Reviewerly/exHarmony huggingface dataset:
Applicability of the Capstan Equation to Guitar Strings

The effects of friction were observed in electric guitar strings passing over an electric guitar saddle.The effects of changing the ratio of the diameter of the winding to the diameter of the core of the string, the angle through which the string is bent, and the length on either side of the saddle were measured.Relative tensions were deduced by plucking and measuring the frequencies of vibration of the two portions of string.Coefficients of friction consistent with the capstan equation were calculated and were found to be lower than 0.26 for wound strings (nickel plated steel windings on steel cores) and lower than 0.17 for unwound (tin plated steel) strings.The largest values of friction were associated with strings of narrower windings and wider cores and this may be due to the uneven nature of the contact between the string and saddle for wound strings or due the surface of the windings deforming more, encouraging fresh (and therefore higher friction) metal to metal contact.It is advised to apply lubrication under the saddle to string contact point after first bringing the string up to pitch rather than before in order to prevent this fresh metal to metal contact.
Vector-Degree: A General Similarity Measure for Co-location Patterns

Co-location pattern mining is one of the hot issues in spatial pattern mining. Similarity measures between co-location patterns can be used to solve problems such as pattern compression, pattern summarization, pattern selection and pattern ordering. Although, many researchers have focused on this issue recently and provided a more concise set of co-location patterns based on these measures. Unfortunately, these measures suffer from various weaknesses, e.g., some measures can only calculate the similarity between super-pattern and sub-pattern while some others require additional domain knowledge. In this paper, we propose a general similarity measure for any two co-location patterns. Firstly, we study the characteristics of the co-location pattern and present a novel representation model based on maximal cliques. Then, two materializations of the maximal clique and the pattern relationship, 0-1 vector and key-value vector, are proposed and discussed in the paper. Moreover, based on the materialization methods, the similarity measure, Vector-Degree, is defined by applying the cosine similarity. Finally, similarity is used to group the patterns by a hierarchical clustering algorithm. The experimental results on both synthetic and real world data sets show the efficiency and effectiveness of our proposed method.
Software design to search data images of satellites LAPAN-A2/Orari and LAPAN-A3/IPB

LAPAN-A2/Orari and LAPAN-A3/IPB satellites are LAPAN-made satellites which are successors of the previous LAPAN-made satellites, namely the LAPAN-TUBSAT satellite made in Germany, the LAPAN-A2/Orari and LAPANA3/IPB satellites launched with Indian PSLV rockets from the Satish Dhawan Space Center, Sriharikota, India in 2015 (LAPAN-A2/Orari) and 2016 (LAPAN-A3/IPB). As time goes by, the two satellites produced large amounts of digital camera image data, for now before software search data images of satellites designed, images results only stored in conventional folder this method makes difficult for operators to search images with certain specifications. LAPAN hoped that the imagery of the two satellites could be integrated in the storage and web-based search media, so the internal user and external user could easily access the image data. This paper describes the design of database storage media, web-based data search which have three function that is search data by location’s name, date range of data retrieval, and also search data images by types of product (camera image data from LAPAN-A2 or camera image data from LAPAN-A3). With this software it is expected that LAPAN services for digital camera images data to every user will be more effective and efficient.
Algorithm of constructing lectures in the training of future Specialists in the field of Preschool Education

Improving the quality of higher pedagogical education is an urgent need and social demand of society, as it is emphasized in the National Strategy for the Development of Education in Ukraine until 2021, the Concept for the Development of Teacher Education, in the Standards and Recommendations for Guaranteeing the Quality of Higher Education in the European Space. These documents focus on the need to review and modernize the traditional forms and methods of organizing the educational process of higher education institutions. One of these organizational forms and methods is a lecture that has maintained the function of direct information transmission for many centuries. The purpose of the article is to highlight the technological aspects and features for constructing and introducing lectures in the process of professional training of future specialists in the field of preschool education under studying conditions at a pedagogical university. The author describes the main functions of the modern lecture, identifies the reasons for the overriding need to review functions and modernize the methodology for constructing a lecture in a modern institution of higher education, and determines the directions of this modernization. The article presents in detail the algorithm of constructing lectures step-by-step; the author illustrates some examples of the application of the proposed technological tools in the course of teaching the discipline “Preschool Pedagogy”, which is an obligatory subject included into the educational and professional training program of Bachelor students majoring in specialty 012 “Preschool education”. Key words: future specialists in the field of preschool education, a lecture, pedagogical university, functions, technological tools, construction algorithm.
Building Your First Bot Using Watson Assistant

