Reviewerly/exHarmony huggingface dataset:
Multiple working conditions cobalt ion concentration prediction model based on correlate relation of different working conditions judged by fuzzy correlation factor

The cobalt removal process is an important step of Zinc Hydrometallurgy, outlet cobalt ion concentration is a significant indicator to manipulate the cobalt removal process and it can provide an important reference for on-site control. However, the cobalt ion concentration cannot be detected online and the test has hysteresis which leads to the delay of the control action or miss the optimal control time. In this paper, a multiple working conditions prediction model based on correlate relation of different working conditions judged by fuzzy correlation factor is proposed to predict the outlet cobalt ion concentration of the reactor. First, a mechanism model based on the material balance and chemical reaction principles is proposed. During the test, it was found that the accuracy of its prediction was reduced when the reaction state changed. Then, considering that the cobalt removal reaction process is greatly affected by the inlet flow rate, the cobalt removal process is divided into different working conditions according to the inlet flow rate of the reactor using K-means algorithm. Finally, considering the correlation coupling of different working conditions, the fuzzy correlation factor is proposed to describe the degree of relevance between the inset flow rate and the specific model, the result obtained by fusion of different models through fuzzy correlation factors is the final prediction result. The results demonstrate the model can predict the outlet cobalt ion concentration more accurately.
CYBER CRIME AND CYBER SECURITY AWARENESS AMONG STUDENTS: A COMPARATIVE STUDY IN ISRAEL AND SLOVENIA

In this paper we present the results of a survey that examined the attitude of users to cyber crime as well as their awareness of cyber security threats.The study was conducted among Israeli and Slovenian students.The study results reveal significant differences in the level of cyber awareness between evaluated groups of cyber users in both countries.The results may serve as the initial phase of a practical education approach aimed at evaluating the impact of cyber education on the level of cyber awareness of individuals.The study implication can also be utilized for the process of development courses to enhance individuals' awareness of cyber hazards and to reduce the level of cyber victimization amongst users.Finally, the paper's findings contribute to a better understanding of which factors are involved in the cyber awareness gap.
Research on Digital Forensics Based on Uyghur Web Text Classification

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.
Security Flaws and Design Issues in Cloud Infrastructure

Information security plays a vital role in cloud computing. Sensitive information should be kept in secure mode for providing integrity and confidentiality from insiders and outsiders. An insider is an employee who has legitimate access to cloud resources which are hosted at cloud data center. They can perform malicious activities on consumer sensitive data with or without malicious intent. This security beach is obvious and the provider needs to protect from such attacks. In this chapter, insider attacks are demonstrated with empirical approach to breach consumer-sensitive data. In this chapter, the authors present the threat models where an insider can manipulate user VMs in the node controller of cloud platform. Here, they assume that cloud service provider is malicious and cloud consumer does not have any security constraints to access their cloud assets. The model described two locations in the cloud infrastructure.
Securing Healthcare Information in Cloud

Due to massive growth of technologies in past few years healthcare professionals used to store their patients information in the form of Electronic Medical Record (EMR) which includes huge multimedia medical big data (MDB) such as X-rays, Ultrasounds, CT Scan, MRI Reports etc., in healthcare clouds. By means of healthcare cloud, a healthcare professional can be able to access the patient information in an efficient manner when the patient moves from one hospital to another. Even though, healthcare cloud provides easy access to patient data, security issues may happen that may be a leakage of data, privacy &amp; lack of transparency, etc. The main scope of this study paper is to discuss about various terminology, technology and techniques about the latest methods of cloud computing used in healthcare maintenance system
A secured modular exponentiation for RSA and CRT-RSA with dual blinding to resist power analysis attacks

