Reviewerly/exHarmony huggingface dataset:
Sistem Pakar Diagnosa Penyakit Insomnia Menggunakan Metode Certainty Factor Berbasis Web

Insomnia is a complaint that often arises because of difficulty sleeping and restless sleep, resulting in decreased health of sufferers. The amount of costs incurred for consultation with a psychologist causes patients rarely do this insomnia examination. Then the insomnia disease diagnosis system was built using a web-based certainty factor method. Patients can make a diagnosis by filling in the symptoms that are felt. These symptoms are obtained from psychologists as experts. By using the certainty factor method, experts enter their knowledge in the MB and MD values ​​(level of confidence) of a symptom that is a definite or uncertain symptom of insomnia. The application performs the calculation of the value of certainty factor to exclude the patient's insomnia. This level is divided into 3, namely mild, moderate or severe insomnia. In addition, the application also provides advice for dealing with insomnia according to the level of diagnosis. The accuracy of the results of the application diagnosis has been tested by comparing it against an expert diagnosis. There are two experts who are used as a reference. System accuracy is obtained by 85%. With the functioning of all features of the application and a patient satisfaction level of 87% this application can help to find out the diagnosis of the level of insomnia.Keywords: Expert System, Certainty Factor, Level of Insomnia, Users
베트남어권 한국어 학습자의 요청화행 실현양상에 관한 연구

This study aims to examine the usage of request speech act’s strategies of Vietnamese Korean learners, and to discuss the implications of improving their pragmatic abilities. In order to achieve this purpose, this study used the most commonly used Discourse Completion Test (DCT) data collection method in speech act research. The respondents to the DCT were 39 Vietnamese learners of Korean language with a Topik Certificate of Grade 4 or higher. Studies have shown that in the scenarios of head act, Vietnamese learners generally preferred indirect request strategies over direct request strategies. Vietnamese learners were found to have used more request strategies in cases of high intimacy, compared to those with low intimacy. In addition, there were strategies that appeared only in situations where intimacy was high, but did not appear in situations where intimacy was low (such as discourse cover strategies, joking strategies, criticizing strategies) and, on the contrary, only when intimacy was low (understanding/self-introduction). Therefore, for Vietnamese learners, we need a request painting style education that can represent various strategies for making various requests. Moreover, it is necessary to educate Vietnamese learners on the speech act of request so that they can use appropriate strategies in appropriate situations by including various variables such as intimacy and social status.
Predictive Staffing Model for Transit Security

Digby's security is a firm that offers security and investigation services. They assign their staffs to transit locations, monitor and report real-time crimes or incidents. However, there are so many train and metro stations, while they have limited number of staffs. To address this issue, we build predictive models to estimate when and where are more possible to have incidents so that they can assign the staffs to the right place at the right time.
Key Structural Points Extracting Algorithm for Dongba Hieroglyphs

Dongba hieroglyph is a kind of very primitive picture hieroglyphs; it has a characteristic of pictograph to express meaning by using pictures, but also have some features of pictographic, ideographic, self-explanatory and echoism like hieroglyphs. The order of writing makes Dongba hieroglyphs and images essentially different. Extracting the key structural points of Dongba hieroglyphs is very important for studying the writing order and automatically extracting the strokes. Therefore, we combined the structural features of Dongba hieroglyphs and the multiple-scales decomposition theory of shape in computer vision to give the key structure point extraction algorithm. The algorithm uses a discrete curve evolution algorithm to decompose the feature curve by multiple-scales decomposition layer by layer and finally extract the points that can show the key structure of the character. And the experiments show that the algorithm has high accuracy, good stability, and good scaling, rotation and translation invariance.
CLASSIFYING TEXTURE IMAGES USING ARTIFICIAL AGENTS, FRACTAL DIMENSION AND EXTREME LEARNING MACHINES

This work proposes a new method of representing textures on digital images through their maximum (or minimum if the negative of the image is used) and dierent intensity borders by means of artificial beings called articial hikers that search for the maximum of a texture image and in doing so, represent the diferent characteristics of the image. The technique has two main parameters that can be adjusted in order to emphasize the greatest maximum of an image and diferent frequency borders. For the classication of  textures, the technique of articial hikers was combined with fractal dimension analysis and extreme learning machine and it presented superior results compared to previous works dealing with texture classication with artificial agents.
A Study to Assess the Level of Knowledge on Ill Effects of Mobile Usage among Adolescents in Selected College at Kancheepuram District, Tamilnadu, India

