Reviewerly/exHarmony huggingface dataset:
Development of Competency-Based Poetry Learning Materials for Class X High Schools

Poetry teaching materials are still textual and have not developed student competence. For this reason, poetry teaching material is needed that truly develops students' competence in mastering poetry material. The purpose of this study is to develop poetry teaching materials for class X high schools by the principles of competency-based poetry material. The research method used is research and development. Data collection techniques using questionnaires and observation. Research data analysis techniques: (1) identification of the need for analysis of available teaching materials, (2) identification of teaching material needs for teachers and students, (3) making poetry based on competency and curriculum, (4) field trial I is validating the device teaching materials, (5) Trial II (field / class trials) until teaching materials are developed, (6) initial design revisions based on findings, and (7) compiling ready-to-use teaching materials (products). Test the validity of research data using the validation test of poetry experts, language experts, and design experts. The results of this study indicate that the competency-based high school poetry class X material is feasible to use because it is arranged based on: (1) analysis of the needs of teaching materials that available in Bengkulu city high school, high school teacher needs analysis in Bengkulu City, (2) preparation of curriculum and syllabus based on the analysis of needs analysis, (3) preparation of syllabus and lesson plans based on needs analysis, (4) preparation of a draft model of teaching materials of poetry based on the lesson plans, needs analysis, and theory of poetry, and (5) test experts and field trials which showed that the model of teaching materials of poetry have a criteria is very good to use.
Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime &amp; anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO.
Cervical Cancer Cell Identification &amp; Detection Using Fuzzy C Mean and K nearest Neighbor Techniques

Across the globe, woman has been diagnosed two major forms of cancer, in which one is identified as cervical cancer and its micro classification. Morphology changes in cells or dead nucleus in the cervix causes cervical cancer. These cells are characterized with multiple nucleuses, faulty &amp; lack of cytoplasm and so on. Detection of cervical cancer using smear test is extremely challenging because such cells does not offer texture variations or any significant color from the normal cells. Therefore for identification in abnormality of cells we required high level Digital image processing technique which compromises an automated, comprehensive machine learning skills. An advanced Fuzzy based technique has been implied to separate nucleus and cytoplasm from the cell. KNN is instructed with the color features and shape features of the segmented units of the cell and then an unknown cervix cell samples are classified by this technique. The proposed technique gives shape and color features of nucleus and cytoplasm of the cervix cell.
About this title - Metamorphic Geology: Microscale to Mountain Belts

In Earth evolution, mountain belts are the loci of crustal growth, reworking and recycling. These crustal-scale processes are unravelled through microscale investigations of textures and mineral assemblages of metamorphic rocks. Multiple episodes of metamorphism, re-equilibration and deformation, however, generally produce a complex and tightly interwoven pattern of microstructures and assemblages. Over the last two decades, the combination of advanced computing and technological capabilities with new concepts has provided a vast array of novel petrological tools and high-resolution/high-sensitivity techniques for microanalysis and imaging. Such novel approaches are proving fundamental to untangling the enigma represented by metamorphism with an unprecedented level of detail and confidence. As a result, the first decade and a half of this century has already seen the tumultuous development of new research avenues in metamorphic petrology. This book aims to provide a timely overview of the state of the art of this field, of newly developed petrological techniques, future advancements and significant new case studies.
Cyberspace And Women: A Research

With the fast-growing reach of the internet, speedy spread of mobile information and the escalated use of social media, our lives being inextricably mixed with online media has purported the emergence of crimes committed in cyberspace. This advent of technology has led to the emergence of cyber violence against females of all age groups which is posing a problem at the global level as it causes danger to the security, dignity and privacy of a human being as a whole. The cyber space has a virtual reality in which the criminals commit crimes by faking their identities and then hide in the same space provided by the internet. The legal structure of India is not yet well-equipped to deal with cybercrimes and also the lack of awareness leads to the unstoppable commission of these offences. The paper discusses the major reasons for the growth of cyber violence against women in the arena of cyber socialisation. This paper also recognizes the common types of cybercrimes against women, which are namely cyber stalking, cyber pornography, circulating images or video recordings of females becoming involved in intimate acts, morphing, sending obscene or defamatory messages or e-mails, blackmailing or threatening, trolling or bullying and impersonation. The paper discusses the substances of every type of offence, analyse the relevant laws and highlight the cases and their judgements on this matter.This paper seeks to introspect the gaps between cybercrimes against women and the laws made to protect them. This paper will also recommend the steps that are required to be taken up in order to mark cyber crimes against women and girls in a holistic and efficient way.
Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning

Covid-19 pandemic urges countries to limit interaction of their people to reduce transmission. Indonesia requires people to do activities at home, one of which is online school. Many people share their thoughts through social media Twitter. Therefore, authors conducted sentiment analysis using supervised machine learning algorithm to determine distribution of words used in commenting on online schools, relationship between sentence, length and sentiment, and best algorithms that can be used to get most accurate results. In this study, authors used the method of crawling with RapidMiner to get data from Twitter. Then authors do data cleansing, data processing with classification methods using Random Forest Classifier , Logistic Regression , BernoulliNB and SVC algorithm. After that authors evaluate using confusion matrix, accuracy rate and classification report. In this research, authors found there are positive, negative, and neutral sentiments expressed on the online school implementation through comments. Authors ranked top three most used words used to express positive sentiments which includes bahagia, rajin and senang. On negative sentiments, top three words are capek, muak and bosen. On neutral sentiments, top three words are tidur, capek, and buka. Lengthy Tweets are usually imbued with negative remarks. On the other hand, the tweet tends to be positive and neutral tweet is usually stable. Authors conclude that the weakness of online school is the amount of workload that makes students tired alongside ineffective teaching method which makes it hard for students to understand the material given by school. However, on the positive side, some people agree with policies that are implemented and they feel like they gained some benefits from the implementation. From the four supervised machine learning algorithms that have been tested, Logistic Regression shows the highest accuracy, 0,87. The analysis shows that society tends to be neutral to the implementation of online school.
Performance Analysis of Sparse Matrix Representation in Hierarchical Temporal Memory for Sequence Modeling

Hierarchical Temporal Memory (HTM) is a special type of artificial neural network (ANN), that differs from the widely used approaches.It is suited to efficiently model sequential data (including time series).The network implements a variable order sequence memory, it is trained by Hebbian learning and all of the network's activations are binary and sparse.The network consists of four separable units.First, the encoder layer translates the numerical input into sparse binary vectors.The Spatial Pooler performs normalization and models the spatial features of the encoded input.The Temporal Memory is responsible for learning the Spatial Pooler's normalized output sequence.Finally, the decoder takes the Temporal Memory's outputs and translates it to the target.The connections in the network are also sparse, which requires prudent design and implementation.In this paper a sparse matrix implementation is elaborated, it is compared to the dense implementation.Furthermore, the HTM's performance is evaluated in terms of accuracy, speed and memory complexity and compared to the deep neural network-based LSTM (Long Short-Term Memory).
Secret Image Sharing using 2-Pixel Visual Cryptography Encryption

Visual Cryptography Scheme (Vcs) Is A Procedure For Encrypting Visual Data Such As Hand-Written Text, Image, Digital Text, Captured Image Etc. In This Scheme, The Shares Are Generated By Scrambling The Pixels Of The Original Image. Further, These Shares Are Sent To Receiver For Decryption At The Receiver End. In this paper, we have proposed an automated tool that takes the binary image as an input, and generates initial shares with one pixel encryption algorithm. Further, these initial shares are encrypted using 2-pixel encryption algorithm. The encrypted shares are then sent to the receiver for decryption to obtain the initial shares. The receiver then stacks the shares to get the original image. The proposed work which is presented in the methodology is the extension of VCS algorithm.
WAVELET PACKET-GAUSSIAN PROCESS REGRESSION MULTIVARIATE AND UNIVARIATE MODEL FOR FORECASTING DAILY SOLAR RADIATION