Watson Assistant is a cognitive cloud service offering by IBM that makes it easier for developers to build conversational interfaces and embed them into any application. Watson Assistant integrates with a variety of cognitive services provided by the IBM Watson platform to enrich the conversation through spell checks, a tone analyzer, and other services.
An Target-Based Privacy-Preserving Approach Using Collaborative Filtering and Anonymization Technique

As the development of informative market grows, the different platform available who take the information of the user from various sources and provide this content to needy business organizations on the demand. The business organization analyses the user information to target the user for their business growth. If proper care of user’s information is not taken than their personal information may be disclosed to third party who analyze the user information. Here, it’s solely responsibility of the content provider to satisfy need of business parties by preserving the privacy of the user content. When the business holder asks for specific users information this request handled efficiently and securely so that the proper information display to the business parties and even the user identity will not be disclosed. To achieve this target-based solution, we propose filtering of data based on target audience requirement and anonymize the filtered data approach which makes secure analysis on user’s information by preserving the privacy as well as provide maximum informative result to the business holders based on their needs.
Summary of the third HIRO creative RoboCon and characteristics of the robots participating in that

Robot competition is holding with the theme of decommissioning of nuclear reactor organized by Ministry of Education, Culture, Sports, Science and Technology: MEXT since 2016. The competition is called ”HIRO creative RoboCon”. The first and second year, the targets of the competition were performances of the robots mobility. The this third year in 2018, the targets are changed total performance for decommissioning of nuclear reactor of the Fukushima Dai-ich. This report summarizes the outline of the three times competitions. We show that summary mainly the this third competition and simply the first and second.
Individual Soldier Gesture Intelligent Recognition System

In order to solve the problem that the combatants cannot communicate face to face while performing combat missions, a soldier identification intelligent recognition system was designed. First, the system performs data normalization and endpoint detection on the collected gesture information. Then, feature extraction of the processed data such as mean, peak-to-peak and root mean square values in the time domain. Finally, the dynamic time warping (DTW) algorithm is used to calculate the similarity between the test gesture and the template gesture for the extracted feature parameters, and then the recognition result is obtained. The experimental results show that the system has the characteristics of high recognition accuracy, fine real-time performance and strong adaptability to individual differences. Therefore, it is suitable for gesture recognition and communication when performing combat missions.
Corpus-Based Statistical Analysis of Polysemous Words in Legislative Chinese and General Chinese

Legislative language is an effective carrier of legal and judicial justice. It has many characteristics that are different from general language. However, currently the study of the language of legislation, especially legislative Chinese, is still relatively weak. This paper extracts high-frequency words from a legislative Chinese corpus and annotates their word meaning in this corpus. By taking them as target words, this paper then randomly extracts sentences from a large-scale general Chinese corpus (the CCL corpus or the corpus of National Language Committee) for word sense annotation. By comparing word meanings in legislative Chinese and general Chinese, this study finds that there are significant differences between them in terms of the total number of meanings and the frequency of meanings. The reasons of the differences are closely related to the accuracy, written style and contextual features of legislative Chinese in comparison with general Chinese. The comparative study between the two types of languages is helpful for exploring the characteristics of polysemous words in legislative Chinese, deepening the teaching and research of legislative Chinese, and providing references for lexical research in legislative Chinese.
DERIVATIONAL AND INFLECTIONAL MORPHEMES IN THE JAKARTA POST

Many people do not know that morpheme and word are interrelated. A word can be a morpheme and can be form by combination of morphemes. More over, there are many types of morpheme with its function that affects either the sentence or the meaning. Because of the morpheme impact, writer wants to analyze about morpheme in the articles from The Jakarta Post Newspaper by its appropriatness for analyzed. Writer is going to relate to the theory from Victoria Fromkin about the types of morpheme; those are derivational and inflectional morpheme. This analyzing purposes to find out which word forming by derivational morpheme, which word forming by inflectional morpheme, how the indicated word forming, and what kind of alteration are found. Writer is going to use the descriptive qualitative method. From 6 data for derivational morpheme, writer found 8 words forming by derivational morpheme and from 8 data for inflectional morpheme, there are 29 words forming by inflectional morpheme. From the data, writer found that each morpheme has its function and impact the meaning, class of word or the grammatical function of the base word. By this paper, people are going to know how prefixes and suffixes affect the word.
Systematic Reviews on Domestic Job Analysis: Focusing on Papers Published in Domestic Academic Journals Since 2010

