Reviewerly/exHarmony huggingface dataset:
Unsupervised hierarchical text summarization

Text Summarization is a process of shortening the longer text document(s) into a summary of informative sentences in an orderly way. Extractive summarization retrieves the important sentences without making any change to actual sentences. Each sentence in the document is represented as a vector of real numbers, which is termed as embedding. While embedding a sentence, values of specific features are analyzed and are plotted in n-dimensional space. This results in effective prediction of following and preceding sentences of the current sentence. Thus, the sentences with similar semantics lie closer to each other. Unsupervised Summarization groups the similar sentences by estimating the distance between the vectors and decides if sentence must be included in summary or not. Hierarchical Summarization constructs a tree-structure with the input text data, where the truncation of tree is done based on the number of clusters. Each cluster holds sentences that are semantically similar. By determining the nearest neighbor in each cluster, a specified number of sentences were retrieved from each cluster to be included in summary which holds at-least half the size of the input document(s). The performance of hierarchical summarization is measured on CNN/Daily Mail dataset and is determined using performance metrics. The evaluation score concludes that BIRCH algorithm performs better than the other Hierarchical Clustering techniques.
ANALISA KEPUASAN PEMBELAJARAN DARING MAHASISWA STMIK SINAR NUSANTARA SURAKARTA DI MASA PANDEMI COVID-19

The learning process during the COVID-19 pandemic is carried out online, students and lecturers carry out lecture activities at their respective homes. Online learning can be carried out using hardware devices (laptops, smartphones, internet) and application software (google classroom, google meet). With both students interacting in online lectures with lecturers, but from some information/news we get that students prefer to study offline rather than online. This study aims to determine student satisfaction in online learning seen from the components of hardware, software and lecturer involvement. The result is that the variable of lecturer involvement has a significant and positive effect on student satisfaction in online learning with a path coefficient value (β) = 0.565. SmartPLS also produces a determination coefficient value of 39%. Exogenous variables affect endogenous variables.
Mengenalkan Peran Keluarga dalam Meningkatkan Motivasi Belajar Anak di Desa Kuta Parit Kabupaten Langkat

The purpose of implementing this service activity is to provide education and understanding of the role of the family and children's learning motivation so that they can help parents improve children's learning outcomes. The method applied in the service is discussion and finding solutions. The result of this service is that currently participants have an understanding of the role of family and learning motivation that can be implemented for their children, so that parents are able to increase their child's learning success and can find solutions when children are lazy to study.
Analisis Sentiment Pelanggan Terhadap Penilaian Produk Pada Toko Online Shop Amreta Menggunakan Metode Naïve Bayes Classification

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application, namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersis an amreta online shop store , based on the results of the identification of the problem, it was found that the fact was that many comments did not match the stars given so it can be said that the rating cannot represent that the store's performance is good or not. Therefore, to increase the profit of shop work, the amreta still needs to evaluate the store. In conducting an evaluation, the store needs to classify positive, negative or neutral comments. Analysis of customer sentiment towards product assessments in amreta online shop stores using the naive bayes classification method. The use of test data in this study was obtained from the sentiment of amreta online shop consumers as much as 2014 data,then the data was processed through the data cleaning process resulting in net data of 1899 data. Furthermore, the data preprocessing process is divided into 3 stages, namely Tokenize Data, Transform case and Stopword removal. After that, the analysis of data for the automatic labeling stage using Text Vectorize from the process obtained data division into 3 data groups of 71% or 1343 positive data, 3% or 52 negative data and 26% or 504 neutral data. furthermore, it is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model through automatic calculations. The results of the study can be concluded that the test data obtained have an accuracy level of 97.16% using the Naive Bayes Classification model.
Human psychological and biometric factor in the development and use of social engineering methods in peacetime and wartime

