants for U-Net optimization are analyzed. Finally, the article summarizes the current development direction of U-Net variants and provides an outlook on the future directions that can continue to be optimized.
The advance of non-digital-currency application with blockchain

With the vigorous development of blockchain technology, its decentralization, privacy, convenience, and other characteristics make it shine in the financial field. Today, blockchain technology has gradually been widely integrated into government, the Internet of Things, supply chain management, healthcare, and other areas. It can change the traditional centralized data management model and provide distributed and decentralized data storage and management methods. This method has higher security, transparency, and reliability, which can solve the problems existing in traditional data management methods and improve the efficiency and reliability of data management. In the meantime, using new technologies also brings many practical problems to solve. These include insufficient technology maturity, privacy concerns, and volatile legal and regulatory policies. This paper delves into these real-world scenarios and summarizes some recommendations. This will give a greater understanding of the principles and characteristics of blockchain technology, promote the popularization of blockchain technology, have a more comprehensive understanding of the use of blockchain technology in different fields, and provide valuable research materials for researchers in related fields in the future.
The Role of Artificial Intelligence in Improving the Quality of Education and Research

The purpose of the study is to determine the role of artificial intelligence in improving the quality of education and research. To achieve this goal, a content analysis was used. Of note is the SWOT analysis method, which was used to identify the positive and negative aspects of the integration of artificial intelligence into scientific and pedagogical life. The results show that the use of artificial intelligence technologies in the field of education is a promising and promising area for further development. The analysis conducted in this context revealed that innovative technologies have become an essential component of modern education. The use of artificial intelligence (AI) is becoming more widespread and. However, for the further development of this area, it is important to improve the level of digital literacy among users. Based on the results of the study, both positive aspects and disadvantages in the use of these technologies can be identified. Among the advantages are the high adaptability of AI, the possibility of individualised learning, accessibility, and a wide range of applications, from primary school to higher education and research. It is important to note that these technologies can be used to achieve learning success regardless of geographical region, making them global in nature. The scientific novelty lies in the consideration of certain shortcomings, among which the issue of security is particularly relevant, especially in the context of global hybrid threats and the overall process of digitalisation. The conclusions note that to overcome the identified negative aspects of the use of AI in research and teaching, it is important to focus on the ethical aspects of using digital tools in general. It is also recommended to consider the possibility of organising additional online courses.
Innovation of Middle School Education Management Model and Countermeasures for Improving Quality

Currently, there are some common problems in the management of middle school education and teaching, which have existed for a long time and have caused obstacles to education and teaching management. As a manager, we should attach importance to these issues, propose solutions from the perspective of innovation and development, and actively practice them in practical management work. In management work, it is necessary to not only supervise teachers and students, but also innovate and reform the basic rules and regulations of education and teaching management based on their immediate interests and needs, making them more scientific and humane.
Recognition and Evaluation Algorithm for English Pronunciation Syllables Based on Neural Prediction Model

As the most widely used language in the world, English has always had the largest number of learners. Therefore, this study has a practical foundation for the recognition of English stressed syllables. As is well known, listening and speaking are crucial aspects of language learning, as they are directly related to communication. Therefore, this article aimed to design a mature syllable recognition algorithm and assist it based on neural prediction models. In the end, this article used the algorithm system for a month of auxiliary training for a certain English major class, and conducted a comparative test on phrase recognition rate and pronunciation accuracy before and after. The results showed that the phrase recognition rate increased from 89.34% to 96.05%, and the pronunciation accuracy rate increased from 73.65% to 92.84%, comprehensively improving students' English learning ability.
DIGITAL LITERACY AND UTILIZATION OF COMMUNITY READING CORNER

