Reviewerly/exHarmony huggingface dataset:
A Novel Mixed Integer Programming Scheme for Passive Shimming in MRI

In a magnetic resonance imaging system, the magnetic field homogeneity plays a decisive role on the image quality. As an effective technique, passive shimming (PS) uses ferromagnetic material (shim pieces) to eliminate the field impurity in the imaging area. In existing PS algorithms, most attempt to efficiently find continuous solutions using standard linear programming (LP). However, discrete solution has to be used in shimming practice. Direct rounding off of the continuous solution may lead to an unacceptable homogeneity loss. In theory, integer programming (IP) can be used to find discrete solution, however, it usually requires a huge amount of computing time, and the optimization procedure can hardly to converge. In this work, a mixed integer programming scheme was proposed, which combines the LP and IP algorithms to find the practical shimming solution. In the hybrid optimization algorithms, the first step is to obtain the initial continuous solution using an improved linear programming model, which not only minimize the peak-peak field error, but also seek the optimal target magnetic field. The second step is to re-optimize those shim pieces having a significant influence on the field homogeneity using an integer linear programming model. In the established optimization model (step 1), the LP algorithm guarantees a global minimum solution for the given problem, without extra computational parameters involved in the transition from first-step to second-step optimization. Compared with other integer programming strategies, the hybrid algorithm can greatly save the computing time and maintain excellent shimming results achieved by continuous solution. This has been demonstrated by a shimming study for the 1.5 T superconducting magnets.
Single and Multiple Failures Diagnostics of Pneumatic Valves using Machine Learning

Predictive maintenance of aeronautical systems is an important field of study to reduce airlines operational costs and non-scheduled events. Valves are important components of several aircraft systems. This fact motivates the need for health monitoring and prognosis. In this context, this work compares machine learning methods to predict pneumatic valves health conditions. A multilayer perceptron artificial neural network model was able to discriminate the three single failures modes with a mean accuracy of 99.9% and a support vector machine model was able to diagnose single and concurrent failure modes with an average accuracy of 94.3%. The results reveal an accuracy improvement compared to a previous pneumatic valves health assessment study.
Burrows - Wheeler Transform in lossless Data compression Problems on hybrid Computing Systems

Currently, hybrid computing systems and clusters based on them are used to solve an increasing number of various tasks. This article addresses the issue of lossless data compression on hybrid computing systems. There are sections describing the development and implementation of a stack of lossless data compression algorithms based on the Burrows - Wheeler transform (BWT), as well as a section on data sorting on hybrid computing systems as one of the BWT steps. At the end are the test results of the proposed algorithms.
Exploring the Roles, Functions and Competencies of Library and Media Teachers in Managing School Resource Centers and Supporting 21st Century Learning in Malaysian Schools

21st century skills are embedded in the Malaysian school curriculum to prepare and produce knowledgeable and responsible young citizens in coping with the challenges of the global knowledge economy. The trend has an impact on the roles of school resource centers (SRC), which are currently managed by the so-called library and media teachers (LMT). As the LMTs are appointed among school teachers who need to allocate and divide their responsibilities into academic teaching and SRC management, several issues are found from literature pertaining to their overburdened teaching tasks, lack of time and inadequate library management competencies. This has caused further shortcomings in service provision including poor implementation of information literacy and reading programs for school students. A qualitative research approach is adapted to investigate the roles, functions and competencies of LMTs in managing SRCs and supporting 21st century teaching and learning in the school setting. This study uses semi-structured interviews for data collection involving five (5) LMTs who provided insightful feedback towards understanding the aforementioned issues. The findings reveal essential information with respect to the roles, functions and competencies of LMTs and also identify relevant challenges that can disrupt the provision and delivery of SRC services, facilities and programs. This requires the attention of the government and school administration to enforce a better platform that enables the LMTs to uphold their roles and functions accordingly towards enhancing the 21st teaching and learning environment in Malaysian schools.
Correction to: Nonlinear analysis of solar wind parameters

Equation 7 was included in error. It is irrelevant to the calculation of the correlation entropy and should be ignored.
PENINGKATAN KETERAMPILAN BERBAHASA INDONESIA DENGAN SIMULASI TES UKBI SEBAGAI TES STANDAR BAHASA INDONESIA

