Reviewerly/exHarmony huggingface dataset:
Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map

COVID-19 has been declared a pandemic and countries are tackling this disease either through preventative measures such as lockdown and sanitization or through curative ones such as medication, isolation, and so on. Some people believe that vaccination is the best way to prevent this disease, while others disagree. Society’s attitudes toward vaccination can be influenced by a variety of factors such as misunderstanding, ambiguity, lack of knowledge. The proposed study’s goal is to better understand people’s attitudes regarding vaccination by focusing on key topics related to COVID-19 anti-vaccine tweets. Tweets are obtained over a period based on the number of COVID-19 cases by utilizing the “anti-vaccine” keyword rather than the “vaccine” keyword. Furthermore, in addition to people perceptions and attitudes toward anti-vaccination, the causal relationship between each topic is investigated. As a result, latent dirichlet allocation (LDA), fuzzy association rule mining (FARM), fuzzy cognitive map (FCM), and fuzzy c-means are used to conduct a complete study. Topics are analyzed independently using clustering and scenario analysis. The findings demonstrate the most common topics in anti-vaccination tweets, as well as the influence of each topic on the others.
The Place of Voting in the Ethics of Counterspeech

Abstract The literature on counterspeech has been debating how institutions and citizens should respond to offensive or dangerous communicative acts. This article identifies a gap in this debate, namely, the lack of attention paid to the individual vote in large-scale democratic elections as an effective act of distancing from candidates who use explicitly derogatory forms of expression to unify and mobilize supporters. In studying the place of voting in the ethics of counterspeech, this article investigates what counterspeakers can expect other counterspeakers to do in large-scale democratic elections framed as a vote for or against hateful representatives. I argue that even in large-scale elections, the individual act of voting, understood as a contribution to increasing the clarity of the indicators of force of a collectivized speech act, can be an effective form of counterspeech. Voting is one of the first forms of counterspeech that must be taken so that other, and perhaps more controversial, actions can stand criticism on moral grounds. For this reason, I also argue that counterspeakers can expect other counterspeakers to contribute to an electoral outcome that, under certain circumstances, can be received as a vote against representatives who use explicit derogatory forms of expression to gain consensus.
Two-step phase shifting algorithm based on cost function minimization

A computationally efficient phase shift estimation algorithm is proposed for two-step phase-shifting interferometry based on a cost function minimization technique. The proposed method can accurately estimate the phase shift over a wide range resulting in reliable phase estimation. Due to the analytical solution offered by the proposed method, its computational efficiency is found to be higher than the existing state-of-art methods. While the performance of some methods is user-defined parameter-dependent, the proposed method is non-parametric and user-independent. The accuracy of phase shift and subsequent phase estimation using the proposed algorithm is found to be maximum around phase shift of π/2rad which further decreases for the phase shift values closer to 0rad and πrad. The experimental results demonstrate the practical applicability of the proposed method.
Simple Patient Registration with The Waterfall Method

At this time, patients seek treatment at the hospital by registering first in the registration section. The purpose of this research is to design software that can be used to assist patient registration at the time of treatment. The research applied the Delphi software design with the waterfall method. Accordingly, the research shows that the software can work well by accepting patient data input and storing it in the database, and the software also runs smoothly.
Comparison of Different Deep Learning Approaches to Arabic Sarcasm Detection

Irony and Sarcasm Detection (ISD) is a crucial task for many NLP applications, especially sentiment and opinion mining. It is also considered a challenging task even for humans. Several studies have focused on employing Deep Learning (DL) approaches, including building Deep Neural Networks (DNN) to detect irony and sarcasm content. However, most of them concentrated on detecting sarcasm in English rather than Arabic content. Especially studies concerning deep neural networks, including convolutional neural networks (CNN) and recurrent neural network (RNN) architectures. This paper investigates several deep learning approaches, including DNNs and fine-tuned pretrained transformer-based language models, for identifying Arabic sarcastic tweets. In addition, it presents a comprehensive evaluation of the impact of data preprocessing techniques and several pretrained word embedding models on the performance of the proposed deep models. Two shared tasks' datasets on Arabic sarcasm detection are used to develop, fine-tune, and evaluate the different techniques and methods presented in this paper. Results on the first dataset showed that fine-tuned pretrained transformer-based language model outperformed the developed DNNs. The proposed DNN models obtained comparable performance on the second dataset to the fine-tuned models. Results also proved the necessity of applying preprocessing techniques with the various Deep Learning approaches for better detection performance of these models.
Identifying Equivalent Words from Different Arabic Dialects Using Deep Learning Techniques

The Arabic language comprises many spoken dialects. These dialects vary from a standard written Modern Standard Arabic (MSA) in terms of syntactic, lexical, phonological, and morphological. Arabic Dialects differ, not only along a geographical continuum, but also with other sociolinguistic factors such as the urban, rural, Bedouin dimension. Currently, Dialectal Arabic (DA) is the essential written language of unofficial communication in the Arab World. These Dialects can be found on social media platforms, emails, Twitter, etc. There has been a high interest in research on computational models of Arabic dialects in the last decade. Most of these studies focus on Arabic dialect identification (classification) and building Arabic dialect corpora. However, finding Arabic dialect word synonyms from another Arabic dialects has received limited attention. To bridge this gap, this study will develop a model to identify the equivalent words from different Arab world dialects using deep learning techniques such as word2vec. This research merged and extended the existing Arabic dialects corpora and then applied some deep learning techniques to achieve the best results for dialectal word synonyms. The outcomes of this research are a new dataset of Arabic dialectical word synonyms and a model with acceptable accuracy of 81%.
Rice Field Production Prediction Using Genetic Algorithm (Evidence from Sukoharjo District, Indonesia)