Blinding has been one of the most effective approaches to resist power analysis attacks on asymmetric cryptosystems like RSA. Blinding is similar to masking in symmetric cryptosystems, but masking can be implemented in various ways like Boolean, affine, polynomial masking, etc. However, for asymmetric cryptosystems with modular exponentiation as a fundamental operation, arithmetic masking or simply blinding has been extremely popular. In this paper, we have presented a secured approach for modular exponentiation in RSA and CRT-RSA cryptosystems with dual blinding. Through dual blinding, we have masked both secret exponent and message twice before executing the fundamental operations. We have also injected two ineffectual instructions between the fundamental operations and blinded the intermediate results to felicitate hiding and resist simple power analysis. The implementation results shows that with a nominal penalty, RSA and CRT-RSA with dual blinding can effectively resist some popular simple power analysis and differential power analysis attacks to a significant extent.
From Service Providers to Collaborators and Partners: A Nondiscipline-Based Approach at a Liberal Arts College

Research libraries have been engaged in discussions of the functionalist approach versus the liaison model. While some have adopted a hybrid model that overlays functionalists with liaisons, few have abandoned the liaison structure. This article demonstrates a successful nonliaison approach that prioritizes teaching and focuses on building supportive organizational cultures. It discusses how the adoption of a nondiscipline- based approach has allowed the Lafayette College Library to integrate various forms of expertise, sustain and expand its services, and create a culture of innovation. The decentralized approach gives librarians freedom to pursue their own interests. Due to their work with faculty members from across academic departments and their deep and collective knowledge of faculty members’ research and teaching interests, librarians have been able to cultivate meaningful collaborations. Through teamwork and sustained engagement with faculty members they have built a vibrant information literacy program and a community of Digital Humanities practice.
Querying Big Data Clusters Through T-SQL

Now that we have some data to play with, let’s look at how we can process and query that data through the multiple options provided through Azure Data Studio.
THE IMPACT OF MOBILE APPLICATIONS ON MATERNITY EDUCATION: A QUANTITATIVE STUDY OF KENYA

Maternal health mortality refers to death that occurs during pregnancy, pregnancy termination, at birth, or within the first two months of the birth.According to World Health Organization (2015), the average number of maternal deaths in sub-Saharan Africa was 239 for a population of 100,000 people, compared to 12 in the developed countries in 2015 despite that health organizations worldwide had aimed to reduce the maternal mortality ratio by 75% from 1990 to 2015.To reduce maternal mortality in Africa, this research developed a mobile health application system that provided maternal health information and education to pregnant mothers in rural Kenya so that mothers could identify problems early on in their pregnancies.A quantitative survey of these mothers showed high satisfaction, usability, and systems effectiveness ratings of the mobile health system.These insights will benefit policy makers to promote mobile health to help improve maternal education and reduce maternal mortality.
Distribution of the Library's Product: The Need for Innovation

Marketing as a conceptual structure was nearly totally unknown to the library community a decade ago. It has been convenient for marketing scholars to categorize the controllable marketing variables into the four P’s: Product, Price, Place and Promotion. The “P” under consideration here is third in this series: “Place”. The Marketing Audit is a needs assessment tailored to the function and purpose of determining where the library stands in terms of its marketing strategy, its competition, and the gaps which exist in terms of effective marketing techniques. Current marketing approaches are audited in an attempt to assess current practices and to identify possible improvements in either service or cost of service. Libraries have distribution alternatives on a variety of levels: request and dissemination of information; programmatic activities; user browsing; cooperation with corollary agencies. User-oriented marketing and planning strategies can frequently smooth the approach to these alternative modes of service delivery.
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 5 p. I-I Full Issue Full Issue First published: 04 July 2019 https://doi.org/10.1111/j.1467-6346.2019.08924.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume56, Issue5July 2019Pages I-I RelatedInformation
AI to prevent cyber-violence: harmful behaviour detection in social media