Indonesian people in the correct language practice often find language barriers, one of them is the application of Indonesian language rules. In fact, in appreciating the Indonesian language, the general public is not aware of a standard test that measures language skills, namely UKBI. The purpose of writing this article is to convey that the UKBI simulation test can be used in an effort to measure ability and improve the language skills of the general public. The method used in this study is direct action by providing training. Value retrieval is carried out in two cycles, so that the differences can be seen before training. The author found a significant increase in the Semanan community's language skills. Cycle I shows the existence of a community that has a predicate: limited to 7%, marginal 7%, average 23%, middle 36%, and superior 27%. Conversely, cycle II shows an increase in predicate, namely: 17%, middle 47%, superior 33%, and very superior 3%. It can be seen that the results of the second cycle have no more people with a limited predicate.
Formalization of SOA concepts with mathematical foundation

With the development of the IT industry, agility and dynamicity are the two expected characteristics from any business enterprise. Service oriented architecture (SOA) is emerging as a promising approach aiming to address the current trends/needs of enterprise business applications. The current work aims at giving an overview of the conceptual model of SOA based on set theory approach. Also, it posits mathematical definitions for service, logical service, environment service and application definition with the help of general systems theory and mathematical foundation. With the help of mathematical model, the definition and functionality of services enhances SOA principles which can be used efficiently in integrating heterogeneous components.
Learning the Malay Traditional Musical Instruments by using Augmented Reality Application

This study proposed the implementation of Augmented Reality (AR) technology to promote the learning experience about Malay traditional musical instruments.The application uses a book as a marker to help the user visualizes the traditional musical instruments, learn the history behind it and how the instruments sounds.As a starting point, a library research has been conducted in order to understand the current state of the art.ADDIE Model has been used and the design and development phases has also been discussed.This AR application is expected to be a tool for promoting the Malay traditional musical instruments for the new young generation.
Mixed Kalman/H? Filter for Multi-Object Tracking in Video Frames

In this paper, the mixed H-infinity and Kalman filter is proposed for multiple target tracking in the video arrangements.Here, the proposed system will be the joined execution of Kalman filter and the H-infinity filter.The Kalman filter is the best filter that is a linear combination of the measurements.That is why; it is widely used in tracking systems.The H∞ filter, also called the mini-max filter.The H∞ filter does not make any assumptions about the noise and it required only last time step and current state estimation for object tracking.Consequently, there would be no necessity for a high limit of computational stockpiling.This mixed filter uses a lower gain in order to obtain better performance, where as the pure H-infinity filter uses a higher gain because it does not take Kalman filter performance into account.The mixed Hinfinity and the Kalman filter, used to find the location and speed of the objects when objects are moves with a certain motion law.The Kalman filter doesn't limit the mean square error.In this way, the H-infinity filter limits the mean square error and also utilized to limit the impact of unexpected noise whose insights are obscure.Usage of the proposed system was implemented in MATLAB and the execution of this system has better execution.
Google AI Approach and Statistical Results of Using Google Applications in Mobile Learning

The aim of this study is to determine the view of students in the Near East University regarding the use of Google applications on mobile learning. A data collection tool consisting of 20 items developed by the researchers were used in the study. The cronbach’s alpha rate of the data collection tool was found to be .942 as a result of the applications. General information is provided in the abstract section of the study. In the methods section the age and gender of the students and faculties in which the students are educated in is shown in tables. The data collected throughout the research is given in the results and discussion section and evaluated by the researcher in the conclusion section. As a result of the research it was concluded that the students have a positive view about the Google Applications in Mobil Education. When the data analyzed statistically it has found that using using Google Applications for students makes the most of the tasks easier to complete. Thus, students saves more time and energy by using this applications. Providing education on using these applications and benefitting from these applications will be good. With the data collected from this study it is aimed to guide other researchers from different universities and countries working on this subject
PENGELOMPOKAN JUMLAH PENDUDUK BERDASARKAN KATEGORI USIA DENGAN METODE K-MEANS

Control of population is one of the tasks of the government in Indonesia. The increase and movement of population in each region makes a certain area to defeat changes in population surging, and this can affect the economic level of the area. This study aims to process the population of Pematangsiantar City in 2018 which is divided into age groups, namely: Toddlers, Young Children, Early Adolescents, Late Adolescents, Early Adolescents, Late Adulthood, Early Adulthood, Elderly, Late Elderly, and Upper Seniors. Data processing is done by using K-Means method clustering in accordance with the population of Pematangsiantar City per district. With this grouping, we can see that the number of population in each sub-district is based on each age group so that we can implement programs that are more appropriate in improving human resources.
Faithfully Explaining Predictions of Knowledge Embeddings

