Reviewerly/exHarmony huggingface dataset:
Asymptotic Analysis

An important task of algorithm analysisAnalysis of algorithms is the estimation of the number of operations executed by the algorithm over a certain class of input data. The exact value of the number of elementary operations does not play any considerable role here, since it depends on the software implementation of the algorithm, the computer’s architecture, and other factors. This is why the algorithm’s performance indicator is the growth rate of this value with the growth of the input data volume. For analysis of the algorithm’s efficiency, it is necessary to estimate the operation time of the computer that solves the set problem, as well as the volume of memory used. The estimate of the computation system operation time is usually obtained by calculating the elementary operations performed during calculations (such operations are referred to as basic). In a supposition that one elementary operation is executed within a strictly defined period of time, the function f(n), defined as the number of operations when calculating over the input data of size n, is called a time-complexity function. Universal method of estimating the growth rate of functions, based on the Master Theorem, is given.
Fast Legendre Moments in Securing Digital Image

Moments are set of values used to describe the information contained in the image. In this paper the content of the image is represented with the help of fast legendre moments. Legendre moments has the advantage that these moments are calculated exactly without any loss in information while other moments like geometric moments, zernike moments etc suffer from approximation errors and geometric error when applied to digital images. Legendre moments are also more suitable for image reconstruction. Experimental results show that the proposed system is very efficient in computing moments at much faster time than the Zernike moments since fast legendre moments calculates the moments as two ID function rather as a 2D function of a digital image and the results of reconstruction in case of tampering is also shown
HUBUNGAN MINAT BELAJAR DAN PEMANFAATAN BUKU TEKS DENGAN HASIL BELAJAR MATA PELAJARAN EKONOMI DI SMA

Interest in learning and the use of textbooks are two aspects that are very influential and have a relationship with student learning outcomes. This can be seen in the learning activities in the XI IPS class of SMAN 1 Luhak Nan Duo on economic subjects, the low student learning outcomes are influenced by students' lack of interest in learning and the lack of use of textbooks by students themselves. The research conducted at the XI IPS class at SMAN 1 Luhak Nan Duo aims to determine the relationship between learning interest and the use of textbooks with learning outcomes. This type of research is quantitative research with correlational methods. The instrument used in this study was a research questionnaire. The population is 130 students with samples randomly selected for 100 students. From the results of the analysis it can be concluded that: (1) Interest in learning and the use of textbooks have a positive and significant relationship together with learning outcomes seen from the Sig. F change 0,000 &lt;0,05. (2) There is a positive and significant relationship between interest in learning and learning outcomes seen from the value of Sig. 0,000 &lt;0,05. (3) There is a positive and significant relationship between the use of textbooks and learning outcomes of economic subjects seen from the Sig. 0,000 &lt;0,05.Keyword : Interest in learning, utilization of textbooks, learning outcomes
Sound objects

This chapter discusses two practical projects that explore the nature and possibilities of sound in theatre and generative audio in particular. The first of these projects, RayGun, is an augmented prop prototype that has potential advantages related to the responsiveness to user interaction and sound localisation. The second project, INTERIOR, consists of a generative audio play embedded in a tangible, radio-like artefact intended to offer an accessible, bi-modal (audio and haptic) experience in a self-contained and portable package. The authors' approach is informed by two bodies of theory: discourse around sound in theatre and related areas such as film and video-game sound; and research in procedural generation and specifically, procedural audio. It is useful to make explicit a distinction between sound effects related to props and more environmentally orientated ambient sounds. This implies that there are four possible categories of sounds in theatre: dialogue, sound effects (prop sounds), ambience, and music.
FSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data

Dimensionality reduction is one of the pre-processing phases required when large amount of data is available. Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. Till now these methods were using separately so the resultant feature contains original or transformed data. An efficient algorithm for Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data (FSEFST) has been proposed in order to select and extract the efficient features by using feature subset method where it will have both original and transformed data. The results prove that the suggested method is better as compared with the existing algorithm
Dual Security Fingerprint that is Utilizing as Well as in Bank System