The literacy program is aimed at villagers (4 target residents) through reading activities through enrichment of knowledge, skills and personality to add insight in accordance with the field of work of villagers in order to improve their welfare. The specification of the Village Literacy Program Assistance (PROLISA) is a literacy corner that functions as a community learning center that has a certain non-formal learning curriculum. Then for the topics covered in the Literacy Corner, it is intended to broaden people's insight, knowledge, attitudes and skills that are useful for improving their welfare. And the activities that we carry out will be directed at getting changes in attitudes and behavior by facilitating literacy books on motivation, general knowledge, and crafts. The holding of digital literacy socialization explains how to use digital tools properly and correctly so that people can use these tools as capital for online businesses.
Innovative Research on the Management Model of College Student Education in the New Era

With the rapid development of society and the continuous progress of technology, the new era presents new challenges and opportunities for the education and management mode of college students. The traditional educational management methods are no longer able to meet the needs of current college students, and more innovation and reform are needed to better adapt to the requirements of the new era. This study aims to explore the innovation of college student education management models in the new era, in order to achieve the comprehensive and healthy development of college students, improve the quality of education, promote the efficient implementation of teaching, and provide specific innovative strategies and methods to guide college student education management into the new era.
8.B. Workshop: Fit for purpose surveillance - strategies to implement effective surveillance systems in EU/EEA countries under the new Serious Cross Border Health Threats Regulation (2022/2371)

Abstract Background As part of the European Health Union package, the EU Regulation 2022/2371 on serious cross-border threats to health has entered into force on 26 December 2022. Chapter III (Articles 13-17) of this Regulation provide new provisions for Epidemiological surveillance, the Digital platform for surveillance, and EU reference laboratories. Methods Several Implementing Acts are currently in preparation, which will define in more detail the concrete implementation of these provisions, such as the list of communicable diseases, case definitions, procedures for the operation of the epidemiological network for epidemiological surveillance, the specifications of the digital platform for surveillance and its specific rules for functioning, as well as to designate EU reference laboratories. Via the EU4Health Programme, the European Commission is furthermore providing funding for Integrated Surveillance, Surveillance capacity building Results The workshop will inform on the current state of implementation, on the results of the preparatory works carried out so far as well as on an outlook to the short and longer term plans for the implementation of the regulation on serious cross-border threats to health. Furthermore, it will inform on a funding mechanism for the improvement of EU Surveillance Systems as one of the central lessons learnt from the COVID-19 pandemic and showcase a good national practice. Conclusions The COVID-19 pandemic has clearly illustrated that the creation and further development of sound, reliable, integrated and real-time digital surveillance systems are a very crucial component for preparedness against serious cross border health threats. The current work under the new regulation is a valuable opportunity for public health practitioners to improve surveillance at EU, national and regional level. The workshop will host panel presentations followed by a round-table discussion.
DESIGNING E-BUKHARY SHOP APPLICATION USING THE BUSINESS TO-BUSINESS (B2B) MODEL BASED ON A WEBSITE

Ongoing technological developments have brought progress in the form of online sales applications. This online sales technology is often also referred to as E-Commerce. Services at the Sinar Bukhari Store for resellers who want to buy goods are still manual and simple, the sales process still uses the WhatsApp group, making it difficult for resellers and also for the shop if there are purchases of goods simultaneously. Resellers also have trouble ordering if the admin they contact is inactive, if new items are sent via the WhatsApp group, the old items will be buried in the group, making it difficult for resellers to order items that have been stockpiled. Then the solution to the problem where the E-Bukhary shop application will be made with a website-based business to business (B2B) model uses the application of prototyping techniques which make plans quickly and gradually so that potential users tend to be quickly assessed. In the E-bukhary shop application, there is a shop feature that involves admins and resellers to simplify the sales process according to the items available. through trials using black box testing in terms of interface scale 1-5 the value is 88% with very good assessment criteria, in terms of application performance a score of 88.8% is included, including very good criteria. in terms of the application database, a score of 86.6% was generated which included very good assessment criteria, then on the missing or damaged application function aspect, a value of 90% was produced in very good criteria, the last on the termination aspect resulted in a value of 86.2% or in very good criteria
Bulletin WKU

