Reviewerly/exHarmony huggingface dataset:
Imbalanced Dataset Problem in Classification Algorithms

Nowadays, companies continuous calculations and research with the available data to minimize the cost of personnel and time. Within the company, they provide an environment in which employees can enter their suggestions for improvement or complaints with the purpose of provide better service. Accordingly, use of Personal suggestion has been increasing by corporate companies during the recent years. In order to automate suggestion systems and make some analyzes, commonly used and developing machine learning technologies are used today. On the other hand, one of the problems encountered of machine learning as is the data set problem having imbalanced distribution. In the real world, data sets have imbalanced are quite a much. In this study, the results of ROS, RUS, and ADASYN methods on the classification algorithms were analyzed and the best method SMOTE and Gradient Boosting Classifier classification algorithm which gave the best results were preferred.
Model Capacity Vulnerability in Hyper-Parameters Estimation

Machine learning models are vulnerable to a variety of data perturbation. Recent research mainly focuses on the vulnerability of model training and proposes various model-oriented defense methods to achieve robust machine learning. However, most of the existing research overlooks the vulnerability of model capacity, which is more fundamental for model performance. In this paper, we study an adversarial vulnerability of model capacity caused by the poisoning on the estimation of model hyper-parameters. We further implement this vulnerability catering for the polynomial regression model, on which the evading of model-oriented detection is challenging, to illustrate the effectiveness of the adversarial vulnerability. Extensive experiments on one synthetic and three real-world data sets demonstrate that the vulnerability can effectively mislead the hyper-parameter estimation of the polynomial regression model by poisoning a few numbers of camouflage samples that cannot be detected by model-oriented defense methods.
Impending arrival - a sequel to the survey on central bank digital currency

Research and experimentation on central bank digital currencies continue to fuel discussion and debate. This BIS paper updates an earlier survey that asked central banks how their plans in this area are developing. The latest responses show emerging market economies reporting stronger motivations and a higher likelihood that they will issue digital currencies, with central banks representing a fifth of the world's population reportedly likely to issue very soon.
Technology readiness and usability of office automation system in suburban areas

Readiness and usability issues are important attributes of an information system (IS) success. Currently, IS studies that have been assessing the issues in suburban areas are still limited. The purpose of this study was to measure the above-mentioned phenomena by combining the readiness and usability models. The proposed model consisted out 10 constructs with 25 indicators. The four constructs were from the readiness model and the six ones were the usability model. The 89 samples were collected using an online survey based on purposive random sampling. Partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 3.2.8 was used for analyzing the data. The findings may have contributed to the IS research field, in terms of the readiness and usability issues on the IS use in a suburban area in Indonesia.
Minimal bases and minimal sub-bases for topological spaces

This paper investigates minimal bases and minimal sub-bases for topological spaces. First, a necessary and sufficient condition is derived for the existence of minimal base for a general topological space. Then the concept of minimal sub-base for a topological space is proposed and its properties are discussed. Finally, for Alexandroff spaces, some special results with respect to minimal bases and minimal sub-bases are illustrated.
Teaching Disability Self-Awareness and Self-Advocacy Using the Me! Lesson Materials

Many educators use the Me! Lessons to teach high school students with disabilities self-advocacy and self-awareness knowledge and skills. We conducted a mixed-methods study to examine the usefulness of the lessons, obtain student and parent perceptions of the lessons and to examine the lessons’ impact on student knowledge. This study indicates the usefulness of the Me! Lessons for teaching high school students with various disabilities needed self-awareness and self-advocacy knowledge. Qualitative findings suggest that students and parents value the content in the Me! Lessons and parents want students to learn about disability awareness and self-advocacy at school. Subscribe to LDMJ
De Jongh’s Theorem for Intuitionistic Zermelo-Fraenkel Set Theory

We prove that the propositional logic of intuitionistic set theory IZF is intuitionistic propositional logic IPC. More generally, we show that IZF has the de Jongh property with respect to every intermediate logic that is complete with respect to a class of finite trees. The same results follow for constructive set theory CZF.
Some Frequently Used Classification Methods

This chapter gives a survey of traditional classification methods that have not been developed with the specific objectives of high predictive accuracy, nor comprehensibility. The chapter covers, in particular, k nearest neighbours classification, Bayesian classifiers, the logit method, linear and quadratic discriminant analysis, and two kinds of classifiers belonging to artificial neural networks. The methods are illustrated on examples of all considered kinds of internet applications.
Towards a Technique for Extracting Relational Actors from Monolithic Applications

