Reviewerly/exHarmony huggingface dataset:
Cross - Language based Multi-Document Summarization Model using Machine Learning Technique

Cross-Language Multi-document summarization (CLMDS) process produces a summary generated from multiple documents in which the summary language is different from the source document language. The CLMDS model allows the user to provide query in a particular language (e.g., Tamil) and generates a summary in the same language from different language source documents. The proposed model enables the user to provide a query in Tamil language, generate a summary from multiple English documents, and finally translate the summary into Tamil language. The proposed model makes use of naïve Bayes classifier (NBC) model for the CLMDS. An extensive set of experimentation analysis was performed and the results are investigated under distinct aspects. The resultant experimental values ensured the supremacy of the presented CLMDS model.
Machine-Learning Based Memory Prediction Model for Data Parallel Workloads in Apache Spark

A lack of memory can lead to job failures or increase processing times for garbage collection. However, if too much memory is provided, the processing time is only marginally reduced, and most of the memory is wasted. Many big data processing tasks are executed in cloud environments. When renting virtual resources in a cloud environment, it is necessary to pay the cost according to the specifications of resources (i.e., the number of virtual cores and the size of memory), as well as rental time. In this paper, given the type of workload and volume of the input data, we analyze the memory usage pattern and derive the efficient memory size of data-parallel workloads in Apache Spark. Then, we propose a machine-learning-based prediction model that determines the efficient memory for a given workload and data. To determine the validity of the proposed model, we applied it to data-parallel workloads which include a deep learning model. The predicted memory values were in close agreement with the actual amount of required memory. Additionally, the whole building time for the proposed model requires a maximum of 44% of the total execution time of a data-parallel workload. The proposed model can improve memory efficiency up to 1.89 times compared with the vanilla Spark setting.
Integration Into the Global Mainstream of Engineering Education Based on the Digital Paradigm

The economy digitalization has demanded the training of specialists with competencies for the development and implementation of digital technologies.The purpose of the study is to characterize a virtual workshop for training a process engineer.The methodological basis of the study was a digital paradigm that promotes the introduction of digital content adapted to modern requirements and associated with information bases of a restricted professional profile.The main results of the study are to determine the essence and content of the virtual workshop of mechanical assembly production, to clarify the criteria for the effectiveness of its implementation in the teaching process.The essence of the virtual workshop as a digital educational resource is aimed at developing professional skills of the 21st century; the content presents a complex multi-level menu.Efficiency criteria (digital, integrative, competence, adequate, productive ones) are determined by the didactic functions of the virtual shop.Research constraints constitute a random selection of respondents.The didactic functions of the virtual workshop (interactive, transdisciplinary, competence, organizational, evaluative ones) reflect the practical significance.The social significance lies in the introduction of digital content into the educational process that imitates professional activities.The originality of the study lies in substantiating the engineering education integration into the world mainstream based on the digital paradigm.
AN INTEGRATED FRAMEWORK FOR SLA-AWARE MULTI-OBJECTIVE TASK SCHEDULING IN CLOUD COMPUTING

Cloud services are offered to consumers based on Service Level Agreements (SLAs) signed between Cloud Service Provider (CSP) and consumer. Due to on-demand provisioning of resources there is exponential growth of cloud consumers. Job scheduling is one of the areas that has attracted researchers to improve performance of cloud management system. Along with the on premise infrastructure, Small and Medium Enterprises (SMEs) also depend on public cloud infrastructure (leading to hybrid cloud) for seamless continuity of their businesses. In this context, ensuring SLAs and effective management of hybrid cloud resources are major challenging issues to be considered. Hence, there is a need for an effective scheduling algorithm which considers multiple objective functions like SLA (deadline), cost and energy while making scheduling decisions. Most of the state of the art schedulers in hybrid cloud environment considered single objective function. However, in real world, it is inadequate for scheduling effectiveness. To overcome this problem, we proposed an integrated framework which ensures SLAs (deadline), cost effectiveness and energy efficiency with an underlying scheduling algorithm known as SCE-TS. This algorithm is evaluated with different workloads and SLAs using a cloud platform. The empirical study revealed that the proposed framework improves scheduling efficiency in terms of meeting SLAs, cost and energy efficiency. It is evaluated and compared with the state of the art and found to be effective in making scheduling decisions in cloud environment.
ALPHABETS IMAGE IDENTIFICATION USING ADVANCED LOCAL BINARY PATTERN AND CHAIN CODE ALGORITHM

