Reviewerly/exHarmony huggingface dataset:
LITERATURE SURVEY ON RECONNAISSANCE AND RELATED REPORTS FOR THE 1872 HAMADA EARTHQUAKE

The 1872 Hamada earthquake occurred just after the Meiji revolution and caused damage in present Shimane Prefecture, the western part of Honshu faced to the Japan Sea. At that time modern seismology was not started yet and the first scientific paper on this earthquake was appeared after 12 years and detailed field investigations were done 40 years later. In this paper, we carried out literature surveys on this event and traced the process of the establishment of the total damage features and distributions. The effect of snow was also discussed because of her snowy location and occurrence season.
Penerapan Model Concept Sentence untuk Meningkatkan Keterampilan Menulis Karangan Narasi Siswa

This research was motivated by a lack of students' vocabularies so that they have difficulty narrating ideas into their writing. Students also often repeated the same words and used the local language. The purpose of this study was to determine students' narrative essay writing skills before the use of the concept sentence model, as well as student's learning activities using the concept sentence model at MI Al-Khoiriyyah III Garut Regency. This research was a Classroom Action Research consisted of two cycles. The results showed the activities of students before the concept sentence model was still lacking (43.04%), and the average of learning outcomes was only (48.69). The use of the concept sentence model run according to its stages, the activities, and students' learning outcomes have increased in every cycle. In the first cycle, the students' activities reached 66.85% and then increased in the second cycle by 78.81%. While students' learning outcomes in the first cycle of action I reached 62.82 then increased in action II by 69.35. As for the second cycle of action I students' learning outcomes reached 75, and for the action II reached 82.61 by very good criteria.
POLITENESS STRATEGIES OF THE COMMENTS TOWARD TRUMP'S INSTAGRAM POST ON 'INTERNATIONAL WOMEN’S DAY'

The purpose of this study was  to describe politeness strategies employed by the users to give comment toward Donald Trump’s Instagram Post on International Women’s Day and to explain the factors that influence the users to use the strategies. The researcher used the theory of politeness strategy by Brown and Levinson (1987).  In this research, the researcher applied descriptive-qualitative method. Because this research is to more explain about the findings politeness strategies employed by the users to give comment toward Donald Trump’s Instagram post. The data were taken from comment/utterance and source of data use document such Donald Trump Instagram Post. The instrument of this research is the researcher herself. Technique of collecting data use documentation methode and technique of analysing data use data reduction, data display, and conclusion. Then, the researcher uses source triangulation to validate the data. The researcher asks lecturer who master in Linguistic as validator of source data trianggulation. In this reserach, the researcher found positive politenes strategy is the highest strategy with total 276 (53%) data out of 525 data used by the users to give the comment toward Donald Trump’s Instagram post on International Women’s Day . The researcher also found social distance factor which factor is mostly influence the users used politeness strategy. It means when the users use social media to give comment, they want to show solidarity, treat the other users as a member of in-group, a friend, a person who are known and liked. So it can save the users and Donald Trump’s face and can minimize the distance between the user and Donald Trump.
한국 아동복지 행정의 통합적 관리 방안에 대한 고찰 - 전문가 대상 초점면담(Focus Interview)을 중심으로 -

This study discusses policy recommendations for integrated child welfare management that extends the current managerial approach, public-private cooperations in child welfare management. To explore a better approach to child welfare management, we conducted focus interviews with experts and analyzed experts’ advices and opinions. We found that experts put a value to legislate children and the establishment of the Framework Act on Children. It was also requested to establish a coherent administrative and reporting system for child welfare. Also, we should develop ‘children’s rights’ and ‘children’s first interests’ as measurable child policy evaluation indicators, and it is critical to expand the target of child welfare policies from ‘children’ to ‘the entire family’. Last, children should be guaranteed dignity and rights as human beings. And the healthy life of children along with inclusive and fair education and welfare should go with policies and administrative services. As a result, there are two main reasons for putting together experts’ opinions. First, integration in an organic and complementary sense for child welfare management of sustainable development. Second, integration in the sense of establishing a cooperative network that reflects the voices of children. This suggests the need for an integrated management plan of Korea’s child welfare management in policy terms, and means that it should be done in a sustainable cooperative network in terms of children’s rights.
An Insight of Cloud Computing

Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services).The word is recent but the idea of centralizing computation and storage in distributed data centers maintained by third party companies is not new, it came in the way back 1990s along with distributed computing approaches like grid computing. Cloud Computing aimed at providing IT as a service to the cloud users with greater flexibility, availability, reliability and scalability with utility computing model. The key characteristics of cloud computing are agility, cost, device and location independence, maintenance, performance, productivity, scalability and elasticity, security. There are substantial arguments for the adoption of Cloud Computing: the lasting improvement of cost structures, faster reaction to changes in the market and potential for increases in productivity.
건강가정·다문화가족지원 통합센터 사업운영 현황 연구: 충청지역 통합센터를 중심으로

Objectives: The purpose of this study is to analyze integration level of family service program contents, subjects of participation, and methods of progress conducted by the family support integrated center in Chungcheong area, and to make suggestions for balanced integrated program operation in regional integrated centers by considering relevance of the direction of family policy implementation and the purpose of integrated program operation. Method: The subjects of this study are programs on the website of 29 integrated centers for Healthy Family and Multicultural Family Integrated Center in Chungcheong Province. We analyzed the data by reclassifying the characteristics of each program based on the integrated program guidelines of Korea institute For Healthy Family. Results: First, four parts of the basic program have been carried out; family relationship(204 programs in five areas), family care(20 programs in two areas), family life(45 programs in five areas), and local community along with family(211 programs in seven areas). Second, the participant in each area included various families or individuals, but the approach to the participants reflecting characteristics of the integrated center was minimal. Third, most of the program process were family education and family culture, respectively, or mixed types. Fourth, when we looked through the number of participants and formation of the program, it was difficult to expect synergy from its effectiveness or integration of the delivery system owing to the fact that those factors can not be achieved by reflecting the characteristics of the individual basic programs. Conclusion: Based on the results of this study, it is necessary to improve the way that meets the purpose of integrated delivery system.
PEMBUATAN MAJALAH PEMBELAJARAN BAGI ANAK – ANAK

From the results of a survey of children we have done, the most vital issue of the topic of humanity in children is about access to education and equal opportunities to explore their potential. Therefore we agreed to Society Empowerment Program (SEPORA) project for neglected children by making a children's learning magazine that can be consumed at any time, of course, packaged with as attractive as possible. The children’s learning magazine called Nusantara, with the content about nationalism. Hopefully that they will be helped with literacy access and of course hopefully they will be motivated by the contents of the magazine.
A Method on Face Recognition Based on Single Sample Per Person with a Contaminated Biometric Enrolment Database

At present, face recognition stands out among various biometric identification technologies due to its unique advantages, and a lot of face recognition algorithms based on a large number of standard samples have been proposed and applied in real life. In order to deal with more complex scenarios, single sample per people (SSPP) face recognition has become one of the most difficult challenges. In practice, the only remaining sample may be contaminated by illumination, expression, and occlusion, resulting in performance degradation of various existing recognition algorithms, which is called SSPP with a contaminated biometric enrolment database (SSPP-CE). The synergistic generic learning (SGL) method studied in this paper is just a kind of solution based on general auxiliary data set to solve this problem. It uses sparse coding and dictionary learning to extract the less discriminative part (LDP) features of the auxiliary set samples as the variant dictionary, and the more discrimination part (MDP) features of the enrolment database samples as the prototype dictionary. Based on the framework of the prototype plus variant (P+V) model, the method in this paper realizes SSPP-CE face recognition. It is verified that the method has better recognition performance on contaminated samples.However, the existing researches did not identify whether the testing sample belongs to the enrolment database, which will cause misjudgment. To solve the problem, this paper proposes a method based on SoftMax function to identify whether the testing sample belongs to the enrolment database by confidence probability. The experimental results on the AR database prove that when the threshold is set to 0.7 and the contamination degree of the enrolment database is less than 70%, more than 90% of the non-enrolled samples can be identified. This method solves the problem of new testing samples outside the contaminated enrolment database, which improves the overall recognition accuracy for SSPP-CE problem.
Quantum certainty mechanics

Quantum certainty mechanics is a theory for measuring the position and momentum of a particle. Mathematically proven certainty principle from uncertainty principle, which is basically one of the most important formulas of quantum certainty mechanics theory. The principle of uncertainty can be proved by the principle of certainty and why uncertainty comes can also be proved. The principle of certainty can be proved from the theory of relativity And in the uncertainty principle equation, the principle of certainty can be proved by fulfilling the conditions of the principle of certainty by multiplying the uncertain constant with the certain values of momentum-position and energy-time. The principle of certainty proves that the calculation of θ π/2 between the particle and the wave involved in the particle leads to uncertainty. But calculating with θ=0 does not bring uncertainty. Quantum certainty mechanics has been established by combining Newtonian mechanics, relativity theory and quantum mechanics. Quantum entanglement can be explained by protecting the principle of conservation of energy.
Content-Based Image Retrieval based on Hybrid Feature Extraction and Feature Selection Technique Pigeon Inspired based Optimization