This article provides for the process of gradual saturation of contextual tasks with educational content. To achieve modern requirements for learning outcomes (not only subject, but also meta-subject and individual), it is necessary to use tasks whose chemical content is integrated with practice. Such tasks are contained in contextual tasks. In this regard, the 1st year students of the Faculty of Chemistry and Chemical Technology of the specialty Chemical Technology of Organic Substances took part in the research work. There were 7 students in total at the time of observation. Previously, a relatively contextual task was prepared with a traditional task on the topic "chemistry of nonmetals", which was given at the general chemistry lesson. The levels of students on tasks were monitored. As a result, the task given by the contextual method, students quickly cope and get high scores. And according to the traditional task, the reduction indicator shows. In addition, methods of analysis are used in the study. Based on the analysis method, a specific problem of research work is determined.
Enhanced Organizational Performance: Integrating Dimensions for Sustainable Growth

This study examined how different factors—environmental, human resources, managerial, technological, and innovation—influence the organizational performance of small- and medium-sized enterprises (SMEs). For the study methodology, a correlational research design was used to examine the relationships between variables in a sample of 476 SMEs in Colombia. Advanced statistical techniques were used, such as regression and correlation analyses with Monte Carlo simulation. We evaluated four dimensions, 16 factors, and 197 related variables to understand their impact on business performance. The results show that all the studied factors, i.e., environment, structure, management, and people, have a positive relationship with organizational performance. Additionally, the results indicate that SMEs tend to have strong performance in the dimensions of finance, infrastructure, and production but weaker performance in those of international, socioenvironmental, technological, institutional philosophy, process, and human resources management. These findings provide valuable insight into the areas that SMEs may need to focus on to improve their performance while underscoring the importance of considering a variety of factors when developing strategies to improve organizational performance.
A Comparison of Grammatical Error Correction Models in English Writing

Grammatical error correction (GEC) is the task of correcting grammatical, spelling, and word usage errors in texts. Detecting and correcting these errors makes it difficult for the reader to understand the text and negatively affects communication. Editors and proofreaders can identify and correct grammatical errors while reading texts. However, this manual correction takes a long time. With the development of technology, artificial intelligence-supported tools have been developed that automatically correct errors. In artificial intelligence-supported systems, this task is usually performed using natural language processing methods. In this study, a traditional neural network architecture (Attention BiLSTM) and a transformer-based language model (T5) were trained. Then, the success of these trained models and Large Language Models (LLMs) (ChatGPT, Google Bard, LanguageTool) in English grammatical error correction has been compared on the JFLEG dataset. According to experimental results, the pre-trained T5 language model beats others in all evaluation metrics for grammatical error correction.
Automatic Turkish Image Captioning: The Impact of Deep Machine Translation

This paper presents a research study on the impact of deep machine translation on automatic Turkish image captioning. In the current literature of image processing, related studies on Turkish image captioning are quite limited. One of the main reasons why Turkish image captioning studies are quite limited is that large-scale datasets in Turkish for image captioning have not been constructed yet. In this study, an image caption set for Turkish was generated by using a recent deep machine translation model that becomes prominent with its high performance. In this context, for the MS COCO database, which is a commonly known and widely used image set, the original image captions written in English for images in this database were translated into Turkish using the NLLB (No Language Left Behind) deep machine translation model. In addition, a Turkish image captioning model based on the LSTM (Long Short-term Memory) which uses ResNet, ResNext and Swin deep learning structures as a backbone has also been evaluated on this derived Turkish image caption set. In performance evaluation tests, generally close performances were observed for all image encoder backbone models, and an average of 0.31 BLEU-1, 0.10 BLEU-2, 0.04 BLEU-3, 0.02 BLEU-4, 0.11 METEOR, 0.26 ROUGE-L and 0.04 CIDer values were measured. The related caption set created by using the NLLB deep machine translation model within the scope of the study has also been made available for general use over the web so that researchers working on similar topics can also benefit from it.
Semantic Differentiation of Uzbek Homonyms Using the Lesk Algorithm