본 연구는 체계적 문헌고찰을 활용하여 국내 직무분석을 주제로 한 관련 선행연 구를 분석하는데 그 목적이 있다. 이를 위해 국내 데이터베이스들을 이용하여 광범 위한 참고문헌의 수집을 진행하였으며, 수집된 참고문헌은 체계적 문헌고찰 방법을 통해 최종적으로 29편의 연구물들이 본 연구의 분석대상으로 선정되었다. 이에 따른 결과는 다음과 같다. 첫 번째, 연구 분야가‘직업’인 분야에서 활발히 진행되었 다는 점, 두 번째, 직무분석 시 가장 많이 활용된 기법으로는 DACUM기법이었으며, 대상은 공공교육 분야, 직무교육분야, 의료분야로 구성되어있었고 주요분석 내용은 직무정의, 책무와 과업, 중요도 등인 점, 마지막으로 직무분석 연구결과는 대부분 교육과정 개발 및 개선에 활용됨을 알 수 있었다. 이러한 결과를 바탕으로 본 논문은 DACUM기법을 비롯하여 다양한 연구방법을 활용한 직무분석이 진행되어야 하며, 직무분석의 연구결과를 교육과정 개발 및 개선뿐만이 아니라 직무모형, 직무 매뉴얼, 직무개선 등 평가와 보상의 측면에서 활용 되어야 한다는 점, 직무분석이 일회성이 아닌 일정 시점이 지나면 새롭게 직무분석 함으로써 조직과 구성원이 지속적으로 발전할 수 있는 토대를 마련해야 한다는 결론을 도출하였다. 본 연구를 통해 향후 직무분석 연구에 대한 시사점을 도출하고 이를 통해 직무분석에 대한 연구방향을 제시할 수 있다는 점에서 본 연구의 의미를 도출할 수 있다.The purpose of this study is to explore recent research trends about job analysis using systematic literature review. For this purpose, a wide range of references was collected by using databases for domestic research papers. Finally, a total of 29 articles are selected and analyzed through our own selection process prepared in this study. The findings of this study are as follows. First, the field of research in job analysis was actively studied in the field of occupation . Second, DACUM survey is the major methods used for job analysis. The subjects of job analysis are in the public education field, job education field and medical care field. Thus the important analysis contents include job definition, responsibilities, tasks, and task’s importance etc. Finally, various suggestions for job analysis results are appear. Among these suggestions, development and improvement appeared most frequently in this training program. Based upon these findings, this study from job analysis should be used in terms of evaluation and compensation of curriculum, including job model, job manual and job improvement, as well as development and improvement of curriculum. Job analysis should be newly performed after a certain point in time rather than a one-off event, laying the groundwork for continuous development of the organization and its members. The implications of this study can be found in eliciting implications for future job analysis studies and in presenting research directions for current and future job analysis.
A Comparative Evaluation of SOM-based Anomaly Detection Methods for Multivariate Data

Anomaly detection for multivariate data is of vital importance in academic research and industry. In real scenes, there is usually a lack of labels of anomalies. Self-Organizing Map (SOM) can map data to the output layer and maintain the original topology, which has been used as a semi-supervised learning method to solve the above problem. In this paper, we first explain the mechanism of classic SOM for anomaly detection, then compare it with two variants of SOM named kernel SOM and K-BMUs SOM. Kernel SOM replaces Euclidean distance with kernel functions, while K-BMUs SOM changes the number of matching neurons. The three types of SOM are applied to multivariate datasets in three different domains. We find that the performance of the three SOM-based methods is related to the characteristics of data.
Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform

Most denoising methods are designed to deal standard images with specific type noise, which do not perform well when denoising real noisy images contain uncertain types of noise. However, underwater image is a typical uncertain type noise image. To solve this problem, this paper presents a method using spatial feature classification jointing nonsubsampled shearlet transform (NSST) for denoising uncertain type noise images. Justifiable granule is employed to solve the problem of parameter selection. The raw image was decomposed by using the NSST to get one low frequency subband and several high frequency subbands. Then, the preliminary binary map is built, the binary map is employed to decide whether a coefficient contains spatial feature or not. And we employ justifiable granule to solve the difficulty of parameter selection. The high subbands coefficients are classified into two classes by fuzzy support vector machine classification: the texture class and the noise class. At last, the adaptive Bayesian threshold is used to shrink the coefficients. Simulation results show the proposed method is effective in uncertain type noise images(also have good performance in specific type noise). The method we proposed has been compared with other popular denoising methods and get excellent subjective performance and PSNR improvement.
A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform

With the development of internet technologies, the use of video sharing sites has increased. Video sharing sites allow users to watch videos of others. In addition, users can create an account to upload content and upload videos. These platforms stand out as the places where individuals are both producers and consumers. In this study, data about YouTube which is a video sharing site was used. The content of the content, which is also called as a channel on YouTube, was made by using a set of producers. The data set with 5000 samples on YouTube channels is taken from Kaggle. The data were classified using 4 different data mining tools such as Weka, RapidMiner, Knime and Orange using Naive Bayes and Random Forest algorithms. The parameters are requested from the user in order to obtain a more efficient result in the application of data mining algorithms and in the data preprocessing steps and in the data mining steps. Although these parameters are common in some data mining software, they are not included in all data mining software. Data mining software provides management of some parameters while other parameters cannot be managed. These changes affect the accuracy value in the study and affect the accuracy value in different ratios. Changing the values of the parameters revealed differences in the accuracy rates obtained. A data mining software model has been proposed by emphasizing to what extent the management of the parameters of the study and the extent of the management of the parameters should be connected to the data mining software developer.
Smartphone Sensor Based Physical Activity Identification by Using Hardware-Efficient Support Vector Machines for Multiclass Classification