Relational actors, or reactors for short, integrate the actor model with the relational data model, providing an abstraction for enabling actor-relational database systems. However, as a novel model of computation for databases, there is no extensive work on reasoning about reactor modeling. To close this gap, this paper aims to propose as well as evaluate a technique to extract reactors from a monolithic system. For evaluation, we selected a REST-based open-source OLTP system in which a decomposition to microservices was conducted and applied our technique on its predecessor monolithic version. Our technique led to the same set of decisions, regarding table and behavior selection, taken by experts when decomposing the same system into microservices. The proposed technique can be seen as a first step towards supporting practitioners in decomposing OLTP systems into reactors.
PENERAPAN AGILE DEVELOPMENT METHODS DENGAN FRAMEWORK SCRUM PADA PERANCANGAN PERANGKAT LUNAK KEHADIRAN RAPAT UMUM BERBASIS QR-CODE

Software is the backbone of business processing in all aspects that require automation. Software Development must be supported by choosing the right Software Development Method. Many methods can be chosen by the user, but most of the methods used were 20 to 30 years ago, so they do not meet current needs. In this research, software development using Agile Development Methods with the chosen model is SCRUM, where the software development methodology is based on the same principles and is a short-term system development that requires rapid adaptation from developers to changes in any form. As the object of implementation in this study, the trial was applied to the case of the Application of Attendance at the QR-Code-based General Meeting . Keyword : Software, Application, Presence, QR-Code DOI : http://doi.org/10.5281/zenodo.3631045
HANDLING HIGHLY IMBALANCED OUTPUT CLASS LABEL

In practice, a balanced target class is rare. However, an imbalanced target class can be handled by resampling the original dataset, either by oversampling/upsampling or undersampling/downsampling. A popular upsampling technique is Synthetic Minority Over-sampling Technique (SMOTE). This technique increases the minority class by generating synthetic class labels and assigned the class based on the K-Nearest Neighbour (K-NN). SMOTE upsampling can only upsample at most one minority class at a time, which means for a multiclass dataset, it needs to undergo multilayer SMOTE to balance the class label distribution. This paper aims to find a suitable method in handling imbalanced class using dataset from Fantasy Premier League (FPL) virtual player to predict price changes. The cleaned dataset has a highly imbalanced class distribution, where the frequency of “Price Remain Unchanged (PRU)” is higher than “Price Fall (PF)” and “Price Rise (PR)”. This paper compared between the baseline (original) dataset, SMOTE-applied dataset and shuffled, linear and stratified sampling in split train-test subset, based on a deep learning algorithm. This paper also proposed criteria of low values in standard deviation (distribution of true positive on each class label on accuracy) as a measurement for finding the best method in handling imbalanced class labels. As a result, multilayer SMOTE until all the classes distribution is the same, combined with stratified sampling in split training and testing subset, get the lower standard deviation (5.7873), high accuracy (80.06%) and less execution runtime (1 minute 41 seconds) compared to the original highly imbalanced dataset.
The Development of Android-Based Mobile Learning in Learning Sundanese Script for Elementary School Students

This research is motivated by many teachers who have not optimized learning such as developing the learning media needed.One of the technology-based learning media is learning media that is based on Android, one of the media that has an important role in improving the quality of education.This research method uses research and development with research procedures, namely (1) needs analysis, (2) Development of learning media (3) validation of learning media that has been developed.The readability test results from this learning media also show that the readability criteria are easily understood with the average student score being 87%.Based on the exposure of the research that has been conducted, the development of android-based mobile learning media on Sundanese literacy material for primary school is declared valid.So as a whole, android-based mobile learning media in the Sunda acknows material for elementary school students produced have very good qualifications.
CAMShift tracking algorithm for metro entrance and exit security

