Reviewerly/exHarmony huggingface dataset:
Mobile Payments in the FinTech Era

Mobile payments have become increasingly popular as consumers prefer to pay for goods and services using their mobile phones. FinTech companies are disrupting the traditional banking system by providing innovative payment solutions that are faster, cheaper, and more convenient. Blockchain technology is at the forefront of this disruption, with its applications enabling secure and efficient mobile payments. This chapter examines the impact of blockchain on the fintech era and how it is disrupting the banking system. The chapter discusses the advantages of using blockchain for mobile payments, including increased security, transparency, and efficiency. It also explores the challenges and limitations of blockchain technology in mobile payments, such as regulatory hurdles and scalability issues. The chapter concludes that blockchain's applications have the potential to revolutionize mobile payments and disrupt the traditional banking system.
Decision Support System for Selecting Exemplary Students with Simple Additive Weighting Method

The selection of exemplary students carried out by the school is expected to trigger the enthusiasm of students to be able to develop their interests, talents, and abilities in the academic and non-academic fields. However, decision-making has not been measured with data so a decision support system is needed. With the use of this method, it is hoped that it can make it easier and minimize the occurrence of errors in making decent decisions therefore this system is needed to be able to make good decisions. In this study, one of the decision support system methods that are often used was chosen, namely Simple Additive Weighting (SAW). The use of the SAW method is due to its uncomplicated calculations. Research conducted at this school in determining decisions is still done manually so it is less effective and efficient. Therefore, this decision support system must be able to calculate exemplary students to be more effective and efficient. This system displays the final results of the ranking of exemplary students using the SAW method. From the overall results of the research that has been carried out, the calculation results that got rank 1 were obtained, namely Kenzo Ecclesio Taha with a total score of 0.9025. From the results of the study, it can be concluded that the results meet the criteria, so this study can be considered in calculations to determine exemplary students in the future
Teaching Digital Systems Engineering Courses and Students Performance and Satisfaction

Teaching digital electronics, microprocessor systems, embedded systems and other similar courses requires teachers to constantly adapt their teaching methods in order to improve the learning results. Courses in the field of digital systems are present in every curriculum of specialties such as electronics, information technologies, computer engineering and even mechanical engineering. Therefore, these courses shall be taught effectively, so that students can apply the knowledge learned to solve their practical engineering problems. In the introduction of this paper an analyses of different learning styles and delivery methods as well as their applications in the context of the pandemic is presented. For the purposes of the study, the training in three courses Digital electronics, Microprocessor systems and Digital systems design learned by students of several specialties in the Faculty of Electrical Engineering, Electronics and Automation at University of Ruse is considered. A comparative analysis of student performance results during online learning and traditional learning was performed. A survey conducted among students on their satisfaction from the online teaching in two of these courses during the pandemic is presented. The results of the survey were analysed and some conclusions were drawn.
Augmented Reality Applications in Mathematics and Science: Exploring Pedagogical Viability in the Amazon Region

Augmented Reality (AR) is becoming more popular in the educational scenario as the number of possibilities for educational tools increases. Teaching experience has revealed a certain difficulty in motivating and capturing the students’ attention in math and science subjects, making it difficult to understand the subjects explained by the professors. In this way, applications based on AR can reduce this problem, helping professors to capture the students’ attention, increasing the students’ engagement, and helping in content fixation. Based on this perspective, this paper presents an AR-based application that uses educational resources to make the teaching process more attractive and interactive, in order to help teachers in the fields of mathematics and science to demonstrate a different approach to traditional activities. The application assessment was carried out through a case study performed by professors of a public school from the municipal teaching network of a town in Oriximiná/PA, located in the interior of the Amazon region, aiming to analyze the design and pedagogical viability of the application. The data collection instrument was a structured survey with a quantitative approach and, after analyzing the results, it was found that the overall average satisfaction was satisfactory, meeting the intended purpose.
Digital Technologies for Lecturing and Learning

