Reviewerly/exHarmony huggingface dataset:
Intelligent Resume Retrieval Based on Lucence

With the development of Internet, the electronic resume has gradually replaced the paper one. It is the basic requirement of recruitment for enterprises to retrieve the talent information that fulfills the requirement quickly and without omission.Based on the framework of SpringBoot and Lucence full-text search engine, this paper implements a resume intelligent filtering algorithm, which improves the query speed of the system by establishing an index database. At the same time,the scoring function improves the accuracy of the filtering results, reduces the pressure of high concurrency of the database, improves the work efficiency of the Human Resources Department, and avoids the talent loss.
PEMBOBOTAN PARAMETER DENGAN PAIRWISE COMPARISON PADA CASE BASED REASONING PENYAKIT TANAMAN ANGGREK MENGGUNAKAN ALGORITMA SIMILARITAS KNN BERBOBOT

Nowadays, orchid farmers or hobbyists are starting to increase, because the diversity of the unique flowers and their rarity has attracted farmers and plant hobbyists. Lots of people want this beautiful plant, but caring for this orchid plant is not easy, it requires high accuracy, so that orchid plants do not die from disease. When orchid plants are attacked by a disease, orchid farmers and hobbyists need consultation about diseases that attack orchid plants, but experts or orchid plant extension agents are relatively difficult to find. Based on this, farmers and hobbyists need a system or application that can be accessed anytime and anywhere. An expert system is the right answer to make it easier to conduct consultations without having to visit an orchid plant expert. To provide information about how to diagnose types of diseases and how to deal with orchid plant diseases, an expert system application that can represent an expert in their field is needed to provide solutions to orchid plant disease problems using the pairwise comparison method and the KNN similarity algorithm. Pairwise comparison method on case-based reasoning of orchid plant disease produced weights with 3 categories of symptoms, namely mild symptoms with a weight of 0.09, moderate symptoms with a weight of 0.24 and severe symptoms with a weight of 0.67. Weighting parameters with pairwise comparison on case-based reasoning of orchid plant disease using the weighted KNN similarity algorithm will recommend several diseases with similarity above 0.5 and diseases with similarity below 0.5 will be entered into the revise table to find a solution.
Blockchain-based smart contracts for e-health-care management 4.0

Chapter Contents: 14.1 Introduction 14.1.1 Evolution of Healthcare 1.0 to 4.0 14.1.2 Blockchain in health-care applications used for preventing diseases 14.2 Related works on blockchain technology in health-care sectors 14.3 Blockchain-based health-care and management applications 14.4 Benefits of blockchain technology in the health-care industry 14.5 Ethereum – system design 14.6 5G networks and Ethereum for the health-care sector 14.6.1 Challenges in the health-care sector 14.7 Real-time examples of Ethereum in the health-care sector 14.8 5G networks and smart contracts 14.9 Advantages of smart contracts 14.10 Choosing the smart contract platform 14.11 Applications of smart contracts in healthcare 14.12 Case study – design and architecture 14.12.1 Client layer 14.12.2 Blockchain layer 14.13 System implementation 14.13.1 Smart contracts 14.13.2 Algorithm 14.14 Experimental setup 14.14.1 Performance evaluation 14.15 Results 14.16 Conclusion References
Ontological Structures of the Modern Educational Process

Three fundamental provisions are introduced here, which lay the basis for studying the ontological structures of modern education: 1) education is what remains when everything that has been learned is forgotten; 2) education is achieved through independent work, while what they can teach one in schools and universities only helps to get it; 3) a well-oiled infrastructure of the cognition process is needed, i.e. auxiliary areas and resources for the formation of the structures under consideration. These provisions are investigated in the context of the concept of dynamic limit equilibria and the world coordinate system based on it. Of the four concepts of consciousness singled out by V.A. Lektorsky, the paper used the first two: identifying consciousness with knowledge and considering intentionality (focus on a certain object) as the main sign of consciousness. When re-assembling knowledge, two main trends are possible: 1) purification, which presupposes discarding of all that is superfluous (it can have to do with the degradation of the subject, his or her reorientation to completely new knowledge, or the need to prepare memory for perceiving more knowledge); 2) self-organization of the acquired knowledge, which is at the cutting edge of contemporary research. The latter trend has several important features. From the point of view of dynamic limit equilibria, knowledge tends to close itself into a stable optimal system, pushing everything unnecessary and secondary to the periphery. Moreover, in the process of self-organization knowledge gravitates towards increasingly accurate identifications that clarify the connections between its key elements. Further, the paper dwells on the characteristics of three main components of the classical educational-hermeneutic triangle teacher–text–student and their interaction with each other. In addition, the hermeneutic circles of teaching methodology are analysed: 1) preparation–teaching–perception; 2) course and its parts, course and its context. Cognitive structures and objects of consciousness, as well as catalyzation and self-organization of educational activity are also considered in the paper.
Interactive Machine Anatomy Explorer using Augmented Reality

