Reviewerly/exHarmony huggingface dataset:
RANCANG BANGUN APLIKASI ENSIKLOPEDIA ELEKTRONIK TANAMAN OBAT BERBASIS ANDROID MENGGUNAKAN METODE LUTHER

This Design of android-based application of electronic encyclopedia of medicinal plant was created as a way to provide information to people about the benefits of herbal medicinal plants.This application used the website as the site admin to enter data of medicinal plants and the application (android) as a place for users to view information of medicinal plants. The software engineering design was Luther method. This method is also called Multimedia Development Life Cycle which is a model developed through 6 stages; Concept, Design, Material Collecting, Assembly, Testing, and Distribution. This Luther model has similar stages to Waterfall model so it can be easily understood and implemented, easy to follow and can be used by small developers. The result of this application was hoped to help users to know which herbal plant that can be made into medicine that can cure some specific diseases.
Classification/Feature Extraction Using SVM and K-NN Classifier

This chapter presents in detail a detection algorithm for image-based ham/spam emails using classification/feature extraction using SVM and K-NN classifier. The structure and texture of an image will be examined, and the detection technique encompasses optimisation, nearest neighbour search, handling inconsistent constraints and error corrections. The proposed technique’s performance is also assessed. Thus, the proposed is an automatic, stable, quick response automatic segmentation, followed by feature extraction and classification to detect spam from the images and the text. The K-NN classifier is used to extract features by predicting nearest neighbour, while SVM analyses the data for classification and regression. The present chapter covers the proposed improvised SVM and K-NN classifiers, elaborates on the experimental results and covers the summary of the chapter.
Integrating Variable Reduction Strategy with Evolutionary Algorithm for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems while they are also difficult to solve due to their characteristics of nonlinearity and multiple roots. Evolutionary algorithm (EA) is one of the methods for solving NESs, given their global search capability and an ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, the problem domain knowledge of NESs is particularly investigated in this study, using which we propose to incorporate the variable reduction strategy (VRS) into EAs to solve NESs. VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through the variable relationships existing in the equation systems. It enables to reduce partial variables and equations and shrink the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper integrates VRS into two existing state-of-the-art EA methods (i.e., MONES and DRJADE), respectively. Experimental results show that, with the assistance of VRS, the EA methods can significantly produce better results than the original methods and other compared methods.
Text Extraction from Scrolling News Tickers

While a lot of work exists on text or keyword extraction from videos, not a lot can be found on the exact problem of extracting continuous text from scrolling tickers. In this work a novel Tesseract OCR based pipeline is proposed for location and continuous text extraction from scrolling tickers in videos. The solution worked faster than real time, and achieved a character accuracy of 97.3% on 45 min of manually transcribed 360p videos of popular Latvian news shows.
Research on Bilingual Teaching Practice of Computer Basic Course

This paper discusses how to enhance the ability of students to master the professional knowledge of basic computer courses and the second foreign language media knowledge through bilingual teaching, analyzes how to improve the students' knowledge level and foreign language ability in the process of bilingual teaching of basic computer courses, and balances the relationship between the teaching of professional knowledge and the improvement of foreign language level.The teaching practice shows that the bilingual teaching method can improve students' academic performance to a certain extent.
Learning Unsupervised Video Summarization with Semantic-Consistent Network

The aim of video summarization is to refine a video into concise form without losing its gist. In general, a summary with similar semantics to the original video can represent the original well. Unfortunately, most existing methods focus more on the diversity and representation content of the video, and few of them take video’s semantic into consideration. In addition, most methods related to semantic pursue their own description of the video and this way will learn a biased mode. In order to solve these issues, we propose a novel semantic-consistent unsupervised framework termed ScSUM which is able to extract the essence of the video via obtaining the greatest semantic similarity, and requires no manual description. In particular, ScSUM consists of a frame selector, and a video descriptor which not only predict the description of summary, but also produce the description of original video used as targets. The main goal of our propose is to minimize the distance between the summary and original video in the semantic space. Finally, experiments on two benchmark datasets validate the effectiveness of the proposed methods and demonstrate that our method achieves competitive performance.
THE EFFECT OF THE FEEDBACK IN LEARNING READING LITERACY MODEL ON HIGHER-LEVEL THINKING ABILITIES EQUIVALENT TO THE PISA (PROGRAMME FOR INTERNATIONAL STUDENT ASSESSMENT)

This study uses an experimental method with a sample of junior high school students. From the hypothesis test results it was concluded that (1) the literacy model based on feedback does not affect interpretative reading ability, (2) there is an influence of feedback based literacy model on integrative reading ability, (3) there is the effect of feedback model based on literacy of feedback based on evaluative reading ability, (4) there is an effect between feedback-based literacy models on reading attitudes, and (5) there is an influence between feedback-based literacy models on reading strategies.
Patriotic education of students on the example of the life of outstanding Russian physicists