In view of security issues, this paper studies how to track suspicious human at the metro entrance and exit accurately and quickly. A novel tracking method combining Kalman filter and SURF algorithm embedded in CAMShift target tracking algorithm was proposed. Firstly, the algorithm chooses target template for tracking by hand, then the Bhattacharyya coefficient is calculated by comparing the comprehensive histogram of chromaticity and gradient direction between the candidate target and the template target. If the Bhattacharyya coefficient between the template with the candidate target area is greater than the given threshold for detection, the tracking process will be failed. At this time, the SURF algorithm is used for feature matching on tracking result window in current search window and previous frame, and the size and position of the suspicious person are recalculated. At the same time, in order to avoid the failure of suspicious personnel tracking caused by occlusion, Kalman filter is used to predict and update the moving window so as to determine the central position of the search window in the next frame. Experiments show that the proposed algorithm can stably track the suspicious target personnel even when adverse effects such as color disturbance, partial occlusion, and complex image background occur.
A new discrete wavelet transform appropriate for hardware implementation

Summary A new method for computation of discrete wavelet transform (DWT) is introduced. The impulse response of the FIR filter, as the main block in filter bank (FB) method, is realized such that orthonormal wavelet transform is achieved without using any multiplier as the most challenging part of the FIR filters. The occupied slices and LUTs in the FPGA realization of the proposed wavelet in comparison with those of Daubechies wavelet are decreased by %50 and %56, respectively.
Recommender System in Higher Education: A Preliminary Study of State of the art

This paper is a preliminary study of the state of the art on Recommender Systems (RS) in Higher Education. This work presents the initial phase towards the development of a prototype of RS of personalized learning path (PLP). The prototype can be seen as a choice for higher education professors' training in ICT, considering Mobile Learning and Microlearning as a working paradigm. The literature about RS focused on Technology Enhanced Learning (TEL) is varied and extensive. However, the presence of RS in the application of PLP is scarce. This review was conducted by exploring the core subject in specialized databases of education and computing. The revised corpus consisted of open access and high impact publications developed from 1990 to 2018 in the context of education. In the process of reviewing the literature, ten studies based on RS for TEL were identified, analyzed and compared. These studies reveal the possibility of applying various techniques and frequent tasks of RS in E-Commerce to the field of RS with educational purposes. It demonstrates that RS to access to Microlearning educational resources in mobile environments are limited, thus making this research appropriate and relevant.
An Adaptive Method for Cross-Platform Browser History Sniffing

To accelerate browsing, most browsers keep track of visited URLs and create a browser history.Unfortunately, history sniffing attacks exploit the saved state to learn users' private information and infer about their visits to other sites.Although browser developers have taken measures to defend against these attacks, Smith's method, a kind of side-channel sniffing attack, could still work effectively.However, since different devices perform differently, this method requires that a list of parameters should be manually set up according to the device platform.In this paper, we propose an adaptive method that introduces multiple auxiliary links and adopts a dynamic parameter search algorithm to improve Smith's method.Our adaptive method attains nearly 100% accuracy on most popular browsers in different operating systems and platforms.
Defying Out-group Impoliteness: An Analysis of Users’ Defensive Strategies in Disputing Online Criticisms

Online social networking sites have become an integral platform in the development of relationships among Internet users around the world. The Internet has made connectivity faster and stronger than the old days. However, studies have shown that many have misused this platform to incite hatred and animosity when expressing themselves. Online derogatory expressions include the usage of slurs, pejoratives, hate-speech, flaming, and trolling, among others. The present study aims to analyse defensive strategies employed by Facebook commenters on the issue of 1MDB. Three threads of comments and responses towards the political scandal of 1MDB were identified and extracted from Facebook newspaper page. The data was then analysed using the summary of response options and the list of defensive counter-strategies offered by Bousfield (2007). The findings demonstrated the use of defensive strategies by the participants in disputing criticisms of 1MDB issues, particularly comments affecting the reputation of their social group and ethnicity. Most of the participants chose to contradict the opposition directly; and some of them included statements of clarification, explanation or details about the issue together with the arguments. The discussion of the findings offers some novel insights into the field of impoliteness by drawing attention to online users’ defensive linguistic behaviour in dealing with out-group impoliteness strategies in online communication.
The Effect of a Goal-Setting Program in Physical Education on Cognitive and Affective Outcomes of the Lesson

This study investigated the effects of a goal-setting intervention on students’ physical education and leisure-time physical activity motivation cognition. One hundred sixty-nine primary school pupils in fifth and sixth grades (11–12 years old) participated in the study and were randomly divided into two groups. Ninety-four students participated in a goal-setting intervention program that lasted five physical education lessons, and seventy-five students served as a control group. Perceived autonomy support in physical education classes, autonomous motivation in physical education, enjoyment during physical education, vitality, attitudes, perceived behavioral control, and intention toward out-of-school physical activity were measured at the beginning and end of the intervention program through anonymous questionnaires. The results indicated that goal setting served as a useful strategy for the promotion of autonomy support in physical education lessons, producing positive effects on leisure-time physical activity–related cognition.Subscribe to TPE
OCGNN: One-class Classification with Graph Neural Networks.

