Reviewerly/exHarmony huggingface dataset:
Forecasting ASEAN countries exchange rates using auto regression model based on triangular fuzzy number

&lt;p&gt;Exchange rate forecasting is important to represent the expectation of exchange rates future values. The forecasting task is due to the economic factor and the historical data used to forecast are exposed to uncertainty and observational error during data collection. The existing auto regression model only deals with uncertainty exist in the model, not in the data preparation. Uncertainties may contained in the data input and should be treated during data preparation which is an early stage of forecasting process. To date, only few researches discuss intensely on a fuzzy data preparation. However, data treatment during data preparation is important to reduce model’s error due to uncertainty problem. Hence, this paper presents an approach to construct Triangular Fuzzy Number to handle uncertainty in data during data preparation. As the Triangular Fuzzy Number is often used to represent uncertain information in a form of interval, this study proposed a procedure to construct Triangular Fuzzy Number from single point data. In this study, the Triangular Fuzzy Number is built in a form of symmetric triangular with 1%, 3% and 5% spread value. Autoregressive model is then used to forecast the exchange rate of Association of South East Asian Nation (ASEAN) countries. The result in this study shows that the forecasting exchange rate is significantly important to trace the movement of ASEAN countries exchange rates and beneficial in forecasting planning.&lt;/p&gt;
IMPROVING DESCRIPTIVE WRITING ABILITY THROUGH MIND MAPPING

The study aims to get the effect of the mind mapping on students descriptive writing ability and the process of mind mapping implementation in writing descriptive learning. The research study is qualitative and quantitative research through action research method. The sample was taken by 34 students. The validity of data used credibility, transferability, dependability, and conformability. The process of collecting data used test, observation, and interview. The researcher used Independent sample t-test to analyze the differences of students’ ability before and after using mind mapping. The finding of analysis that mind mapping implementation has increase the students’ motivation of descriptive writing learning. They can make a good imagination in writing. The t-test result is t-stat (5.38) &gt; t-table (1.99). It means that there is a significant difference was found in descriptive writing ability before and after using mind mapping.
Example based facial aging simulation via facial detail transfer

The key problem of the facial aging simulation is protecting the identity information during the texture synthesizing procedure. In this paper, we address the aging simulation problem by using the facial detail transfer method, which provides a fundamental view of the aging simulation problem and shows its superiority in the small dataset. The proposed method decomposes the facial image into several layers, namely, structure layer, detail layer and color layer. The structure layer protects the identity information of the test image, while the detail layer contains the age information from the selected template image. We transfer the detail layer from the example images to the test images to generate the aging effects on the synthesized images. The images near the target age are also considered in the proposed method to further refine the synthesized results in the aging sequence. Experimental results conducted on the FG-NET database, Morph database and the IRIP-Aging database show that the proposed method can not only protect the identity information and the age information, but also generate more smooth aging sequence, which demonstrates the effectiveness of the proposed method.
The Implementation of Iso 9001: 2015 Quality Management System at PT Tarumatex Bandung

ISO management becomes an important issue in line with the increasing competition in the industrial world. The company strives to maintain its position and increase the trust of consumers. ISO is used as a tool to measure corporate credibility globally and improve the quality of company management. The research aims to find out how ISO 9001 Quality Management System is applied in a company, what profit will be achieved by the company when implementing ISO 9001 Quality Management System, identifying problems and solutions and what steps should be taken by the company to ensure that implementation of Quality Management System ISO 9001 work properly. The approach used is descriptive qualitative method. The results obtained in the form of division of authority and responsibility of each division is more clear and communication and response more quickly.
Combined Heat and Mass Transfer during Condensation of Vapours Mixture and Non-Condensable Gas in a Vertical Tube