Content-Based Image Retrieval (CBIR) is also known as Query by Image Content (QBIC) that presents the technologies allowing to organize digital pictures by their visual features. They are based on the application of computer vision techniques to the image retrieval problem in large databases. CBIR consists of retrieving the most visually similar images to a given query image from a database of images. Retrieval of images is based not on keywords or annotations but on features extracted directly from the image data. Therefore, in this research study, the final output is retrieved from the database using feature extraction from the input query images.In this research study, Haralick Features are extracted, which is also known as Gray-Level Co-Occurrence Matrix (GLCM) along with Local Binary Pattern (LBP), and histogram of oriented gradients (HOG) features. When more number of features are used, it will increase the complexity of classifier to achieve better results. Therefore, feature selection technique called Pigeon Inspired based Optimization (PIO) is used in this research study. In the testing process, when the user gives a query images, the process will be same as training process and finally, to extract the relevant images, the training images are taken from the dataset and compared with the query images using Artificial Neural Network (ANN) classifier. The experiments are carried out using publicly available dataset called WANG and compared with existing techniques in terms of accuracy, precision, recall and F-measure.
Neural Re-rendering for Full-frame Video Stabilization

Existing video stabilization methods either require aggressive cropping of frame boundaries or generate distortion artifacts on the stabilized frames. In this work, we present an algorithm for full-frame video stabilization by first estimating dense warp fields. Full-frame stabilized frames can then be synthesized by fusing warped contents from neighboring frames. The core technical novelty lies in our learning-based hybrid-space fusion that alleviates artifacts caused by optical flow inaccuracy and fast-moving objects. We validate the effectiveness of our method on the NUS and selfie video datasets. Extensive experiment results demonstrate the merits of our approach over prior video stabilization methods.
ClinicNet: Clinical Practice Oriented Medical Representation Learning for Electronic Medical Records

Medical representation learning with deep learning methods is a popular research topic in recent years. Researchers build complex deep learning-based models for learning health status representation from electronic medical records (EMR) and performing downstream clinical prediction tasks. Previous works have achieved impressive performance on various clinical prediction tasks. However, almost no work analyzes about their performance in clinical practice. Nevertheless, as a form of clinically assisted decision making, an important target for clinical prediction is giving useful information for physicians when diagnosing patients. In order to eliminate this gap, we propose ClinicNet, an end-to-end deep representation learning framework for personalized and clinical practice-oriented health status representation learning from EMR. With analysis in real clinical scenes, the health status representation learned by ClinicNet is closer to the need of medical practice with specially designed loss function in training. Furthermore, verified by experiments on real-world datasets, ClinicNet achieves competitive performance compared with previous works for clinical prediction tasks.
Rethinking Curriculum Learning With Incremental Labels And Adaptive Compensation

Like humans, deep networks have been shown to learn better when samples are organized and introduced in a meaningful order or curriculum. Conventional curriculum learning schemes introduce samples in their order of difficulty. This forces models to begin learning from a subset of the available data while adding the external overhead of evaluating the difficulty of samples. In this work, we propose Learning with Incremental Labels and Adaptive Compensation (LILAC), a two-phase method that incrementally increases the number of unique output labels rather than the difficulty of samples while consistently using the entire dataset throughout training. In the first phase, Incremental Label Introduction, we partition data into mutually exclusive subsets, one that contains a subset of the ground-truth labels and another that contains the remaining data attached to a pseudo-label. Throughout the training process, we recursively reveal unseen ground-truth labels in fixed increments until all the labels are known to the model. In the second phase, Adaptive Compensation, we optimize the loss function using altered target vectors of previously misclassified samples. The target vectors of such samples are modified to a smoother distribution to help models learn better. On evaluating across three standard image benchmarks, CIFAR-10, CIFAR-100, and STL-10, we show that LILAC outperforms all comparable baselines. Further, we detail the importance of pacing the introduction of new labels to a model as well as the impact of using a smooth target vector.
Quantum Finite Volume Method for Computational Fluid Dynamics with Classical Input and Output

