Reviewerly/exHarmony huggingface dataset:
Pengembangan Sistem Informasi Asosiasi Jasa Konstruksi dengan Menerapkan Tanda Tangan Digital

Currently, Indonesian government is changing the government system into an Sistem Pemerintahan Berbasis Elektronik (SPBE) or often heard as e-government. With this change in the government system, it has an impact on various sector of life. One of many sectors is construction service sector. LPJK as a non-structural institution under Ministry of Public Works and Public Housing issued a letter to the construction services association regarding the development of integrated application with SIKI LPJK. LPJK and OSS institutions has implemented digital signatures on business entity licensing document. Construction service associations has responded to develop of these regulations by creating an association information system application that implements digital signatures. This research was conducted to apply an digital signatures to the validation of the Membership Card using the secure hash algorithm (SHA) and advanced encryption standard (AES) methods generated through the association information system. This application generates an digital signatures which is implemented with QR Code. The existence of this application is expected to be a form of support for the government which is making changes to the government system.
EVALUASI PENGENDALIAN INTERNAL SISTEM INFORMASI AKUNTANSI ATAS SIKLUS PENJUALAN DAN PENERIMAAN KAS (Studi Kasus pada PT GEOFF MAKSIMAL JAYA)

This study aims to examine and find about implementation of internal control evaluation of Accounting Information Systems (Case Study at PT Geoff Maksimal Jaya. Sales and cash receipts are one of the most important components for a company and are very easy to abuse. The purpose of this study is to evaluate the weaknesses and strengths of the internal control system of the accounting information system on cash receipts for the sales cycle at PT Geoff Maximal Jaya. This research is a qualitative research using an evaluation program by comparing it with internal control based on COSO theory. The results of this study are the internal control of cash receipts over the sales cycle is quite good, but there are still aspects that are not in accordance with COSO theory, namely the merger between the sales function and the credit function.&#x0D; Keywords: Internal Control, Sales and Cash Receipt Cycle, COSO
Keyword Extraction from Arabic Text using the Page Rank Algorithm

This paper describes how keywords are extracted from Arabic text using the page rank algorithm, by constructing a graph whose vertices are formed by candidate words that are extracted from the title and the abstract of a given Arabic text after applying a tagging filter to that text. Next, a co-occurrence relation is applied to draw the edges between the vertices within specified window sizes. Then, the page rank algorithm is applied to the graph to rank the importance of each keyword. Finally, the vertices are sorted in descending order by their page rank scores and the tokens with highest scores are chosen as the keywords. Several experiments were conducted on a dataset that consisted of 100 Arabic academic articles for training and 50 for testing. The results were evaluated by using precision, recall, and the F-measure. The maximum recall achieved on the dataset was 63%, as not all the manually identified keywords and keyphrases existed in the article abstracts and titles. The proposed method achieved 25% of recall, which is acceptable as it is comparable to that of a method in the literature that was applied to an English language testing dataset that consisted of 500 English documents, which achieved 42% of recall where the maximum recall percentage of the testing dataset was 78%. Despite the difficulties and challenges in searching for keywords in the Arabic language and using fewer documents in the Arabic testing dataset than in the English, it can be concluded that the proposed keyword and keyphrase extraction system using the page rank algorithm works well.
Feature Selection towards Soil Classification in the context of Fertility classes using Machine Learning

Soil is recognized as one of the most valuable entity which is responsible for sustaining life on the earth. It is clear that Indian economy is largely dependent on agriculture. In India, for a large section of the population the sole or major source of earnings is agriculture. There are many factors which are responsible in yield production and also affect agriculture. Soil is one of them and plays a crucial role in yield production. Soil nutrients are important aspects that contribute to soil fertility. In this paper we classify soil in the form of fertility index level using machine learning. We induced rule model on training data and apply this model on test data for making predictions for fertility level classes. In this paper we have used Rough Set method to classify the data based on fertility level class and calculate the accuracy of the used classifier.
On Probabilistic λ-Calculi