Knowledge embeddings are key ingredients of advanced question-answering and recommender systems. Even though their predictions are accurate, they are rather hard to interpret by human users; interpretability techniques are needed so as to provide meaningful human-friendly explanations for prediction generated by embeddings. We propose a novel model-agnostic method inspired by local surrogate approaches that generates faithful explanations for knowledge embedding predictions.
Introduction to the Special Issue “Applications in Self-Aware Computing Systems and their Evaluation”

The joint 1st Workshop on Evaluations and Measurements in Self-Aware Computing Systems (EMSAC 2019) and Workshop on Self-Aware Computing (SeAC) was held as part of the FAS* conference alliance in conjunction with the 16th IEEE International Conference on Autonomic Computing (ICAC) and the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO) in Umeå, Sweden on 20 June 2019. The goal of this one-day workshop was to bring together researchers and practitioners from academic environments and from the industry to share their solutions, ideas, visions, and doubts in self-aware computing systems in general and in the evaluation and measurements of such systems in particular. The workshop aimed to enable discussions, partnerships, and collaborations among the participants. This special issue follows the theme of the workshop. It contains extended versions of workshop presentations as well as additional contributions.
LINE application: An alternative tool to develop writing abilities

This study tried to reveal the potential use of LINE application in developing students’ writing abilities through blended learning. It employed a quasi-experimental design which involved thirty-nine students of the advanced writing course. The experimental group was taught by using process and product approach combined with LINE application with teacher’s and classmates’ feedbacks while the control group was taught by using a conventional method without LINE application. The data were the result of students’ writings collected by using a writing test and scoring rubric as the instruments. The analysis of the students’ scores was carried out by employing the independent sample t-test. The finding showed that there was a significant effect of the use of LINE application on the students’ writing abilities. Furthermore, the differences were found in the components of vocabulary, grammar, organization, content, and mechanics. The study concluded that LINE application was a potential tool to develop students’ abilities in writing provided that it was combined with teachers’ and classmates’ feedback which facilitated students’ writing improvement.
Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films

Quantification of parasitemia is an important part of a microscopic malaria diagnosis. Giemsa-stained thin blood smear is the gold standard method for detecting malaria parasite enumeration. However, manual counting reveals the limitations of human inconsistency and fatigue, as well as the unreliability of accuracy and non-reproducibility. Inaccurate parasitemia affects clinical diagnosis and therapeutic procedure. Automated quantification is therefore useful to improve the performance of quantifying parasite density. In this paper, the texture-based classification approach is investigated. The methods consist of the following processes: pre-processing, segmentation, feature extraction and the classification of erythrocytes. The pre-processing is applied for image conversion and enhancement. The segmentation combines local adaptive thresholding, morphological process and watershed transform to extract red blood cells, separate touching and overlapping cells. Texture analysis is performed to establish parameters obtained from first-order, second-order and higher-order statistical analysis and wavelet transform. Two feature selection approaches, the sequential forward selection method and sequential backward elimination method, integrated with a support vector machine classifier are examined to obtain the optimal feature set for identifying the Plasmodium falciparum stages. We found that gray-level co-occurrence matrices based textural features were highly selected. The optimal feature set selected by the sequential forward selection yields lesser number of features and tends to give a higher degree of accuracy than the feature set selected by sequential backward elimination. The proposed method produces 98.87% accuracy for binary classification, 99.56% accuracy for ring stage classification, and 99.48% accuracy for tropozoite stage classification. Grey-level co-occurrence matrices based texture analysis is the dominant method compared to first-order and higher-order statistical texture analysis as well as wavelet transform.
Coalgebraic Geometric Logic: Basic Theory

Using the theory of coalgebra, we introduce a uniform framework for adding modalities to the language of propositional geometric logic. Models for this logic are based on coalgebras for an endofunctor on some full subcategory of the category of topological spaces and continuous functions. We investigate derivation systems, soundness and completeness for such geometric modal logics, and we specify a method of lifting an endofunctor on Set, accompanied by a collection of predicate liftings, to an endofunctor on the category of topological spaces, again accompanied by a collection of (open) predicate liftings. Furthermore, we compare the notions of modal equivalence, behavioural equivalence and bisimulation on the resulting class of models, and we provide a final object for the corresponding category.
Infoesthetics of Modern Mediaculture. Stating the Problem

