Reviewerly/exHarmony huggingface dataset:
Feature analysis for stage identification of Plasmodium vivax based on digital microscopic image

&lt;em&gt;Plasmodium&lt;/em&gt; parasite is identified to confirm malaria disease. Paramedics need to observe the presence of this parasite prepared on thick and thin blood films under microscope. However, false identification still occurs which is caused by human factor during the examination. Thus, malaria identification based on digital image processing has been widely developed to overcome the error possibility. This paper proposes a scheme to identify and classify the stages of &lt;em&gt;Plasmodium vivax&lt;/em&gt; parasite on digital microscopic image of thin blood films based on feature analysis. Shape and texture features are extracted from segmented parasite objects. Feature selection based on wrapper method is then conducted to obtain relevant features which may contribute in improving the classification result. The classification process is conducted based on Naïve Bayes classifier. The performance of proposed method is evaluated using 73 digital microscopic images of &lt;em&gt;P.vivax&lt;/em&gt; parasite on thin blood films comprising of 29 trophozoites, 10 schizonts and 34 gametocytes stages. By using six selected features including perimeter, dispersion, mean of intensity, ASM, contrast GLCM and entropy GLCM, the proposed scheme achieves the best classification rate with the accuracy, sensitivity and specificity of 97.29%, 97.30% and 97.30%, respectively. This indicates that the proposed scheme has a potential to be implemented in the development of a computerised aided malaria diagnosis system for assisting the paramedics.
A Study on English Vocabulary Size of Students Majoring in International Natural Science Education, Yogyakarta State University

The current study is an attempt to discover the vocabulary size of students majoring in International Natural Science Education and the developmental pattern of their vocabulary size. The proposed research questions are: (1) What are the vocabulary sizes of students of international natural science education? and (2) Is their vocabulary size different across levels (freshmen, sophomore and junior)? To achieve the research aim, a cross-sectional research design was used. The total number of 79 students from three semesters studying in International Natural Science Education participated in the study. The instrument used was a Vocabulary Size Test developed by Nation and Beglar (2007). A descriptive statistic and an inferential statistics One-Way ANOVA continued by a pos-hoc test (Scheffe) were run in order to answer the proposed research questions. The results of computation revealed that the vocabulary size was 5,271-word families for the freshmen level, 6,142-word families for the sophomore level and 6,524-word families for the junior level. Based on the result of inferential statistics One-Way ANOVA, the student’s vocabulary size across levels are different. The F-ratio indicates that the difference between these groups was 7,369 in the level of significance 0,001. Furthermore, based on the Scheffe test, the significant difference lies between semester 2 and semester 4. Meanwhile, the difference of the vocabulary size between semester 4 and semester 6 was found to be less significant. In general, it can be concluded that there is a difference on the development of student’s vocabulary size reflected by the result of statistical analysis. The developmental pattern of students’ vocabulary size seems to be positive since the higher the level, the larger the students’ vocabulary size.
Research on Course Selection Algorithm in Colleges Based on Collaborative Filtering Recommendation Algorithm

In this era, it is very difficult to find information that you are interested in from a large amount of information. The recommendation algorithm is an important tool to solve this contradiction. This article will focus on the selection of the recommendation algorithm for college students to recommend the best curriculum and the application of the algorithm to college students in the course of elective courses. In the recommendation algorithm based on collaborative filtering, this article will link the high relevance of the students with the high relevance of the curriculum and take the course performances of the information management and information system major of Zhongnan University of Economics and Law as an example to draw a curriculum network diagram for the curriculum. The use of multiple linear regression analysis is for the first performance prediction. At the same time, a student-course performance matrix is established to calculate the similarity of the matrix, and the student performances with high similarity score is used as the predicted result of the students. That is the second-step performance prediction. The final prediction result consists of these two parts. With the Top-N ranking of the predicted results of the elective courses, the recommendation algorithm will recommend high-quality courses to the students so that students can choose the right course for them faster and more objectively.
Improving Recommendations by Embedding Multi-Entity Relationships With Latent Dual-Metric Learning