Church's λ-calculus has become a universally accepted model of pure functional programming, and its properties have been thoroughly scrutinised by the research community in the last 90 years. Many variations of it have been introduced for the sake of capturing programming with various forms of effects, thus going beyond pure functional programming. This chapter is meant to be a gentle introduction to a family of such calculi, namely probabilistic λ-calculi, in their two main variations: randomised λ-calculi and Bayesian λ-calculi. We focus our attention on the operational semantics, expressive power and termination properties of randomised λ-calculi, only giving some hints and references about denotational models and Bayesian λ-calculi.
2018 International Conference on Computer Information Science and Application Technology

It was a great pleasure to welcome all the participants from different countries to the 2018 International Conference on Computer Information Science and Application Technology (CISAT 2018) in Daqing, China during 7th-9th December, 2018. This three-day conference was hosted by Northeast Petroleum University, China, and supported by American Industrial Technology Research Centre, Northeast Normal University.
Process Optimization of Big-Data Cloud Centre Using Nature Inspired Firefly Algorithm and K-Means Clustering

During the last decade, the growth of big data is immeasurable in information technology. Big data has the potential to take all the decisions necessary for a company or business. But it has many challenges as well. As its size and volume are immeasurably ample it is a very challenging task to store, process and mines it. At the same time as a boon to it cloud computing has a large capacity to store this big data and provides tremendous processing power. It is a challenging task to process large amount of data frequently in the big-data cloud center through the thousands of interconnected servers. Due to the day by day growth of the big-data, big-data cloud center is forced to improve its Quality of Service (QoS) metrics like throughput, latency and response time. Hence, to develop an optimal data processing optimization method is a current research problem that has to be solved. The major intention of this paper is to develop an application that provides maximum throughput, minimum latency and reduce the response time. Toward this, we have developed an optimization technique using nature-inspired firefly optimization algorithm and k-means clustering (FA-KMeans). The developed optimization method has been evaluated with state of art algorithms. Its experimental result elucidates that our proposed method provides good throughput, reduces latency and response time.
Studying the motives of volunteering among students

Е. А. Коган, Д
Preface

XIII International Conference on Mathematics, Science and Technology Education A E Kiv 1 , V N Soloviev 2 , S O Semerikov 2,3,4,5 , A M Striuk 3 , V V Osadchyi 6 , T A Vakaliuk 7,4 , P P Nechypurenko 2 , O V Bondarenko 2 , I S Mintii 2,4 and S L Malchenko 2,8 1 Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, 8410501, Israel 2 Kryvyi Rih State Pedagogical University, 54 Gagarin Ave., Kryvyi Rih, 50086, Ukraine 3 Kryvyi Rih National University, 11 Vitalii Matusevych Str., Kryvyi Rih, 50027, Ukraine 4 Institute of Information Technologies and Learning Tools of the NAES of Ukraine, 9 M. Berlynskoho Str., Kyiv, 04060, Ukraine 5 University of Educational Management, 52-A Sichovykh Striltsiv Str., Kyiv, 04053, Ukraine 6 Bogdan Khmelnitsky Melitopol State Pedagogical University, 20 Hetmanska Str., Melitopol, 72300, Ukraine 7 Zhytomyr Polytechnic State University, 103 Chudnivska Str., Zhytomyr, 10005, Ukraine 8 National Pedagogical Dragomanov University, 9 Pyrohova Str., Kyiv, 01601, Ukraine E-mail: kiv.arnold20@gmail.com, vnsoloviev2016@gmail.com, semerikov@gmail.com, andrey.n.stryuk@gmail.com, poliform55@gmail.com, tetianavakaliuk@gmail.com, acinonyxleo@gmail.com, bondarenko.olga@kdpu.edu.ua, irina.mintiy@kdpu.edu.ua, malchenko.svitlana@kdpu.edu.ua This paper represents a preface to the Proceedings of the XIII International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2021) held at the Kryvyi Rih State Pedagogical University, Ukraine, 12{14 May 2021. Background information and the organizational structure of the meeting, paper overview and acknowledgments of the contributions of the many people who made the conference a success are presented.
An Optimization of Feature Selection for Classification using Modified Bat Algorithm

