Reviewerly/exHarmony huggingface dataset:
Open Source Software Engineering Theory: Intelligent Educational Tool Increases Placement of Graduates in STEM-related Careers

Comparative Programming Languages and Data Structures, and Algorithms.El-Bathy's area of research is distributed computing and computer networking.He is interested in applying the concepts and techniques of distributed computing, information extraction, intelligent information retrieval, and data warehousing using service-oriented architecture (SOA) to develop intelligent web applications.El-Bathy received a B.Sc in business administration from Cairo University in Egypt and a post-bachelor's degree and M.S. in computer science from Wayne State University.He also has completed all computer science courses and passed the proficiency exam of the Ph.D. program at Wayne State University.In addition to his academic experience, El-Bathy has more than 25 years of experience in industry, working in areas including software development, database design, and computer networking design, implementing projects across many technology platforms, DBMS, network topologies, and programming languages.As a practitioner of information technology, he reached the pinnacle of his career and held a Vice President position of the Department of Information Services for a $1.6 billion financial institution, providing leadership in applying and aligning information technology with business strategies, goals, and needs.While the study of and research in computer science is his prime objective, his interests in literature, music, travel, and nature help him to maintain a sense of perspective in life.He likes to write and has
Efficient Nyberg-Rueppel type of NTRU digital signature algorithm

Message recovery is an important property in Nyberg-Rueppel type digital signature algorithms. However, the security of Nyberg-Rueppel type digital signature algorithms depends on the hard problems which might be vulnerable to quantum attacks. Therefore, quantum resistant Nyberg-Rueppel type digital signature algorithms with message recovery property are needed. Since NTRU-based cryptosystems are one of the best studied quantum-resistant schemes, using traditional NTRU encryption scheme has several advantages on the message recovery property. In this paper, we define Nyberg-Rueppel type of NTRU digital signature algorithm. It is carried out by combining NTRU-based encryption and signature algorithms. In the proposed scheme, efficient message recovery property is achieved with the help of NTRU. Then, we compare the computational cost of our Nyberg-Rueppel type signature scheme with the others in terms of the arithmetic complexity. According to the asymptotic complexity results, the proposed scheme has better arithmetic complexity than Nyberg-Rueppel type schemes. We also discuss the security properties of the proposed scheme by modifying attacks on Nyberg-Rueppel type algorithms and lattice-based algorithms.
Augmented Machine Learning Ensemble Extension Model for Social Media Health Trends Predictions

Social Networks are the source of rich, interactive, textual, and other media.Users of the social media generate data at a tremendous pace.This data consisting of user opinions and attitudes is so large that it has necessitated automated methods to analyze and extract knowledge from the same. Social networks have been studied and analyzed using various graph-based analysis techniques. Prominent analysishas centered on features like ego-networks, distance, centrality, sub-networks etc. The areas of study for social media analysis have been centered around populations, boundaries, Cohesion, Centrality and Brokerage, Prestige and Ranking. In the past several models have been propounded for various machine learning based analytics for the Social Networks study but there is a perceived need for studying social networks for health data using Ensemble Learning wherein an array of various Machine Learning techniques can be employed to achieve better classification or clustering results. We introduce an Analytical Model which will identify most discussed terms/ topics of health/ healthcare on social networks to predict the emerging health trends. The model is to use temporal datasets to deduce multi-label classification of health-related topics. The Model employs the technique of Temporal Clustering (using Machine Learning) on the Topic Classification done on datasets using Ensemble Machine Learning to deduce the most discussed topics. Using this model, we will see how Ensemble Machine Learning based Analytical Model for analyzing social network data for health topics is efficient than traditional Machine Learning technique(s).
COMPLIANCE AND WHISTLEBLOWING: HOW TECHNOLOGY WILL REPLACE, EMPOWER AND CHANGE WHISTLEBLOWERS