The subject of this article is the process of influence of social engineers on people by manipulating consciousness, emotions, feelings without and with the use of modern information technology processing and simulation of biological characteristics of mankind in peacetime and wartime. The purpose and objectives are: consideration of existing methods of social engineering such as phishing, trojan, pretexting, watering hole, betting, quid pro quo, persecution, reverse social engineering through the prism of human feelings: naivety, inattention, greed, gullibility, curiosity, belonging fear; Consideration of modern technologies: handwriting forgery - My Text in your Handwriting algorithm, voice substitution - Descript's Ovrlab service, artificial dialogue - Replika application and face replacement - Deep Fake technology, which can help fraudsters to achieve their own goals of obtaining confidential data and carrying out further attacks based on them, as well as manipulating the consciousness of the real perception of events in wartime. The methods used are: psychological and sociological research methods, such as observation, social engineering methods, forecasting methods based on analysis and conclusions about future trends. The following results were obtained. Associations of methods of social engineering with feelings to which this or that psychological influence of the social engineer is directed are created. The potential of using emerging technologies with social engineering techniques to carry out more effective and sophisticated attacks, taking into account the favorite effects of criminals: haste, surprise, confusion, feelings of pity, anxiety, excitement and so on. The possibility of threats related to the procedures of authentication and authorization of users of biometric data on the basis of the considered modern technologies of imitation of biometric data of mankind: handwriting, voice, face. The scientific novelty of the research results is as follows: the importance and necessity of careful treatment of confidential information that people disseminate about themselves in all aspects of their social life; identified the main feelings on which social engineers play to achieve their own goals and benefit; the potential of development of modern technologies of imitation of biometric data of people in efficiency of carrying out attacks by methods of social engineering is specified, efficiency of use of means and methods of social engineering during information wars for influence on unstable psychological state is proved.
Recognizing Traffic Signs with Synthetic Data and Deep Learning

Recently, in-depth learning about computer vision and object classification tasks has surpassed other machine learning (ML) algorithms. This algorithm, alike similar ML algorithms, requires a dataset for training. In most real cases, developing an appropriate dataset is expensive and time-consuming. Also, in some situations, providing the dataset is unsafe or even impossible. In this paper, we proposed a novel framework for traffic sign recognition using synthetic data and deep learning. The main feature of the proposed method is its independence from the real-life dataset, which leads to high accuracy in the real test dataset. Creating one-by-one synthetic data is more labor-intensive and costlier than providing real data. To tackle the issue, the proposed framework uses a procedural method, which gives the possibility to develop countless high-quality data that are close enough to the real data. Due to its procedural nature, this framework can be easily edited and tuned.
PERANCANGAN SISTEM INFORMASI PENILAIAN KINERJA KARYAWAN BERBASIS WEB PADA PT DWI WAHANA EKUALINDO JAKARTA

Web and internet-based information technology transforms all data-dispersed communication within theconcept of information system deployment. The spread of data in the form of information can be a contributionin making decisions based on the information that is diapat in order to achieve the desired goal. At PT DwiWahana Ekualindo in the Human Resource Department (HRD) there is no web-based information system inthe management of employee performance assessment data. The process of managing employee performancedata is still done manually, which is still typing one by one the employee performance values into the MicrosoftExcel application to perform the calculation of values. The results of the employee performance assessment canbe measured as a measure of career positions and labor wages or salaries every month. In addressing the issue,a web-based information system is required that makes it easier to manage employee performance assessmentdata and generates employee performance assessment results.
Management of Facilities and Infrastructure in Improving the Learning Process

Abstract: The background of this research is that every school has facilities and infrastructure, whether adequate or inadequate. Facilities and infrastructure need to be utilized and managed for the benefit of the learning process. This management is intended so that the use of facilities and infrastructure can running effectively and efficiently. Purpose: The aims of this study were to find out facilities and infrastructure management based on literature review from previous research from several countries around the world. Methodology: The method used in this research is human instrument, descriptive qualitative, case study, with retrieval data in the form of interviews, observations, and documentation. Results: The result of this review that already fulfilled by means of: requirements planning, procurement, use, maintenance, inventory, write-off and liability. For teacher can use a means and infrastructure for teaching, for school to have a facilities and infrastructure and can help a students and teachers for study, to other authors that further research using facilities and infrastructure of study applied to other materials. Facility management and infrastructure at SMP N 2 Sragi already according to management techniques, which include planning, procurement, Distribution, use and maintenance. Limitations: One of the factors that became obstacles and obstacles in this study was the time and place of research. Contribution: This research can contribute in Educational Institutions, school management, educational foundations, and also implemented by teachers in educational institutions. Keywords: 1. Management 2. Facilities and Infrastructure 3. Learning
Klasifikasi Teks menggunakan Genetic Programming dengan Implementasi Web Scraping dan Map Reduce

