Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

Objectives:The main objective of this study is to compare the performance evaluation of ensemble based methods and neural network learning on various combinations of unigram, bigram, and trigram feature vector along with feature selection (IG) and feature reduction (PCA) for sentiment classification of movie reviews.Methods: Bagging and Adaboost are the techniques used in ensemble learning to learn the sentiment classifier to get better classification accuracy, using SVM, NB as a core learner for different models of attribute vectors.The classification results of the ensemble approach are compared with neural network learning for classification of movie reviews.Among the ensemble methods, AdaBoost with base learner SVM outperforms in classifying attribute vectors for model m-iii.The backpropagation algorithm is used to improve classification accuracy in the neural network learning and IG and PCA are used in sentiment classification to reduce the feature length and training time.Findings:The classification results of ensemble based approach are compared with neural network learning.Between the two ensemble based methods, Adaboost + SVM outperform in classifying the sentiment of movie reviews for m-iii feature vector.IG and PCA are used in sentiment classification in order to reduce the feature length.Between the IG and PCA methods, IG performs better than PCA.Among IG+Adaboost+SVM and neural network learning methods, IG+Adaboost+SVM performs better than neural network learning.Improvement: In our application, we are using the ensemble based methods and neural network learning, these methods are compared and analyzed the performance for various levels of feature vectors.A classification algorithm may be designed to analyze the performance with other neural network methods.
Modified Particle Swarm Optimization Algorithm Facilitating Its Hardware Implementation

This paper presents various modifications and developments in the PSO algorithm. PSO is an algorithm based on the behavior of swarms. In this work we focus mainly on simplifying the algorithm by replacing random number generators, which can be a problem when implementing algorithms in hardware. We investigate how changing the methods for algorithm updates from random to more deterministic approach influences the results. This paper shows whether it is possible to achieve same as random or better results using a simplified algorithm, which uses simple mathematical operations in the algorithm optimization process.
Implementation of CAESAR CIPHER Cryptography Techniques for Android Based Information Data Security

Information data security is an important aspect of exchanging data and information. Data and information security can be done in various ways, including by using the cesarean cipher method in cryptographic techniques. The cesarean cipher method of cryptographic technique is a substitution coding system which is done by replacing each alphabet character with other characters along the 26 alphabet characters so that the coding only occurs in the alphabet itself without any other punctuation. This study proposes a modification to the cesarean cipher technique by adding to the number of characters used, namely 256 characters in the ASCII code. The caesar cipher application or CaesarApp uses the methodology of library study, consultation, application design and application testing. The implementation of the CaesarApp application was created using the open-source Android Studio 3.5 application. The results of tests conducted on the CaesarApp application note that the modification of the caesar cipher with 256 ASCII characters results in a secure information data security application, this application has a deficiency in reading limitations on ASCII characters, so characters cannot be read properly on android users.
Improved Background Subtraction Technique for Detecting Moving Objects

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.
PENINGKATAN HASIL BELAJAR SISWA DENGAN MENGGUNAKAN MEDIA GAMBAR BERBASIS KOLASE PADA PEMBELAJARAN TEMATIK DI MADRASAH IBTIDAIYAH PADANGSIDIMPUAN

The problem that is found in this research is low learning outcomes of IV grade student in MI Padangsidimpuan Angkola Julu in thematic learning. The problem is caused learning system given by teacher still not fun, so it does not make student desire in following the learning. This research is conducted to improve learning outcome of IV grade student in MI Padangsidimpuan Angkola Julu in following thematic learning in the theme the beauty of diversity in my country, material magnetic force and gravitational force using collage picture media. This is classroom action research that is consisted of two cycles and every cycle consist of four steps, they are (1) planning, (2) implementation, (3) observation, (4) reflection. The technique of data collection uses observation, test, and interview. Data analysis techniques are conducted qualitatively and quantitatively using questionnaire data analysis in the form of percentages (%) to get the success of image media implementation toward student’s learning outcome. Based on the results of teacher’s observations using collage picture media that it obtains 85,5% in the first cycle and 92,5% in the second cycle. The data shows an increase in teacher’s activity in improving student’s learning outcomes. Based on observation result, there is increase in student’s learning outcome where cycle I gets 60% and cycle II gets 84%. The average of student’s learning outcome gets increase percentage of 24%. Research result can be concluded that the use of collage picture media can improve student’s learning outcome on thematic learning.
Assessing the effect size of users’ consciousness for computer networks vulnerability