Whistleblowing has a long lineage. In recent decades, there has been growing societal awareness of the public interest in whistleblowing and the personal, professional and financial toll endured by those who expose wrongdoing. Regulators have taken notice, with an increasing number of jurisdictions enacting whistleblower protections laws. These developments have coincided with rapid technological innovation. Orthodox communication technologies along with big data, artificial intelligence and blockchain are beginning to have a dramatic effect on the nature of whistleblowing. The rise of data transparency has enabled technology to replace whistleblowers by algorithmically preventing and detecting misconduct. Technology is also supporting traditional human whistleblowers, by providing secure and anonymous channels for disclosure. Finally, technology is altering the nature of whistleblowers and whistleblower protections, including by blurring the distinction between whistleblowers, hackers and leakers. This chapter analyses these developments to consider the future of compliance and how technology is replacing, empowering and changing whistleblowers.
Role-Play Simulation of Negotiations between the EU and the Eurasian Economic Union: Teaching while Enhancing a Transnational Dialogue

The article reviews the experience of the role-play simulation (RPS) ‘Modelling negotiations between the EU and EAEU’, arranged in 2018 and 2019. Its specific goals were building knowledge (about the European Union (EU) and Eurasian Economic Union (EAEU), and enhancing a transnational dialogue between EU (Western) and Russian students in the context of a deteriorated EU-Russian relationship. This latter aspect of international RPS has received insufficient attention in the literature, yet it might contribute to the improvement of real-life EU-Russian relations through fostering a transnational dialogue and mutual understanding among young citizens. The article outlines the setting of the RPS and addresses three major difficulties that the organisers faced (designing the RPS, preparing students and fostering their dialogue), the solutions that were developed and the assessments of the results.
Classification of Imbalanced Big Data using SMOTE with Rough Random Forest

Learning from datasets is an important research topic today. Amongst the various data mining tools available for the purpose, none works satisfactorily in the case of imbalanced data mainly because this type of data gives rise to various minority classes, which may affect the learning process. In addition to the large volume, characteristics of Big Data also include velocity and variety. The Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to balance imbalanced data. Here, we have focussed on extending this concept to conform to the Big Data environment by combining it with the concepts of rough random forest (RRF). This hybrid approach comprising SMOTE and RRF algorithms for learning from imbalanced datasets has been applied on various benchmark datasets from the KEEL Dataset Repository. The results obtained are satisfactory. The velocity aspect of Big Data has been handled by this method on the dynamic dataset of the stock market. The results obtained have been verified using popular online websites related to stock markets.
The use of Ontologies in the Development of a Mobile E-Learning Application in the Process of Staff Adaptation

The article discusses the method of using ontologies for developing a mobile application of the corporate e-learning system in the process of adaptation of companies’ new employees. The problems of shortening the period of adaptation of newly recruited employees force companies to look for effective methods of personnel management, their training and development. Well-known publications and the best developments in the field of mobile devices in corporate training systems, which allow to determine the vector of development of corporate training systems from e-learning to mobile learning, were considered. The widespread use of mobile devices makes the process of learning and adaptation of employees flexible, convenient, which leads to reduction of the time for employees to join the company's business processes. The problems of motivation and staff engagement in the process of early adaptation remain, but they are solved in the course of our other studies. The original method is use of ontologies when building individual learning paths in the process of adaptation of employees. The article examines the main approaches to the presentation and analysis of employees’ competencies based on ontologies when building modern corporate systems of adaptation and personnel training. An original approach to creating mobile applications is proposed, which reduces the time for adaptation and training of new employees.
Designing Integrated Data Security System

By looking at what happens to the world today, we need to know that the issue of information security has become a common problem that is very important to note. Especially for organizations or companies that have a high level of risk to data and possible losses with loss of data. Many data breaches continue to occur due to accidental, deliberate and human factors that cause financial loss or company reputation. The approach towards improving behavior and culture is by applying awareness activitiesof how important data security information systems are in progress. This journal presents an approach to identifying factors related to human security by incorporating people into the design and implementation of information system security awareness. Theauthor presents six steps in progress that can be done in business activities and evaluate the author's approach to the business case study. The authors' findings suggest that an information-centered approach to information security awareness has the capacity to adapt to the time and resources required for its implementation in the business, and offers a positive contribution to reducing the risk of information security through awareness of the importance of that security
Planning operators of concurrent RDF stream processing queries