Classification of text documents on online media is a big data problem and requires automation. Research has developed a text classification system with pre-processing using map-reduce and web scraping data collection. This study aims to evaluate text classification performance by combining genetic programming algorithms, map-reduce and web scraping for processing large data in the form of text. Data collection was carried out by observing web-based scraping. Data was collected by reducing 8126 duplicates. Map-reduce has tokenized and stopped-word removal with 28507 terms with 4306 unique terms and 24201 duplication terms. Text classification evaluation shows that a single tree produces better accuracy (0.7072) than a decision tree (0.6874), and the lowest is a multi-tree (0.6726). For the acquisition of genetic programming support values with the multi-tree, the highest average support is 0.3854, followed by the decision tree with 0.3584 and the smallest single tree with 0.3494. In general, the amount of support is not in line with the accuracy value achieved.
Research on the Construction of Enterprise Accounting Data Analysis Platform Based on Cloud Computing

With the emergence of massive data inside and outside the enterprise, paying much attention to the processing and analysis of accounting big data can bring huge value-added value to enterprises, and adapt to complex and changing economic environment.Based on this, by analyzing the related theories of accounting big data, accounting information and cloud computing, we build a cloud computing storage module of big data analysis platform, and apply Apriori data mining algorithm based on association rules to deal with massive data of accounting.A comparative prediction of the financial status of a group shows that the maximum error is less than 8% compared with the actual results, thus verifying the reliability and superiority of the established accounting big data analysis platform based on cloud computing.
Rainfall Prediction Using Backpropagation with Parameter Tuning

Rainfall is one of the important elements in the process of weather and climate. The high intensity of rainfall every year can hamper the mobility of the population and the distribution of goods, especially in the port area. Rainfall prediction is needed to handle the impacts caused by high rainfall. The data was obtained from the website dataonline.bmkg.go.id with observations made by the Tanjung Perak Surabaya Maritime Meteorological Station. The prediction method uses an artificial neural network with Backpropagation. Autocorrelation function is used to determine the number of input neurons with the best features in the Artificial Neural Network. Rainfall data is divided into two parts,: January 2008 to December 2019 used for training data and January to August 2020 for testing data. The validation technique used is 10-Fold Cross Validation. The experiment uses parameter tuning of iteration and learning rate. The training process obtained the best learning rate was 0.2 and 1000 iterations with a MSE validation score of 0.02591.Finally, the testing process has a Mean Square Error value of 0.02769 and a percentage of true rain character of 62.5%.
A Software Development Lifecycle Case Study on: Diet Recommendation System based on User Activities

This paper focuses on the methods of software engineering which can be helpful to various people working on huge projects with teams. This paper considers diet application as a case study. The main aim of the proposed system (diet application) is to give its users a healthy and balanced diet. The application is made in Flutter to ensure it reaches most of the audience and maximum people can receive its benefits. This paper is mostly based on the process used to create the application and all the views, processes, and architecture of the same. Some of the methods which are mentioned in the paper are the implementation of product backlog for the proposed system in association with a planning poker activity. The paper also has various Unified Modelling Language (UML) views in order to explain the design, implementation, use case and deployment of the proposed system.
Preparation and Characterization of Mangrove (Rizhopora Mucronata) Charcoal-Epoxy Polymer Composite

Composite materials have been widely developed and applied for various purposes. Polymer composites have excellent properties on mechanical strength and chemical resistance. This study aims to determine the strength of composite made of mangrove charcoal and epoxy since mangrove charcoal has a good character and is abundantly available. The mangrove charcoal was added into the epoxy-hardener matrix with a volume fraction of 10%, 20%, and 30%. The charcoal size was 40, 60, and 100 mesh. The highest tensile strength was found with a volume fraction of 10% with an average of 19.33 N/mm 2 at a size variation of 100 mesh mangrove charcoal. The highest elongation at break was found with a volume fraction of 10% with an average of 5.233% at 100 mesh mangrove charcoal size.
Sistem Manajemen Pendistribusian Barang Menggunakan Metode Drp Pada Pt Niko Elektronik Indonesaia Berbasis Client Server