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on University of California Irvine datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly. The significance of this research is it will show a great impact in selecting the best features out of all the existing features which gives best accuracy rates which helps in extracting the information from raw data in Data Mining Domain. The Value of this research is it will manage main fields like medical and banking which gives exact and proper results in their respective field. The best quality of the research is to optimize the selection of features to achieve maximum predictive accuracy of the data sets which solves both single variable and multi-variable functions through the generation of binary structuring of features in the dataset and to increase the performance of classification by using nature inspired and Meta Heuristic algorithms.
Research on Image Connection using Neural Networks

Image compression assumes a critical job in correspondence application, to expel the repetition from the Image information so that it permits a similar Image reproduction at the beneficiary end. Likewise, the neural system has turned out to be valuable in Image compression in light of their parallel engineering and adaptability. This Survey paper covers neural system based on Image compression technique. Image compression plays out a vital part in correspondence application, to decrease the excess of pixels from the Image, communicate cast and the transmission cost of Image information so that it permits a similar Image rebuilding at the beneficiary end. Image compression based on back engendering neural system and this is accomplished by separating the quantity of pixels of a Image and select one neural system for each square as per its multifaceted nature esteem. Back proliferation calculation is utilized to diminish the union time and enhance the execution of high compression proportion of Image. Additionally, neural system's parallel engineering and adaptability made it to progressively helpful in Image compression. In this study paper, we intentional different systems and will realize how neural systems are acclimatized in Image compression.
An Efficient and Robust Temporal Video Segmentation

Temporal video segmentation is the primary step of content based video retrieval. The whole processes of video management are coming under the focus of content based video retrieval, which includes, video indexing, video retrieval, and video summarization etc. In this paper, we proposed a computationally efficient and discriminating shot boundary detection method, which uses a local feature descriptor named local Contrast and Ordering (LCO) for feature extraction. The results of the experiments, which are conducted on the video dataset TRECVid, analyzed and compared with some existing shot boundary detection methods. The proposed method has given a promising result, even in the cases of illumination changes, rotated images etc.
Introduction to the Minitrack on Security and Privacy Challenges in Healthcare

The growing use of information technology in healthcare provides many opportunities for improving quality of care, better patient support, and health outcomes.However, it also opens the door to many individuals with malicious intentions who are looking for ways to compromise healthcare systems, data, and various medical devices.This minitrack provides a venue for researchers and practitioners to share their state-of-the-art and recent advancements in the field of healthcare security and privacy.These findings come as a result of the increasing need to protect patients' data through initiatives such as the General Data Privacy Regulation (GDPR) in the European Union or the integration of smart devices for healthcare monitoring and clinical decision-making.The following studies present a much needed analysis of the current issues in the field of security and privacy for healthcare and provide directions for future research contributions.
Correction: Treatment Adherence and Secondary Prevention of Ischemic Stroke Among Discharged Patients Using Mobile Phone- and WeChat-Based Improvement Services: Cohort Study

[This corrects the article DOI: 10.2196/16496.].
Efficient Anonymization Algorithm for Multiple Sensitive Attributes

The data of medical applications over the internet contains sensitive data. There exist several methods that provide privacy for these data. Most of the privacy-preserving data mining methods make the assumption of the separation of quasi-identifiers (QID) from multiple sensitive attributes. But in reality, the attributes in a dataset possess both the features of QIDs and sensitive data. In this paper privacy model namely (vi…vj)-diversity is proposed. The proposed anonymization algorithm works for databases containing numerous sensitive QIDs. The real dataset is used for performance evaluation. Our system reduced the information loss for even huge number of attributes and the values of sensitive QID’s are protected.
An Efficient Method to Extract Geographic Information