The problem of laminar film condensation from binary vapours mixture with the presence of non-condensable gas (air) flowing in a vertical tube is numerically investigated. The set of the non-linear parabolic equations expressing the mass conservation, momentum, energy, and species diffusion in both phases with the boundary conditions are resolved by using a finite difference numerical scheme. A comparative study between the results obtained for three cases (water-ethanol-air, water-methanol-air, and ethanol-methanol-air) under the same conditions is made. The impact of varying the wall temperature, the inlet vapour mass fractions, and the inlet liquid mass flow rate on the conjugate the heat and mass transfer during the condensation of the studied mixtures are examined. It is found that the condensation of water-methanol-air corresponds to a higher latent heat flux QL2 and accumulated condensation rate Mr2 when compared with water-ethanol-air and ethanol-methanol-air. Moreover, the nature of the fluid plays an important role in the heat and mass exchanges.
Registration and Analysis of Acceleration Data to Recognize Physical Activity

The purpose of the article is to check whether the acceleration signals recorded by a smartphone help identify a user's physical activity type. The experiments were performed using the application installed in a smartphone, which was located on the hip of a subject. Acceleration signals were recorded for five types of physical activities (running, standing, going up the stairs, going down the stairs, and walking) for four users. The statistical parameters of the signal were used to extract features from the acceleration signal. In order to classify the type of activity, the quadratic discriminant analysis (QDA) was used. The accuracy of the user-independent classification for five types of activities was 83%. The accuracy of the user-dependent classification was in the range from 90% to 95%. The presented results indicate that the acceleration signal recorded by the device placed on the hip of a user allows us to effectively distinguish among several types of physical activity.
The Extent of Use of Analytical Procedures by External Auditors in Jordan in the Light of ISA 520

The aim of this study was to identify the substance of the Audit Standard No. 520 concerning the analytical procedures, and evaluating the use of auditors in Jordan for the analytical procedures. Moreover, the study sought to make valuable recommendations to develop audit methods and address weaknesses to take advantage of financial analysis methods. To achieve the objectives of the study, questionnaire was developed included (38) questions distributed to a sample of auditors practitioners of the audit profession and numbered (46) auditors, have been recovered, (42) of them were collected, (41) Questionnaires were used in analysis; One sample t-test was used to test the hypotheses of the study. the researcher found: that the auditors are not using enough analytical procedures as the arithmetic average (3.49). Because they use financial ratios moderately where the arithmetic average (3.45), the arithmetic average of the comparable financial ratios and information extracted with pre-specified criteria (3.69). Finally, researchers suggested some of the recommendations of the most important need for the competent authorities to hold training courses, workshops and seminars related to the application of analytical procedures, and to verify the commitment and the work of audit firms auditors applying quality control standards, and be subject to peer review.
PENGUKURAN USER EXPERIENCE PADA E-LEARNING DI LINGKUNGAN UNIVERSITAS MENGGUNAKAN USER EXPERIENCE QUESTIONNARE (UEQ)

User experience is the term user in feeling an ease and efficiency in human interaction with computers. The term user experience is increasingly becoming a concern because of the wider and more complex user needs that are not only about functional and aesthetic needs. Human and computer interaction in utilizing information systems has become part of daily activities in student activities, not least in the availability of e-learning. E-learning which functions as a tool in the teaching and learning process does not have an important role in the development of education. E-learning at XYZ University has been used for a long time with users being active lectures and students. The success of an information system can be obtained by analyzing user requirements that are more focused on user experience and comfort. This study was conducted to determine the level of User Experience Questionnare (UEQ) measurement method. The measurement results show the below average level on the attractiveness scale of 0.87, perspiculty of 0.85, efficiency 0.89, dependability of 0.84, and stimulation of 0.84 shows that the five scales are at the below average level, while the novelty scale is at the bad level of 0.29. The measurement results can be used as recommendation for the new user experience design e-learning.
Effort Estimation in Agile Software Development using Evolutionary CostSensitive Deep Belief Network

In today's scenario, frequent requirement changes in software development are a notable issue in the software field.Because of the frequent changes, fulfilling the user's requirement is very difficult.As a solution to such issues, Agile Software Development (ASD) has efficiently replaced the traditional methods of software development in industries.Due to various aspects of ASD, it is extremely hard to follow, maintain and estimate the general item.Hence, in order to tackle the Effort Estimation Problem (EEP) in ASD, various types of EEP have been identified in existing methods.The Evolutionary Cost-Sensitive Deep Belief Network (ECS-DBN) model implemented in this paper for effort prediction in any agile technique.The ECS-DBN method has no impact on agility because it uses simple and small inputs.The proposed method used in planning stage of software development to support the project managers in further development of agile software.The project managers characterize the structure of the ECS-DBN, while the parameter estimation consequently gained from a dataset.This paper used different statistics like accuracy, prediction at level to evaluate the accuracy of the model.The ECS-DBN method achieved nearly 99% accuracy compared to the existing methods.
Understanding Teachers’ Integration of Moodle in EFL Classrooms: A Case Study