Optical Character Recognition (OCR) is a method for computer to process an image that contains some text and then try to find any characters in that image, then convert it to digital text. In this research, Advanced Local Binary Pattern and Chain Code algorithm will be tested to identify alphabets in the image. Several method image preprocessing are also needed, such as image transformation, image rescaling, grayscale conversion, edge detection and edge thinning.
Enhanced Geospatial Functionality for NOAA's Ocean Exploration Data

National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) is transitioning its suite of ocean exploration geospatial products and services into NOAA's GeoPlatform - an instance of ArcGIS Online. This cloud-based platform supports accelerated data visualization, spatial analyses, collaborative workspaces (e.g., groups), and map product creation. NCEI hosts ocean exploration data access layers, multiple bathymetric image services, and various remotely operated vehicle (ROV) feature services. NCEI also supports near real-time operation tracking for NOAA Ship Okeanos Explorer via a web map as well as a 3D web scene compiling all ROV-related services. NCEI plans to build upon these initial services as NOAA Office of Ocean Exploration and Research (OER) expands its exploration efforts.
BAR CODE RECOGNITION IN STRONG DISTORTED AND LOW-RESOLUTION IMAGES

Almost all products and items that exist on the market today have a unique code or identifier associated with them. We call this special identifier a barcode. Barcode detection is very common and is a necessity today as it makes our job easier. You can simply scan the code and easily get all the product information. Barcodes are still the prevalent machine-readable encoding mechanism for most products and services. At this stage of development, the barcode was quite common. Most items and products present in the household now have their own unique barcodes. They can be scanned and easily get all the necessary information, such as price, product details and description. There are even various apps available to scan the barcode through our phones. This is due to the high-quality pixel cameras that are present in all iPhones, android devices, or windows phones. Today, a wide range of portable devices for general use, such as mobile phones, come with an optical imaging system. Providing these general-purpose portable devices with barcode recognition is a cost-effective alternative to conventional barcode scanners. The equipment is available to billions of people. Combined with new services, this could revolutionize day-to-day shopping, including checking in-store prices, getting product information, accessing product reviews, finding similar products and services, and comparing prices locally. Detecting barcodes in images captured by general-purpose portable devices is particularly challenging due to the limitations of the integrated imaging system and on-device processing capabilities. These devices often have lower quality lens systems and lower resolution imaging circuits when compared to dedicated digital cameras. The optical system is often not designed for shooting close to the lens, resulting in out-of-focus and poorly lit images with rich colors. These limitations often mean that using conventional threshold detection methods cannot create a suitable template for barcode recognition. These limitations need to be considered in an algorithm that can invariably be implemented on a wide variety of portable devices. Barcode reading must be resistant to harsh conditions such as blur, noise, low resolution or low-quality camera lenses, which are very common. Surprisingly, even the most advanced barcode reading algorithms fail when some of these factors come into play. In this article, we present a new approach to barcode image detection. Our algorithm is specially designed to recognize barcodes when an image may be of low resolution, low quality, or suffer from significant blur, defocus, uneven lighting, noise and color saturation. The algorithm is accurate, fast, scalable and can be easily configured to find a valid result within a time constraint. Our algorithm is useful for real-time barcode recognition on handheld devices with limited processing capabilities such as mobile phones.
Predictive Analysis using Convolution Network on Sentiment Analysis of Text Classification using Machine Learning

Today we have large amounts of textual data to be processed and the procedure involved in classifying text is called natural language processing. The basic goal is to identify whether the text is positive or negative. This process is also called as opinion mining. In this paper, we consider three different data sets and perform sentiment analysis to find the test accuracy. We have three different cases- 1. If the text contains more positive data than negative data then the overall result leans towards positive. 2. If the text contains more negative data than positive data then the overall result leans towards negative. 3. In the final case the number or positive and negative data is nearly equal then we have a neutral output. For sentiment analysis we have several steps like term extraction, feature selection, sentiment classification etc. In this paper the key point of focus is on sentiment analysis by comparing the machine learning approach and lexicon-based approach and their respective accuracy loss graphs.
Automatic detection of cyberbullying on social media platforms

The presence of cyberbullying on the Internet has grown alarmingly in recent years.Teenagers and children are the most affected by this phenomenon that is often the cause of higher suicide rates and social isolation.The detection and prevention of cyberbullying depends firstly on its correct understanding and secondly on the correct selection of a classification model trained on features that have a high discrimination factor between cyberbullying and noncyberbullying.In this paper, we aim to create an automatic detection model for cyberbullying posts that is not biased towards a specific social media platform or a certain type of bullying.We describe the method we used for selecting the best features for two different classifiers trained on datasets collected from Twitter and Formspring.Next, we explain how we use the predictions made by these classifiers for labelling a new dataset collected by us from Twitter.The results of the automatic classification of the dataset have been compared to the manual classification of a sample of data from it, resulting in a rate of agreement larger than 50% between automatic detection and human annotation.
PENERAPAN METODE COOPERATIVE LEARNING TIPE GROUP INVESTIGATION UNTUK MENINGKATKAN HASIL BELAJAR DALAM BAHASA INGGRIS PADA KELAS XI BOGA SMK NEGERI 2 PARIAMAN SEMESTER 2 TAHUN PELAJARAN 2016/2017

