Reviewerly/exHarmony huggingface dataset:
Transformer-based model for Vietnamese Handwritten Word Image Recognition

Handwritten text recognition plays an important role in transforming handwritten-based documents into digital data, which is necessary to intellectualize social management and production processes in the fourth industrial revolution. To overcome this challenge, several recent studies have assumed that each character appeared on the image is independent so that they could make predictions solely on the visual features. However, it leads to a lack of language characteristics because of the fact that the occurrence of a character is somehow related to the previous characters. Therefore, the attention mechanism between the text and the image to create the character predictions sequentially have outperformed the above method on the word level because it could make use of the context of the predicting word text. In this paper, which is inspired by the Transformer architecture in Neural Machine Translation tasks, we further proposed a model that exploits the dependencies between the last predicted character and the previously predicted characters based on the attention mechanism to translate from the word image to a word text. Our method has achieved the state-of-the-art result with 2.48% CER and 7.70% WER on the VNOnDB-word data set compared to similar works on the same data set.
예비한국어교원의 교안 분석을 통한 교안 작성 지도 방안 연구

The purpose of this study is to analyze the lesson plans of prospective Korean language teachers and to identify the problems and suggest educational measures to improve the teaching ability of the prospective teachers required for the organization and operation of Korean language classes. “Korean language education practicum” belongs to five section of the major subjects to obtain a Korean language teaching certificate, and if the subjects from 1 section to 4 section are related to the knowledge of the major, “Korean language education practicum” is a very important subject that can actually experience the Korean language education site in advance.Students of the “Korean language education practicum” course will draw up a teaching plan for the mock class, which is the final course of the class. The importance of the teaching plan cannot be overlooked in that it is the beginning and end of the “Korean language education practicum”, and even after becoming a Korean language teacher the preparation of the lesson plan is carried out frequently. Therefore, the study plans to analyze 117 lesson plans of prospective teachers written in the “Korean language education practicum” to identify problems in each stage of the class and propose guidance measures to resolve them.(Sejong Cyber University)
Efficient Data-Secured Cryptography Using FRQI Algorithm

The Web clients are consistently expanding step by step. After the starting of the 4G or IMT—Advanced administrations, correspondence over Web expanded definitely. Individuals over every one of the networks like social, monetary, business, money related, and so forth are doing correspondence or trading their esteemed reports over Web. The interest for verifying the information in productive manner has been expanded. As cryptography is the most ideal approach to verify the correspondence. In FRQI, the compression ratio of quantum images is more efficient and achieves quadratic speed in quantum images. Here, BB84 protocol convention is used to execute QKD. It manages the harmonization states that want to transmit the media transmission information with elevated level of security utilizing a glass or optical fiber.
터키 경제 불확실성에 관한 한국의 이해: 텍스트 마이닝을 통한 대안적 방법론

The guiding research question of this paper is to discover whether the machine learning-based Text Mining can provide us with a better understanding of economic uncertainty in Turkey. Data covers the period of the year 2006 to 2019 and is collected by crawling news texts from Naver News Online Service. The main findings are: (1) main topics concerning economic uncertainty in Turkey are US-Turkey economies and global growth(Topic 1), export and market outloook(Topic 2), UK-Turkey relationship and President Erdogan(Topic 3), economic uncertainty in Turkey and Korean companies(Topic 4), Emerging Country risk and stock price(Topic 5), and US President and world economy(Topic 6), (2) the focal topic is Topic 5 and the topics are closely associated with each other, (3) generally Korea reveals a continuous positive sentiment for Turkish economy, while a negative sentiment was discovered in the years of 2014 and 2018.
Cross Language Information Retrieval for Accessing the English Web in Sinhala

Searching the web in Sinhala language does not provide satisfactory results and hence, Sri Lankans who are not fluent in English find it difficult to browse the web for knowledge. This issue can be solved by Cross Language Information Retrieval (CLIR) where the query in Sinhala is matched with documents in English using a query translation approach. This study has experimented with different models which uses the concept of word embeddings to transform the Sinhala query to English where results were retrieved using the Google Search API by providing the equivalent English query obtained. The retrieved results were translated back to Sinhala and re-ranked using two different approaches. A user evaluation showed that re-ranking the results did not show a positive impact but obtaining results using the equivalent English query proved to be effective. Hence this study shows that the quality of the results obtained when searching the web in Sinhala can be improved by performing CLIR.
Novel Global Harmony Search Algorithm with New Position Updating for Optimization Problems

