Reviewerly/exHarmony huggingface dataset:
Analysis of the Talent Training Model for Teaching Chinese to Speakers of Other Languages Major

With the continuous development of global economic integration, China's foreign cultural dissemination and exchange have shown a rising trend. Over a hundred Confucius Institutes and Confucius Classrooms around the world have become important platforms to promote China's excellent traditional culture to the world. Therefore, it is very necessary to strengthen the cultivation of Chinese international talents in China. With the deepening development of science and technology, digitization, informatization, and intelligence have played an important role in promoting the development of education reform in China. Promoting digital education, building a society of lifelong learning for all, promoting China's digital transformation of education, and reshaping the high-quality education system are the focus of our country's education work. Under the globalization trend, strengthening the digital construction of Teaching Chinese to speakers of other languages major is an important strategic content. Guided by modern educational concepts, Chinese language education majors in China should actively explore new models of talent cultivation, attach importance to the cultivation of composite, practical, and applied talents, and gradually improve the teaching level of Teaching Chinese to speakers of other languages major majors. At present, there are still some problems in the target cultivation, course content, and actual teaching plans of Teaching Chinese to speakers of other languages major majors in China, which have caused some obstacles to the further development of Teaching Chinese to speakers of other languages major. The paper focuses on the talent cultivation model of teaching Chinese to speakers of other languages major in China, aiming to further promote the reform of the talent cultivation model and promote the high-quality development of teaching Chinese to speakers of other languages major in China.
Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University)

Purpose: To detect cracks in the walls of buildings using digital image processing and the Naïve Bayes Algorithm.Design/methodology/approach: Using the YCbCr color model for the segmentation process and the HSV color model for the feature extraction process. This study also uses the Naïve Bayes Algorithm to calculate the probability of feature similarity between testing data and training data.Findings/result: Detecting cracks is an important task to check the condition of the structure. Manual testing is a recognized method of crack detection. In manual testing, crack sketches are prepared by hand and deviation states are recorded. Because the manual approach relies heavily on the knowledge and experience of experts, it lacks objectivity in quantitative analysis. In addition, the manual method takes quite a lot of time. Instead of the manual method, this research proposes digital-based crack detection by utilizing image processing. This study uses an intelligent model based on image processing techniques that have been processed in the HSV color space. In addition, this study also uses the YcbCr color space for feature extraction and classification using the Naïve Bayes Algorithm for crack detection analysis on building walls. The accuracy of the research test data reached 88.888888888888890%, while the training data achieved an accuracy of 93.333333333333330%.Originality/value/state of the art: This study has the same focus as previous research, namely detecting cracks in building walls, but has different methods and is implemented in case studies.
Research based on computer artificial intelligence recognition technology and its application

Under the background of the continuous improvement of human science and technology, the artificial intelligence industry has developed rapidly. At present, computer artificial intelligence recognition technology has been widely used in social production and life, from the perspective of social production, this technology can not only improve the automatic control level of enterprises, but also greatly improve the quality and efficiency of production, so as to create greater social and economic benefits; From the perspective of social life, it helps to improve people's convenience in life. Starting from the connotation and categories of computer artificial intelligence recognition technology, this paper analyzes the specific application of this technology, and discusses its future development direction, hoping to provide reference for relevant colleagues.
Check It Out! A Proposed Public Library-Based Meal Program to Combat Literacy Struggles in Children

Purpose: To highlight the relationship between hunger in children and struggles with reading and to propose a library-based social program that is directly incorporated within the local libraries&amp;rsquo; already established summer reading program to address it. All local libraries throughout the country have an established summer reading program to provide learning stimulus for students when school closed for summer recess. Design/methodology/approach: The method is to analyze the problem using the lens of an economic theory and he research method is the literature review. The theoretical approach or underpinning is that Arthur Laffer&amp;rsquo;s, trickle-down economic theory has failed our children who go to bed hungry. Findings: School-aged children who are food insecure suffer academically and the problem is worse during the summer months because there is no access to school meals. The local public library is in a unique position to provide social services in the form of a library-based meal program that is incorporated with the summer reading program. Research limitations/implications: Due to the nature of the study, since there were no participants, the findings are only generalizable to a sample of libraries. Future studies could explore topics related to the actual implementation of a library-based meal program that is tracked over a period of three years.
Classification of Naive Bayes Algorithm on Dengue Hemorrhagic Fever and Typhoid Fever Based on Hematology Results

