Reviewerly/exHarmony huggingface dataset:
Investigating Measurement Invariance under Different Missing Value Reduction Methods

This study aims to comparatively examine the resultant findings by testing the measurement invariance with structural equation modeling in cases where the missing data is handled using the expectation-maximization (EM), regression imputation, and mean substitution methods in the complete data matrix and the 5% missing data matrix that is randomly obtained from the same matrix. The data were collected from 2822 students. Of these students, who participated in the study, 1338 (49.2%) were females while 1434 (50.8%) were males. The data were collected using the “School Attitude Scale” developed by Alıcı (2013). In this study, the measurement invariance was tested with structural equation modeling in the complete data matrix and in cases of handling the missing data it was tested using EM, Regression-Based Imputation, and Mean Substitution methods. In the present study the measurement invariance decisions taken for the complete data matrix coincide with the mean substitution method in all sub-factors, with regression imputation from the other suggested methods in two sub-factors, and with expectation-maximization in one sub-factor. It was concluded that different data imputation methods change measurement invariance decisions, considering all the factors. The three techniques provided consistent results only in one sub-dimension. The ways of dealing with the missing data change the results; thereby, increasing fundamental studies in this regard is necessary. With reference to these results, it would be worthwhile to study different missing data structures and different proportions of missing data in terms of invariance decisions.
GENERATION OF VALUES FROM DISCRETE PROBABILITY DISTRIBUTIONS WITH THE USE OF CHAOTIC MAPS

The values of random variables are commonly used in the field of artificial intelligence. The literature shows plenty of methods, which allows us to generate them, for example, inverse cumulative density function method. Some of the ways are based on chaotic projection. The chaotic methods of generating random variables are concerned with mainly continuous random variables. This article presents the method of generating values from discrete probability distributions with the use of properly constructed piece-wise linear chaotic map. This method is based on a properly constructed discrete dynamical system with chaotic behavior. Successive probability values cover the unit interval and the corresponding random variable values are assigned to the determined subintervals. In the next step, a piece-wise linear map on the subintervals is constructed. In the course of iterations of the chaotic map, consecutive values from a given discrete distribution are derived. The method is presented on the example of Bernoulli distribution. Furthermore, an analysis of the discussed example is conducted and shows that the presented method is the fastest of all analyzed methods.
Speech Bandwidth Extension Using DWT-FFT-Based Data Hiding

A novel transform-domain speech bandwidth extension algorithm is proposed to transmit information about the missing speech frequencies over a hidden channel, i.e., the related encoded spectral envelope parameters are hidden within the narrowband speech signal using discrete wavelet transform-fast Fourier transform-based data hiding (DWTFFTBDH) technique.The hidden information is recovered reliably at the receiver to produce a wideband speech signal of much higher quality.Obtained results confirm the excellent reconstructed wideband speech quality of the proposed method over traditional methods.
Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

The article presents an analysis of approaches to the development of enterprise information systems that are in use today. One of the major trends that predetermines the agenda of information technology is the focus on parallel computing of large volumes of data using supercomputing technologies. The article considers the resulting ubiquitous move to distributed patterns of building enterprise information systems and avoiding monolithic architectures. The emphasis is placed on the importance of such fundamental characteristics of enterprise information systems as reliability, scalability, and maintainability. The article justifies the importance of machine learning in the context of effective big data analysis and competitive gain for business, vital for both maintaining a leading position in the market and surviving in conditions of global instability and digitalization of economy. Transition from storing the current state of a enterprise information system to storing a full log and history of all changes in the event stream is proposed as an instrument of achieving linearization of the data stream for subsequent parallel computing. There is a new view that is being shaped of specialists at the intersection of engineering and analytical disciplines, who would be able to effectively develop scalable systems and algorithms for data processing and integration of its results into company business processes.
Promise Theory as a Tool for Informaticians

Promise theory was designed and developed from 2005 onwards by Mark Burgess and his coworkers. It totalises the notion of a promise so that it applies to both animate and inanimate promisers. The focus of promise theory is on applications in informatics and systems design. This paper extends the account of promises by providing more detailed requirements on promises. In particular, the requirement of determinacy, the requirement of decomposition of aggregate agents, and the feature of a promise bias are introduced. The paper further includes an account of threats, as well as of risks, both viewed as an extension of promise theory. It is finally indicated by means of a series of informal examples how and where various kinds of promises and threats may occur in informatics.
Role of Red Cell Distribution Width (RDW) in the Diagnosis of Iron Deficiency Anemia