Mobile phone is a device used to communicate verbally all over the world who are not near to us, it is common in adolescents. Mobile phone became more and more a tool in our daily life.Therefore a study was done to assess the knowledge on ill effects of mobile usage among students at selected college,Kanchipuram district, Tamilnadu, India. Was undertaken with the objectives: to assess the knowledge on ill effects of mobile usage among students and to associate the knowledge on ill effects of mobile usage with selected demographic variables. Non experimental - Descriptive research study was conducted. The study sample consisted of total 55 adolescents. Data was analyzed by inferential statistics and presented through tables and figures. Findings revealed that Variables like, number of mobile phone is found to be significant. The number of students having moderate knowledge were 28(51%), inadequate knowledge was 20(36%) and adequate knowledge was 7(13%). Mean value 4.8 and standard deviation (SD) 1.6 are as follows.
NCS 학습모듈 활용을 통한 항공여객운송서비스 민간자격제도 개선에 관한 연구

In the field of travel services, the overall level of service provided depends on the work capacity of the employees at the customer contact point. In addition, the performance of travel service employees affects the satisfaction and repurchase of travelers. Therefore, it is very important to emit personnel with job qualifications for certain tasks in the labor market through training and qualification system in accordance with the NCS module developed to objectify the expertise and work capacity of the travel service employees. This is also the purpose of establishing a college. This is a core national task to build a core infrastructure by developing talented people with talent, and to improve the nation's competitiveness based on this and to create a society that is capable of giving priority. The purpose of this study was to find ways to improve the learning module of air passenger transport services through prior research on NCS learning modules, private qualification research, and the domestic qualification system, and to present the direction of improvement of the qualification system through research on the private qualification system of air passenger service through preliminary research review and focus group interviews with experts. In order to carry out the research purposes, the results obtained were summarized through focus group interviews conducted from September 3 to December 20, 2018 for educators in charge of aviation passenger service and experts in developing a private air passenger service qualification system.
The Connotation Meaning of Education in Rumah Kaca Novel of Pramoedya Ananta Toer

This study discussed the connotation meaning of expressions containing the sense of education. This study used a semantic review. The method used was descriptive qualitative, which analyzed and described the data objects. The data of word research contained connotations related to education. The data was the novel of Rumah Kaca by Pramoedya Ananta Toer. Data collection used were observation and note-taking technique. The validity used the source and theory triangulation. The results found that words were containing 16 good connotations and five bad connotations.
Interfaces, Platforms, and Three-Tier Programming

After completing this chapter, the student will be able to
TINJAUAN YURIDIS PERAN PEMADAM KEBAKARAN BP BATAM TERHADAP KEBAKARAN HUTAN DAN LAHAN

The purpose of writing this journal is to describe and analyze the role of the BP Batam Sub Directorate in mitigating and mitigating fire hazards against forest and land fires, then using the theory of effectiveness, the theory of usefulness of the law, and the theory of good governance. Collecting data obtained is by interviewing directly to the head of the fire department of BP Batam itself, as well as obtaining actual and official data. The document used is a recorded conversation that is relevant to this research, field data obtained directly from the BP Batam Fire Department Headquarters. The output of this study will explain the role of the Sub Directorate of mitigation and overcoming fire hazards against forest and land fires based on Perka BP Batam number 19 of 2019 which is reviewed based on the above law namely Permen PU Number 20 / PRT / M / 2009 concerning technical management guidelines fire protection in urban areas as a national standard reference for fire fighting
Nested variational autoencoder for topic modelling on microtexts with word vectors

Abstract Most of the information on the Internet is represented in the form of microtexts , which are short text snippets such as news headlines or tweets. These sources of information are abundant, and mining these data could uncover meaningful insights. Topic modelling is one of the popular methods to extract knowledge from a collection of documents; however, conventional topic models such as latent Dirichlet allocation (LDA) are unable to perform well on short documents, mostly due to the scarcity of word co‐occurrence statistics embedded in the data. The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets. To solve the lack of information of microtexts, we allow our method to take advantage of word embeddings for additional knowledge of relationships between words. For speed and scalability, we apply autoencoding variational Bayes, an algorithm that can perform efficient black‐box inference in probabilistic models. The result of our work is a novel topic model called the nested variational autoencoder , which is a distribution that takes into account word vectors and is parameterized by a neural network architecture. For optimization, the model is trained to approximate the posterior distribution of the original LDA model. Experiments show the improvements of our model on microtexts as well as its runtime advantage.
A Novel Image Encryption Scheme Based on Poker Cross-Shuffling and Fractional Order Hyperchaotic System