In recent years, the accuracy of the prediction of daily global solar radiation (GSR) has received great attention by researchers. In this study, hybrid models using the Gaussian process regression algorithm, wavelet, and wavelet packet decomposition (W, WPD) GPR have been proposed to predict daily solar radiation in Ghardaia city (Algeria). In addition to the hybridization methods, the problem is examined initially by entering meteorological data as input and is extended to the univariate case in the process of estimating the optimum model. For this purpose, three years of data series (2013-2015) have been used in model training, while data from 2016 were used to validate the model. The results showed the effectiveness of the hybrid model, especially WPD-GPR; compared to the classic GPR model, this model gave excellent results when using univariable inputs in terms of root mean square error (RMSE = 0.58 MJ/m2 day), relative root mean square error (rRMSE = 2.83%), mean absolute error (MSE = 0.34 MJ/m2 day), and determination coefficient (R2 = 99.54%).
Anomalous Human Activity Recognition in Surveillance Videos

This paper is a survey on different approaches for Human Activity recognition which has utmost significance in pervasive computing due to its many applications in real-life. Human-oriented problems such as security can be easily taken care of by detecting abnormal behavior. Accurate human activity recognition in real-time is challenging because human activities are complicated and extremely diverse in nature. The traditional Closed-circuit Television (CCTV) system requires to be monitored all the time by a human being, which is inefficient and costly. Therefore, there is a need for a system which can recognize human activity effectively in real-time. It is time-consuming to determine the activity from a surveillance video, due to its size, hence there is a need to compress the video using adaptive compression approaches. Adaptive video compression is a technique that compresses only those parts of the video in which there is least focus, and the rest is not compressed. The objective of the discussion is to be able to implement an automated anomalous human activity recognition system which uses surveillance video to capture the occurrence of an unusual event and caution the user in real-time. So, the paper has two parts that include adaptive video compression approaches of the surveillance videos and providing that compressed video as the input to detect anomalous human activity
A Novel Approach of De duplication of Records using Febrl Algorithm and Data Mining

Record linkage is that issue about recognizing comparative records over distinctive information sources.The similitude the middle of two records will be characterized In light of domain-specific comparability capacities over a many attributes.One of the information set is De-duplicating or linking a many information sets are enhancing significant tasks in information preparation phases for numerous information extracting activities.Those point will be will match the sum records identifying with those same substance.Diverse measures need been used to describe the nature and unpredictability about information linkage algorithms, Furthermore a few new measurements need been suggested.A review of the problems included in evaluating information linkage of de-duplication caliber and unpredictability.Matching tree is utilized to succeed correspondence overhead & provide for matching selection illustration got by utilizing that conventional linkage technique.Formed novel indexing systems for versatile record linkage and also de-duplication systems under febrl structure that examines about learning strategies for accurate &effective indexing.
PENGARUH DUKUNGAN ORANG TUA DAN OPTIMISME MASA DEPAN TERHADAP MOTIVASI BEPRESTASI SISWA EKONOMI

The study aims to find out and analyze: Parental support, optimism for the future toward achievement motivation Student at SMAN 2 Solok. The type of this research was a correlational. The technique of sampling in this study was the simple random sampling, with a sample of 162 students. Data collected through in terviews, questionnaires, and documentation as a reference in research. Method analysis was the Path analysis. classified in to quantitative study using survey research methods. To test the hypothesis by using test. The results showed that: 1) Parental support significantly influence the achievement motivation. 2) optimisme future significantly influence the achievement motivation. 3) the Parental support significant effect the optimisme future.Keyword: Parental support, Peer social support, optimisme future, achievement motivation
Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.
Sepsis Detection Using Missingness Information

Sepsis is one of the leading causes of death in hospital.An early detection is crucial to reduce its consequences and mortality.The challenge of Computing in Cardiology 2019 is addressing this issue by providing about 40,000 records from intensive care unit patients.As clinical measurements are collected at irregular frequencies, this dataset is missing many observations.Simply discarding missing values is counterproductive.Indeed, it has been observed that missing data patterns hold relevant information regarding the patient health state.To take advantage of this information, we propose a sepsis detection model incorporating representations of missingness information.This model is a recurrent neural network network composed of two gated recurrent unit (GRU) layers to capture long-term dependencies and a sigmoid layer to output a probability of sepsis.First, the model is trained by simply imputing missing values in the dataset.Then, the dataset is extended with the pattern of missing values.Finally, the model with a modified GRU cell taking into account missing data is evaluated.Our best model achieves an utility score of 0.00 on the final test set.
Sentimental Classification of News Headlines using Recurrent Neural Network