Background: Iron deficiency anemia (IDA) is the most prevalent micronutrient deficiency in the world. Morphologically, IDA is microcytic and hypochromic as is anemia on chronic disease (ACD) thereby creating confusion on peripheral blood smear examination. Red cell distribution width (RDW) has been proposed to be a more sensitive indicator to establish the possible origin of microcytic hypochromic anemia. Various previous studies have debated the role of RDW in diagnosis of IDA, with no conclusive word on the utility of RDW in diagnosing iron deficiency anemia.Objective. To study the utility of RDW in the diagnosis of IDAMethods: 93 patients with microcytic (MCV&lt;80 fl) anemia were classified into iron deficient (IDA) and anemia on chronic disease (ACD/non-IDA) on the basis of serum ferritin and total iron binding capacity (TIBC). RDW values were obtained on an automated hematology analyzer. Receiver operator curves (ROC) were constructed and the utility of RDW in diagnosis of iron deficiency was studied.Â Results: The mean RDW value was 20.07Â±7.67% in IDA group (57 patients) compared to 17.60Â±3.23 % in the ACD group (36 patients) (p&lt;0.001; CI 95%: 4,14-33,42). At a cut-off value of 17,35%, as obtained from the ROC curve, the sensitivity and specificity of RDW in diagnosis of IDA were 85,96% and 69,44% and a positive and negative predictive value of 81,66% and 75,75% respectively.Â Conclusions: RDW has a good sensitivity for screening diagnosis of IDA among patient with microcytic hypochromic anemia.
Peculiarities of Metacognitive Awareness in the Students’ Training

The aim of the article was to analyze theory and to research empiricallymetacognitive awareness of students in training. In the article, we have analyzedtheory and diagnosed empirically the levels of metacognitive awareness(involvement in activity) of students in the process of their training at theuniversity.The methods of our research included theoretical analysis of the presentconceptual studies of the phenomenon of metacognitive awareness and empiricalevaluation of its indicators with the use of the questionnaire «MetacognitiveAwareness Inventory» by G. Schraw and R. Dennison (adapted by A. Karpovand I. Skytiaieva). We also used the D. Everson’s questionnaire of metacognitivecharacteristics of personality, and in particular the scale of metacognitiveinvolvement in activity.Results of the research allow us to conclude that in the individual workwith students it is advisable to use the Metacognitive Awareness Inventoryby G. Schraw and R. Dennison, adapted by A. Karpov and I. Skytiaieva, andthe Everson’s questionnaire «Metacognitive Characteristics of a Personality».These methods allow to identify students with either very low or low levels ofmetacognitive awareness, and find out, which metacognitive processes that areimportant for the efficiency level they do not use, but potentially can use.In the article, the results of the theoretical analysis and empirical researchof the issue of metacognitive awareness have been presented. The author hasprovided data on the levels of metacognitive awareness (involvement in activity)of students, among them the average level has significantly prevailed. The dataobtained with the use of the two methods can improve the efficiency of the useof relevant materials in the educational process based on the general analysisresults of the metacognitive element, which students use most often.Conclusions. According to the results of the research, we can conclude thatidentification and analysis of metacognitive awareness of students can be aneffective tool for the pedagogues, which would consider individual characteristicsof students and appropriately build their activity focused at improvement oftheir own metacognitive knowledge and skills. This is important in the contextof the attitude towards students as the subjects of their own cognitive activity.
Metric learning with submodular functions

Most of the metric learning mainly focuses on using single feature weights with Lp norms, or the pair of features with Mahalanobis distances to learn the similarities between the samples, while ignoring the potential value of higher-order interactions in the feature space. In this paper, we investigate the possibility of learning weights to coalitions of features whose cardinality can be greater than two, with the help of set-functions. With the more particular property of submodular set-function, we propose to define a metric for continuous features based on Lovasz extension of submodular functions, and then present a dedicated metric learning approach. According to the submodular constraints, it naturally leads to a higher complexity price so that we use the ξ-additive fuzzy measure to decrease this complexity, by reducing the order of interactions that are taken into account. This approach finally gives a computationally, feasible problem. Experiments on various datasets show the effectiveness of the approach.
Decision Support System untuk Swasembada Jagung Nasional dalam mendukung Sustainable Food Security

Corn is one of the main commodities in supporting Indonesia's food crop economy and is one of six strategic targets to accelerate the development of Indonesia's agricultural infrastructure.To achieve self-sufficiency in corn that can meet national needs and availability, it can be done by reducing import activities.Because there are many variables within system that have interdependencies, system dynamics simulation is an appropriate approach to be used to cope with the complexity and uncertainty of this system continuously.Therefore, this study aims to create a decision support system (DSS) regarding zero imports of corn to support sustainable food security.This research was conducted by running three scenarios, namely Scenario I (using 80% of hybrid superior seeds), Scenario II (proportion of corn sales to the industrial market by 85%), and Scenario III (a combination of Scenarios I and II).Based on the results of the study, it was found that the best scenario was Scenario III which had an impact on imports by 30% and corn export revenues by 77%.
Sentiment Analysis Using Hybrid Feature Selection Techniques