Abstract In this paper the conditions and the findings of a simulation study is presented for assessing the effect size of users’ consciousness to the computer network vulnerability in risky cyber attack situations at a certain business. First a simple model is set up to classify the groups of users according to their skills and awareness then probabilities are assigned to each class describing the likelihood of committing dangerous reactions in case of a cyber attack. To quantify the level of network vulnerability a metric developed in a former work is used. This metric shows the approximate probability of an infection at a given business with well specified parameters according to its location, the type of the attack, the protections used at the business etc. The findings mirror back the expected tendencies namely if the number of conscious user is on the
Stacking Based Ensemble with Instance Selection in Random KNN

Nearest neighbor classification is a simple and quite effective method. But it has some drawbacks in that it suffers from both so called the curse of dimensionality and computational burden when there are lots of examples. A lot of features in high dimensional data degrade the performance of KNN due to the noisy and redundant variables. Hence some kinds of feature selection mechanism is essential to KNN. Random KNN simplifies the feature selection problem by utilizing an ensemble of KNNs constructed with randomly selected subsets of features. Two kinds of enhancements of the random KNN methodology are proposed in this paper. One is to add an instance selection procedure and the other is to determine the weights by training another combining model instead of simple voting in the random KNN. These two additional procedures applied to the random KNN can improve the classification performance of the original random KNN, which is verified by some data analyses with a real data set.
Machine learning based detection of digital documents maliciously recaptured from displays

We used to say “seeing is believing": this is no longer true. The digitization is changing all aspects of life and business. One of the more noticeable impacts is in how business documents are being authored, exchanged and processed. Many documents such as passports and IDs are being at first created in paper form but are immediately scanned, digitized, and further processed in electronic form. Widely available photo editing software makes image manipulation quite literally a child's play increasing the number of forged contents tremendously. With the growing concerns over authenticity and integrity of scanned and image-based documents such as passports and IDs, it is more than urgent to be able to quickly validate scanned and photographic documents. The same machine learning that is behind some of the most successful content manipulation solutions can also be used as a counter measure to detect them. In this paper, we describe an efficient recaptured digital document detection based on machine learning. The core of the system is composed of a binary classification approach based on support vector machine (SVM), properly trained with authentic and recaptured digital passports. The detector informs when it encounters a digital document that is the result of photographic capture of another digital document displayed on an LCD monitor. To assess the proposed detector, a specific dataset of authentic and recaptured passports with a number of different cameras was created. Several experiments were set up to assess the overall performance of the detector as well as its efficacy for special situations, such as when the machine learning engine is trained on a specific type of camera or when it encounters a new type of camera for which it was not trained. Results show that the performance of the detector remains above 90 percent accuracy for the large majority of cases.
Health App User Experience Analysis Research : Focusing on the Step Count App

Due to the increasing interest of people in health and life sports, health is a major content in various media.This background and development of information technology has led to the release of hundreds of thousands of mobile health applications.Nevertheless, studies evaluating the user experience for mobile app usability and app success are insufficient.Therefore, this study aims to describe the phenomenon and identify problems and improvements by comprehensively exploring the user experience of the step count app of users in their 20s.FGI was performed to grasp the rich empirical data and satisfaction level of users in detail.FGI is a research method conducted by exchanging perceptions and experiences on subjects and cases according to the purpose of the research.As a result of the study, it was found that it is necessary to improve the level of playfulness of the step count app, various additional functions, and reliability.
SOCIO-EDUCATIONAL ASPECTS OF FORCE MAJOR ACTUALIZATION OF THEORY AND PRACTICE OF DIGITAL UNIVERSITY UNDER THE CONDITIONS OF PANDEMIA COVID-19