The article presents the material showing how to organize Patriotic education of schoolchildren and university students on the example of actions and statements made by Russian and Soviet physicists. The facts testifying numerous Physicists’ deep love for Russia – their homeland – are brought about. Consideration of the topic begins with the activities of the first Russian academician M. V. Lomonosov, which is a vivid example of the scientist-patriot’s love for the Motherland. Similar facts from the life and scholarly endeavor of Russian electrical engineers V. V. Petrov, P. L. Schilling, electrical engineersinventors B. S. Jacobi, E. H. Lenz are given. The historical facts testifying the true service to the Motherland by a number of other physicists, including Zh. I. Alferov – a Nobel laureate, known throughout the scientific community for his work in the field of modern physics.
Blockchain Application Based Smart Power Grid System

With the prosperity of wireless communication and ubiquitous sensing technology, Smart grid has achieved rapid development in recent years. While smart grid technology has provided Convention and Efficiency to both power suppliers and consumers, it also raises new challenges. In this paper, we propose a secure and smart power grid system. By utilizing blockchain technology enables both secure transaction data storage and smart execution of contracts. The results of a large scale simulation with 1000 users show that besides its secure and smart features, smart power grid system is still as effective as traditional centralized methods.
A REAL-TIME PHOTOGRAMMETRIC SYSTEM FOR MONITORING HUMAN MOVEMENT DYNAMICS

Abstract. The human body posture is rich with dynamic information that can be captured by algorithms, and many applications rely on this type of data (e.g., action recognition, people re-identification, human-computer interaction, industrial robotics). The recent development of smart cameras and affordable red-green-blue-depth (RGB-D) sensors has enabled cost-efficient estimation and tracking of human body posture. However, the reliability of single sensors is often insufficient due to occlusion problems, field-of-view limitations, and the limited measurement distances of the RGB-depth sensors. Furthermore, a large-scale real-time response is often required in certain applications, such as physical rehabilitation, where human actions must be detected and monitored over time, or in industries where human motion is monitored to maintain predictable movement flow in a shared workspace. Large-scale markerless motion-capture systems have therefore received extensive research attention in recent years.In this paper, we propose a real-time photogrammetric system that incorporates multithreading and a graphic process unit (GPU)-accelerated solution for extracting 3D human body dynamics in real-time. The system includes a stereo camera with preliminary calibration, from which left-view and right-view frames are loaded. Then, a dense image-matching algorithm is married with GPU acceleration to generate a real-time disparity map, which is further extended to a 3D map array obtained by photogrammetric processing based on the camera orientation parameters. The 3D body features are acquired from 2D body skeletons extracted from regional multi-person pose estimation (RMPE) and the corresponding 3D coordinates of each joint in the 3D map array. These 3D body features are then extracted and visualised in real-time by multithreading, from which human movement dynamics (e.g., moving speed, knee pressure angle) are derived. The results reveal that the process rate (pose frame-rate) can be 20 fps (frames per second) or above in our experiments (using two NVIDIA 2080Ti and two 12-core CPUs) depending on the GPU exploited by the detector, and the monitoring distance can reach 15 m with a geometric accuracy better than 1% of the distance.This real-time photogrammetric system is an effective real-time solution to monitor 3D human body dynamics. It uses low-cost RGB stereo cameras controlled by consumer GPU-enabled computers, and no other specialised hardware is required. This system has great potential for applications such as motion tracking, 3D body information extraction and human dynamics monitoring.
Fuzzy Logical Techniques in Social Science Research - Application and Adoption

Fuzzy logic is extremely useful for many people involved in research and development including engineers (electrical, mechanical, civil, chemical, aerospace, agricultural, biomedical, computer, environmental, geological, industrial, and mechatronics), mathematicians, computer software developers and researchers, natural scientists (biology, chemistry, earth science, and physics), medical researchers, social scientists (economics, management, political science, and psychology), public policy analysts, business analysts, and jurists. The objective of this article is to explore the advances of fuzzy logic in a large number of real-life applications and commercial products in a variety of fields.
Sınıflandırıcı Performanslarının Gauss Karışım Modeline Uygulanan Beklenti-Maksimizasyon Algoritmasına Göre Analiz Edilmesi