RDF stream processing (RSP), which aims to query data streams and linked datasets using common data model and query languages extended from RDF and SPARQL, is gaining popularity. However, most of the existing RSP engines do not provide any optimisation techniques for shared join operators among query plans from concurrent queries. Many number of shared join operators can incur the waste of a lot of CPU resources like a processing memory. Moreover, queries on shared operators cause a slow response time because they must be re-evaluated without reusing intermediate results. To solve these problems, we propose an efficient method of optimising query plans on multiple queries. First, the proposed method evicts some data that get notified from the streams in order to maintain an efficient memory usage. Second, the proposed method optimises query plans to maximise the reuse of shared join results. Experimental results show that the proposed method has significant improvements in terms of memory consumption and latency, compared to the state-the-of-art methods.
DisUnknown: Distilling Unknown Factors for Disentanglement Learning

Disentangling data into interpretable and independent factors is critical for controllable generation tasks. With the availability of labeled data, supervision can help enforce the separation of specific factors as expected. However, it is often expensive or even impossible to label every single factor to achieve fully-supervised disentanglement. In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor. Under this setting, we propose a flexible weakly-supervised multi-factor disentanglement framework DisUnknown, which Distills Unknown factors for enabling multi-conditional generation regarding both labeled and unknown factors. Specifically, a two-stage training approach is adopted to first disentangle the unknown factor with an effective and robust training method, and then train the final generator with the proper disentanglement of all labeled factors utilizing the unknown distillation. To demonstrate the generalization capacity and scalability of our method, we evaluate it on multiple benchmark datasets qualitatively and quantitatively and further apply it to various real-world applications on complicated datasets.
Human Action Recognition using STIP Techniques

The activities of human can be classified into human actions, interactions, object- human interactions and group actions. The recognition of actions in the input video is very much useful in computer vision technology. This system gives application to develop a model that can detect and recognize the actions. The variety of HAR applications are Surveillance environment systems, healthcare systems, Military, patient monitoring systems (PMS), etc., that involve interactions between electronic devices such as human-computer interfaces with persons. Initially collected the videos containing actions or interactions were performed by the humans. The given input videos were converted into number of frames and then these frames were undergone preprocessing stage using by applying median filter. The median filter identifies the noises present in the frame and then which replaces the noise by the median of the neighboring pixels. Through frames desired features were extracted. The recognize of action present in the person of the video using these extracted features. There are three spatial temporal interest point (STIP) techniques such as Harris SPIT, Gabour SPIT and HOG SPIT were used for feature extraction from video frames. SVM algorithm is applied for classifying the extracted feature. The action recognition is based on the colored label identified by classifier. The system performance is measured by calculating the classifier performance which is the Accuracy, Sensitivity and Specificity. The accuracy represents the classifier reliability. The specificity and sensitivity represents how exactly the classifier categorizes it’s features to each correct category and how the classifier rejects the features that are not belonging to the particular correct category.
IoT

Currently, we are living in an era in which each Internet of Things (IoT) device is becoming an intelligent device. An intelligent device is a smart device or embedded appliance that is connected everywhere and touches almost every corner of the globe with the help of the Internet. The fundamental goal is to install every logical and physical item directly into the computing community. It emphasizes the assimilation of the web directly into every entity as part of non-living or existing, intangible or tangible, logical or physical type. This chapter deals with the present IoT fundamentals, huge exploration problems, and application program domains within IoT. The main aim of this chapter to help the readers and researchers understand the IoT fundamentals and the real-world challenges in IoT. This effort spreads over the different viewpoints of several academicians, providers, organizations, and scientists regarding IoT, and aims to classify, examine, and encapsulate the issues, demand answers, and provide recommendations associated with IoT. It investigates conflicts that can be categorized as technological, environmental, and societal. Although knowledge can be utilized for good or bad, we need to adopt new technologies and devices if they are available.
Understanding users Display-Name Consistency across Social Networks

Online users create their profiles on numerous social platforms to get benefits of various types of social media content. During online profile creation, the user selects a username and feeds his/her personal details like name, location, email, etc. As different social networking services acquire common personal attributes of the same user and present them in a variety of formats. To understand the availability and similarity of personal attributes across various social networking services, we propose a method that uses the different distance measuring algorithms to determine the display-name similarity across social networks. From the experimental results, it is found that at least twenty percent GooglePlus-Facebook and Facebook-Twitter users select the same display name, while forty five percent Google and Twitter user select identical name across both the social networks.
Feature Selection Method based on Fisher’s Exact Test for Agricultural Data