Smartphone sensor-based activity identification has been recently received significant attention in versatile applications such as elderly people physical condition monitoring, general health monitoring, disease likelihood and other vital contexts to make human life more productive, secure and sound. Due to sensor derived data popularity, along with smartphone researchers are using other ad-hoc wearable devices like smartwatch, fitness tracker, fitbit for activity data collection. This paper emphasizes on heterogeneous optimal feature selection process based on Sequential Floating Forward Search (SFFS) approach. At the first stage, prominent discriminant features are elected from both time and frequency domain signal in order to create a robust model with better accuracy and generalization capability. Then the prime features are trained by Multiclass Support Vector Machines (SVMs) to identify twelve human activities by analyzing accelerometer-gyroscope sensory data taken from a published dataset. In this paper, SVM is applied to create nonlinear classifiers by adopting the kernel trick. Lastly, we have validated our model with online based benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification with trivial time duration as opposed to neural network.
Aerial traffic planning for autonomous multi-rotor in a dynamic known environment based on a Decomposition-Coordination method

This paper deals with the problem of aerial traffic planning for autonomous Unmanned Aerial Vehicle (UAV) and deploys a successful Decomposition-Coordination Method (DCM) to provide the optimal solution. Our purpose is to make the UAV’s flight smoother and avoid the dynamic obstacles. We work under the assumption that the environment is known and the obstacles positions can be identified in real-time by a deciding entity. We choose an initial position, for which we plan a first safe and optimal path which remains valid until a moving obstacle crosses it. To avoid the collisions, the deciding entity provides a new safe path, allowing the UAV enough freedom to reach the desired position safely. For this matter, we choose a nonlinear model that describes veraciously the rotational and translational dynamics of the UAV, we then use the DCM to compute the optimal control, thus enabling the UAV to navigate toward the safe path. This approach consists of breaking down the nonlinear system into several interconnected subsystems which allows the non-linearity to be treated at a local level, we then use the Lagrange multipliers to achieve coordination. Consequently, parallel processing minimizes remarkably the computation time which offers a fast solution to the traffic problem. to demonstrate the convergence and the stability of the method we make use of two theorems. The numerical application and the simulation presented confirms the efficiency of this technique.
Apriori Algorithm Optimization using Temporary Table

Apriori algorithm has been widely used to establish association rules and be applied on various areas. However, when applying this algorithm to some applications with relational database, the iteration process become ineffective. This study proposes the use of transaction temporary table to increase the time performance of apriori algorithm specifically in the scanning process of support value. The result showed that the algorithm ran faster on the process that using temporary transaction table.
Comparison between K-Nearest Neighbor and Support Vector Machine Algorithms for PPG Biometric Identification

with the great evolution in information technology, data mining and its techniques became a need in almost every application. One of the data mining techniques is data classification, in this paper 2 classification techniques are compared, the K-nearest neighbor and the Support vector machine due to their several advantages. The classification techniques are implemented and tested on Photoplethysmography signals after extracting 40 features from the signals. This work was made in order to check the efficiency of using the Photoplethysmography signals as biometric identification technique and choose the best classification technique for this application.
Estrous Cycle Classification through Automatic Feature Extraction

We study and propose, for the first time, an autonomous classification of the estrous cycle (the reproductive cycle in rats). This cycle consists of 4 stages: Proestrus, Estrus, Metestrus and Diestrus. The short duration of the cycle in rats makes them an ideal model for research about changes that occur during the reproductive cycle. Classification is based on the cytology shown by vaginal smear. For this reason, we use manual and automatic feature extraction; these features are classified with support vector machines, multilayer perceptron networks and convolutional neural networks. A dataset of 412 images of estrous cycle was used. It was divided into two sets. The first contains all four stages, the second contains two classes. The first class is formed by the stages Proestrus and Estrus and the second class is formed by the stages Metestrus and Diestrus. The two sets were formed to solve the main problems, the research of the reproductive cycle and the reproduction control of rodents. For the first set, we obtained an 82% of validation accuracy and 98.38% of validation accuracy for the second set using convolutional neural networks. The results were validated through cross validation and F1 metric.