The development of a semantic analyzer of natural language is considered one of the factors that develop the language. Homonymy is one of the main elements of semantic analysis. Different methods can be used for semantic analysis of homonyms. Homonyms can also be determined using Lesk's algorithm. Lesk's algorithm is based on WordNet of natural language. The weight of the compounds of the homonymous word in the sentence entered through WordNet is determined. The meaning of the word homonym was determined according to the compounds with high weight.
Cost-Sensitive Cold Start Latency Optimization Mechanism in Function-as-a-Service

Function-as-a-Service (FaaS), as a cloud computing service, builds, runs, and manages application packages directly in a functional way, greatly improving development and delivery efficiency, and is a major trend in the future development of cloud services. However, FaaS is executed via event-driven execution and has a cold-start problem at runtime. Most of the existing research focuses on function runtime optimization and ignores cold start time. For business scenarios with high real-time requirements, prolonged cold starts can affect business results. Therefore, cold-start optimization is particularly important for the application of function computing in latency-sensitive scenarios. To reduce the impact of cold start latency on services, this paper proposes a memory configuration to reduce cold start. Firstly, a memory-cost model is constructed based on memory resource rules and service computation time rules, and the model is optimized using a gradient descent algorithm. The results of large-scale simulations show that the memory selection scheme proposed in this paper can reduce the cold start latency by about 25% compared to the memory selection of conventional function services in existing cases.
Locally Differential Private Federated Learning with Controllable Perturbation Domains

As a kind of distributed machine learning, federated learning allows each party to upload local model parameters instead of sensitive data to train a more accurate global model collaboratively. However, since it turns out that the original training data can be inferred by intercepting and analyzing the passed model parameters, local differential privacy is introduced into the federated learning to provide robust privacy guarantees and high efficiency. At present, most existing approaches prefer to adjust the perturbation domain range of the model parameters only according to the privacy budget, which is difficult to ensure the accuracy of the model under the condition of limited privacy budget or fewer participating clients, resulting in low usability and practicability of the global model. To tackle this, we propose a locally differential private federated learning with controllable perturbation domains. First, we design a local differential privacy mechanism, which generates three perturbation domains centered on the true value. The range and perturbation probability of each perturbation domain are adjusted by controlling two factors, the privacy budget and the unit size of the perturbation domain. Specially, the true value is mapped to the center perturbation domain with a large probability and to the two-side perturbation domains with a small probability. Second, to ensure the training process of federated learning is protected from inference attacks, we apply the designed mechanism to the transmission of parameters in federated learning. Finally, we analyze and prove the privacy and utility of the mechanism in detail, and conduct comparative experiments in terms of accuracy on three standard datasets. Theoretical and experimental results show that the proposed approach outperforms other state-of-the-art approaches.
Maximizing CNN Accuracy: A Bayesian Optimization Approach with Gaussian Processes

Convolutional neural networks (CNNs) are widely used in deep learning and the performance of their training process heavily depends on hyperparameters. Optimizing the hyperparameters can enhance the learning process and model performance. In this study, we utilized Gaussian Processes and Bayesian optimization techniques to explore the relationship between the performance of CNN models and their hyperparameters. Bayesian optimization is used to update the posterior distribution over the objective function using prior distribution information. We used the Expected Improvement (EI) acquisition function to select the next point that minimizes the objective function. To evaluate the effectiveness of our optimized models, we trained and tested them on standard benchmark datasets including MNIST, CIFAR-10, and SVHN. Our optimized models achieved high accuracy: 98% on MNIST, 90% on CIFAR-10, and 96% on SVHN, across a variety of CNN architectures.