Niko Elektronik Indonesia is an electronics company located on Jl. Semarang Industrial Estate. This company distributes its products, via land transportation (cars). Until now PT. Niko Elektronik Indonesia still has problems with office applications, especially when it comes to the validity process and data retrieval. If certain data is needed, it will take a long time because the search will be done manually by looking for the data one by one in the files one by one. This allows duplicate data to occur because the writing of customer and supplier data is done repeatedly.&#x0D; Based on the above problems, the authors would like to propose a design of a Client-Server-based distribution system to be considered in helping speed up the process of recording purchase and sales transactions and can provide the information needed by PT Niko Elektronik Indonesia in the form of stock reports, sales level reports and daily income reports in the form of an Inventory System application at PT Niko Elektronik Indonesia using Visual Basic 6.0 and MySql database. Making this application is expected to be one of the efforts that can be made to overcome the problems that often occur so as to increase work effectiveness which of course will affect the quality of service to customers.
“S × UKILAM” Collaboration to Connect Local Digital Resources and School Education: Workshop and Archiving to Construct Network of “People” and “Data”

The purpose of this study is to construct a network of people and data to connect diverse local digital resources and school education. Accordingly, we propose the S × UKILAM (School × University, Kominkan, Industry, Library, Archives, and Museum) collaboration, in which school teachers and institutions that hold and publish resources cooperate to create learning materials. As part of the project, three national workshops were held involving participants from 83% of prefectures and 197 institutions. The questionnaire suggested the effectiveness of the workshops and also identified the problems encountered in utilizing the resources. The 62 diverse learning materials co-created based on the workshops were made available as a secondary-use archive, creating a database in a format that can be machine-readably linked to various learning contents and resources.
Design and Development of an Interactive Software for the Teaching-Learning of Fractal Compression of Images

In the study of courses with complex topics, as is the case of data compression, students experience difficulty in their learning due to the abstract nature of these theories and the absence of interactive digital resources. Fractal image compression seeks to deduce a set of functions, called iterated function system (IFS), which by iterations reconstructs the image to be compressed; In this way, compression levels are achieved that are not achieved using other techniques, because the image is considered compressed in the description of the functions that generate it, this is what is stored, and to recover the image, the functions are simply iterated. This article describes the design and development of interactive software that supports the teaching-learning of the fundamental concepts of fractal compression of images: contractive affine transformations, iterated function systems (IFS), IFS encoding (compression) and decoding (decompression) IFS.
Remote sensing study of cloud top absolute temperature with surface rainfall over Lahore (Pakistan) during monsoon

The study attempts to build a relationship between clouds top absolute temperature and rainfall during monsoon period over Lahore during 2019. For this purpose, meteorological data was taken from Pakistan Meteorological Department (PMD) of few parameters (temperature and rainfall) for Lahore during monsoon period, i.e., from July to September, 2019. The study revealed interesting results for the observed three months. In July and August, the rainfall and temperature trend showed an inverse relation, whereas a decreased trend was observed for both temperature and rainfall during September, 2019. In Pakistan, most of the rainfall is a result of the depressions created over Bay of Bengal and Arabian Sea. High temperatures cause lower pressure that becomes a reason for originating of low pressures/depressions resulting in monsoon rainfall. During September, the average temperature of the study area was lower comparatively that ultimately resulted in low rainfall, and only 54 mm of total rainfall was recorded over Lahore during September, 2019 which was quite less than in July and August. Satellite cloud top temperatures were also taken from EUMETSAT to establish a relation between cloud top absolute temperature and surface rainfall over Lahore.
Convolutional neural network optimisation to enhance ESPI fringe visibility

The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI).
Impact Online Learning on Students' Learning Ability in the Time of Covid 19 in Kelurahan Pabatu Kecamatan Padang Hulu Kota Tebing Tinggi

The coronavirus forces us to keep our distance and stay away from crowds which has an impact on children's learning methods. Furthermore, the government gives an appeal to do promotions at home or on the web. Web-based learning will find that is completed online by utilizing a web network where web-based learning affects student profits in learning. Students' interest in learning at the beginning of covid in 2019 - 2020 children did not have interest in learning, but once face-to-face 50% of students' interest in learning slowly increased and after 100% face-to-face it was much different from the time of covid compared to the period before covid which greatly decreased. This study aims to determine the effect of web-based learning on learning abilities during the COVID-19 pandemic in adolescents in Kelurahan Pabatu, Kecamatan Padang Hulu, Kota Tebing Tinggi. This exploration uses subjective examination with an interview, perception, and documentation techniques. The results of the study show that the impact of learning on students during online learning is the decline in children's learning abilities and some even cannot read and count in grade 3.