Parametric density estimations i.e., maximum likelihood, mixture model, bayesian inference, maximum entropy are frequently used when type of distribution is known or predictable. Expectation-Maximization (EM) or a variable step learning algorithm are most successful ways for obtaining maximum likelihoods of distribution parameters. In this paper, we aim to present implementation of the EM algorithm to multidimensional Gaussian mixture model (GMM) that includes three different distributions. In this study, the statistical distribution is obtained from Gaussian distribution and parameters which are mean and covariance matrices for each distributions are used for estimation process. Original feature vectors and their estimates are compared in term of similarity as well as obtained results are presented and discussed in details. In addition, each distribution for bifurcated dataset is indicated. Finally, Bayesian, k-NN and Discriminant classifier methods are implemented to GMM and the performance of these methods are analyzed.
Null-sampling for Interpretable and Fair Representations

We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant correlations w.r.t. class label annotations, and a robustness to irrelevant correlations with protected characteristics such as race or gender. We introduce a non-trivial setup in which the training set exhibits a strong bias such that class label annotations are irrelevant and spurious correlations cannot be distinguished. To address this problem, we introduce an adversarially trained model with a null-sampling procedure to produce invariant representations in the data domain. To enable disentanglement, a partially-labelled representative set is used. By placing the representations into the data domain, the changes made by the model are easily examinable by human auditors. We show the effectiveness of our method on both image and tabular datasets: Coloured MNIST, the CelebA and the Adult dataset.
Risk Assessment and Management of File Server Components in Production Activities

The paper is devoted to mathematical modeling of the analysis of the survival of an automated information system in enterprises, namely the survival of a file server for production management.Based on the studies, a methodology was developed for constructing a survival analysis model for an automated information system, which consisted of using beta distributions as a survival analysis model.This model can effectively predict the behavior of a system after an attack and the prospective life expectancy of both the whole system and its separate components, calculating risk parameters for a beta distribution of the probability density of damage and building the dependence of the values of the estimates of the survival function and instant risk function on specific factors.
Flow visualization methods and their application in electronic systems

The literature abounds in flow visualization methods and their application to a very large range of situations. This chapter presents techniques for the visualization of air and water (liquid) flows. The principle for each technique is provided in addition to implementation details, equipment used, sources for procurement, and example visualizations. The chapter presents the holographic interferometry technique and example visualizations. Smoke entrainment is the most common visualization technique for laminar air flows, but has somewhat limited use in turbulent flows due to its rapid diffusion by turbulent mixing. Flow visualization is usually easier to perform in water, and also yields better quality results. As a result, water-flow tunnels are frequently used to study air flows by testing scale models at low velocity. As with smoke visualization, dye entrainment is successful mostly in laminar flow. The enhanced mixing in turbulent flow causes the dye streaks to diffuse too rapidly to be of value as tracers.
Semi-supervised Visual Feature Integration for Pre-trained Language Models

Integrating visual features has been proved useful for natural language understanding tasks. Nevertheless, in most existing multimodal language models, the alignment of visual and textual data is expensive. In this paper, we propose a novel semi-supervised visual integration framework for pre-trained language models. In the framework, the visual features are obtained through a visualization and fusion mechanism. The uniqueness includes: 1) the integration is conducted via a semi-supervised approach, which does not require aligned images for every sentences 2) the visual features are integrated as an external component and can be directly used by pre-trained language models. To verify the efficacy of the proposed framework, we conduct the experiments on both natural language inference and reading comprehension tasks. The results demonstrate that our mechanism brings improvement to two strong baseline models. Considering that our framework only requires an image database, and no not requires further alignments, it provides an efficient and feasible way for multimodal language learning.
A role-based collaborative process design on crowdsourcing systems

Crowdsourcing is a collaborative business process model, in which tasks are carried out by a crowd. In crowdsourcing systems, there are two types of stakeholders namely, requesters who outsources tasks, and the crowd or contributors, performing those tasks. We consider a stakeholder as an actor or a standalone software component, evolving on a platform and having both mechanisms of interaction with its environment and business skills. A set of stakeholders interacting in a dynamic context for solving a problem, is a distributed collaborative system, and we term it crowsourcing system. In such a system, the role concept is central, because each stakeholder must have a specific framework within which he collaborate. Traditionally, collaborative systems lose flexibility if their design is role-based, because only static role description mechanisms based on intuitive concepts are available. We propose in this paper, an improvement consisting of four things: (1)defining clearly what an outsourceable task or crowd task is, (2)specifying roles clearly and rigorously, while ensuring flexibility for collaboration, (3)providing role switching mechanisms and, (4) providing an abstract basis, for crowdsourcing system design and workflow monitoring and checking mechanisms, for potential activities, dynamically carried out by a system.
AN ANALYSIS OF CODE SWITCHING IN BILINGUAL SCHOOL IN COMMUNICATION BY NON-NATIVE SPEAKER ENGLISH TEACHER IN BALI