The application of increasing technology developed explicitly in the health field would significantly have an urgent role in guaranteeing quality service. Application deep data mining techniques classifier method, one among them used for classify something possibility, for example for classification disease. Dengue Hemorrhagic Fever is a disease caused by the dengue virus biting the Aedes aegypti mosquito. Meanwhile, Typhoid Fever is a disease caused by the bacterium Salmonella typhi. The second disease could attack all types of circles, fine children or mature ones. The second disease is almost the same symptom, so a proper diagnosis is needed to differentiate it. Study this applies the Naive Bayes algorithm to classify Dengue Hemorrhagic Fever and Typhoid Fever using 250 yield data test blood routine hematology at Tugurejo Hospital. Attributes used in the study, age, type sex, temperature, leukocytes, erythrocytes, hemoglobin, hematocrit, platelets, anti-dengue IgG, anti-dengue IgM, salmonella typhi o and salmonella Typhi h. The Naïve Bayes method is one of the techniques that can be used to perform analysis in determining the diagnostic results from a number of data studied with the aim of producing optimal results. The use of the Naïve Bayes method in this application is due to the probability that the accuracy value of the Nave Bayes method is close to the accuracy value of the experts.[12] The results of testing the Naive Bayes method using a confusion matrix show Recall value is 97.62%, Precision is 93.89%, accuracy is 93.33%, and Error Rate is 6 %. It can be concluded that this method is suitable for classifying Dengue Hemorrhagic Fever and Typhoid Fever and can be applied in studying this.
Construction of Evaluation Datasets for Trend Forecasting Studies

In this study, we discuss issues in the traditional evaluation norms of trend forecasts, outline a suitable evaluation method, propose an evaluation dataset construction procedure, and publish Trend Dataset: the dataset we have created. As trend predictions often yield economic benefits, trend forecasting studies have been widely conducted. However, a consistent and systematic evaluation protocol has yet to be adopted. We consider that the desired evaluation method would address the performance of predicting which entity will trend, when a trend occurs, and how much it will trend based on a reliable indicator of the general public's recognition as a gold standard. Accordingly, we propose a dataset construction method that includes annotations for trending status (trending or non-trending), degree of trending (how well it is recognized), and the trend period corresponding to a surge in recognition rate. The proposed method uses questionnaire-based recognition rates interpolated using Internet search volume, enabling trend period annotation on a weekly timescale. The main novelty is that we survey when the respondents recognize the entities that are highly likely to have trended and those that haven't. This procedure enables a balanced collection of both trending and non-trending entities. We constructed the dataset and verified its quality. We confirmed that the interests of entities estimated using Wikipedia information enables the efficient collection of trending entities a priori. We also confirmed that the Internet search volume agrees with public recognition rate among trending entities.
Dual-image reversible data hiding based on encoding the numeral system of concealed information

The dual-image reversible data hiding technique is dominant in terms of security and embedding capacity. Because the secret data is encoded and then embedded into two cover images instead of a single image. In this paper, the proposed method based on the dual-image RDH scheme utilizes a transferring numeral system to enhance the embedding rate. The secret data is encoded and converted to 2-digit base-9 numbers, with the encoding values hidden in two cover images. The results of the experiments reveal that the strategy achieves considerable results in terms of visual quality and payload. This method provides several options in the application.
Article Elements Recognition of Old Sinhala Newspapers Using Vision Heuristic Algorithm

This paper presents an approach to segmenting degraded and low-quality old Sinhala newspaper articles into separate elements such as Title, Subtitle, Columns, Highlight phrases, special paragraphs, author name or reference phrase, and general content. Human vision inculcated machine vision algorithms were introduced to elicit above categorical content with groundbreaking accuracy. This paper highlights the importance of column-wise analysis of newspaper articles to segment elements. The results of this research are intriguingly comparable to other existing literature. The outcome application of this study is applicable in old Sinhala newspaper digital archival to be enriched with search and retrieval.
Knowledge Weightage Calculation: an AI &amp; ML based smart modelling for text- content summarization and quantification