In this review and analytical article, based on recent publications and empirical studies, an attempt is made to clarify how traumatic or mobilizing the rapid transition to distance educational technologies and online learning in the world university community and Russian universities has become in the face of curbing the spread of coronavirus disease 2019 (COVID-19). The text gives the factors of the development of crisis alarmist university futurology, substantiates the strengthening of a group of analysts - adherents of the position of inevitable fundamental and radical changes in universities. The first practices of national transitions to digital universities without additional technological equipment and advanced training of teachers, emerging existential and organizational-technical issues are considered. The statistical data of foreign and domestic empirical studies related to clarifying the features of the forced complete transition to digital educational technologies, both in the assessments of teachers and students, are presented. The conclusion is substantiated that practicing teachers assess the existing shortcomings and new resource opportunities in a rather balanced way, expressing the need for mutually complementary classical classroom and digital educational technologies, sharing the ideas about the development of hybrid education and cooperation between universities.
Finding the Look of Souls

Finding the look of the soul characters for the movie Soul was a challenging process. The art direction was based on a ethereal design and turned out to be a moving target during the look development collaboration between the different departments involved. While on a tight schedule, multiple technical approaches were taken in order to find solutions to the difficult design challenges. The final approach features a volumetric shading with a unique line treatment on extreme flexible yet simple and appealing character designs.
Investigating Awareness and usage of Electronic Resources by Research Scientists in Ghana

The study investigated the use of electronic resources/information by research scientists in Ghana. Specifically, to investigate the awareness and level of use of electronic resources; perceived reliance, benefits and impact of use of electronic resources on the research activities. The research design for the study was a survey. Questionnaire schedule was used to collect data from 103 scientists from selected institutes at the Council for Scientific and Industrial Research (CSIR), Ghana. The result revealed that majority of the scientists (92 %) used electronic resources. The most used type of resources were Electronic databases, electronic research reports and electronic journals. The purpose of use was mostly for research work (71 %). However only 34 per cent had had training in information literacy skills. Some of the impediments to accessing the electronic resources were poor ICT infrastructure (38 %) and lack of skills in accessing the resources (35 %). Generally, the use of electronic resources have improved research activities of research scientists It is recommended that continuous training (formal and informal) should be organised for research scientists to keep them abreast with new electronic applications and tools that will promote their level of competence and improvement in their research outputs. In Addition, ICT infrastructure should be improved in the various institutes for easy access to the electronic resources
Analytical solutions of advection-dispersion equation using fuzzy theory

The aim of this research is to apply mathematical models for the solution of the one-dimensional convective-dispersive solute transport equation.For the derivation of the advection-dispersion equation it is assumed that the flow in the medium is unidirectional and that the average velocity is constant throughout the length of the flow field.Moreover, it is assumed that the porous medium is homogeneous and isotropic and that no mass transfer occurs between the solid and liquid phases.Unfortunately, the boundary conditions of the problem are not always intuitively apparent and in many cases convey uncertainties.For that reason, the problem is solved by utilizing fuzzy systems and fuzzy logic.The significance and the main advantage of this research is the introduction of fuzzy logic in order to solve similar problems presenting uncertainties.Since the aforementioned problem involves differential equations, a method of generalized Hukuhara derivative for total derivatives was applied, as well as the extension of the corresponding theory, concerning partial derivatives.So the fuzzy problem was transformed in a system of two classical differential equations, which were resolved with a Laplace transformation.The development of the fuzzy concentration profile is presented, as well as the membership functions of concentration.The results have given some beneficial conclusions for the effects of the uncertainties.In conclusion, it is expected that this conception will help the researchers and the engineers to take the right decision in similar problems.It is a special effort in this research to solve an advection-dispersion equation presenting uncertainties in boundaries and to follow the effect of these uncertainties in time.
Propose a new approach for key generation based on Chicken Swarm Optimization and HTML Parser