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods to solve fluid flows. The finite volume method (FVM) is an important one. In FVM, space is discretized to many grid cells. When the number of grid cells grows, massive computing resources are needed correspondingly. Recently, quantum computing has been proven to outperform a classical computer on specific computational tasks. However, the quantum CFD (QCFD) solver remains a challenge because the conversion between the classical and quantum data would become the bottleneck for the time complexity. Here we propose a QCFD solver with exponential speedup over classical counterparts and focus on how a quantum computer handles classical input and output. By utilizing quantum random access memory, the algorithm realizes sublinear time at every iteration step. The QCFD solver could allow new frontiers in the CFD area by allowing a finer mesh and faster calculation.
A Case Study on Rubbing Character Recognition Based on Deep Learning

This paper presents an case study which explored the classification performance of rubbing characters by utilizing deep neural networks. In the evaluation experiments, several mainstream deep neural networks are employed to realize the recognition of the rubbing characters. The purpose of this work is to examine the classification performance of the prevalent neural networks and collect the necessary information for our future works.
Food Ordering System based on Human Computer Interaction and Machine Learning Techniques

There is a saying,”the best interface is no interface”. Many people consider voice interface as an  excellent approach  to communicate with computer system. In the following paper,  we are trying to build up a system which is based on Human Computer Interaction to put in an order for food in restaurants. People tend to go to a restaurant where service and convenience of the customer are given top priority. A major problem faced  by the customers in a restaurant is waiting for the staff to take the order, this is because during rush hours the availability of staff decreases. This mishap can be removed by implementing a Food ordering system, a device which is built using Raspberry   Pi 3; along with voice controlled interface, as presented in this paper. Techniques like Natural Language Processing, Rule-based system, Sentiment Analysis are used in the proposed system.
A Study on Improved Authentication Technique in Cloud Computing

Cloud computing has emerged as a new field of IT industry as it has made many changes in the computing environment by acceleration of communications environment and the rapid spread of mobile devices recently. Many IT companies are providing various types of cloud services and have switched a variety of services that the companies have into the cloud service. However, Cloud computing has a slow growth rate and technology development related fields are being behind due to several security vulnerabilities that cloud computing has. In this paper, we analyze the vulnerabilities of the security element that cloud computing has and propose an authentication technique suited to cloud computing. PDM performs the self-authentication of many computing devices after receiving authentication from the authentication agency in the proposed authentication technique. The safety and efficiency of the device authentication can be increased in this way.
Improving the accuracy of the energy estimation by combining quantum annealing with classical computation

Quantum chemistry calculations are important applications of quantum annealing. For practical applications in quantum chemistry, it is essential to estimate a ground state energy of the Hamiltonian with chemical accuracy. However, there are no known methods to guarantee the accuracy of the estimation of the energy calculated by quantum annealing. Here, we propose a way to improve the accuracy of the estimate of the ground state energy by combining quantum annealing with classical computation. In our scheme, before running the QA, we need a pre-estimation of the energies of the ground state and first excited state with some error bars (corresponding to possible estimation error) by performing classical computation with some approximations. We show that, if an expectation value and variance of the energy of the state after the QA are smaller than certain threshold values (that we can calculate from the pre-estimation), the QA provides us with a better estimate of the ground state energy than that of the pre-estimation. Since the expectation value and variance of the energy can be experimentally measurable by the QA, our results pave the way for accurate estimation of the ground state energy with the QA.
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data

Despite considerable progress, most machine reading comprehension (MRC) tasks still lack sufficient training data to fully exploit powerful deep neural network models with millions of parameters, and it is laborious, expensive, and time-consuming to create largescale, high-quality MRC data through crowdsourcing.This paper focuses on generating more training data for MRC tasks by leveraging existing question-answering (QA) data.We first collect a large-scale multi-subject multiple-choice QA dataset for Chinese, Ex-amQA.We next use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance to convert it into a weakly-labeled MRC instance.To better use the weakly-labeled data to improve a target MRC task, we evaluate and compare several methods and further propose a self-teaching paradigm.Experimental results show that, upon state-of-the-art MRC baselines, we can obtain +5.1% in accuracy on a multiple-choice Chinese MRC dataset, C 3 , and +3.8% in exact match on an extractive Chinese MRC dataset, CMRC 2018, demonstrating the usefulness of the generated QAbased weakly-labeled data for different types of MRC tasks as well as the effectiveness of self-teaching.ExamQA will be available at
Working with Data and Datasets

In this chapter, we will review the basics on how to apply functions to a list using Map and Apply; how to define user functions, which can be used throughout a notebook; and how to write code in one of the powerful syntax used in the Wolfram Language called pure functions. Naturally, we will pass, to the associations, how to associate keys with values and understand that they are fundamental for the correct construction of datasets in the Wolfram Language. We conclude with a final overview on how associations are abstract constructions of hierarchical data.