A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Firstly, the plain image is transformed into block image, and each block is confused by Poker shuffling operation with key. Secondly, bit plane of every pixel in each block is confused. Lastly, the scrambled image is encrypted by fractional order hyperchaotic sequence. This encryption scheme provides a secure and efficient key stream, and guarantees a large key space. The experimental results and security analysis imply that the new encryption method has secure encryption effect and property of resisting common attacks.
Supply-Chain Management System for Plastic Pipes Market Based on Open Blockchain Framework

Blockchain-based solutions can significantly decrease potential losses from counterfeit products in the majority of existing supply chains. In this paper, we present an architecture of a supply-chain management solution for the plastic pipes market and demonstrate its' feasibility of industrial scenarios. We emulate such a system for plastic pipes factory based on the Exonum blockchain framework and show the robustness and reliability of the proposed system.
The Social Benefits of Water Sports Events and Their Impact on Environmental Pollution

Zhang, B., 2020. The social benefits of water sports events and their impact on environmental pollution. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 111–115. Coconut Creek (Florida), ISSN 0749-0208.In today's society, sports events have become the focus of attention, occupy an important position in the sports industry, while bringing economic and social benefits. The social benefits of sports events play a positive role in the development of local society, and the effect of sports events is determined to a great extent according to social benefits. The purpose of this paper is to analyze and study the social benefits of water sports events, an important branch of sports events. In addition, water sports events are bound to have a certain impact on the environment, cannot be ignored, so this paper will also study the impact of water sports events on environmental pollution. According to the definition of social benefits and related factors, the social benefits of water sports events are analyzed and the positive effects of water sports events are analyzed. Through systematic analysis and combing, for the future development of water sports events put forward relevant views, while the environmental protection of targeted measures to improve social benefits at the same time, to ensure that the environment is not broken, to achieve a win-win situation.
Improving Unsupervised Neural Machine Translation with Dependency Relationships

Nowadays, neural networks have been widely used in the domain of machine translation (MT) and achieved good results. Neural machine translation (NMT) models need large bilingual parallel corpora to perform training. However, in many languages or domains, such corpora are scarce. Therefore, the technology of unsupervised neural machine translation (UNMT) which does not need bilingual parallel corpora attracted wide interest. State-of-the-art UNMT models use Transformer for training and cannot learn the syntactic knowledge from the corpora. In this paper, we propose a method to improve UNMT by using dependency relationships extracted from dependency parsing. The extracted dependency relationships are concatenated with the original training data after Byte Pair Encoding (BPE) to obtain new sentence representations for UNMT training. Models that combine dependency relationships allow for a better understanding of the underlying syntactic structure in sentences and thus affect the quality of UNMT. We leverage linearized parsing trees of the training sentences in order to incorporate syntax into the Transformer architecture without modifying it. Compared with state-of-the-art UNMT method, our method increased the BLEU scores by 5.11 and 9.41 respectively on WMT 2019 English-French and German-English monolingual news corpora with 5 million sentence pairs.
BeCAPTCHA: Behavioral Bot Detection using Touchscreen and Mobile Sensors benchmarked on HuMIdb

In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology and improve bot detection algorithms. For this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the touchscreen information obtained during a single drag and drop task in combination with the accelerometer data. The goal of BeCAPTCHA is to determine whether the drag and drop task was realized by a human or a bot. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile Interaction database), a novel multimodal mobile database that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.
Face Mask Detection System for COVID_19 Pandemic Precautions using Deep Learning Method