The need for naturalistic interaction and communication between humans and machines is essential for high-level artificial intelligence. Matured Natural Language Processing is the first step towards attaining artificial intelligence by automation equal to human beings. It has been a hot and active area of research among researchers for a long. In the advanced forms of natural language processing-based intelligent application building must-have capabilities of generating summaries, understanding context, representing and combining multiple information, and knowledge extraction. Artificial Intelligence and various computing ways (Statistical Reasoning, Machine Learning, and Deep Learning) for achieving it, have been proven the next big weapon for attaining a true sense of intelligent automation and smart system building. Many tasks related to natural language processing have shown consistent and comparable high performances since the introduction of deep learning modelling. Using sequence-to-sequence models has been advantageous and demonstrated promising outcomes in many other areas, including machine translation, speech recognition, picture captioning, etc. This paper aims to propose a novel framework to extract important features from an unstructured text by removing the noisy, irrelevant, and redundant components, and to provide accountability for the information delivered by retrieving image-based text, deep text summarization by knowledge weight calculation, and representation- based on the extracted features.
Electronic Matadan: Blockchain Based E-Voting System

Building a model that people can access from remote locations for casting votes in the election process has always been a challenge. Our project aims to make a web app using blockchain technology to build an e-voting system. Blockchain technology is immutable, distributed, and incontrovertible, making this web app more secure and robust. The suggested solution is a web application with better security capabilities, such as authentication and authorization. A special identification key is used for authentication. The vote is cast as a transaction, and a blockchain is constructed to keep track of the transactions. Through this the ACID properties of a transaction are satisfied. This web app would be more suitable for small-scale election processes to ensure secure and clean voting.
Day-ahead photovoltaic power prediction based on a hybrid gradient descent and metaheuristic optimizer

The integration of Photovoltaic (PV) power into energy market requires the development of accurate PV power forecasting models. Apart from the prediction model the selection of the proper optimizer may contribute towards the forecasting accuracy increment. At this paper, we propose a hybrid Adam- Unified Particle Swarm Optimization (UPSO) optimizer to increase the accuracy of day-ahead PV power forecasting. At first, stage the gradient descent based Adam optimizer is executed to initialize the position of the particles. Then, the metaheuristic UPSO is executed for the model’s training. The proposed optimizer is employed to a Long Short-Term Memory model and its efficiency is compared to stand-alone Adam and UPSO. To confirm the robustness of the proposed optimizer, Adagrad, Adadelta, Genetic Algorithm and Artificial Bee Colony optimizers have been also examined. The proposed optimizer is tested on two datasets and four test cases. The results highlight that Adam-UPSO outperforms for 50% of the examined test cases. It may reduce the forecasting error up to 15.21% compared to Adam optimizer and up to 22.6% compared to the persistence model.
Information Risks of the Educational Activities of Universities Under Digitalization

Digitalization of educational activities in universities is based on the use of the latest telecommunication technologies, the Internet, intelligent systems, big data analysis, information services to form education space, and flexible approaches to learning. Along with positive factors, digitalization creates different information security risks in educational processes. It is necessary to identify and analyze such risks and plan the activities to neutralize them. The analysis of risks requires the development of models to assess the impact of these risks on the educational process in the university. In the development of models, the factors that determine the risks of information security in the university’s educational process are characterized by considerable uncertainty. The research proposes an approach to develop the risk analysis model based on fuzzy logic. While developing the model, the authors identify the parameters that determine the risks and develop a production model and fuzzy rule base. Mamdani’s algorithm is the basis of the fuzzy conclusion. The example of the information security risk modeling of the educational process in the university is considered.
Deep learning-based Turkish spelling error detection with a multi-class false positive reduction model