Novel global harmony search algorithm performs better in many engineering optimization problems, but its optimization accuracy is valued to further study. Therefore, a modified version of NGHS algorithm will be proposed to solve some optimization problems. Inspired by differential evolution algorithm mutation operation, stochastic harmony variable is selected and applied in the position updating to enhance the global search capability. Then double evolution operation be used based on a probability control parameter in evolution process, which aim to reduce the probability of algorithm trouble in local optimum value. Compared to some well-known approaches such as HS, IHS, GHS, IHS_MM, IHS_SM, NGHS, all the results demonstrated that the new proposed algorithm has better potential performance.
Is Environmental Hostility in the Eyes of the Beholder? Foreignness, Environmental Perception and Response Mechanisms in Nigeria’s Banking Industry

Environmental hostility is posited to halt firms’ development and growth. This leads to the expectations that foreign firms that developed their capabilities in more munificent environments and are able to draw on resources via their global network to compensate for local scarcity would emerge as the predominant competitors in hostile environments. The dominance of local banks of Nigeria’s banking industry conflicts this theoretical expectation. An exploratory study points at differences between foreign and local firms in their perception of Nigeria’s environmental conditions and their subsequent responses to environmental hostility. We use this insight to advance a typology of environmental hostility distinguished by the effectiveness of response mechanisms employed by local and foreign firms to confront it, and employ it to explain variations in the competitive outcomes between foreign and local firms across environments with different types of hostility.
One-Dimensional Clustering

The clustering is a task of grouping objects, which are similar with respect to some metric. Clustering is a well-known problem in statistics, data mining, and machine learning.
A Novel Watermarking Scheme based on The Curvelet transformation method for Medical Images

Authentication and confidentiality of a patient's data records is an extremely important issue in digital store or telehealth services. Authentication solutions based on the watermarking method have been widely used in image processing fields due to its simplicity. However, the watermarking process introduces noise into the covered image that can affect the medical image quality. To overcome that, this paper proposes a curvelet-based authentication method to keep the medical image quality in certain watermark information. Moreover, the proposed scheme uses a machine learning method to recover the patient's record information in extracted watermark data. Comparative numerical results show a certain effect on image quality after embedding and the ability to recover embedded information when compared with the discrete wavelet transformation method.
Part-Of-Speech Tagging in French: State-of-the-Art and Obstacles

POS Tagging (Part-Of-Speech Tagging) is considered as a basic step for automatic natural language processing. It consists in identifying the grammatical category of each word in a text. It is tricky for a machine to understand the grammatical form of words in some languages including French. These difficulties are related to the labeling of unknown words, the ambiguitsy of certain words, the understanding of many linguistic syntaxes etc. However, several approaches and tools have been proposed in the literature. In this article, we are exploring the Part-Of-Speech Tagging in French.
Research on the Development Trend of Artificial Intelligence Based on Papers and Patent Analysis—Data Comparison Between China and the United States

With the development of artificial intelligence ushered in the third wave, how to develop artificial intelligence in the future has become the focus of attention. This article first introduces the basic situation of the development of artificial intelligence, and secondly analyzes the development context of artificial intelligence based on the data of the paper. It then analyzes the hotspots of artificial intelligence application according to the patent data, and finally, it summarizes and forecasts the development trend of artificial intelligence related fields.
Emotional Intelligence Research Trends and Future Research Directions in Korean Journals

Purpose: The purpose of the study is to analyze the characteristics of emotional intelligence and the variables related to emotional intelligence in a...
DFOG-Image Processing Application for Real-Time Defogging

The enhancement of real-time video taken under bad visibility or bad weather is a vital necessity in consumer transport industry and computer vision applications. During the past decade, many researchers have been devoted to the problem of how to remove fog noise from real-time video. Nowadays vehicle industry uses various computing systems to assist in the transport of travelers from one location to another .now most of the cars have revers camera front cameras and sensors who give the signal when the vehicle is near to another object. These detections and identification are useful for the safe operation of vehicles. When looking through vehicle accident history, many accidents caused bad weather conditions. Fog, haze, rain, and other natural weather conditions cannot remove physically. Fog and haze block vison above 1 kilometer. There is a defogger in the windscreen, but it is only removed Mist on the windscreen. For the driver's vision above the rode, there is no such thing for that. The purpose of this research paper is introducing a new system to remove fog from real-time video and give detailed visual to the driver in foggy or other bad weather condition. This D-Fog system includes functions such as give clear realtime visual in bad weather condition, recognize, and give details about the object above the road, give the distance between objects and vehicle. In this system, main function is producing real-time defogged, clear video. Combination of Ha and Hoon method and Dark channel priority method used to get this real-time defog video. To recognize the object, this system has use thermal sensors and heat maps. To get the distance between object and vehicle this system has use LIDAR sensors. Because of this facility, we can name this system as three in one system.
MM-GCN: Multi-Mutual Learning Networks of Graph Convolution for Node Classification