Social media has allowed people to communicate freely. This total freedom has led to the emergence of cyber-violence with a growing number of victims. Many researches in psychology and e-health have been conducted to detect the act of cyber-violence. In computational field, most of works have focused on multiple aspects of cyber-violence, but none of them, to our knowledge, have studied the perpetrator's harmful behaviour from an emotional dimension. Our goal in this work is to discover the relationship between the emotional state of social media users and their harmful behaviour while engaged in the act of cyber-violence. Our approach is based on Ensemble Machine Learning and engineered features related to Plutchik wheel of basic emotions extracted with semantic similarity and word embedding. The results show a significant association between the individual's emotional state and the harmful intent, which may be a good indicator for cyber-violence detection.
Flooding

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 5 p. 22570B-22570C Policy and Practice Flooding First published: 04 July 2019 https://doi.org/10.1111/j.1467-6346.2019.08936.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue5July 2019Pages 22570B-22570C RelatedInformation
Development of Online Learning Models to Improve Digital Literacy Capabilities of Students: a Preliminary Analysis

The purpose of this study was to identify the use of the web in student learning and analyzing the needs regarding the development of online learning models. In general, this study uses a research and development approach by adopting the Analysis, Design, Development, Implementation and Evaluation (ADDIE model). At this stage of the analysis, it is done by identifying the use of the web in student learning and the opinions of students and lecturers regarding the development of online learning models, using online surveys on students and lecturers. The results of preliminary studies on the use of the web in learning showed that the percentage for reading using digital technology tended to be low at 22.5% of students stated that they rarely read and 59.2% stated that they sometimes read, 47.9% of students sometimes sought academic information and 57.1% sometimes - sometimes use the web to complete tasks related to student studies. As for opinions on the development of learning models from 76 students stated, 2 (2.63%) stated that there is no need to develop online learning models, 69 (90.79%) stated that the development of online learning models was needed and 5 (6.58%) stated that it was necessary to develop online learning models. The same opinion was also conveyed by lecturers regarding the development of online learning models from 13 9 lecturers (69.23%) stating that the development of online learning models was needed and 4 (30.77%) stated that it was necessary to develop an online learning model. In general, the use of the web for learning in the low category and the high opinion regarding the need for online learning models for learning, so that further research is needed to develop an Online Learning Model to Improve Students' Digital Literacy Capabilities.
Benefits by using AI-CAD for Screening Mammography According to Different Reading Strategies: A simulation study comparing the performances between single vs. double reading

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
Imprint: Geomechanics and Tunneling 5/2020

Geomechanics and TunnellingVolume 13, Issue 5 p. 579-579 ImprintFree Access Imprint: Geomechanics and Tunneling 5/2020 First published: 07 October 2020 https://doi.org/10.1002/geot.202070508AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume13, Issue5October 2020Pages 579-579 RelatedInformation
Statistical Machine Translation between Myanmar and Myeik

This paper contributes the first evaluation of the quality of machine translation between Myanmar and Myeik (also known as Beik) .We also developed a Myanmar-Myeik parallel corpus (around 10K sentences) based on the Myanmar language of ASEAN MT corpus.In addition, two types of segmentation were studied word and syllable segmentation.The 10 folds cross-validation experiments were carried out using three different statistical machine translation approaches: phrase-based, hierarchical phrasebased, and the operation sequence model (OSM).The results show that all three statistical machine translation approaches give higher and comparable BLEU and RIBES scores for both Myanmar to Myeik and Myeik to Myanmar machine translations.OSM approach achieved the highest BLEU and RIBES scores among three approaches.We also found that syllable segmentation is appropriate for translation quality comparing with word level segmentation results.
Introduction

In this chapter, we broadly outline the scope of digital image processing and list its applications in various fields. We clarify certain basic concepts such as the concept of a pixel and grayscale value related to digital images.
Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel

Abstract — The application of machine learning technology in various industrial fields is currently developing rapidly, including in the retail industry. This study aims to find the most accurate algorithmic model so that it can be used to help retailers choose a store location more precisely. By using several methods such as Pearson Correlation, Chi-Square Features, Recursive Feature Elimination and Tree-based to select features (predictive variables). These features are then used to train and build models using 6 different classification algorithms such as Logistic Regression, K Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Neural Network to classify whether a location is recommended or not as a new store location.&#x0D; Keywords— Application of Machine Learning, Pearson Correlation, Random Forest, Neural Network, Logistic Regression.
Project Scheduling using Event Based Scheduler with ABC