We are currently finalizing the Erasmus+ project DiT4LL – Digital Technologies for Lecturing and Learning. The project is primarily focused on innovative education practices in the digital age. It responds to the challenges revealed by coronavirus pandemic. Its outputs directly support the transition towards online and distance education. The project deals with the introduction of digital technologies and innovative and open pedagogical procedures, especially in the field of professional training in technology. It also seeks to overcome the limitations introduced by the conventional forms of online digital education having been used so far.In the paper we describe in detail the individual activities and outputs of the project: digital online learning tools making use of advanced technologies; MOOC courses; experimental virtual internships; international student digital competition; large-scale pilot testing; and verification of the modern digital teaching approaches by semi-professionals.
Cyber security in technical education

In today's digital age, cyber security threats are among the most serious threats facing countries around the world. In the context of Russia's aggression in Ukraine, many countries have increased their measures related to potential cyber-attacks, and cyber threats can affect anyone of us.The paper identifies innovative, visionary educational ideas and their application in e-learning in the field of cyber security for university and technical high school students.The main deliverable will be an intelligent website that will map educational materials in the area of cyber security, categorized by threats, vulnerabilities and their potential impact with respect to the target audience. Other deliverables will be practical online tasks and educational materials in the field of cyber security. The authoring team and other members of the project consortium will achieve the desired web usefulness by combining their years of e-learning experience with practical experience in the field of cyber security.The new CYB-FUT (Cybersecurity for the Future) project is applying for support under the Erasmus+ call for proposals.
Reflection of the challenges facing cyber deterrence on cyber attacks

&#x0D; As a result of the cyber-attacks to which many countries are exposed, and the increase in their numbers in the recent period, reliance on cyber deterrence has made an important issue. It Is related to the challenges of cyber deterrence, as the latter faces many attacks, which are a great pressure factor on this deterrence, as the challenges may be a contributing factor in reducing cyber-attacks and at the same time it may be a catalyst in increasing it, and this is what we will address in this research.&#x0D; &#x0D;
Content-Based Recommender Systems Taxonomy

Abstract In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks.
Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

Abstract Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.
Application of Bayesian network for farmed eel safety inspection in the production stage

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.
Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset.
Detection of Communication Tunnels in Network Using DNS Logs

Domain Name Framework (DNS) assumes a significant part as an interpretation convention in ordinary utilization of the Web. The motivation behind DNS is to make an interpretation of space names into IP locations as well as the other way around. Its straightforward engineering can, without much of a stretch, be abused for pernicious exercises. One tremendous security danger concerning DNS is burrowing, which assists aggressors with bypassing the security frameworks inconspicuous. A DNS passage can be utilized for three purposes: as an order and control channel, for information exfiltration or in any event, for burrowing one more convention through it. In this paper, various procedures for DNS tunneling identification are overviewed. The research characterizes those originally founded on the kind of information and afterward inside the classifications of the sort of examination and finishes up with a correlation between the different identification procedures. Research also presents one genuine High level Persevering Danger crusade that uses DNS tunneling, and hypothetically analyse how well the overviewed identification strategies could distinguish it.
Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions

Abstract We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.
Data Redundancy, Data Phishing, and Data Cloud Backup

Data today is extremely complex and massive in size. By taking these factors into account, machine learning and ETL techniques will find it much easier to separate useful data from unusable raw data. In web technology, OLTP and OLAP are used to manipulate data on a daily basis. During the transaction, a massive amount of data is maintained in the database or data warehouse. There was no Cloud storage to back up data in the past. Because of the significant increase in internet usage, people are sharing more of their personal information online. Cybercriminals can now access a vast amount of personal data and financial activities. Phishing is one instance of a very efficient type of cybercrime that allows offenders to trick users and obtain crucial data. Phishing has advanced into a more complex attack vector since the first known instance was published in 1990. One of the most common online fraud types now is phishing. Phishing attacks can cause serious losses for their victims, including the theft of confidential information, identities, businesses, and state secrets. This article evaluates these attacks by addressing existing phishing techniques and discussing the current state of phishing. Phishing attacks have been classified in studies based on common phishing methodologies and defences, ignoring the importance of the entire phishing lifecycle. This article provides a new, in-depth model of the anatomy of phishing attacks, including attack phases, attacker types, threats, targets, attack media, and attacking strategies. The suggested physiology would also help readers understand the lifecycle of a phishing attack, which would aid in the development of a comprehensive anti-phishing system. This will improve public awareness of phishing attacks and the methods used. New approaches are also proposed, and certain preventive measures are investigated.
The Detection of Skin Cancer and Oral Cancer : A comparison of the proposed Hybrid Model with the Existing Detection Algorithms