The wide potential of Augmented Reality (AR) for future applications has to gain the interest of the big companies to implement AR technologies in their production. AR have a various function in especially for educational, maintenance, design and training purpose. The importance of AR in the industry is to reduce the errors in maintenance and to increase the skill of the new workers with less involvement of the experienced workers in line with the industry revolution 4.0. The purpose of this project is to design and develop an augmented reality (AR) based application that allows users to be able to learn and understand the anatomy of a machine, and to measure the application usability and receptions. The design and developing process will be using the Unity3d engine and Vuforia SDK as the main software. To measure the usability and of the developed application its reception, a survey has been distributed to the students of the Universiti Tun Hussein Onn Malaysia (UTHM). The application developed is able to display the anatomy of the worm gear reduction by using a quick response (QR) code as the image target. The Interactive Machine Anatomy Explorer have 85% performance accuracy to detect the QR code. The application is able to detect the QR code with minimal light. The suggested distance to obtain the best result of the worm gear reducer anatomy is between 45 cm to 90 cm. From the survey done, the majority of the respondents (67%) agree that they will use the AR application that is able to show the machine’s components anywhere and anytime and 88% of the respondents think that it will reduce the time taken for them to understand the components detailed. These results show that AR involvement in the education process could be implemented to facilitate users in understanding the machine’s components better.
Jaringan Implementasi Disaster Recovery Plan Menggunakan Cloud Computing Pada Web Server

The need for information that is always up-to-date and can be accessed anywhere and anytime is increasing. This causes the need for data storage services that can guarantee data availability. With the availability of data, can help reduce the occurrence of server failure. One way to improve the quality of these services is to implement disaster recovery plan using Google Cloud Platform. The implementation carried out applies several techniques such as data replication, failover and failback. Data replication uses MySQL Replication Master-Master and GlusterFS, failover and failback using Nginx. From the results of this study it was found that on average the data successfully replicated using MySQL Replication Master-Master and GlusterFS was 100% and the use of google cloud as a secondary server along with nginx can guarantee availability of data on primary servers and secondary servers. The results of failover and failback methods get 100% success and in the failover process requires an average time of 7,35 seconds for the secondary server to take over primary server services and when a failback occurs, it requires an average time of 7,11 seconds for the primary server to retrieve service transfer on secondary servers. The existence of anomaly data in the failover and failback process causes the process transfer time to be unstable.
The Use Of News Item On YouTube To Improve Students' Speaking Ability At Grade XII Of SMAN 2 Bandar Lampung

In learning speaking, the students of Senior High School should be able to speak English appropriately. In the real condition, some students of Senior High School found difficulties in speaking English that is caused of some factors. One of the factors was how the teacher delivered the material. The materials were given by the teachers were not interesting. To make them interested and wanted to share their feelings in speaking English, the researcher used news item video as the media in the process of teaching and learning English. The research was intended to find out statistically significant improvement of the students’ speaking ability after they got treatment by using news item video. The subjects of the research were 27 students at SMAN 2 Bandar Lampung. There were three raters who assessed students’ speaking ability. The data were collected through speaking test (pretest and posttest). The paired simple t-test was used to analyze the data collected from both the pretest and posttest. The result showed that the news item video had statistically significant improvement on the students speaking ability with the significant level 0.05. This suggest that the news item video facilitates students to improve their ability of speaking. Keywords: Speaking, news item video, news item text, YouTube.
The effectiveness of edmodo and schoology in descriptive writing for Universitas Muhammadiyah Yogyakarta students

This research was aimed to figure out (1) the students’ achievement in descriptive writing taught by using Edmodo and Schoology, (2) the differences of students’ achievement in descriptive writing taught by using Edmodo, Schoology, and the conventional media, and (3) the most effective media among the media compared in descriptive writing for the students of Yogyakarta Muhammadiyah University. This research was a quasi-experiment study with a nonequivalent (pretest and posttest) control group design. The data were collected through written tests. The reliability of the instruments was gained using inter-rater reliability. The data were analyzed using the covariant analysis (ANCOVA) and the Scheffe test. The results of the study showed that: (1) the use of Edmodo and Schoology gave a positive contribution to students’ achievement in descriptive writing, (2) there were differences among students’ achievement in descriptive writing taught by using Edmodo, Schoology and the conventional media, (3) the use of Edmodo and Schoology was more effective than the conventional media.
AN EXPERIENCE REPORT ON THE ADAPTATION OF A FACE-TO-FACE DISCIPLINE TO THE VIRTUAL CONTEXT IN TIMES OF COVID-19 PANDEMIC