The study explores the integration and implementation of the Moodle platform at the English Language Center of the Salalah College of Technology. To achieve this purpose, a qualitative, interpretive approach with a case study research design was used to collect the data and to deepen our understanding of the phenomena and how it was constructed in social reality of the school.Two teachers have been chosen to be the interviewees, to give their opinions and views on the topic under study, and the factors affecting both the implementation and integration of the Moodle programme. It was evident from the narratives of the two interviewees that the integration of Moodle was successful, and that it has proven to be a useful tool in the teaching and learning processes of English. In spite of some existing factors that may hinder the working mechanisms of the implementation and integration of Moodle, it may be concluded that this platform could be recommended to be extended to the other skills of the English language that it currently does not support. Following this process will inevitably improve the comprehension and production of the English language and related materials, online and real, respectively.
THE EFFECT OF SOCIAL MEDIA IN IMPROVING STUDENTS’ WRITING SKILL

The effect of social media in improving students’ writing skill is investigated in this paper, which can be used to improve students’ creativity in writing. This paper takes covering of second semester English education students of Universitas Negeri Medan that study written language skills. The method used in this research is the development research aims to produce written language skills learning materials using social media (instagram, facebook &amp; whatsapp). Its intention to answer the students’ need of the innovated material and to find out the level of students creativity after using such materials. Further, this paper gives a simpler and practical method to develop learning material using social media to improve students’ creativity in writing.
Methodological Framework to Guide the Development of Continual Evolution Methods

Companies live in a fast-changing environment imposing to constantly evolve in order to stay competitive. Such an evolution is carried out through continuous improvement cycles or radical changes often based on innovation that concern their products, their processes, their internal organization, etc. We refer to this situation as continual evolution. There are two implications of such continual evolution from our viewpoint: (a) the instillation of the "no end point" philosophy in organizations and (b) the use of methods based (1) on continual evolution cycles (by opposition to project-based approaches that have delimited budget and dates) and, (2) on autonomous and collective implication of the organization's actors. This article presents a methodological framework, called As-Is/As-If framework to support method engineers in handling such continual evolution. The framework offers a process model and a product meta-model that are both reusable instruments, aiming to guide the construction of continual evolution methods. The process model and product meta-model can be seen as prototypical examples to be adapted in each situation at hand using heuristics proposed as part of the framework. The usefulness of the framework is illustrated through two methods adaptations.
Breast mass detection and diagnosis using fused features with density

BACKGROUND:The morbidity of breast cancer has been increased in these years and ranked the first of all female diseases. Computer-aided diagnosis techniques for mammograms can help radiologists find early breast lesions. In mammograms, the degree of malignancy of the tumor is not only related to it s morphology and texture features, but also closely related to the density of the tumor. However, in the current research on breast masses detection and diagnosis, people usually use the fusion feature of morphology and texture but neglect density, or only the density feature is considered. Therefore, this paper proposes a method to detect and diagnose the breast mass using fused features with density. METHODS:In this paper, we first propose a method based on sub-region clustering to detect the breast mass. The breast region is divided into sub-regions of equal size, and each sub-region is extracted based on local density feature, after that, an Unsupervised ELM (US-ELM) is used for clustering to complete the mass detection. Second, the feature model is constructed based on the mass. This model is composed of the mass region density feature, morphology feature and texture feature. And Genetic Algorithm is used for feature selection, and the optimized feature model is formed. Finally, ELM is used to diagnose benign or malignant mass. RESULTS:An experiment on the real dataset of 480 mammograms in Northeast China shows that our proposed method can effectively improve the detection and diagnosis accuracy of breast masses, where we obtained 0.9184 precision in detection of breast masses and 0.911 accuracy in diagnosis of breast masses. CONCLUSIONS:We have proposed a mass detection system, which achieves better detection accuracy performance than the existing state-of-art algorithm. We also propose a mass diagnosis system based on the fused features with density, which is more efficient than other feature model and classifier on the same dataset.
Parallel Backpropagation Neural Network Training Techniques using Graphics Processing Unit