Nowadays, people from every part of the world use social media and social networks to express their feelings toward different topics and aspects. One of the trendiest social media is Twitter, which is a microblogging website that provides a platform for its users to share their views and feelings about products, services, events, etc., in public. Which makes Twitter one of the most valuable sources for collecting and analyzing data by researchers and developers to reveal people sentiment about different topics and services, such as products of commercial companies, services, well-known people such as politicians and athletes, through classifying those sentiments into positive and negative. Classification of people sentiment could be automated through using machine learning algorithms and could be enhanced through using appropriate feature selection methods. We collected most recent tweets about (Amazon, Trump, Chelsea FC, CR7) using Twitter-Application Programming Interface and assigned sentiment score using lexicon rule-based approach, then proposed a machine learning model to improve classification accuracy through using hybrid feature selection method, namely, filter-based feature selection method Chi-square (Chi-2) plus wrapper-based binary coordinate ascent (Chi-2 + BCA) to select optimal subset of features from term frequency-inverse document frequency (TF-IDF) generated features for classification through support vector machine (SVM), and Bag of words generated features for logistic regression (LR) classifiers using different n-gram ranges. After comparing the hybrid (Chi-2+BCA) method with (Chi-2) selected features, and also with the classifiers without feature subset selection, results show that the hybrid feature selection method increases classification accuracy in all cases. The maximum attained accuracy with LR is 86.55% using (1 + 2 + 3-g) range, with SVM is 85.575% using the unigram range, both in the CR7 dataset.
Environmental Awareness in the Educational Field with the use of Information and Communication Technologies

The general theme of this article is environmental awareness in the educational field with the use of ICTs.Our problem is whether teachers use and recognize technology as a partner in the process of environmental awareness?The objective is to analyze the use of technology as an environmental awareness process for the teacher in the investigated school, seeking to verify the use of educational technologies in the process of environmental awareness, and also to identify their developed activities and educational practices.As for the method, we used data collection with a likert scale, with 552 teachers and validated with a factorial analysis.Research was carried out in Elementary Schools in Goiânia, Brazil.In the main conclusions we highlight that teachers use the technological resource in their classes in order to raise environmental awareness, favoring an interdisciplinary educational work with innovative practices.
The Clustering of Households in Madura Based on Factors Affecting Their Ingestion of Clean Water Using Similarity Weight and Filter Method

Clean Water and Sanitation is one of SDGs’ indicators that relates to human’ demand for clean water. Three of four regencies in Madura Island reportedly have suffered in drought, thus it leads this research to fulfill Madura people need of water. Madura Island has 3097 households in need of water. However, not all households could fetch their need. This research aims to classify the households of Madura Island regarding factors which affect their ingestion of clean water using cluster analysis. There are clustering numerical data and categorical data. Therefore, this research uses Similarity Weight and Filter Method. SWFM is one of clustering mix methods in which there are clustering numerical, using hierarchical ward, and clustering categorical, using k-modes. To analyze the clustering numerical data, there are 3 variables and it gains two optimum groups by using ward method with pseudo-F 1001,172. Clustering categorical analysis uses 6 variables with k-modes and gains three groups and SWFM gains five groups. Five groups are selected because they produced the smallest ratio 0,006627 in the group.
Nearest-neighbour Joint Probabilistic Data Association Filter Based on Random Finite Set

The joint probabilistic data association (JPDA) filter is effective for multitarget, but it suffers from the track coalescence problem. To solve this problem, an improved nearest-neighbour JPDA filter based on random finite set is proposed. First, the standard JPDA filter is utilized to compute the target posterior density. Then, the posterior density is optimized by reordering the target index in each global association event using a novel nearest-neighbour method. Finally, the marginalized posterior densities of targets are obtained as independent Gaussian densities. Compared to conventional data association methods, the proposed approach needs less computing time and achieves satisfactory tracking accuracy.
Online Versus Traditional Shopping in Developing Countries Study Case - Kosovo