Graph Convolutional Network (GCN) has been proved to be an effective method to process graph-structured data. Concurrently, deep mutual learning has shown significant improvements in network performance. In this paper, we propose a model: Multi-Mutual learning Networks of GCN (MM-GCN), which combines these two lines of work. At its core, MM-GCN trains multiple GCNs, which may be the same or different, and the final loss function is jointly determined by these networks, which can be used for backpropagation to train the network. Our experiments show that the effect of MM-GCN proposed by us improves state-of-the-art baselines on node classification tasks.
VIRTUAL INTERACTION IN THE EDUCATIONAL ENVIRONMENT: ONTOLOGICAL ASPECT

It is shown that as a result of the lack of synchronicity and balance between the virtual and real sectors of higher education, desynchronization of the ontology of education can occur. It is indicated that, in accordance with eco-nomictrends in the national higher system there can be a clear division into elite science-intensive high-tech competitive education and mass low-value low-tech “higher education” that is not in demand outside the national labor market.
Improved Synonym Queries Bolstered by Encrypted Cloud Multi-watchword Ranked Search System

Cloud storage becomes more prominent in recent years due to its benefits such as scalability, availability, and low price carrier over standard storage solutions. Organizations are stimulated to migrate their information from neighborhood sites to the central business public cloud server. By outsourcing information on the cloud, customers receive alleviation from storage maintenance. Although there are many benefits to migrate facts on cloud storage, it brings many security problems. Therefore, the statistics owners hesitate to migrate the sensitive data. In this case, the manager of information is going toward a cloud carrier provider. This safety trouble induces fact owners to encrypt records at the client-side and outsource the data. By encrypting data improves the fact’s safety, however, the fact effectivity is diminished because searching on encrypted information is difficult. The search strategies which are used on undeniable textual content cannot be used over encrypted data. The existing options help only equal keyword search, fuzzy keyword search, but the semantic search is now not supported. In the project, the Semantic Multi-keyword Ranked Search System (SMKRSS) is proposed. It can be summarized in two factors: multi-keyword ranked search to acquire additional accurate search results and synonym-based search to support synonym queries.The experimental result shows that our proposed system is better than the unique MRSE scheme.
Governance, CO2 Emissions and Inclusive Human Development in Sub-Saharan Africa

This study investigates the relevance of government quality in moderating the incidence of environmental degradation on inclusive human development in 44 sub-Saharan African countries for the period 2000-2012. Environmental degradation is measured with CO2 emissions and the governance dynamics include: political stability, voice and accountability, government effectiveness, regulation quality, the rule of law and corruption-control. The empirical evidence is based on the Generalised Method of Moments. Regulation quality modulates CO2 emissions to exert a net negative effect on inclusive development. Institutional governance (consisting of corruption-control and the rule of law) modulates CO2 emissions to also exert a net negative effect on inclusive human development. Fortunately, the corresponding interactive effects are positive, which indicates that good governance needs to be enhanced to achieve positive net effects. A policy threshold of institutional governance at which institutional governance completely dampens the unfavourable effect of CO2 emissions on inclusive human development is established. Other policy implications are discussed.
Development status and trend of water saving agriculture in China

Abstract As an agricultural country, the shortage and waste of water resources seriously restrict the rapid development of agriculture in China, so how to realize water-saving agriculture is imminent. This paper expounds the current situation and existing problems of water-saving agriculture in China, studies and discusses the development trend of water-saving agriculture in the future, which provides a certain theoretical basis for the rapid development of China’s agriculture.
Linguistic Duality

Currently, the approaches from the results of word or document vectorization significantly achieve their success in large-scaled language processing, such as high-quality neural network machine translation trained on a large-scale parallel corpus. The data driven approach with minimum concern in language knowledge efficiently captures its syntactic information. However, there are some deficiency occurred in capturing the semantic information, such as in the similarity measure when performs on both sides of the language interpretation. We propose an add-on process to correct the similarity measure of the concepts defined in WordNet. Similarity measure is the fundamental procedure for language processing highly depending on the context integration and its representation method.
Non-Local Self-Attention Mechanism for Real-Time Context Embedding Deep Shadow Removal Network