Sentiment analysis combines the natural language processing task and analysis of the text that attempts to predict the sentiment of the text in terms of positive and negative comments. Nowadays, the tremendous volume of news originated via different webpages, and it is feasible to determine the opinion of particular news. This work tries to judge completely various machine learning techniques to classify the view of the news headlines. In this project, propose the appliance of Recurrent Neural Network with Long Short Term Memory Unit(LSTM), focus on seeking out similar news headlines, and predict the opinion of news headlines from numerous sources. The main objective is to classify the sentiment of news headlines from various sources using a recurrent neural network. Interestingly, the proposed attention mechanism performs better than the more complex attention mechanism on a held-out set of articles.
Performance Improving of ANN with Preprocessing Stage in Human Face Expression Recognition System

In many face recognition systems, the important part is face detection. The task of detecting face is complex due to its variability present across human faces including color, pose, expression, position, and orientation. So, by using various modeling techniques it is convenient to recognize various facial expressions. The system proposed consists of three phases, the facial expression database, pre-processing and classification. To simulate and assess recognition efficiency based on different variables (network composition, learning patterns and pre-processing), we present both the Japanese Female Facial Expression Database (JAFFE) and the Extended Cohn-Kanade Dataset (CK+). Comparative approaches of data preprocessing include face detection, translation, normalization of global contrast and histogram equalization. Significant results were obtained with 85.52 percent accuracy particularly in comparison with some other pre-processing phases and raw data in single pre-processing phases. The result indicates the ANN classifier representation produces a satisfactory result which reaches more accuracy.
An Apriori Method for Topic Extraction from Text Files

In this data age peta-bytes of data is generated every day. One of the biggest challenge today is to convert this data into useful information, this is known as data mining. Important kinds of data include text-based data, audio-based data, image-based data, video-based data etc. An important challenge in mining useful information from text-based data source (text mining) is topic modeling which is to find out the topic the text is talking about. The solution to this problem finds application, in clustering files based on the topic, pre-processing method in information retrieval, ontology of medical record etc. A lot of research work has gone into this area of topic modeling, and many approaches have been formulated. Some of these approaches take into account the occurrence and frequency of occurrence of words/terms, these models come under the Bag Of Words(BOW) approach. Others take into account the underlying structure in the corpus of text used, Wikipedia category graph is an example of this approach. This paper, provides an unsupervised solution to the above problem by extracting keywords that represent the topic of the text document. In our approach, topic modeling is carried out with a hybrid model which makes use of WordNet and Wikipedia Corpus. Promising experimental results have been obtained for well- known news dataset (BBCNews) from our model. We present the experimental result for our proposed approach along with the results of others in the same domain and show that our approach provides better results.
Semiotic Analysis and Persuasive Discourse to the Billboard of Legislative Candidates 2019 In Indonesia

This study aims to scrutinize the utilization both of visual and written language toward the billboard of legislative candidates in the general election 2019. The sources of the data are 2 billboards from 9 parties winning the general election 2019 in Indonesia and the data that has been taken by the researchers are slogans and photos which have been deployed on the billboards. This qualitative research uses the method of descriptive analysis. On billboard 1 (Golkar), the dominance of the persuasive discourse is a wise clause which has been deployed to build a good reputation about legislative candidates. Meanwhile on billboard 2 (PKS), the dominance is the invitation which indicated that the legislative candidates directly invited the people to choose them. Through the visual and written language, the legislative candidates showed their identities strongly. Briefly, these billboards which showed in this research contained the strong persuasive language which combined both of written and visual languages.&#x0D; Keywords: Language, Persuasive, Visual, Parties
Interestingness Calculation for Cricket Summary Generation

This paper analyses the way how to make cricket summary automation more efficient. Thus, from the score card details, by adding various interestingness factors to make the summary more interesting to the user is the solution. That can be achieved by considered both the history of the teams and the current match details and analyzed by the system.