Training of artificial neural network using backpropagation is a computational expensive process in machine learning.Parallelization of neural networks using Graphics Processing Unit (GPU) can help to reduce the time to perform computations.GPU uses a Single Instruction Multiple Data (SIMD) architecture to perform high speed computing.The use of GPU shows remarkable performance gain when compared to CPU.This work discusses different parallel techniques for the backpropagation algorithm using GPU.Most of the techniques perform comparative analysis between CPU and GPU.
An experimental study on feature engineering and learning approaches for aggression detection in social media

With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites.
Fusion Estimation for Discrete Time-Varying Systems With Bounded Nonlinearities

This paper studies the distributed fusion estimation problem for a class of time-varying systems with bounded nonlinearities, where nonlinear terms are considered under two different cases. By using matrix analysis approach, each local estimator with time-varying gain is designed by minimizing an upper bound of the square error of the estimator. Based on the obtained local estimators, the distributed fusion estimators are designed by establishing a class of convex optimization problems. Notice that the designed local estimators and fusion estimators are stable, and the established convex optimization problems can be easily solved by standard software packages. Localization of a mobile robot is given to demonstrate the effectiveness of the proposed methods.
Gabal El Fereyid - Wadi Rahaba Area, South Eastern Desert, Egypt: Mineralization and Spectrometric Prospecting

Most of the radioactive occurrences in the basement rocks of Egypt are present in the younger granites and associated pegmatites (Abd El Naby and Saleh, 2003).The high level of radioactivity of these rocks is attributed to the presence of accessory minerals like zircon, monazite, thorite, uranothorite and allanite (Shurmann, 1966).Uranium and thorium are generally enriched in the youngest, most felsic and most potassic members of comagmatic suites of igneous rocks (Rogers and Adams, 1969).Although pegmatites have been considered as favorable uranium and thorium environments (Page, 1950), they are equally considered as poor mining targets (Gaberlman, 1977).Radioactive pegmatites have been recorded in many areas in the Eastern Desert of Egypt.Omar (1995) recorded radioactive anomalies associated with pegmatitic bodies of the G. Um Anab, the pegmatitic bodies of the G. Ras Baroud younger granite (Sayyah et al., 1993 andRaslan et al., 2010) and radioactive pegmatites related to the G. El Sibai alkali feldspar granites (Ali, 2001).Ibrahim et al. (1997) recorded uranium and columbite mineralization in the pegmatites related to the G. Abu DOB
Grey Wolf Optimizer with Linear Collaborative Discriminant Regression Classification based Face Recognition

For the past few decades, biometric Face Recognition (FR) is the active research area in different domains such as image processing, pattern recognition, etc.The existing FR system has several limitations such as Single Sample Problem, maximum Reconstruction Errors (REs), these problems decreases the FR rate.In this research paper, an efficient FR method is proposed, namely Grey Wolf Optimizer based Linear Collaborative Discriminant Regression Classification (GWO-LCDRC).The optimization technique of GWO algorithm is applied in LCDRC to select the relevant weight value in LCDRC.For every training sample, optimal weight values are selected to improve the recognition rate.The proposed GWO-LCDRC method maximize the collaboration of Between Class RE (BCRE) and minimize the Within Class RE (WCRE).An experimental analysis conducted on the two standard facial databases namely ORL and YALE.The proposed GWO-LCDRC method improved the performance of recognition accuracy with respect to different training samples.Also, the performance is compared with the traditional methods Linear Regression Classification (LRC), Linear Discriminant Regression Classification (LDRC), and LCDRC.The overall experiment demonstrated that the proposed GWO-LCDRC method achieved approximately 3% and 6.5% of FR accuracy improvement with respect to ORL and YALE respectively.