Reviewerly/exHarmony huggingface dataset:
PENINGKATAN KEMAMPUAN MENELAAH STRUKTUR FABEL DENGAN MODEL DISCOVERY LEARNING DAN MEDIA POHON TELAAH

ABSTRACT This study aims to describe planning, performance, and the learning assesment studying the fable text structure by applying the discovery learning model and media trees to class VII SMPN 11 Satu Atap Menukung student participants.. The method used in this study is descriptive method with qualitative forms of research. The research subject has been teacher and a class VII SMPN 11 Satu Atap Menukung Kecamatan Menukung Kabupaten Melawi with 15 participants and datas are the result teacher ability assesment 1(APKG 1), result of teacher ability assesment 2 (APKG 2), interview result, and learning result of student VII class VII SMPN 11 Satu Atap Menukung. The result shows that the overall paln for learning is increasing from cycle 1 to cycle 2. Result on the plan to implement learning acquired on the cycle 1 ends up in 79 with adequate predicate and increases on cycle 2 to 84 with a good predicate. A teacher’s performance in managing learning experiences an improvement from cycle 1 with score 82 with a good predicate and increasing on cycle 2 to 89 with a good predicate. The result shows that the average score that students earn on Learning in cycle 1 shows the average score 56 with 8 students completed apprentices (54%). The study of cycle 2 is also improving with average score 74 with 12 students completed apprentices (80%). So the learning curve studied the structure of the fable text using the discovery learning model and media of the tree learning stage of cycle 1, and cycle 2 are enhanced. Keywords : DISCOVERY LEARNING, STRUCTURE OF FABLE TEXT, TREE OF LEARNING
E-Commerce and E-Technology Contribution in Engineering and Scientific Laboratories Equipments Contracts at Jordanian Universities

This paper presents the role of E-commerce and E-technology in simplifying operations of preparing quotations and contracts of supporting engineering and scientific laboratories by required equipments and tools at Jordanian universities.The experimental and practical sides of education in scientific colleges in Jordanian universities is very important, so many quotations and contacts are performed and required each year, and E-commerce also offers the required information and specifications to persons responsible to do such contacts by using Business-to-Consumer, Consumer-to-Consumer or Business-to-Business principles.By searching about such equipments using search engines, many companies present their products and their specifications and give you the opportunity to select the suitable devices and tools depending on their specifications and prices.Case studies are discussed for some devices to show how much E-commerce simplifies this operation.It was found that E-commerce and E-technology reduces both time and efforts required to support both engineering and scientific labs with their needs from equipments, catalogs and tools required to such devices.
Classification of Mammograms using Various Feature Extraction Methods and Machine Learning

Breast cancer is an alarming disease which takes millions of lives every year. In 2018, it was anticipated that 627,000 women died due to breast cancer – which is around 15% of all deaths caused due to different types of cancers among women. Currently, risk factors of breast cancer cannot be avoided, and early detection is the only way of survival. Automated detection of breast cancer with the help of image processing methods and machine learning algorithms helps in giving more accurate results and less human power. In the proposed system, multiple features are extracted using HSV histogram, LBP, GLCM, 2-D DWT. Support vector machine and LIBSVM classifiers are used for the classification of mammogram images if it’s benign or malign in nature. For classification, the INbreast dataset have been used which includes 115 cases containing 410 images. The dataset is divided into benign and malign category based upon BI-RAIDS scale. According to this partition we have 243 benign images and 100 malign images present in this dataset and a feature matrix of 595 features in total is generated for balanced and unbalanced datasets respectively and fed into SVM and LIBSVM to distinguish the data. The balanced datasets on LIBSVM gave best results with 92% accuracy, 84% sensitivity, 100% specificity and 91.30% F1 score followed by SVM which gave 75% accuracy, 73.61% sensitivity, 76.66% specificity and 75.8% F1 score.
2928 Retrieval Number: F8342038620/2020©BEIESP DOI:10.35940/ijrte.F8342.038620 Journal Website: www.ijrte.org Published By: Blue Eyes Intelligence Engineering &amp; Sciences Publication Approbation of Leaves with QR Code

The objective and scope of the project are to record the details and grant leave permission to the students and faculty in an institution in a secured way by eliminating the present system. Usually, leave permission is granted by giving the physical gate pass and this is a time taking process and the paper gate pass can be manipulated very easily. In this project, the paperwork is eliminated and a highly secured electronic version of the paper gate pass is provided in the form of a QR Code. Here in the project, only the administrator or HoD is approving the user with a QR Code, if they want to allow the student to leave the college premises. Appropriate training is provided to the user which suit their specific needs. When a student or a faculty need a leave, they can approach the concerned authorities, the credentials like unique ID, leaves left, etc are checked and then leave is granted in form of a QR Code. This QR Code is scanned by the security guards before allowing a person to leave the campus. The security guards have to be trained with scanning of QR Code which is a simple task. The system is intuitive and user-friendly. It simplifies the task and eliminates the paperwork. This project enables free, secured, fast and easy leave approval.
Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