This paper is aimed to analyze the feature selection process based on different statistical methods viz., Correlation, Gain Ratio, Information gain, OneR, Chi-square MapReduce model, Fisher’s exact test for agricultural data. During the recent past, Fishers exact test was commonly used for feature selection process. However, it supports only for small data set. To handle large data set, the Chi square, one of the most popular statistical methods is used. But, it also finds irrelevant data and thus resultant accuracy is not as expected. As a novelty, Fisher’s exact test is combined with Map Reduce model to handle large data set. In addition, the simulation outcome proves that proposed fisher’s exact test finds the significant attributes with more accurate and reduced time complexity when compared to other existing methods.
Hate Speech Detection in Portuguese with Naïve Bayes, SVM, MLP and Logistic Regression

Even though social networks can provide free space for discussing ideas, people can also use them to propagate hate speech and, given the amount of written material in such networks, it becomes necessary to rely on automatic methods for identifying this problem. In this work, we set out to verify the use of some classic Machine Learning algorithms for the task of hate speech detection in tweets written in Portuguese, by testing four different models (SVM, MLP, Logistic Regression and Naïve Bayes) with different configurations. Results show that these algorithms produce better results (in terms of micro-averaged F1 score) than the LSTM used for benchmark, being also competitive to other results by the related literature
The Teaching Techniques Used in Teaching Speaking to Young learners on English Massive Program Kediri

Learning language at an early age has several benefits. However, young leaners attending formal schools in Indonesia get little chance to speak in the classroom. In this case, non-formal education plays a role in helping young learners. English Massive Program is non-formal education given for free by the government of Kediri City. This study aimed to describe the teaching techniques used and the differences of the technique used by an experienced tutor and a less experienced tutor. Descriptive qualitative approach was used as the research design. The data were collected by observing teaching learning process, conducting interview with two tutors, and documentation. It was found that the tutors used several techniques to get the students to speak: information gap, game, listen and repeat, question and answer, and read aloud. The difference of the technique: the experienced tutor used game in every meeting during three observations, while the less experienced tutor used game once during three observations. The use of game by the experienced tutor in every meeting during three observations showed that the experienced tutor understood the characteristics of young learners better. The decisions made by the experienced tutor during three observations also suit the characteristics of experienced tutors or teachers.&#x0D; &#x0D; Key word: experienced tutor, novice or less experienced tutor, teaching speaking, teaching technique, young learners
The Action Potential

In this chapter we meet the action potential, the fundamental electrical signalling mechanism used by the body. We introduce the cycle of ion movements associated with the generation of an action potential and the role of different ion gates. We then analyse the system of action potential generation in terms of a simple electrical circuit model.
Research on E-commerce Payment Security and Privacy Protection Based on Improved B2C Model

The popularity of Internet and mobile terminalspromotes the development of e-commerce. However, e-commercewhich is not necessary to face to face poses a major challenge to thepayment security and privacy protection of consumers. This paperbriefly introduces the traditional business to customer (B2C)e-commerce model. A third-party privacy server was introduced toprovide a double encryption algorithm for the model, and then thetraditional and improved B2C models were simulated to verify theperformance of the two models in the confidentiality of the transactioninformation and the security of the transaction informationtransmission process. The results showed that the intruder could notdirectly obtain the sensitive privacy information such as paymentinformation and order information of consumers even if he invaded thedatabase of the online store when the improved B2C model was used;after the transaction information was intercepted and decrypted underthe two B2C models, the decryption integrity decreased with theincrease of the transaction information quantity, while under the sametransaction information quantity, the decryption integrity of theimproved B2C model was lower
JPR volume 56 issue 1 Cover and Front matter

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2. Digital identity protection using blockchain for academic qualification certificates

Although it was always an important issue, digital era increases the importance of both questions "what identity is" and "why is important to manage and protect one."There are many views and definitions of digital identity in the literature; however, this chapter explains the identity related to the identification of an individual, his/her qualification, and his/her status in society.Using modern approach, this chapter focuses on the academic qualification of an individual where blockchain is presented as an efficient concept for publishing, storing, and verifying educational certificates/diplomas.Motivation for this research came from lack of practical applications of blockchain and importance of EDU certificate transparency and challenges in their sharing (policy issues, national standard, etc.).By having easy to apply and understand guidelines, it is easier for wider audience to accept and use/reuse sometimes complex digital concepts as part of their solutions and business processes.As part of institution research lab, explained approach and proof of concept solution was developed in Algebra University College.