The aim of this study is to analyse the behaviour of consumers in developing countries with a particular emphasis on traditional shopping versus online shopping in Kosovo. Specifically it is recognized that online shopping replaces traditional shopping in all developed countries but it is worth noting that countries with emerging economies cannot yet claim to replace traditional shopping as a cause of civil awareness, technology awareness, online payments, etc. In this study we used the primary data to achieve the main purpose. The primary data were provided through the online questionnaire in a random sample of 300 respondents. The data were calculated using IBM SPSS concluding in the analyses such as descriptive statistics, frequencies, linear regression, Pearson Chi - Square Test and Pearson correlation. The paper comes with some key conclusions that online shopping in Kosovo is affected by the employment status and education and is not influenced by age and gender. Also we came in conclusion that the respondents prefer online shopping for one main reason – time loss reduction.
Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture

There has been little research on novel approaches to digitising liquid-preserved natural history specimens stored in jars or vials. This paper discusses and analyses three different prototypes for high-throughput digitisation using cheap, readily available components. This paper has been written for other digitisation teams or curators who want to trial or improve upon these new digitisation approaches in liquid preserved collections.
Exhaustive Crisp Parameter Modification in Quantization Table for Effective Image Compression

In recent times, transmission of information over wireless channels is increasing at an exponential rate. Internet is major source of information; it can be in the form of video or images which alone size up to 72% of the global traffic. In order to tackle such immense data, available channel may not be enough for transmission or reception, in this regard it is imperative to use efficient compressions techniques to reduce its size. Compressed image quality depends on the Quantization Table performed after spatial transformation like Discrete Cosine Transform. Size of a raw image captured by Digital Single-Lens Reflex, having all of its traits can easily exceed 20 Mega Bytes. In proposed compression algorithm, a Crisp parameter p modification step is introduced for effective compression of an image by utilizing standard Joint Picture Expert Group Quantization Table as a baseline model. After implementation of the proposed algorithm, Mean Opinion Score is obtained from the masses through an online survey and it provide the scores of 47.633 at p = 1, 62.74 at p = 8, and 83.252 at p = 16. According to Mean opinion score, best trade-off between quality and size of an image is between the values of p ranges from 11-20, this is also proved by Mean Squared Error and Peak Signal to Noise Ratio, as their ranges are 0.00038-0.000301 and 34.09-34.92 dB respectively. Compression Ratio which is from 6.49-5.76 is also acceptable for the given range.
Modelling Type-2 Fuzzy Systems by Optimized Nonstationary Fuzzy Sets with Genetic Algorithm

The high computational complexity of type-2 fuzzy systems causes emerging of alternative methods. Nonstationary fuzzy system, which aims to model the type-2 fuzzy sets with a number of type-1 sets -obtained with the help of perturbation function- is one of these methods. In this study, the type-1 fuzzy subsets which are used in nonstationary systems are optimized by the help of the genetic algorithms. Thanks to the convergence rate of the genetic algorithm, the obtained type-1 fuzzy subsystems are close to the best solution. So, the nonstationary fuzzy set which is generated using the genetic algorithm gives us a better solution instead of the nonstationary fuzzy sets created by perturbation functions which are based on mostly randomness. The success of the obtained nonstationary fuzz set is proven by the simulation results.
Application of Augmented Reality Technology as Alternative Media for Tourism Promotion in Banyumas Regency

Augmented Reality (AR) is known as an interactive technology that is able to project virtual objects into real objects in real time. Banyumas is an example of a district that has a lot of tourism commodities because of its diverse culture and its lack of promotion. This promotional media was created using AR with marked based tracking method. This method utilizes the function of the marker as the media that acts to display the virtual object, the marker will be recognized by the application through the camera device regarding the position and orientation of the object. For the process of making this application using Unity as a tool and Vuforia as a database for markers. The testing phase using the ISO 25010 standard is an evaluation of the quality of the software system specifically based on product quality consisting of eight characteristics, namely functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. This study uses three tests of eight characteristics, namely function suitability, compatibility and usability. The results of this application quality analysis obtained functional suitability test results of 97.5% and compatibility results obtained results of 100% for usability with a percentage of 88.6%.
An Analysis of Teachers’ Strategy in Teaching Listening

&lt;p style="text-align: justify;"&gt;Listening is the ways of picking up the information delivered by others in daily life, and also is the most important elements in studying foreign language. Several people have, however, problem in listening because they think that listening is the hardest skill to be mastered. To solve this barrier, teachers carry a big responsibility in their classroom. Teachers need to encourage students’ participation through preparing an interesting strategy during listening instruction in order to attract students’ attention to take part actively in listening. This study attempted to investigate the strategy used by teacher in teaching listening. Richard’s theory of listening strategy to include bottom-up and top-down was applied. Through utilizing qualitative descriptive analysis, this study involved three listening lecturers of English Department of Universitas Negeri Gorontalo where they were observed and interviewed. The analysis of data revealed that teachers used mostly top-down process activities in listening class.&lt;/p&gt;