This type of research is qualitative research as the subject in this study is 37 students of class XI Boga SMKN 2 Pariaman consisting of 36 female students and 1 male student. In conducting this research, researchers examined the implementation of learning by applying the cooperative learning model type Group Investigation. Based on the results of research carried out at SMKN 2 Pariaman in class XI Boga students in the second semester of the 2016/2017 academic year with the application of the Cooperative Learning Model Group Investigation type, it is known that there is an increase in learning outcomes obtained by students in cycle I on average 70.6 to increase in cycle II, the class average score reached 81.84 above the KKM 75
Using and comparing Rhetorical Structure Theory parsers with rst-workbench

I present rst-workbench, a software package that simplifies the installation and usage of numerous end-to-end Rhetorical Structure Theory (RST) parsers. 1 The tool offers a webbased interface that allows users to enter text and let multiple RST parsers generate analyses concurrently.The resulting RST trees can be compared visually, manually post-edited (in the browser) and stored for later usage.
Pengembangan Media Pembelajaran Berbasis Augmented Reality Book Simulasi Perkembangbiakan Hewan Pada Mata Pelajaran IPA Studi Kasus Kelas VI- SD Negeri 4 Suwug

The purpose of this research is to develop learning media based on the Augmented Reality Book of Animal Breeding on the Android platform, as one of the media to facilitate and provide it’s an attraction in understanding animal breeding material in Natural Sciences (IPA) lessons in SDN 4 SUWUG. The development of the Augmented Reality Book Animal Breeding application uses the ADDIE model which consists of five stages, namely the Analyze, Design, Development, Implementation, and Evaluation stages. In this model, an Evaluation process is carried out in each of the stages that are passed continuously to produce products that meet the needs in the field. The final result of this development is in the form of Augmented Reality Animal Breeding applications that can be used via mobile devices with the Android operating system. The final results of this study indicate that the application of Augmented Reality Animal Breeding is included in the criteria very well. The results obtained based on the black-box test obtained a percentage of success of 100%, white-box test obtained a percentage of success of 100%, content expert test, and media expert test were processed with Gregory Technique with an average of 1.00 calculation results if converted into a table of achievement levels expert validity criteria included Very High, and for the user response test with a percentage of 93.4% so that it falls into the rating criteria that is Very Good.
A Correlation Study of Reading Comprehension with Speaking Skill in English Class at Vocational High School

&lt;p&gt;Students in vocational high school are required to learn their vocational skills and English as a Foreign Language in preparation for their potential profession or job. This research investigates the relationship between reading comprehension and speaking skills in English classes and the degree to which reading comprehension relates to vocational high school students' speaking abilities. Thirty-five students from a vocational high school in Surabaya participated in the study. This study's design was a correlation study, and it was performed using a descriptive quantitative method. The students were given a test to assess their abilities. The results of this study indicated that there was a connection between reading comprehension and speaking ability. The correlation coefficient was 0.61, and the correlation was strong. The teacher should have greater access to what they are reading, and improved reading resources would aid their communication skills.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keyword: &lt;/strong&gt;Correlation, Reading Comprehension, Speaking Skill&lt;/p&gt;
Formation of reading competence of schoolchildren in the process of studying biographies of the writer

The article deals with the problem of the formation of reading literacy, reading competence of schoolchildren in the study of the biography of the writer in middle and high school.
Application Of Permissioned Blockchain For Automated, Efficient, Secure Cross Border Trade

There are lot of documents and confidential data involved in international trade. Banks offer financial services and also act as middleman for cross border trades. The verification process plays a vital role and it takes months to years, to complete. The major problem is to maintain integrity and confidentiality of the data shared, and to maintain fair trading process. Blockchain technology is introduced to increase the efficiency and security of the documents shared to the needed parties in the trade. Permissioned blockchain is used wherein the participants of the network are controlled and governed by the blockchain owner. Hyperledger fabric platform is used to implement the trade finance application. The main idea is to automate the verification processes in order to complete the trade financing within minutes to hours and to enhance security of the documents shared using Attribute Based Encryption (ABE) in cross border trades. The smart contracts in blockchain provides functionalities to assure that the information is tamper-free, auditable and verifiable.
Investigating Literary Terminologies to Accomplish Literary Research and Enjoyment: A Corpus Study