Abstract This study aimed at investigating types of code-switching pattern which used by non-native speaker English teacher and the reason to use the code-switching. This research was a descriptive qualitative research. There were 3 informant samples from non native speaker English speaker in a bilingual school in Bali. The data were collected by using observation and interview. The results showed that, 1) there is an indication of insertion and alternation which occurred by non native speaker English during communication and mostly in the form of simple imperative and instructional utterances, and 2) the reason to use the code switching is to introduce the students the habit of using English
Book Review – 2020

The following publication contains book reviews of these titles:Herodotou, C., Sharples, M. and Scanlon, E. (eds) (2018) Citizen Inquiry: Synthesising Science and Enquiry Learning. London: Routledge, 237 pages ISBN 9781138208698Baxter, J., Callaghan, G., and McAvoy, J. (eds) (2018) Creativity and Critique in Online Learning: Exploring and Examining Innovations in Online Pedagogy. London: Palgrave Macmillan, 287 pages ISBN 978-3-030-08668-8Krause, S.D. (2019) More than a Moment, Contextualizing the past, present, and future of MOOCS, Utah: Utah State University Press. 168 pages. ISBN: 9781607327868 (paperback) 9781607327875 (ebook)Rowell, C. (ed) (2019) Social Media in Higher Education: Case Studies, Reflections and Analysis, Cambridge, UK: OpenBook Publishers 304 pages ISBN 9781783746682. Available as an open access book at: https://doi.org/10.11647/OBP.0162Morris, S. M. &amp; Stommel, J. (2019), An Urgency of Teachers: The Work of Critical Digital Pedagogy. Hybrid Pedagogy, Open Access: https://criticaldigitalpedagogy.pressbooks.com/ ISBN 9780692152690.
Early Warning Analysis of Tube and Shell Heat Exchanger Based on Big Data Technology

In the field of petroleum, chemical industry and food manufacturing, heat exchangers are often used chemical equipment for their simple composition. With advantages like easy to transport and relatively low cost, the shell and tube heat exchanger has a wide range of uses. Although many factories regularly inspect and repair the heat exchanger equipment, missed inspections occur from time to time, resulting in low heat exchange efficiency and even leakage of the heat exchanger. Based on this situation, this research comprehensively uses big data association analysis technology, data mining technology and state assessment technology. This can realize the comprehensive perception of the risk situation of the shell and tube heat exchanger, the comprehensive analysis of the operation and maintenance data, and the real-time early warning of the operation and maintenance risk, thereby improving the ability of the shell and tube heat exchanger to monitor and risk.
Study on the Traffic Safety Risk Assessment Method for Signalized At-Grade Intersection

At-grade intersections are important parts of the road network, and are often the “bottleneck” areas of the road traffic system. Therefore, it is necessary to analyze and study the traffic safety risk generated by at-grade intersections. In view of the traffic safety risk assessment and prediction of two-phase controlled at-grade intersection, and on the basis of the traffic survey, the traffic safety risk analysis has been conducted based on traffic conflict analysis. With the application of the probability theory and traffic conflict theory, the traffic safety risk assessment model of signalized at-grade intersection had been established. The traffic safety risk prediction model of signalized at-grade intersection had been also constructed, to predict the traffic safety risk levels in a specific period. The new methods for the traffic safety risk assessment and prediction as well as the analysis on traffic safety risk controlling countermeasures of signalized at-grade intersection had been proposed.
Detection of Squamous Cell Carcinoma Through Smart Technologies

Oral cancer is the sixth most cancer type that occurs common among population who have addiction habit of chewing gutka, overconsumption of alcohol, excessive smoking and improper care for health. Squamous cell carcinoma is the most common type of oral cancer that occurs in mucosa lining of oral cavity. The proposed work implements simple and smart techniques based on colour feature to detect and localise the squamous cell carcinoma of oral cavity, as malignant tumour occur by changing the colour texture of tissue. Diagnosing colour appearance of any organ in human body plays a major role to locate the difference between healthy and abnormal changes of skin due to some underlying cause. The experimental results of the work locate region of interest (ROI) by converting RGB (red, green and blue) colour space to HSV (hue, saturation and value) colour space. Since HSV are used more common for tracking objects using computer vision and helps human to interpret colour changes easily. Then localised ROI is segmented into binary mask, by varying pixel intensity through track bars manually. Separation of susceptible cancerous region becomes more robust by applying contours on ROI of an image. This work also focuses on noise removal, thus making prediction possible for a physician.
Minimal Prerequisits for Processing Language Structure: A Model Based on Chunking and Sequence Memory

Minimal Prerequisits for Processing Language Structure: A Model Based on Chunking and Sequence Memory