Big Data analysis has been viewed as the processing or mining of massive amounts of data used to retrieve information which is useful from large datasets. Among all the methods employed to deal with the analysis of Big Data, the selection of a feature is found extremely effective. A common approach which includes search making use of feature-based subsets which is relevant to the topic, tends to represent the dataset with its actual description. However, a search that makes use of such a subset is a combinatorial problem which is time-consuming. All commonly used meta-heuristic algorithms to facilitate feature choice. The Stochastic Diffusion Search (SDS) based algorithm has been a multi-agent global search algorithm based on agent interaction is simple to overcome combinatorial problems. The SDS will choose the feature subset for the task of classification. The Classification and Regression Tree (CART), the Naïve Bayes (NB), the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN) have been used to improve the performance. Results proved that the proposed method was able to achieve a better performance than existing techniques.
Инфокоммуникационные технологии для управления общественным мнением

The article is devoted to the study of the influence of information technologies on the formation of public opinion. Models of information perception in a digital society are characterized by non-linearity and are formed as a result of network interactions. The images of reality formed in the mass consciousness of the participants of the network society can be described within the framework of an interdisciplinary approach that takes into account both the opposition of the ancient archetypes of «friend» and «stranger», and the modern decision-making model based on the UML language. The existing digital means of controlling the dissemination of information have a significant impact on the formation of dominant social values and attitudes. The article discusses the technologies of manipulating public consciousness, as well as two main types of strategies used in building social identity using information technologies: rejection (alienation) and empathy. The study of the mechanisms of rejection and empathy formation in the process of social identity formation is useful for developing new practices of coordinating interests for the stability of democratic institutions in the new digital reality.
Blood Cells Counting using Image Processing

The aim of this project is to count the blood cells using image processing . Computer vision methodologies develop to help improve not only image quality of blood smears but also the effectiveness of its analysis. Digital Image processing techniques is help in their analysis and diagnosis .Image processing proves the advantage to blood cell counting since it provides an experience-independent and less time consuming analysis. This paper propose two algorithm for calculating Blood cells 1. Region Growing 2. K mean Clustering.
Transmission and archiving of reduced MRI medical images

Medical images are often large. To minimise their file sizes, different compression standards like JPEG, JPEG 2000 and TIFF have been developed, but they can affect the image quality in case of lossy compression. In this paper, we propose a compression method based on image resizing that can reduce the file size to a quarter before its transmission or archiving. When it is received or before its display, the reduced image is enlarged to ensure better visual comfort. This approach is called REPro. In this context, we have used three image reduction techniques namely square-square mesh decimation, square-square filtered mesh decimation, the square-staggered-square filtered mesh decimation. In addition, we have utilised four enlargement techniques namely zero padding, nearest neighbour interpolation, cubic spline interpolation and binterpolation to resize MRI images. The experimental results prove that the combination of the square-square mesh decimation with the B-spline interpolation ensures the minimum distortion of the original image.
Feature set processing using multi-objective optimisation algorithm to improve content-based image retrieval system

We propose a framework of genetic algorithms to search for Pareto optimal solutions (i.e., non-dominated solutions) of multi-objective optimisation problems. Our approach differs from single-objective genetic algorithms in its selection procedure and elite presence strategy. The selection procedure in our genetic algorithms selects individuals for a crossover operation based on a sum of multiple objective functions. The characteristic feature of the selection procedure is that the weights attached to the multiple objective functions are not constant but randomly specified for each selection. This might most likely decry the classification accuracy and increase noise once it extracts type content type pictures. To avoid these drawbacks, a brand new technique is planned to induce retrieval performance. The implementation results show the effectiveness of projected optimisation technique in retrieving all pictures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other optimised CBIR methods.
Klasifikasi Kategori Buku dengan menggunakan Metoda Naïve Bayes (Studi Kasus: Perpustakaan STKIP PGRI Sumatera Barat)

The number of documents in the form of books, scientific articles, reports etc. is always increasing every day, this certainly makes it difficult for groups or types of documents to classify. Using the Naïve Bayes classifier, which is an approach that refers to the Bayes theorem, is a simple classification algorithm but has high accuracy. Naïve Bayes refers to previous knowledge which is strengthened in advance by processing the title text of a new document so that it can be used to facilitate the classification of new document classes. Based on the results of trials conducted using 125 training data on 10 test data in 5 class groups, the accuracy value is quite high, namely 90% correct classification.
Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-invariant Texture Image Descriptors

1089-778X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often requires domain expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, domain-expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight domain-expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.
Shape

In geometry, shape is the object of theorems associated to a class of transformations. It corresponds to the invariant properties of regions, figures, solids, which are not altered by transformations. The concept of shape implies the concepts of space, upon which transformations are acted, and metric, if distance is preserved, or a measurement function.
Community Oriented Shifting Based Recommonnd Social