The human body's skin and mouth are two organs that are most susceptible to cancer, and early detection is the only way to reduce the number of fatalities. Deep learning algorithms have shown to be more effective than machine learning algorithms for detecting malignant skin or oral/mouth cancer. For this reason, a revolutionary design has already been put out for the diagnosis of oral and skin cancer. Achieving accuracy of 97%, recall of 93%, f1-score of 92%, and precision of 92%, the proposed model has been successful. Additionally, a comparison between the outcomes of the suggested hybrid model (a combination of CNN and VGG19) and the current algorithms (like AlexNet, ResNet-50 and Inception-V3) has been done in this article.
LSTM Based Speaker Recognition System with Optimized Features

Major challenge in speech processing is due to non-linearity and complexity in speech signals. Characterizing this non-linear signals, affects the performance of the automated speaker recognition system. In this paper, autocorrelation features, Approximation statistical features and detailed statistical features are obtained from the speech signals. These features are fed to RNN and LSTM for speaker recognition. LSTM has higher sensitivity and accuracy than RNN. Similarly, detailed statistical features provide the highest sensitivity when compared to auto correlation features and approximation statistical features.
A Deviation based Ensemble Algorithm for Sarcasm Detection in Online Comments

Sarcasm refers to the use of irony to mock or convey contempt and involves the use of words that mean the opposite of what someone truly intends to convey. Online forums which enable users to express sarcasm as a sentiment tend to induce misunderstandings between different parties and obscure the users' true intentions. This leads to ambiguity being one of the prime challenges in detecting sarcasm. Another challenge in sarcasm detection is the rapidly growing size of language vocabularies with the addition of new slang words every day. Additionally, usage of emojis in online text can greatly influence the polarity of a sentence by inducing a sarcastic tone. These setbacks make sarcasm a particularly demanding sentiment to determine. In this paper, the statistical significance of various deep learning models for the purpose of detecting sarcasm in online comments containing emojis is explored. For the task of binary classification, GRU achieves an accuracy score of 73.44% with an F1-score of 73.96%. The proposed ensemble-based approach yields an accuracy score of 74.41% for the combination of LSTM and GRU, which is comparable to the accuracy achieved with conventional ensemble techniques such as max-voting and averaging. Twenty-six different hybrid combinations of deep learning models were explored and the most optimal performing ones were identified. CNN and Global Average Pooling 1D are two other architectures that were explored.
Optimization techniques for preserving privacy in data mining

Data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access.
Task scheduling in cloud computing using Heterogeneous Initial Finish Moment technique

Cloud computing is one of the most widely used infrastructures and services for using virtual servers, also known as processing elements. One of the most basic issues with cloud technology is the scheduling algorithm. Over the years, researchers have put forth a variety of approaches to handle non-deterministic polynomial-time complete optimization of data processing, with the best cloud work schedule criteria being persistence. The Heterogeneous Initial Finish Moment (HIFM) procedure is one of these methods and is renowned for producing the best outcomes for task scheduling in a centrally managed setting in a shorter period of time. According to research, HIFM delivers exceptional results in terms of timeline quality and execution time. Nevertheless, a good solution might not always be produced by the selection of the initial empty slot and the common computational complexity. As a result, we offer here various HIFM technique variants that may result in better results. In the processor selection stage, we also customize the process for choosing standby slots for task scheduling, but in the rank generation stage, we use various methods to determine positions. This article suggests improved iterations of the HIFM technique under consumer budgetary constraints in order to decrease the number of iterations of a specific work - flow submitting on virtual servers. Our research shows that improved HIFM algorithms perform better than the conventional HIFM technique in terms of shorter timeline lengths for process trouble running on numerous virtual servers.