A ton of vital geographic information about spots, including points of interest, areas and personal information such as neighborhoods, phone numbers etc. can be found on the Internet. However, such information is not openly available using legitimate means. Furthermore, the given information is temperamental as it is static and not refreshed every now and again enough. In this paper, using the results of an internet list, an effective method to manage and collect datasets of spot names is demonstrated. The strategy proposed is to use the Google web crawler Application Programming Interface in order to recoup site pages related with express territory names and types of spots and after that analyses the resultant website pages to remove addresses and names of places. Using the data gathered from internet, the final result compiled is a dataset of spot names. We survey our philosophy by using accumulated data found using street view of Google Maps by examining signs belonging to businesses found in images. The conclusion exhibited by the results was that the modelled procedure efficiently created spot datasets on par with Google Maps and defeated the results of OSM.
Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload.However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life.In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC).UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user's internal similarity.The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions.Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.
Proposal to Conceive Multimedia Systems from a Value Creation Perspective and a Collaborative Work Routes Approach

Methodologies for the development of interactive multimedia systems traditionally apply the practices and lifecycles of user-centred design in conjunction with usability and accessibility practices guided by software engineering process models. This work contributes to the conception of multimedia systems through a new way of working using a set of work routes encouraging collaborative work. This new approach to conceive multimedia systems is centred on value creation and value-sensitive design, through a formal characterisation based on theoretical foundations. The results obtained from an evaluation of this definition, using a case study in an academic context, show that our scheme facilitates the conception and planning of factors such as the interactive multimedia experience and the responsible design of the solution.
A Covert Communication System Using Non-zero Mean Normal Distributions

A covert communication system is proposed in this study, in which a non-zero mean Gaussian sequence is used as a random carrier and its mean is modulated by a covert binary bit.The aperiodic transmitted signal exhibits the same statistical characteristics as the ambient noise to confuse an eavesdropper.The received signal is multiplied with the pseudo-random sequence synchronized with the transmitter to recover these positive and negative mean Gaussian sequence.The sample mean estimator and hard decision are used to determine the covert message, and accordingly, theoretical bit error rate in additive white Gaussian noise channel is also derived.Simulation results are very consistent with the theoretical derivation.The proposed system works in the physical layer with the advantages of simple structure, strong concealment, good BER performance and very suitable for low-cost, resource-limited and low-rate transmission devices.
On the Use of Geostatistical Cross-Association Method for Lithostratigraphical Correlation

The aim of this paper is to determine the effectiveness of cross association in detecting the similarity between correlated geological columnar sections.For this purpose, cross association is used to compare several geological columnar sections which are arbitrarily selected from different localities in central and north Jordan.It turns out, for most of the study cases, that the sections which consist of the same rock units (formations) are statistically classified as similar (p-value .05),while sections of different rock units (formations) are statistically classified as dissimilar (p-value .05).
Recognition of Roman Characters using Geometric and Regional Features

This paper presents the Roman Characters Recognition in clean and noisy environment using geometrical and regional features. The geometrical and regional features are extracted from the standard dataset and are combined to achieve better recognition. The combination of these features is classified using neural network (NN) and random forest (RF) classifiers. In our experiments, we have achieved the recognition accuracy of 100% for some characters. However, the average recognition accuracy of 85.7% has been recorded by using NN and 88% has been recorded by using RF classifier, respectively.
The Potential of Integrating Social Media in Saudi EFL Context

Language learning is going through a new dimension with advancement of new technologies. Social media integration with language learning has added a new insight in this field. The main objective of this study is to determine the position of social media in Saudi Arabia and then verify the probability of its use in language learning. This study is based on quantitative method using a questionnaire to collect data from 50 randomly chosen female students and 10 teachers of King Khalid University. Two different structured questionnaires consisted of eight questions based on different satisfactory level of Likert scale was used among the learners and teachers to know their opinion. Primarily it was assumed that most of the students would not agree to use social media for language learning due to their highly conservative socio –cultural background. However, later on it was found that majority of the students showed their interest to engage social media with language learning for the betterment of their learning skills. They also agreed that social media application would increase their practical knowledge related to real life situations communication. Still a few of them showed disinterest to these intermingling initiatives and thus disagreed with this endeavor. This study suggests a careful implication of suitable language learning features of social media with the EFL students and thus encourage them to be involved actively in language learning procedure.
The assessment of deep learning computer vision algorithms for the diagnosis of prostatic adenocarcinoma

for the diagnosis of prostatic adenocarcinoma