Recently, latent vector embedding has become a research hotspot, with its great representative ability to measure the latent relationships among different views. However, most researches utilize the inner product of latent vectors as the representation of relationships, and they develop some embedding models based on this theory. In this paper, we take deep insight into the existing embedding models and find that utilizing the inner product may increase several problems: 1) in latent space, the inner product among three vectors may violate triangle principle; 2) the inner product cannot measure the relationships between vectors in the same category, such as user and user and item and item; and 3) the inner product cannot catch the collaborative relationships (user-user and item-item) for collaborative filtering. Along with this line, we propose a latent vector embedding model for collaborative filtering: latent dual metric embedding (LDME), which utilizes the dual-Euclidean distance in latent space, instead of the inner product, to represent different types of relationships (user-user, item-item, and user-item) with a uniform framework. Specifically, we design an embedding loss function in LDME, which can measure the close and remote relationships between entities, tackle the above problems, and achieve a more clear, well-explained embedding result. Extensive experiments are conducted on several real-world datasets (Amazon, Yelp, Taobao, and Jingdong), where the expiring results demonstrate that LDME can overperform some state-of-the-art user-item embedding models and can benefit the existing collaborative filtering models.
Exploration of Training Mode for EMC Technicians in Beijing Tianjin Hebei Region

Through analyzing the electromagnetic compatibility technology development level in colleges and universities, research institutes of special industry at Beijing, Tianjin and Hebei region are summarized. Combined with the joint construction by Hebei, Tianjin and Ministry of Education and first-class university building for Hebei University of Technology in Hebei province, A-list subject construction of Electronic Science and technology target is proposed. At the same time in development of an opening electromagnetic environment and electromagnetic compatibility research platform, teaching mode of practical personnel in the field of electromagnetic compatibility is explored. Through Innovation by Production, scientific research and teaching cooperation, EMC testing technical level of undergraduate is improved, EMC design rectification level of graduate is enhanced, and technical reserve and EMC talents are prepared to support the development of the industry of EMC in Beijing, Tianjin and Hebei region.
A Brief Discussion on Enhancing the Core Competitiveness of University Teachers

This paper regard the core competitiveness of college teachers as the special advantage for college maintaining fierce energy and enhancing the momentum of the development in the fierce competition, and it is a key to maintain and enhance the competiveness of college teachers. Come to a conclusion that it is a systematic project to enhance the competitiveness of the college teacher, it can only achieved by government, universities and teachers itself working together.
Extracting Illustrated Pages from Digital Libraries with Python

Machine learning and API extensions by HathiTrust and Internet Archive are making it easier to extract page regions of visual interest from digitized volumes. This lesson shows how to efficiently extract those regions and, in doing so, prompt new, visual research questions.
INVESTIGATION OF METHODS OF INTELLECTUAL ANALYSIS OF BIBLIOGRAPHIC DESCRIPTIONS AND DEVELOPMENT OF PROGRAM SYSTEM FOR ANALYSIS OF LITERATURE LIST

Context. The problem of analyzing a bibliographic list is examined with the purpose of automating the verification of the correctness of its compilation. The object of the study was the process of creating bibliographic descriptions based on existing standards.The subject of the study is models and methods of intellectual analysis for checking and correcting bibliographic descriptions. Objective. The goal of the work is to increase the effectiveness of scientific and pedagogical activity through the use of an intellectual system to check the list of literary sources in the students’ course and diploma papers.Method. The standard is analyzed and the elements composing the bibliographic description are listed. Existing tools for forming and checking the list of literature sources are considered, their shortcomings are revealed, the necessity of creating a software tool forchecking bibliographic descriptions is grounded. The research of methods of intellectual analysis is given: methods of regular expressions, fuzzy search with application of fuzzy regular expressions, finite automaton and neural network of Hamming are considered. A previously existing model for checking the bibliographic description for compliance with existing standards was created, based on the methods of regular expressions, fuzzy search, the finite automaton and the neural network of Hamming. An object-oriented model of the projected computer system in the language of visual modeling of UML is presented. The work of computer implementation of the software system implemented in the C # visual programming environment is described. Results. A software system was developed – an intelligent decision-making system – to check the bibliographic description and partially automated correction with user instructions generated based on the knowledge base. Conclusions. The developed intellectual system allows to reveal and correct mistakes of registration of the bibliographic description. The basis of the program is the knowledge base, which can be supplemented if there are any unforeseen situations. In addition, this system makes it possible to accumulate a database of already verified lists of literature. The developed system can be improved without interfering with the source code of the program. At the same time, the level of its capabilities and correctness of corrections, the issuance of comments directly depends on the completeness and correctness of the compiled knowledge base. The prospects for further research include the use of other styles for the design of a list of literature other than DSTU 7.1: 2006, such as the IEEE.
Probabilistic Relaxation Labeling: A Short Survey on Object Recognition