AN EXPERIENCE REPORT ON THE ADAPTATION OF A FACE-TO-FACE DISCIPLINE TO THE VIRTUAL CONTEXT IN TIMES OF COVID-19 PANDEMIC
Multi-layer color QR code dynamic decoder framework with fuzzy color recovery

In this paper, we proposed a dynamic framework for a multi-layer color QR code decoder. The proposed decoder framework shows the general steps to decode color QR code. It contains a configuration setting standard that allows other researchers to refer in order to decode their color QR code based on the colors used in the encoder. The framework starts with color QR code detection, then search for color reference. This is followed by fuzzy sets selection based on the color QR code. Color enhancement for the QR code is implemented based on the fuzzy set decision. Next, is color de-multiplexing to get Black and White (B/W) QR code. The de-multiplexing process is based on a configuration file, for the QR code color setting. Finally, is the decoding and merging of the results for the B/W QR code to obtain the original file. We use two datasets with color reference to evaluate our framework. The first dataset used is generated by Yang et al., 2018 encoder and we obtained 83% success rate for the detection and color de-multiplexing. The second dataset is generated from our encoder and produced 90% decoding success rate. The experiment shows the framework can successfully work with different sizes of color QR code.
ОРГАНИЗАЦИЯ САМОСТОЯТЕЛЬНОЙ РАБОТЫ УЧАЩИХСЯ ПО ФИЗИКЕ В ШКОЛЕ ПО НОВОЙ ОБРАЗОВАТЕЛЬНОЙ ПРОГРАММЕ

The organization of independent work of students is an actual problem in the conditions of the updated training program, the main task of which is to teach students the application of the acquired knowledge in practice. The purpose of the article is the proposed justification of the role of the case method in its application for the organization of independent work of students. This article analyzes the modern views of researchers on the potential of independent work in the educational process. Particular attention is paid to the formation of students ' readiness for self–preparation for the implementation of intermediate and final works, for this purpose the article offers multivariate tasks for self– fulfillment. The authors come to the conclusion that the most effective way of independent work is the case– method. Based on the analysis of modern research reveals the role of the case method in the educational process. The article describes the method of organization of independent work of students to solve the case on the example of sections in physics as Mechanics, Electrodynamics,Optics. The practical significance lies in the application of the given example of the case for the organization of independent work of students in the conditions of the updated training program. Considerable attention is paid to the content of the case and its tasks. The criteria of evaluation of learning outcomes using the case method, methods of evaluation by the teacher, self– assessment and mutual evaluation are described.
Extension of low-SNR coherent signal detection method based on spectral matrix analysis by using wavelet transformation and time delay coordinate

PreviousNext No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021Extension of low-SNR coherent signal detection method based on spectral matrix analysis by using wavelet transformation and time delay coordinateAuthors: Takayuki NagataYusuke MukuhiraTaku NonomuraTakayuki NagataDepartment of Aerospace Engineering, Tohoku UniversitySearch for more papers by this author, Yusuke MukuhiraInstitute of Fluid Science, Tohoku UniversitySearch for more papers by this author, and Taku NonomuraDepartment of Aerospace Engineering, Tohoku UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segj2021-033.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The detection and analysis of low signal-to-noise-ratio (SNR) events are valuable for source positions mapping of microseismicity and understanding of underground reservoirs. In the previous study, we applied the spectral matrix (SPM) analysis to characterize the motion of 3D particles in the time-frequency domain by eigenvector of SPM and detected the P-wave arrivals of low SNR microseismic events. In the present study, we further extended low-SNR event detection based on the spectral matrix analysis with the wavelet analysis and time delay coordinate. By using wavelet analysis, the optimum length of the time window for frequency analysis is automatically applied for each frequency band. In addition, the introduction of time-delay coordinates increases the dimension of the SPM matrix and improves the SNR in SPM analysis. Particularly, the time delay is beneficial when event signals are coherent. The characteristics of the proposed method were evaluated by applying the method to synthetic signals. Synthetic signals include sinusoidal event signals and colored noise, and the SNR sensitivity test was conducted by changing the intensity of the noise component. The proposed method is robust to noise and shows the potential to detect low SNR coherent signals efficiently. Keywords: low-SNR event, spectral matrix, delay coordinate, time-series analysisPermalink: https://doi.org/10.1190/segj2021-033.1FiguresReferencesRelatedDetails Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 349 publication data© 2021 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Nov 2021 CITATION INFORMATION Takayuki Nagata, Yusuke Mukuhira, and Taku Nonomura, (2021), "Extension of low-SNR coherent signal detection method based on spectral matrix analysis by using wavelet transformation and time delay coordinate," SEG Global Meeting Abstracts : 120-123. https://doi.org/10.1190/segj2021-033.1 Plain-Language Summary Keywordslow-SNR eventspectral matrixdelay coordinatetime-series analysisPDF DownloadLoading ...
The Political Economy of Capital Flight: Governance Quality and Capital Flight in the East Africa Community