Spell checking and correction is an important step in the text normalization process. These tasks are more challenging in agglutinative languages such as Turkish since many words can be derived from the root word by combining many suffixes. In this study, we propose a two-step deep learning-based model for misspelled word detection in the Turkish language. A false positive reduction model is integrated into the system to reduce the false positive predictions originating from the use of foreign words and abbreviations that are commonly used in Internet sharing platforms. For this purpose, we create a multi-class dataset by developing a mobile application for labeling. We compare the effect of using different types of tokenizers including character-based, syllable-based, and byte-pair encoding (BPE) approaches together with Long Short-Term Memory (LSTM) and Bi-directional LSTM (Bi-LSTM) networks. The findings show that the proposed Bi-LSTM-based model with the BPE tokenizer is superior to the benchmarking methods. The results also indicate that the false positive reduction step significantly increased the precision of the base detection model in exchange for a comparably less drop in its recall.
Explainable AI for trustworthy image analysis

The capabilities of convolutional neural networks, and in fact all manner of artificial intelligence and machine learning capabilities, to explore data in various fields has been documented extensively throughout the literature. One common challenge with adopting AI/ML solutions, however, is the issue of trust. Decision makers are rightfully hesitant to take action based solely on “the computer said so” even if the computer has great confidence that it is correct. There is obvious value in a system that can answer the question of why it made a given prediction and back this up with specific evidence. Basic models like regression or nearest neighbors can support such answers but have significant limitations in real-world applications, and more capable models like neural networks are much too complex to interpret. We have developed a prototype system that combines convolutional neural networks with semantic representations of reasonableness. We use logic similar to how humans justify conclusions, breaking objects into smaller pieces that we trust a neural network to identify. Leveraging a suite of machine learning algorithms, the tool provides not merely an output “conclusion”, but a supporting string of evidence that humans can use to better understand the conclusion, as well as explore potential weaknesses in the AI/ML components (whether as a result of lack of sufficient training data, adversarial attempts to corrupt the system, etc.). We have applied this system to problems of object detection and semantic segmentation of images. This paper will provide an in-depth overview of the prototype and show some exemplar results.
Non-Functional Requirements Classification Using Machine Learning Algorithms

Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.
Secure federated machine learning for distributed spectrum sensing in communication networks

Federated machine learning (FML) has proved a useful technique for training of artificial intelligence and machine learning (AI/ML) models, using data that is distributed among different constituents of a network which may be geographically dispersed. Typically, the data privacy of individual constituents should be preserved, and it may also be desirable to protect the integrity and secrecy of the algorithms and trained models deployed within the network. Demonstrating the privacy-enhancing technology of Confidential Computing, we present the results obtained using a novel solution for FML implementation that supports model training within a distributed network of data providers. Based upon recent research on the use of FML for distributed spectrum sensing in communication networks, we demonstrate the application of the proposed solution for distributed model training within a simulated sensor network of arbitrary topology. The presented solution provides for graph-based network configuration and model convergence within decentralized network applications. Cross-domain adaptation of the proposed solution and characteristics of confidential computing that support a zero-trust architecture are discussed, along with the integrated model integrity protection provided by attestation of trusted execution environments (TEEs). We conclude by looking ahead to the application of our solution to model training within distributed communications networks and sensor arrays, characterized by devices with limited electrical and computational power. We consider the use of physical unclonable functions (PUFs) to encrypt raw data before processing within a layered hierarchy secured with Confidential Computing technology.
DETERMINAN AKUNTABILITAS PENGELOLAAN DANA BANTUAN OPERASIONAL SEKOLAH DI KOTA JAYAPURA

The objective of this study is to analyses the determinants of the financial management accountability of school operational fund (BOS hereafter) in Jayapura municipality, Papua province. The research method used was a survey with questionnaires. Quantitative analysis using the SPSS analysis tool was used to analyze the data collected in order to test empirically the factors that influence accountability of the management of BOS funds in beneficiary schools in Jayapura City. The results showed that the BOS funds received in Jayapura Municipality in 2019 and 2020 amounted to 24.9 billion which were managed in 14 junior high schools and 54 elementary schools. BOS spending consists of personnel spending, goods and services spending, and capital spending. Data collected from 242 respondents showed that the factors of Transparency, HR Quality, Organizational Commitment, Internal Control System had an effect on the Accountability of BOS funds. The results of the research are to provide empirical factors on the accountable management of BOS funds so that they become technical knowledge for other districts/cities in Papua Province and others in managing their BOS Fund and other public transfer funds.