The random key plays a basic role in the design of any cryptographic algorithms. In this paper, a proposed model for generating a random key is presented, which will be used for security purposes. Chicken swarm optimization (CSO) and HTML parser are used for scattering the bits of the key. The statistical tests of randomness have given good and acceptable security results. The proposed key generation method was programmed by JavaScript language.
Pedagogical conditions for the development of future Music teachers’ interpretative skills in the process of choral and conductorial training

The article analyses the essence, content and specificity of future music teachers’ interpretive skills. The purpose of the article is to substantiate pedagogical conditions favourable for effective development of future music teachers’ interpretive skills in the process of their choral and conductorial training. These methods of theoretical research are used: analysis, generalisation, synthesis, extrapolation, deduction, systematisation. Interpretation is seen as the basis and the necessary condition for understanding a piece of music, the subjective (personal) attitude and creative imagination of a teacher of music being of great importance. The essence of interpretation in the context of the activity of the Musical Arts teacher lies in the interpretation of an artistic text which reflects the content of the author's message in key note symbols. In the activity of the Musical Arts teacher, there are two forms of interpretation: performing and verbal-pedagogical. The interpretation of the choral work is considered in the article as an artistic-performing conception of the conductor which is based on his/her artistic and imaginative ideas, knowledge; it is realised in his/her creative interaction with the choral team. The list of specific interpretive skills to be demonstrated by the future Music teachers in the context of choral and conductorial training has been clarified. A system of interrelated pedagogical conditions has been offered, it includes these constituents: increase of future specialists’ motivation for the choral and conductorial activities through realisation of the axiological potential of choral music; the implementation of the coaching pedagogical technologies into the choral and conductorial training intended for the future Music teachers; a systematic widening of future specialists’ thesaurus of artistic and imaginative ideas. Further research involves the development of a step-by-step methodology for the development of future Music teachers’ interpretative skills in the process of their choral training.
Properly initialized Bayesian Network for decision making leveraging random forest

Bayesian network is one of major methods for probabilistic inference among items. But if it contains particular targeting node and other explanatory nodes for decision making, for example how to select suitable appealing keywords to make customers like a product, edges around the target should be counted with more importance than those among others while constructing the network. In order to achieve this adjustment, this study proposes to configure initial state consisting of a few nodes and their edges connected with the target. The initial state is obtained by leveraging Random forest which is a proven method for decision making. Initial nodes are extracted by measuring mean decrease of Gini coefficient calculated with decision trees of Random forest. Initial edges are designated by comparing Kullback-Leibler divergences of conditional probability distribution among nodes which are corresponding to edge directions. Through an actual experiment, this method is confirmed to be effective for adjusting Bayesian network in decision making. This approach is especially useful for business scenes, such as selecting preferable keywords for appealing products over SNS.
In-vehicle Driver and Passenger Activity Recognition

Recognition of human behaviour within vehicles are becoming increasingly important. Paradoxically, the more control the car has (ie in terms of support systems), the more we need to know about the ...
Ranking of Services for Reliability Estimation of SOA System Using FUZZY TOPSIS Method

In software system, there are different quality attributes like reliability, flexibility, reusability and maintainability.Among these quality attributes, reliability is one of the important parameters.Reliability can successfully complete a given system task in a specific environment in specific time period.Reliability estimation is an essential task, which makes users very selective while selecting software for purchase and use.Therefore, estimation of software reliability is necessary and beneficial.In this paper, an approach to estimate the reliability for most often used software system, i.e.Service-Oriented Architecture System (SOAS) has been used.A fuzzy logic technique for order preference by similarity to ideal solution (TOPSIS) has been employed in fuzzy environment to estimate the systems' reliability.Fuzzy logic is used to remove the uncertainty associated with ranking of alternative services and to select the best alternative services in existing algorithms.At first, we have selected four factors which affects the reliability of SOA system.Secondly, we have ranked the services by using Fuzzy technique for order preference by similarity to ideal solution (TOPSIS).In terms of triangular fuzzy numbers, the weight criteria and rating of alternatives has been assessed by linguistic variables that provide the ranking of services to estimate the SOA reliability.