This paper examines the impacts of political economy factors on an illicit capital outflow in the East African Community using robust panel data models. The main findings of the study are as follows: An increase in Gross Domestic Product is a statistically significant variable and reduces an illicit flow of illegal capital outflow from the Community. However, foreign direct investment, total grant and exchange rate statistically significant and aggravate the outflow of capital. This is due to poor governance and economic policies that governments favor foreign investors over local investors. An aggregate index of poor regulatory quality and government ineffectiveness, state fragile index, and the political instability index are statistically significant and positively influence an illicit capital flight from the Community. However, the existed perceived corruption level does not positively contribute to capital flight, but an intensive corruption level positively influences capital flight overtime, bringing a mixed sign of negative and positive depending on the level of corruption that affects capital flight overtime in the Community.  The study, therefore, recommends that member countries in the Community need to undertake an effective governance and regulatory qualities, political stability and controlling power of corruption in order to control capital flight. Keywords : Capital flight, governance quality, political stability index, regulatory quality index, Government Effectiveness, Control of Corruption, World Bank Residual Methods DOI : 10.7176/JESD/10-11-02 Publication date :June 30 th 2019
VISUAL ACTIVITY AS A MEANS OF DEVELOPING CHILDREN’S CREATIVE ABILITIES DELAYED MENTAL DEVELOPMENT

The article is devoted to the problem of developing the creative abilities of children with a delay in mental development. The author describes in detail how visual activity can contribute to the development of creative abilities in children with a delay in mental development.
Effects of word frequency and phonological neighborhood density on speech recognition in background noise and competing speech

Clinical speech-in-noise tests typically use materials without contextual constraint or balanced for linguistic properties like word/phoneme frequency. However, real-world linguistic context effects can be substantial and vary by listener and scenario. Here, 38 participants completed the Theo-Victor-Michael (TVM) speech test in four types of background: speech shaped noise (SSN), speech-envelope modulated noise (envSSN), one competing talker (1T), and two competing talkers (2T) (Helfer and Freyman, 2009). The TVM is a matrix test using keywords from a corpus of one- and two- syllable nouns that vary considerably in word frequency (FREQ) and phonological neighborhood density (DENS). Bayesian logistic regression was used to estimate the effects of FREQ/DENS on TVM performance. A multinomial model was used for 1T/2T to assess reporting of target and distractor keywords. Overall, percent-correct recognition increased with increasing keyword FREQ and decreased with increasing keyword DENS. Effects were larger in SSN/envSSN than 1T/2T. Statistically significant but small effects of FREQ/DENS were observed on distractor responses in 1T/2T. Adjusting performance for FREQ/DENS substantially shifted the distribution of scores but only for SSN/envSSN. Performance in 1T/2T may be dominated by non-linguistic factors, and/or less sensitive to FREQ/DENS due to higher difficulty or linguistic competition from the background talkers. [Work supported by VA RR&amp;D Service.]
Emotions Recognition from Human Facial Images Using Machine Learning Technique

Systems of facial emotion recognition have witnessed a high significance in the research field. The face emotions are based on human facial expressions which play a crucial role in silent communication. Machine learning algorithms have widely used in systems of human facial emotion detection from images. However, many systems suffer from low accuracy rate. In this paper, we present a system of facial emotion recognition by using images. In this proposed system, the samples of facial emotions have taken from Yale Face database. In addition, the Histograms of Oriented Gradients (HOG) is used to extract features from the images. The extracted features will feed the Fast Learning Network (FLN) algorithm for the classification part to identify the images of facial emotions with respect to their subjects. Many evaluation measurements have used to evaluate the performance of the proposed system. Based on the results of the experiment, the proposed system achieves 95.04% for the highest accuracy, 72.73% precision. Also, the results of the proposed system in terms of recall, f-measure, and G-main are all equal to 72.73%, respectively.
An Online Document Emoji-Based Classification Using Twitter Dataset

An enormous amount of unstructured data is generated by online media like online connecting sites, microblogs, ecommerce sites, etc. Nowadays, online data plays a very decisive part in analyzing and predicting the opinions and sentiments of people toward an organization, brand, famous personality, etc. which in turn tremendously helps company to improve product quality, model market strategy, measure return of investment, determine people’s attitude toward famous personalities, artists, etc. The major component of these online documents is text and emoji which has motivated us to provide an approach to classify the online document into “Online Document Emoji Class, ODEC”, i.e., classifying the various online document on the basis of number of emojis in the sentiment. In this paper, we have also examined the tweets of numerous world-famous personalities as online document based on classification of popular regions. The results show that the number of emoji used for most of the cases is approx. 60% of the total number of tweets data. Thus, this quantification of online data will help us to identify the relevance of emoji study in sentiment analysis and better efficient way of classification of data.