In relation to producing, enjoying, and especially criticizing literature, some literary terminologies are used frequently. Thus, corpus in the field of Literature is urgently needed to compile. This study aims to compile literary terminologies found in Literature and Language Teaching by Gillian Lazar and Literary Movements for Students by Ira Mark Milne books. In addition, it also tries to find the frequency, meaning (in context), and examples of the use of the terminologies. The method used in this research was document analysis, the data of which was obtained from predetermined documents, such as books of general literature, and books of theory and literary criticism. While the stages carried out were data collection, data selection, and presentation. From the books studied, it is found that the terminologies that appear could be categorized into terminologies related to (1) authors, (2) readers of literary works, (3) literary work itself, and (4) literary theory and criticism. Additionally, there is an interesting fact from the data found that the books have different frequencies of literary terminologies. The terminologies that appeared in Literary Movements for Students are more frequent.
Value Orientations of College Students with Different Status of Professional Identity

This study aims to identify the meaningful relationship of personal value orientations depending on professional identity status.The article presents the results of a study of students' value orientations of a pedagogical college with different professional identity status.The data obtained based on the conducted correlation and frequency analysis allow concluding that there are substantial and structural differences in the value sphere of students of a pedagogical college due to professional identity development level.The study results indicate that the value sphere of students with achieved professional identity is characterized by an integral structure, where the leading values are associated with selfleadership, self-actualization, and self-development.Among students with a crisis of professional identity, the value sphere is characterized by partial instability and an active search position concerning resources, support and means of self-development.For students with an indefinite and imposed professional identity, the value sphere's state reflects an insufficient readiness for professional development.The factors of the system formation of value orientations are the conditions promoting professional self-determination, as indicated by the simplified structure of students' values with an immature and emerging professional identity.
MODEL PERAMALAN JUMLAH PENYAKIT DEMAM BERDARAH DENGAN PENDEKATAN METODE FUZZY LINEAR REGRESSION (FLR)

Forecasting is very important in time series data, which is needed in the decision making process.This study predicts the number of dengue fever as the dependent variable output (Y) and the information factor for severe patient condition (X1) and mild condition X2 as independent variables.Time series forecasting uses fuzzy data to describe the data in the form of linguistic values.Fuzzy regression is used to correct errors in classical modeling.The result of conventional forecasting is that there is a difference between the observed value and the estimated value which is known as error, but in fuzzy regression it is known as system ambiguity.This study uses the Fuzzy Linear Regression (FLR) method to model the prediction of the number of dengue fever.This study aims to produce parameters in the form of intervals (upper bound and lower bound) in the forecast results, which can be used in decision making in all the best and worst possible outcomes.The results of the study are based on the interval by varying the value (h) = 0; 0.25; and 0.5; 0.75 was generated using the Time Series Fuzzy Linear Regression method with the smallest coefficient of determination equal to 0.238 at h = 0.25 with the smallest standard deviation error of 3.94.
POSSIBILITIES OF THE TEXTBOOK IN PROVIDING PEDAGOGICAL INTERACTION IN THE CONDITIONS OF DISTANCE LEARNING

The article analyses the features of virtual pedagogical interaction due to distance learning and analyses the possibilities of the textbook in its regulation. Virtual interaction creates conditions for taking into account the leading channels of information perception by students: audio, visual and kinaesthetic; leading style of educational activity: naturalistic, musicalrhythmic, logical-mathematical, verbal-linguistic, motor-mobile; temperament: choleric, sanguine, melancholic, phlegmatic; the level of educational opportunities of group members, etc. At the same time, there are additional opportunities in regulating the composition of groups of students, the division of responsibilities between group members, discussing the results of work and making joint decisions. The textbook in this process becomes a guide who helps to determine the required level of learning material, plan the sequence of its learning, master the methods of self-education, encourages obtaining practical results in educational activities and creativity. The advantages of printed materials include: maximum transportability; convenience; insignificant cost; accessibility. Many distance learning courses can use existing printed materials, saving time and money in developing your own materials. The use of electronic platforms on the Internet contributes to the expansion of the possibilities of using printed materials during distance education. New opportunities for distance learning are created by the Google Class platform. Distance learning involves a specific pedagogical system, which is characterized by the presence of almost all ways of organizing the interaction of participants in the educational process, in the form of dialogue. The effectiveness of their pedagogical interaction is due to a number of factors such as: the content of the discipline (content and purpose of training), background factors (size of the study group, level of intellectual development, ethnicity, etc.), information and communication and other means of communication, form of control.