Social desire is Associate in Nursing mountaineering brand-new characteristic in online social networks. It positions unique issues and opportunities for referral. Throughout this paper, we typically have a tendency to installation clusteria collection of matrix factorization (MF) and nearest-neighbour (NN)- primarily based absolutely recommender systems (RSs) that check out person social network further to group affiliation facts for social desire belief. Via try outs actual social possibility traces, we have a propensity to illustrate that social network and cluster affiliation records will significantly decorate the accuracy of popularity-based totally definitely preference referral, further to social media information controls collection affiliation information in NN-primarily based techniques. We regularly have a tendency to similarly test that social in addition to cluster data is an awful lot added treasured to cold customers than to huge humans. In our experiments, smooth meta route based totally totally in reality truely definitely NN designs defeated computation-massive tool regularity versions in warmth-vote casting referral, on the equal time as users' passions for non warm temperature ballot 's may be higher strip-mined through device frequency models. We have a tendency to greater suggest a hybrid RS, cloth truely truly one in every of a type solitary strategies to accumulate the maximum dependable pinnacle-pinnacle sufficient hit price
Assamese Text Classification using k Nearest Neighbor

Knowledge is the most powerful weapon of a society. And in today’s world it is just a click away from the mouse. There is abundance of knowledge and information in the form of newspaper , electronic newspaper ,articles, online journals, webpages , search results etc. And there is a wide range of news from all over the world. But then the choice of news varies from person to person. Some people may prefer sports news to amusement news and some people may prefer political news over sports news and likewise there can be a number of other choices. It completely relies on individual’s decision. Document Classification is the process of classifying a document into a number of predefined classes. In this paper we have done document classification of Assamese text using k-Nearest Neighbor. We have considered only four classes sports , politics , law and science. Our dataset consists of 200 documents collected from major Assamese newspaper . We have divided our data into 3:1. Majority of our datasets that is 75% data from datasets is used for training and the rest 25% of the datasets is considered for testing.
A New Representation in PSO for Discretization-Based Feature Selection

In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
JavaScript Numbers

This chapter will focus on JavaScript number operations, number representation, Number objects, common number algorithms, and random number generation. By the end of this chapter, you will understand how to work with numbers in JavaScript as well as how to implement prime factorization, which is fundamental for encryption.
A New Method for Software Projects for Predicting Defects using LSTM and SVM

The most hardest situation to most software developers is determining where bugs are in applications. Finding them and repairing defects is expected to cost billions of pounds per year, and any automated assistance in accurately identifying where faults are, and concentrating tester efforts , would have a huge effect on software development and maintenance costs. So Work on defect detection has been going on for several years using regression methods and, lately, ML algos. To determine where there are defects therefore every organization’s main priority is to detect and fix faults in the early stages of the SDLC. This research has provided some insight into where flaws can be identified, but clinicians do not appear to have taken that on board. One explanation for this may be due to the difficulty in choosing and constructing predictive defect models. In the paper we actually analyze the reasons why the standard of the prediction is so varying due to the altering nature of the process of repairing defect. It primarily comprises two stages in the proposed system: a model development stage, and a prediction stage. In the model development our aim is to create a classifier with proven labels (i.e., broken or clean) by using deep learning and ML techniques from past improvements. This classifier would be used in the predictive stage to determine whether an uncertain shift were to be buggy or safe. Next, our Architecture derives a range of functions from a training package. Next, we do preprocessing of data on the characteristics obtained. Preprocessing of the data involves two counter-steps: normalization of the data and re-sampling. In normalization, we turn the values of all featured to values in the interval from 0 to 1. A deep learning technique such as LSTM &amp; SVM is used. In the prediction stage, the classifier is then used to predict whether a change with an unfamiliar label is buggy or safe(clean). We will evaluate on four datasets from four well-known Open source software, including Mozilla, Eclipse, Net beans and Open Office programs.
Software Practitioners Challenges in the Requirement Engineering Phase of Software Development

Software practitioners have always stressed upon the importance of having a dedicated formal Requirement Engineering (RE) stage in software development.Research has found that RE stage is among those factors that contribute towards project success.Decomposition of this stage in terms of elicitation, analysis, modeling and specification are well defined, but yet software developers claim they face issues as regards to the establishment of proper structural components during the RE stage.Project failures have often been associated with poor requirement definition where the practitioners approach to problem arising during RE phase has been criticised.Ensuring proper interaction among team members and maintaining good communication channel with clients have always been enlisted of being of prime importance in promoting project success in the RE phase.However, findings were mainly based on conventional wisdom.There is still lack of empirical evidence in terms of the identification of other factors that influenced the smooth running of the RE phase.This study aims at identifying the problems faced by practitioners in the RE phase through empirical evidence.An analysis of the influential factors with respect to the criticality for project successes is made.Assessment of the relevance of these factors according to length of projects and size of organisations are also made.A survey questionnaire was designed for identifying the problems faced by practitioners in the RE phase and their frequency of occurrences according to length of project were assessed.The questionnaire was administered to software developers at small, medium and large software enterprises.Semistructured interviews were also conducted with practitioners who have long years of experience as project leaders.Findings reveal that besides communication flaws there are other influential factors that hinder successful completion of the RE phase."Ti me boxing", "Insufficient support from customer" and "from own management", "Gray areas in requirements" are among the hindering factors that make the RE phase vulnerable.Besides, it has been observed that there is uniformity across organisations.Small, medium and large development firms face similar issues as regards to the factors that affect the RE phase.