This paper defines utilising the ATM (Automatic Teller device) which supplies clients with all the banknote that is convenient are really typical. Nevertheless, the f crime that is inancial increases over and over repeatedly in current y ears; plenty of criminals tamper with all the ATM terminal and steal individual's charge card and password by unlawful means. When individual's charge card is lost as well as the password is taken, the criminal will draw all money in the shortest time, that may bring enormous losings which can be monetary consumer. How exactly to continue the legitimate identification towards the consumer becomes the main focus in present group that is economic. Old-fashioned ATM systems authenticate generally by making use of the credit card and the password, some defects are had by the technique. Making use of credit password and card cannot verify the client's identification. Generally there is absolutely essential to improve the protection that client make use of the ATM device. As soon as individual's charge card is lost and the password is taken, the criminal will draw all profit the shortest time, that may bring enormous losings which can be monetary consumer, therefore to rectify this dilemma we are applying in this work. The goal that is primary to help make the ATM device with additional safety by giving twin security in other words., fingerprint recognition and also by entering password. S o, to conquer the issues which can be above are designing a work containing little finger printing audience, keypad which can be helpful for entering of password without needing ATM cards as w age do in the ATM’s currently available. As soon as the nger that is fi etails are g iven a window is exhibited regarding the c ontroller, this contains keypad a nd u sing that keypad we are able to enter the password after b eing enrolled. Individual can enter the a mount t o withdraw. And can get the note from note dispenser (stepper motor) interfaced with microcontroller.
Algorithms Used in Cryptography and Role of Cryptography in Network Security

In this modern world, we cannot live without these two things. One is technology and other one is security. Even these two cannot stand alone, they depend on each other. Technology without security and security without technology leads the world into destroying path. Day by day the technology is improving and also the security. In the technology field, the network plays an important role. Every system in the world depends on the network. So the network has been secured before it sends to the relevant people. By this problem can lead us to create a new method called Cryptography. The work of cryptography is to convert the sending message into encryption method by using some key and the receiver receives the encrypted message. The receiver decrypt the message by the key which is used to encrypt the message. In this paper, we discuss how network security works with cryptography and algorithm used for cryptography.
Deep Learning based Sentiment Analysis for Social Media Network

Sentiment Analysis is the process of identifying opinions expressed in a piece of text. It determines whether the writer's attitude towards a product is positive, negative, or neutral. Sentiment evaluation addresses such need by way of detecting evaluations on the social media textual content. Product evaluations are valuable for upcoming shoppers in supporting them make choices. In recent, deep learning is loom as a powerful manner for fixing sentiment classification troubles. The neural network intrinsically learns a beneficial representation without the efforts of human. This paper presents the overall performance evaluations of deep learning classifiers for big-scale sentiment evaluation. In this system the reviews from the online shopping website called flipkart.com is analyzed and divided as positive, negative and neutral by Multilayer Perceptron (MLP) Neural Network depending on the aspect of the product. The proposed work is simulated by using SPYDER. In our system the accuracy, precision, F-measure and recall is calculated for Multilayer Perceptron (MLP) Neural Network, Random Forest and Support Vector Machine (SVM) algorithm. During comparison Multilayer Perceptron (MLP) Neural Network gives the best accuracy of 99% than other two algorithms.
A Comprehensive Survey on Human Action Recognition

The present The present situation is having many challenges in security and surveillance of Human Action recognition (HAR). HAR has many fields and many techniques to provide modern and technical action implementation. We have studied multiple parameters and techniques used in HAR. We have come out with a list of outcomes and drawbacks of each technique present in different researches. This paper presents the survey on the complete process of recognition of human activity and provides survey on different Motion History Imaging (MHI) methods, model based, multiview and multiple feature extraction based recognition methods.
Conference Title: Proceedings of the International Conference of Computational Methods in Sciences and Engineering 2020 (ICCMSE-2020)

First Page
Reinforcement Learning using Convolutional Neural Network for Game Prediction

The paper presents a Deep learning model for playing computer games with elevated level information utilizing Reinforcement learning learning. The games are activity restricted (like snakes, catcher, air-bandit and so on.). The implementation is progressive in three parts. The first part deals with a simple neural network, the second one with Deep Q network and further to increase the accuracy and speed of the algorithm, the third part consists of a model consisting of convolution neural network for image processing and giving outputs from the fully connected layers so as to estimate the probability of an action being taken based on information extracted from inputs where we apply Q-learning to determine the best possible move. The results are further analysed and compared to provide an overview of the improvements in each methods.
Efficient retrieval of trademark images from large databases