Nowadays, graph-structured data are increasingly used to model complex systems. Meanwhile, detecting anomalies from graph has become a vital research problem of pressing societal concerns. Anomaly detection is an unsupervised learning task of identifying rare data that differ from the majority. As one of the dominant anomaly detection algorithms, One Class Support Vector Machine has been widely used to detect outliers. However, those traditional anomaly detection methods lost their effectiveness in graph data. Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data. In this work, we propose One Class Graph Neural Network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of Graph Neural Networks along with the classical one-class objective. Compared with other baselines, OCGNN achieves significant improvements in extensive experiments.
Remarks on the Translation of Noun Phrase Ellipsis from English into Arabic

This paper discusses the translation of noun phrase ellipsis (NP ellipsis) from English into Arabic. It aims to determine how NP ellipsis is rendered into Arabic and what implications its translation has on the target text. To achieve this, the study relies upon the analysis of authentic English texts and their Arabic translations. The analysis of the Arabic translations reveals that NP ellipsis is rendered by ellipsis or lexical repetition. Furthermore, there seems to be a tendency towards using lexical repetition in the translation of ellipsis, which raises the degree of explicitness in the translated text. The use of lexical repetition can be attributed to structural incompatibilities and differences in stylistic preferences between the two languages. Finally, the study shows that NP ellipsis is not always translated accurately, resulting in ambiguity and loss of the source text informative content. Therefore, further studies on the translation of other forms of ellipsis can show the potential renderings of ellipsis and their impacts on the target text.
Algorithm research of spoken English assessment based on fuzzy measure and speech recognition technology

At present, many speech recognition algorithms are difficult to effectively evaluate the fuzziness of the evaluation algorithm. Based on this, this dissertation uses the speech recognition technology based on fuzzy measure to evaluate the spoken English. In the study the fuzzy measure, based on the traditional algorithm, is used to evaluate the spoken English and different characteristic parameters are extracted to construct the corresponding evaluation model. Simultaneously, the pronunciation is evaluated through automatic learning rules. The English speaking assessment model based on fuzzy measure and speech recognition technology is constructed and validated. The research shows that compared with the traditional algorithms, the spoken language evaluation algorithm based on fuzzy measure and speech recognition technology has the incomparable superiority, and can provide a reference for the follow-up related research.
Infographics: Media for Improving Students’ Writing Abilities

Writing is acknowledged as a skill that needs a high order thinking and a deep understanding of developing ideas. It also deals with the students’ ability in organizing a proper paragraph, various vocabularies, and decent grammar application through written text. To create a worthy writing product, it requires the integration of numerous aspects involving diction, organization, grammar, cohesion, and sequence. Due to the complexity of writing, students may find obstacles in transforming and organizing their ideas into written text. Specifically, in this paper, Information Technology (IT) Department students of State Polytechnic of Malang faced problems in writing a text. Thus, the researchers applied infographics media to improve students’ writing skills in composing instruction in the form of procedure text to solve the problems. The results of the research showed that infographics are beneficial and successful in improving students’ writing skills, especially in using sequence adverbs and imperative verbs.&#x0D; &#x0D; Keywords: infographics, writing skills, procedure text
Tuning the performance of automatic speaker recognition in different conditions

Automatic speaker recognition applications have often been described as a ‘black box’. This study explores the benefit of tuning procedures (condition adaptation and reference normalisation) implemented in an i-vector PLDA framework ASR system, VOCALISE. These procedures enable users to open the black box to a certain degree. Subsets of two 100-speaker databases, one of Czech and the other of Persian male speakers, are used for the baseline condition and for the tuning procedures. The effect of tuning with cross-language material, as well as the effect of simulated voice disguise, achieved by raising the fundamental frequency by four semitones and resonance characteristics by 8%, are also examined. The results show superior recognition performance (EER) for Persian than Czech in the baseline condition, but an opposite result in the simulated disguise condition; possible reasons for this are discussed. Overall, the study suggests that both condition adaptation and reference normalisation are beneficial to recognition performance.