The object of research is modern media culture, and the subject is its information aesthetics as a codifier of reality and a new paradigm of humanitarian knowledge. The author focuses on three periods in the development of media culture: modernism, postmodernism and “computer­centrism”, which emerged in the era of globalization and digitalization. Particular attention is paid to screen forms of info­aesthetics as the most popular in he 21st century.
Deposisi ZnO Doping Ag pada Substrat Alumunium Foil untuk Degradasi Methylene Blue

This research has successfully deposited ZnO: Ag on aluminum foil substrates with variations in deposition temperature. The purpose of this study was to obtain the surface morphology of ZnO:Ag on aluminum foil substrates with variations in deposition temperature and obtain optimization of the photocatalytic activity of ZnO: Ag in degrading Methylene Blue dyes. ZnO:Ag deposition on the aluminum foil substrate was carried out by the sol-gel method and spray coating deposition technique. Surface morphology characterization and photocatalytic activity with SEM and with UV-Vis. The surface morphology results obtained from ZnO:Ag on aluminum foil substrate with deposition temperatures of 250 ℃, 300 ℃, 350 ℃, and 400 ℃ are the forms of ganglia with indications as ZnO and Ag grains attached to ZnO. Obtained surface area and roughness level ZnO:Ag decreases with an increase in temperature from 250 ℃ - 300 ℃, and surface area and roughness increases at 350 ℃ -400 ℃. The highest level of roughness is found in the ZnO layer: Ag temperature 400 ℃. The most optimum photocatalytic activity is indicated by the largest percentage of degradation. The highest percentage of degradation is shown by the ZnO: Ag layer with a temperature of 350 ℃ of 87.33%. This is due to the modification of silver at low temperatures which is &lt;400 ℃ effective for photocatalytic activity.
A study on Anomaly Detection GAN-based methods on image data

The anomaly detection task is a well know problem being researched among a variety of areas, including machine learning. The task is to identify data patterns that have a non expected behaviour, that can be a malicious data sent by an attacker or a unexpected valid behaviour, in both cases the anomaly need to be identified. With the advance of deep learning based techniques showing that this class of algorithms can learn high-dimensional and complex data patterns, naturally it became an option to the anomaly detection task. Recent researches in literature are using a sub-field of deep learning algorithms named Generative Adversarial Networks for predicting anomalous samples, since the original method can learn the data distribution. These new techniques make some changes for the anomaly detection task, and this work provides a briefly review on these methods and provides a comparison with well known methods.
Validity and Integrity check in Supply Chain Management using Blockchain

Existing centralized blood supply chain management systems have various drawbacks including lack of information on blood bags, inability to reflect real-time updates in detail and the factor of distrust.Apart from this, our literature survey indicates that the wastage of blood bags is at an all-time high, accredited to the reasons for poor management.We propose a blood collection chain system based on blockchain technology using a distributed ledger structure and transaction execution of the entire process from blood collection till the blood reaches the final stage -hospitals.In this respect, the blockchain technologies offer the possibility to maintain a transparent blood management system, especially since data cannot be counterfeited and tampered with.In addition, the system stores blood contracts between hospitals in the event of emergencies through the transaction and consensus flows.It will also act as a waste management strategy for Blood Bags by splitting and using various contents in the blood such as RBCs, WBCs, Platelets, Plasma to a maximum extent before its expiry.
Development of a VR Nursing Educational Content

The purpose of this study was to develop a virtual reality (VR) educational content for nursing college students to strengthen their capacity to cope with multiple trauma situations.The VR educational content for nursing of patients with multiple trauma was developed based on the ADDIE (Analysis, Design, Development, Implementation, Evaluation).The emergency service was composed of the following process: Patient condition check, preparation for emergency supplies and treatment completion.In the educational content, a visual limit of the existing simulation learning was supplemented.This study has a meaning in that it developed a VR educational content program on emergency service for patients with multiple trauma and assessed possibility and effects of VR education first in Korea.
Comparing the Performance of Winsorize Tree to Other Data Mining Techniques for Cases Involving Outliers

Winsorize tree is a modified tree that reformed from classification and regression tree (CART). It lays on the strategy of handling and accommodating the outliers simultaneously in all nodes while generating the subsequence branches of tree. Normally, due to the existence of outlier, the accuracy rate of most of the classifiers will be affected. Therefore, we propose winsorize tree which could resist to anomaly data. It protects the originality of the data while performing the splitting process. In this study, winsorize tree was compared to other classifiers. The results obtained from five real datasets indicate that the proposed winsorize tree performs as good as or even better compare to the other data mining techniques based on the misclassification rate.