Trademarks law is considered as the most pervasive amongst all the intellectual property laws because all the judgments require contemplating the imaginations of the consumers. Once the trademark has been submitted for registration, the examiners at the trademark office make sure that it is not similar to any of the previous registered trademarks. This motivated the need of an automated trademark retrieval system. This paper makes a contribution in the field of trademark image retrieval by proposing a retrieval technique that allows a flexible combination of colour, texture and shape features. Moreover the proposed technique utilises HSV colour histogram, for colour, multi resolution Gabor wavelet for texture and an integration of Zernike moments for global shape and scale invariant feature transform (SIFT) for local shape feature extraction. The results have been tested on MPEG7, MPEG trademark, WANG and self-compiled datasets. The improvement achieved in precision is 14% on MPEG7, 30% on the WANG and 38% on self-compiled dataset. Similarly, 26% improvement in average recall is achieved and around 16% when our proposed shape feature is compared with other state of the art techniques like Fourier descriptors, Hu moments, wavelet descriptors, Zernike moments and edge gradient co-occurrence matrix.
A New Aggregated Attribute Values Match Technique for Improving the Quality of Probability Estimated Decision Trees

Probability estimations of decision trees may not be useful directly because their poor probability estimations but the best probability estimations are desired in many useful applications. Many techniques have been proposed for obtaining good probability estimations of decision trees. Two such optical techniques are identified and the first one is single tree based aggregation of mismatched attribute values of instances. The second one is bagging technique but it is costly and less comprehensible. So, in this paper a single aggregated probability estimation decision tree model technique is proposed for improving the performance of probability estimations of decision trees and the performance of new technique is evaluated using area under the curve (AUC) evaluation technique. The proposed technique computes aggregate scores based on matched attribute values of test tuples.
Development of Accounting Information System Modul Learning in Universitas Persada Indonesia

The research aims to produce that accounting information systems intruductional modul for college of department of information in indonesia administrasion foundation (YAI). The research was implementation at the department of information YAI for 1 year 2 months in the research and development (R &amp; D) methode with Dick And Carey model of design. The accounting information systems learning consepts was collection of resources,designedto transform financial and other data into information from Bodnar. The results indicated that 30 respondens in field trials produce until the finished of product module accounting was effective and efficient and had an appeal. Results of study subjects accounting information system can be increased after using the accounting information system model that availability of accounting information system model can be used as a source of intructional for college of department of information in institute administration of indonesia. The development of accounting information systems learning module is very important to help teaching process. Therefore the Persada Indonesia YAI is trying to develop it. This module is using ADDIE model, Gerlach and Ely, J.E Kemp, Bella H Banathy, MPI model, Borg and Gali , Dick and Carey model The result area Students are obtaining learning resources for self-study, Students are more eager to learn, because already available SIA print module, Student learning outcomes have increased finally Based on the results of interviews with lecturers SIA module developed to give a positive impact for lecturers and students
Consonant approximation

As we have learned in the last two chapters, probability transforms are a very wellstudied topic in belief calculus, as they are useful as a means to reduce the computational complexity of the framework (Section 4.7), they allow us to reduce decision making with belief functions to the classical utility theory approach (Section 4.8.1) and they are theoretically interesting for understanding the relationship between Bayesian reasoning and belief theory [291].
Architectural Framework for Intelligent and Secure Aadhar Data Management Synchronized with both Offline and Online Transaction Systems

The information of the citizens identity are reposited in the national database system that demands higher degree of security features in order to combat the privacy problems associated with it. The information retained within an identity of a citizen is highly valuable as well as sensitive as it is constructed by integrating various forms of biometric trails e.g. iris, fingerprint etc along with their personal details. Aadhar is one of such initiatives by Government of India by their “Unique Identification Authority of India (UIDAI) project” that generates 12-digit random number as unique identity. The Government of India is in progressive approach to integrate various social and security related applications to Aadhar in order to achieve identification-based service provisioning. In the recent news, Aadhar details have been reported to be encountered a serious security breach that endangered the private details of the Aadhar card holders. Therefore, a robust and efficient secure eco-system is required to keep national ID database secure. This chapter aims to describe novel framework for Aadhar data privacy and storage management by adopting efficient data blockage and transformation over cloud storage using statistical method based on data embedding by blocking and transformation.
Reviewer Acknowledgements