Scheduling is the first and foremost step for every project implementation. Project scheduling could be a mechanism to communicate what tasks has to be compelled to get done and which resources are going to be allotted to finish those tasks in what timeframe. Project scheduling occurs during the planning phase of the project. The problem comprises the correct assignment of employees to the various tasks that frame a software project, casing in time and cost limitations. To accomplish this objective, this paper presents and discusses the EBS with ACO, FCM clustering and EBS with ABC and the conclusions are drawn from it. First, to schedule human resources to tasks we implement Event based scheduler with the Ant colony optimization algorithm (ACO) for probabilistic optimization, second, for fast scheduling we implemented Fuzzy c means clustering to assign similar data points of employees to clusters so that the searching space will be reduced. Third, for optimum scheduling we apply Artificial Bee Colony algorithm with Event Based Scheduler. Artificial bee colony (ABC) is an optimization algorithm based on stochastic calculation which has demonstrated good search capacities on numerous advancement issues. Based on these findings we briefly describe the scheduling with FCM-EBS with ABC prompt optimum values.
Modern Measurement

Extensive measurement from Helmholtz and Holder is the standard physical account. It gives a ratio scale. Stevens introduces interval and ordinal scales. Scott and Suppes give a representational foundation to Stevens’ scales. Suppes, Zinnes, Luce and Narens analyse meaningfulness.
SLY DIGIT: UNFAIR PRACTICES IN SCIENTIFIC CREATIVITY

Are mathematical approaches always justified in the case of evaluating the work of scientific and pedagogical workers? What risks does the quote rating carry? What unscrupulous methods are now used to get rid of charges of plagiarism, to wind yourself a scientific quotation rating? Only the scientific community as a whole will be able to work out answers to these and many similar questions, responding to the bad behavior of individual researchers.
SOLVING TWO-CLASS CLASSIFICATION PROBLEM USING MEMETIC PROGRAMMING

The main target of the two-class classification problem is to design a classifier that discriminates between two objects from a seen dataset, then use this classifier to predict the object's class for unseen instances.Different methods have been used to solve the two-class classification problem, such as Genetic algorithm (GA) and Genetic Programming (GP).However, there is still a need to design new methods that can overcome some limitations in evolutionary algorithms, e.g., the high disruption of the breading operations; mutation and crossover.Recently, the Memetic Programming (MP) algorithm was proposed as an improvement to the GP algorithm.In this paper, we adapt the MP algorithm to produce a new classifier algorithm called the Memetic Programming Classifier (MPC) algorithm to solve the two-class classification problem.The performance of MPC is validated through different datasets from the UCI database and the accuracy is compared along with different methods.As a result, the proposed MPC algorithm shows a competitive performance compared with 179 classifiers in the literature.
Prediction of Phishing Websites Using Machine Learning

The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Phishers try to deceive their victims by social engineering or creating mock-up websites to steal information such as account ID, username, password from individuals and organizations. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. In this paper, we compared the results of multiple machine learning methods for predicting phishing websites.
Joint remote preparation of a single-photon hyper-state with two pairs of hyper-Bell states

Abstract Hyper-entanglement is gaining more and more attentions thanks to its outstanding applications in processing quantum information. It is considered as a quantum resource in many important tasks. In this work, we deal with a tripartite task for three remote parties Alice, Bob and Charlie. Namely, Alice and Bob are two senders who independently share the full information of an arbitrary single-photon state which is encoded simultaneously in two different degrees of freedom, the spatial-mode degree of freedom and the polarization degree of freedom (hereafter called photon’s hyper-state). We show that Alice and Bob can jointly prepare for the receiver Charlie such a hyper-state of the photon when they share beforehand two pairs of hyper-Bell states.