Object recognition problem can be defined as classifying input object(s) to number of predefined classes.Object recognition is one of the most important sections in computer vision.While this filed has been studied from long time ago, but it still suffers from several challenges such as: occlusion, rotation, distortion illumination, and scaling.The conventional object recognition system has two phases.Firstly: extraction of the most important (informatics or key pints) parts from object image (scene image) and predefined class image (model image), secondly matching between object and model.The Probabilistic Relaxation Labeling (PRL) is one of the popular probabilistic approaches in matching among model and scene.In this paper we review two phase and report the most important works based PRL.
Research on Path Planning of Aircraft Based on Improved Genetic Algorithm

As for the optimal design of aircraft, route planning is an important part of mission planning system and a key technology for autonomous control of aircraft. In order to find the optimal route quickly, an aviation network model based on navigation point flight is designed. The cost function is generated by considering the distance between nodes, weather and the density of the aircraft, and the optimal route in the model is planned by using the improved genetic algorithm. The simulation results show that this method can realize the optimal design of the flight path of the aircraft in the aviation network.
Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis

Traditional supervised multiple kernel learning (MKL) for dimensionality reduction is generally an extension of kernel discriminant analysis (KDA), which has some restrictive assumptions. In addition, they generally are based on graph embedding framework. A more general multiple kernel-based dimensionality reduction algorithm, called multiple kernel marginal Fisher analysis (MKL-MFA), is presented for supervised nonlinear dimensionality reduction combined with ratio-race optimization problem. MKL-MFA aims at relaxing the restrictive assumption that the data of each class is of a Gaussian distribution and finding an appropriate convex combination of several base kernels. To improve the efficiency of multiple kernel dimensionality reduction, the spectral regression frameworks are incorporated into the optimization model. Furthermore, the optimal weights of predefined base kernels can be obtained by solving a different convex optimization. Experimental results on benchmark datasets demonstrate that MKL-MFA outperforms the state-of-the-art supervised multiple kernel dimensionality reduction methods.
Application of variational mode decomposition method in Fourier transform profilometry

For the spectrum overlapping problem caused by the fundamental frequency component and the zero-frequency component in the Fourier Transform Profilometry (FTP), a new method based on Two-Dimensional Variational Mode Decomposition (2DVMD) is proposed to extract the fundamental frequency components, and it can adaptively decompose the fringe pattern into background part and fringe part. Comparing 2DVMD method and Bidmensional Empirical Mode Decomposition (BEMD) method in zero-frequency components removing, it is found that 2DVMD method can perfectly separate the fundamental frequency component from the zero-frequency component and can extract the fundamental frequency information completely, thus avoiding the loss of the fundamental frequency information caused by filtering. Then the inverse Fourier transform and the phase unwrapping algorithm are performed to the extracted fundamental frequency component, it can effectively recover the abrupt change information of the measured object. Numerical simulations and experimental results show that the FTP based on 2DVMD method can reconstruct the details of the measured object more effectively than the FTP based on filtering method.
Analysis on the Present Situation of Environmental Education in Middle School of China by Investigation to Teachers and Its Countermeasures

By investigation to Teachers, combining the method of qualitative analysis with quantitative analysisï¼Œ authors analyzed the present situation of environmental education in middle school and its problems. The results show that, the cognitive status to environmental education of teachers is generally good, but the practice status of it is not satisfied, and there are differences in the practice of environmental education of teachers between schools and disciplines or teaching ages. The investigation shows further that the pressure of college entrance examination and the lack of curriculum setting are the main constraints affecting the implementation of environmental education of teachers, and teacher training is the key to break the bottleneck of environmental education practice. Therefore, The author makes the following Countermeasures suggestions: i) to emphasis on the train of environmental education and great an atmosphere of environmental education for all; ii) to strengthen the platform and mechanism construction for online environmental education teacher training; iii) to promote the interface of environmental education between university and middle school for strengthen the pre-service training of environmental education capabilities; iv) to combine environmental education with teaching reform by promoting open teaching research and practice.
Application of Organic Architecture in School of Nature Design