Applied Economics and Finance (AEF) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether AEF publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 6, Number 2 Mahmoud Mohammed Sabra, Al Azhar University-Gaza, PalestineGeorge Theocharides, Cyprus International Institute of Management, CyprusRamona Orastean, Lucian Blaga University of Sibiu, RomaniaIgor Matyushenko, School of Foreign Economic Relations and Touristic Business, UkraineSteven V. Cates, Purdue University-Global, USAMagdalena Zioło, University of Szczecin, PolandVictoria Cociug, Academy of Sciences of Moldova, MoldovaJolita Vveinhardt, Lithuanian Sports University, LithuaniaIan McFarlane, University of Reading, UKPatrycja Kowalczyk-Rolczynska, Wroclaw University of Economics, PolandGetamesay Bekele Meshesha, Debre Berhan University, EthiopiaNicolas Afflatet, University of the Federal Armed Forces, GermanyMarco Muscettola, Independent Researcher-Credit Risk Manager, ItalyY. Saidi, M’sila University, AlgeriaSorin Gabriel Anton, Alexandru Ioan Cuza University of Iaşi, RomaniaRichard Nguyen, Alliant International University, USASzabolcs Blazsek, Universidad Francisco Marroquín, GuatemalaAaron Morey, University of Melbourne, AustraliaFarhat Iqbal, University of Balochistan, Quetta – Pakistan. , PakistanHichem Maraghni, University of Taibah, TunisiaLuca Giordano, CONSOB, ItalyRomeo Victor Ionescu, Dunarea de Jos University, RomaniaSebastian Schich, Organisation for Economic Coopertaion and Development (OECD), FranceNuno Crespo, ISCTE-IUL, PortugalAndrey Kudryavtsev, The Max Stern Yezreel Valley Academic College, IsraelZi-Yi Guo, Wells Fargo Bank, N.A., USAAsad K. Ghalib, Liverpool Hope University, UKPayal Chadha, University of Wales Prifysgol Cymru, KuwaitMagdalena Radulescu, University of Pitesti, RomaniaOlena Sokolovska, Research Institute of Fiscal Policy, State Fiscal Service of Ukraine, UkraineHe Nie, Jinan University, ChinaIbrahim Baghdadi, Lebanese University, LebanonMamdouh Abdelmoula M. Abdelsalam, Minufiya University, EgyptDimitrios Koumparoulis, University of the People, USARajeev Rana, APB Govt. P.G. College, InidaZuzana Janko, San Francisco State University, USAKaveh Dalvand, University of Delaware, United States Nikki GibbsEditorial AssistantOn behalf of,The Editorial Board of Applied Economics and FinanceRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://aef.redfame.com
Attribute rank-based weighted decision tree

Decision tree is a renowned classifier reformed by innumerable tree generation techniques to construct efficient trees. In this paper, a novel framework for constructing decision tree classifiers using ranked attributes and weights is presented. The tree is fabricated using proposed framework hierarchically from root to leaf using ranks and weights of attributes assigned to branches based on their contribution towards classification accuracy. The weights are perpetually updated until the tree furnishes maximum classification accuracy. This framework is experimented with cuckoo search, firefly search, wolf search and proposed limited lazy wolf search for ranking attributes and the experimental results illustrates the framework achieving excellent performance than the antecedent. For demonstrating the stability of proposed framework in all problems, numerous experiments are carried out using prominent benchmark datasets and a proposed crime propensity prediction dataset. The crime propensity prediction accuracy accomplished by fine tuning the proposed algorithm was 99.3617% directing crime free society.
WebSocket

The WebSocket protocol (www.websocket.org) allows a two-way real-time conversation between the web server and client, through a standardized connection to allow both the server and the client to send data at any time. The client sends a request to the server to switch from an HTTP protocol to a WebSocket protocol; and if the server can host the WebSocket protocol, the HTTP connection is replaced with a WebSocket connection, but using the same port as HTTP.
Perceptions of Students and Teachers Regarding Blended Learning Use in the Elementary Schools

The Covid-19 pandemic has altered people's lives in all sectors, including the educational field.This idea resulted in establishing the learning from the home policy as an alternative to academic services for students.This study aims to describe the perceptions of students and teachers towards the implementation of mathematics learning using the blended learning method in elementary schools in the Manggarai regency.This type of research was survey research with the number of samples in this study were 42 elementary school students and 42 elementary school teachers.Based on the instrument validity test results, it was found that both the student's perception instrument and the teacher's perception were all declared valid, with the reliability coefficients being 0.84 and 0.81, respectively, in the high category.The data analysis technique used quantitative descriptive techniques by analyzing respondents' answers to the instrument items on three dimensions.Based on the results of this study, it can be concluded that the implementation of blended mathematics learning for students in Manggarai regency has not done well.This happened because not all students had Android phones and laptops; unstable internet connection, some several student residence areas lacked an internet connection, limited internet quota, teachers were not accustomed to controlling student attendance in online learning or using google forms applications, teachers were not accustomed to giving assessments using certain applications, both teachers and students had not accustomed to using virtual faceto-face learning using video conferencing, which hinders the learning process during the Covid-19 pandemic.