Cast shadows can potentially mask important information in vehicle vision systems. To mitigate this degradation, shadow removal has been studied but challenging by requiring the semantic context understanding of global images. Recently, deep convolutional neural network-based methods show outperforming results; however, they mainly increase the depth to capture global information; this makes it difficult to be mounted on a vehicle vision system. In this paper, we adopt a non-local self-attention mechanism and improve its time complexity to capture the global context in real-time. Experimental results confirm that the proposed self-attention mechanism allows the network to capture semantic image contexts while it requires less computation.
Misogynous Text Classification Using SVM and LSTM

Discrimination and manipulation are becoming predominant in social network activities. Comments bearing attitudes, such as distress, hate, and aggression in Social Networking Sites (SNS) add fuel to the process of discrimination. This research aims to classify texts, which are misogynous in nature using Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) for user-generated texts of English and Hindi languages written using the Roman script. Approximately 87% accuracy was achieved while SVM was trained with Term Frequency-Inverse Document Frequency (TF-IDF) feature and for Hindi comments approximately 93.43% accuracy was achieved for English using Bidirectional LSTM (Bi-LSTM).
Data Correlation Analysis Algorithm of University-enterprise Cooperation and Cultivation Quality

Enterprise cooperation mechanisms play an important role in the cultivation of college students, but a scientific method is needed to evaluate the potential relationship between the university-enterprise cooperation and the quality of student cultivation. This paper proposes a data correlation analysis algorithm between university-enterprise cooperation and college students' training quality, in order to scientifically evaluate the impact of university-enterprise cooperation mechanisms on college students' training quality. The transaction data set and item set reflecting the relationship between university-enterprise cooperation mechanisms and training quality are established. The association rules for the influence of university-enterprise cooperation mechanism on the cultivation quality of college students are designed. The computation method of the item set support degree for student training affairs is defined, and the computation method of the confidence degree of the association rules for the influence of university-enterprise cooperation mechanisms on training quality is defined. The strongest association rules between the university-enterprise cooperation mechanisms and the quality of student training are confirmed by the support degree and the confidence degree. At the same time, the corresponding correlation analysis algorithm is designed, and the effectiveness of the proposed algorithm is verified by experiments.
Progressive Visualization of Epidemiological Models for COVID-19 Visual Analysis

COVID-19 data analysis has become a prominent activity in the last year. The use of different models for predicting the spread of the disease, while providing very useful insights on the epidemics, are not inherently designed for interactive analysis, with time for a single computation ranging in the tens of seconds. In order to overcome this limitation, this paper proposes three techniques for progressive visualization of Susceptible-Infectious-Recovered (SIR) models data, that govern the trade-off between time and quality of the intermediate results. The techniques are quantitatively evaluated showing promising results.
Using Anonymous Protocol for Privacy Preserving Deep Learning Model

Deep learning is an effective approach to many real-world problems. The effectiveness of deep learning models depends largely on the amount of data being used to train the model. However, these data are often private or sensitive, which make it challenging to collect and apply deep learning models in practice. In this paper, we introduce an anonymous deep neural network training protocol called ATP (Anonymous Training Protocol), in which each party owns a private dataset and collectively trains a global model without any data leakage to other parties. To achieve this, we use the technique of sharing random gradients with large aggregate mini-batch sizes combined with the addition of temporary random noise. These random noises will then be sent back through an anonymous network to be filtered out during the update phase of the aggregate server. The proposed ATP model allows protection of the shared gradients even when the aggregating server colludes with other n-2 participants. We evaluate the model on the MNIST dataset with the CNN network architecture, resulting in an accuracy of 98.09%. The results show that the proposed ATP model has high practical applicability in protecting privacy in deep learning.
Hacking Passwords that Satisfy Common Password Policies

The password policies for 14 popular websites were checked and a list of passwords that satisfied the minimal requirements created for each website. 58 users then created realistic passwords that satisfied the minimal requirements. A special purpose cracking computer was built to crack these passwords using dictionary and brute-force attacks. All minimal passwords were cracked and it was found that weaker password policies produced weaker realistic passwords. It is recommended that password policies increase the minimal length to 8 characters, require a more diverse alphabet, prevent simple passwords based on repetitions and sequences, and check a database of already hacked passwords.