The development of education in the room is the main factor for students to feel the boredom of learning in the room. Indoor education is rigid and formal, often causing saturation and boredom when carrying out the process of teaching and learning activities. Whereas to get education is very important. Therefore, a learning system is needed that can eliminate the impression of saturation in the room. Based on Organic Architecture in the design used in natural schools, it also impacts the application of the material used. These materials are often found in areas / areas that are directly adjacent to the construction site, so as to provide ease of adjustment of the material with the surrounding nature. The method used is direct observation and literature study. The data management results are used as a basis for designing activities combined with the theme of Behavioral Insight Architecture. Thus, it is expected to be able to create a school that is environmentally comfortable and safe for its users.
Introduction to “Orogenic Cycles: From Field Observations to Global Geodynamics”

Abstract This Special Section of Tectonics , entitled “Orogenic Cycles: From Field Observations to Global Geodynamics,” has been compiled in commemoration of our friend and colleague Dr. Marco Beltrando, who tragically passed away in a mountain accident on 8 December 2015.
The Mediating Role of Employee Performance on the Relationship between Delegation of Authority and Decision Making

The aim of this study is to enhance understanding in the animated extent of delegation authority and its role in decision making. Precisely, the prime objective of this study is to visualize the concept and propose the effects of delegation authority on decision making along with the mediating role of employeesâ€™ performance in the Police department at Dubai. Based on an inclusive and systematic literature review of delegation authority, decision making, and employeeâ€™s performance, this study will collect data through self-administered questionnaires based survey in a quantitative manner. The collected data will be analyzed through smart partial least square structure equation modelling for validating the model, testing hypothesis, and finding results. This study will highlight some new insights into the relevant knowledge area. Specifically, the results will contribute to enhance the motivational synergy of upper management to include the employees in decision-making process to improve their performance. This study is only limited to the police department of Dubai and future studies can examine the relationship between defined constructs in other service sector and the moderating role of employeesâ€™ performance. By integrating delegation authority, decision making, and employeeâ€™s performance literature, the prime contributions of this study are the analysis of defined variables in the police department of Dubai with emphasize on new challenges and to boost up the gap of relevant literature.
Pembuatan Game Berbasis Pembelajaran Menggunakan RPG Maker MV

The RPG game is one of the games are in great demand by the public because they are able to sharpen their right brain. One method used in game development is GDLC which consists of 6 phases, where each phase is arranged systematically. The aimed of this study is how to develop RPG maker game based learning for children cognitive. The analysis method is used SWOT analysis and testing method used α and β testing methods on 36 respondents who were taken by random and the response rate was 99.8%. The testing instrument used a questionnaire with testing parameters (1) Design consisting of music, sound, and color (2) Moral Message (3) Storyline. The test of results were 42.5% of respondents said that the development design was considered good, 44.3% of respondents said the game had a good storyline, and 56.3% of respondents stated that the Rise of the Zokai Clan game using RPG Maker MV had a good moral message.
The Application of Random Walk Model to Enhance Utilization of Cloud based Services through Virtualization

Cloud computing has transformed the complete operation pattern of just not IT industries but also other sectors like Transportation, Metrological unit, Finances, education, agriculture, health care etc., The transformation is due to the enormous reliable services provided to these sectors at low cost.The Cloud model is able to do this because of the flexibility in providing services like the computing resource, storage or data managing and migration.These services are provided based on the user requirements, the user may opt for a public, private or a hybrid cloud.Cloud based applications are fully deployed in cloud and all the supporting applications run in cloud.The performance of the computing model is influenced by the operation of the model.The computing model should always be available and reliable.Efficient operation depends on good scheduling and resource allocation techniques.In this paper, a mathematical model to configure the required series of Virtual machines termed as Super Virtual machines (SVM) has been proposed.SVM is a combination of Virtual machines (VMs) which comprises of Processor, Storage space and Bandwidth required for computation.The research contribution shows an increase in availability and reliability of resources to requesting jobs.
Nature Inspired Feature Selector for Effective Data Classification in Big Data Frameworks

In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise. And also, time complexity is the major issues in existing approach. In order to rectify these issues our proposed method made use of efficient feature subset selection in high dimensional data. We are considering the input dataset is the high dimensional micro array dataset. Initially, we have to select the optimal features so that our proposed technique employed Social Spider Optimization (SSO) algorithm. Here the traditional Social Spider Optimization is modified with the help of fruit fly optimization algorithm. Next the selected features are the input for the classifier. The classification is performed using optimized radial basis function based neural network (ORBFNN) technique to classify the micro array data as normal or abnormal data. The effectiveness of RBFNN is optimized by means of artificial bee colony algorithm (ABC).