The World Health Organization (WHO) has stated that there are two ways in which the spread of COVID 19 virus takes place that are respiratory droplets and physical contact. So, avoiding the spread of this virus need some precautionary steps to be taken that are social distancing and the wearing of masks. Among these two precautions the mask wearing is considered as the important factor for the spread of COVID 19 virus because these droplets can land on any surface. So, to keep track of the people that are wearing mask or not is more important. Here we have presented a mask detection system that is able to detect any type of mask and masks of different shapes from the video streams for following the rules that are applied by the government. Deep learning algorithm is used here and the PyTorch library of python is used for mask detection from the images/video streams. The proposed system is able to detect the mask wearing people and those one who are not wearing the masks.
An Analysis of the Feasibility of Children's Acquisition of English Sentence Patterns from Picture Books --Taking the Picture Book “I Hear a Pickle” as an Example

Children's acquisition of English sentence patterns mainly depends on sufficient quantity and continuous input reinforcement. In this process, the use of children's English picture books can help children to acquire English sentence patterns. This paper will analyze the feasibility of children's acquisition of English sentence patterns from picture books from various perspectives.
Data preprocessing based on missing value and discretisation

In the real world, data is not available in the appropriate form for mining or extracting information from this. Generally, real-world data is incomplete, inconsistent and dirty so it is very necessary to process data smartly according to the requirement of the dataset. Preprocessing is one of the most crucial steps in data mining and most of the time spent in this about 60% of the time. Unprocessed data takes lots of time in mining. End-user wasted lots of time in getting the desired result. So it is very necessary to process data according to the specific dataset by applying techniques of processing and thereby it reduces the overall mining time, the end user gets the desired result more fastly. In this paper, preprocessing of missing value and discretisation has been done. Preprocessing of missing value handle by three techniques that is a deletion, replacement by mean or averages, and prediction method. From these three techniques, user opt the best technique for handling missing value, which gives maximum accuracy and takes less time for preprocessing. After handling the missing value, discretisation is done for data reduction so it minimises the preprocessing time.
Pose-Independent Facial Action Units Recognition with Attention Enhanced Residual Mapping

Facial action units (AU) recognition is an essential issue of affective computing, which is important to modern human-computer interaction and virtual reality. Recent advances in deep learning have shown great achievements in facial action unit recognition. However, the conventional approaches are sensitive to the pose of head. To tackle this limitation, we propose a pose-independent AU recognition approach based on attention enhanced deep residual mapping. In the deep feature space, the non-frontal face is mapped to frontal face through attention enhanced residual addition to improve the performance of non-frontal AU recognition. The network consist of three parts: the base network, the residual mapping module and the channel attention enhanced module. The base network is the fine-tuned VGG-Face which are trained with frontal faces. Then, the residual mapping and channel-wise attention mechanism are proposed and introduced into the deep feature space to learn the AU consistent features of faces in different poses. The non-frontal facial features are combined with the residual to map it to frontal face. The channel-wise attention mechanism enables the network to understand which features are more important for the facial pose mapping process. We have demonstrated the effectiveness of our approach on FERA2017 dataset. The experiment results have shown that our approach has improved the face recognition performance.
Hashing and Randomized Algorithms

Most of the algorithms discussed in previous chapters were designed to optimize worst-case performance: they are guaranteed to return optimal solutions for every problem instance within a specified running time.
Quality of Internal Audit in Light of Applying Institutional Governance Concepts in Jordanian Banks

This study aimed to measure the impact of the application of the concepts of corporate governance on the quality of internal audit in Jordanian banks, the study community will be from all Jordanian banks, where a questionnaire has been distributed to the study sample, which included financial managers and heads of accounting departments and managers of internal audit and workers in the accounting and internal audit departments in these banks. The results of the study concluded that there is an effect of applying the concepts of institutional governance (disclosure and transparency, accountability, responsibility, justice, independence, laws and regulations) to the quality of internal audit in these banks. In light of the results, the study made several recommendations, including stressing the importance of commitment to applying the pillars of institutional governance for its apparent impact on the quality of the internal audit, and working to develop the efficiency of the professional auditor for the internal auditor by holding training programs to keep up with the latest developments in his field of work and obtaining professional certificates.
Web Security Concepts

Now that some important general security concepts are out of the way, it’s time to talk about web security. If you’re already creating websites with some version of ASP.NET, many of the concepts presented in this chapter will be familiar to you. However, it is important to read this chapter fully before moving on to the next, because in order to understand web security, you need to understand how the web works at a deeper level than a typical web developer would.