Agriculture has an essential role in human life in providing food. Sukoharjo district government is one of the 514 regencies supplying most agricultural products. Agricultural land, such as rice fields in the district, is decreasing yearly. The yield of rice production has fluctuated due to factors such as pests, weeds, weather conditions, drought, and the decrease in agricultural land. This requires a prediction of rice production to help the local government develop strategies to maintain the production quantity and achieve food security. Therefore, this study aimed to propose a rice production prediction in Sukoharjo using a Genetic Algorithm. This method was employed to generate the correct number of individuals in the initialization process to predict rice fields' production accurately. Population size could lead to diversity in the population and influence the genetic algorithm to study the changes in rice production in Sukoharjo. The results showed that the local government has maintained the area of rice fields and made the IP400 program a success until 2025. This could increase production by 2,526.196 tons or 11.11%.
Evaluating Simple and Complex Models’ Performance When Predicting Accepted Answers on Stack Overflow

Stack Overflow is used to solve programming issues during software development. Research efforts have looked to identify relevant content on this platform. In particular, researchers have proposed various modelling techniques to predict acceptable Stack Overflow answers. Less interest, however, has been dedicated to examining the performance and quality of typically used modelling methods with respect to the model and feature complexity. Such insights could be of practical significance to the many practitioners who develop models for Stack Overflow. This study examines the performance and quality of two modelling methods, of varying degree of complexity, used for predicting Java and JavaScript acceptable answers on Stack Overflow. Our dataset comprised 249,588 posts drawn from years 2014-2016. Outcomes reveal significant differences in models’ performances and quality given the type of features and complexity of models used. Researchers examining model performance and quality and feature complexity may leverage these findings in selecting suitable modelling approaches for Q&A prediction.
Mitigating Risk in Neural Network Classifiers

Deep Neural Network (DNN) classifiers perform remarkably well on many problems that require skills which are natural and intuitive to humans. These classifiers have been used in safety-critical systems including autonomous vehicles. For such systems to be trusted it is necessary to demonstrate that the risk factors associated with neural network classification have been appropriately considered and sufficient risk mitigation has been employed. Traditional DNNs fail to explicitly consider risk during their training and verification stages, meaning that unsafe failure modes are permitted and under-reported. To address this limitation, our short paper introduces a work-in-progress approach that (i) allows the risk of misclassification between classes to be quantified, (ii) guides the training of DNN classifiers towards mitigating the risks that require treatment, and (iii) synthesises risk-aware ensembles with the aid of multi-objective genetic algorithms that seek to optimise DNN performance metrics while also mitigating risks. We show the effectiveness of our approach by using it to synthesise risk-aware neural network ensembles for the CIFAR-10 dataset.
Using Knowledge Graph Embeddings in Embedding Based Recommender Systems

This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work.
A Novel Dataset for Known and Unknown Ancient Arabic Manuscripts

This paper presents a new dataset of Ancient Arabic-Islamic Manuscripts to detect unknown manuscripts and classify them from the known manuscripts. Unknown Manuscripts are identified as those that have been affected badly by human or natural forces, such as humidity, temperature, and air pollution, which degraded their quality and missed their identification information, such as the title, author, and date of the manuscripts. Thus, The Known Manuscripts are characterized by having a known title, author, etc. Recognizing the unknown manuscripts is essential to further the analysis process, facilitate information extraction from such degraded manuscripts, enable their indexing, and make them easily accessed and retrieved. The objectives of the constructed dataset are as follows: 1) Collect a set of known and unknown manuscripts of similar forms and highlight the characteristics of the unknown manuscripts. 2) Promote the automatic detection and recognition of unknown manuscripts. 3) Formulate the problem of recognizing unknown manuscripts as a supervised machine-learning problem, and boost this recognition with the advances in machine learning and deep learning techniques. A total of 108 manuscripts were collected, distributed equally by the known and unknown categories. The preliminary results for classifying and recognizing unknown manuscripts showed that using a decision tree classifier achieved an accuracy of 88% in classifying unknown manuscripts.
Sign Language Hand Gesture Recognition Method based on Machine Learning

The dynamic development of computer technology and means of communication and the improvement of network technology have led to an increase in the role of information as a major resource in society. People with hearing impairments, like everyone else, need to benefit from all areas where ICT is applied. Gestures are the only way for people with hearing and speech disabilities to communicate. Automatic recognition of gestures to facilitate communication with gestures is a topical issue, both scientifically and practically. The study provides information on static and dynamic gestures, various sensor technologies used in the collection of gesture data have been researched. The advantages and disadvantages of image-based and non-image-based technologies are analysed. A machine learning method based on neural networks has been developed for high-precision identification of gestures. High results were obtained when testing the developed method on a database open to scientific research. Thus, the method was able to recognize the letters of the dactyl alphabet with an accuracy of 0.95, 0.92, 0.95, 0.94 on the indicators of accuracy, precision, recall, F1-score, respectively.
Automotive Service-oriented Architectures: a Systematic Mapping Study

Service-oriented architectures are emerging as a promising solution to deal with the increasing complexity of automotive software systems. In this paper, we conduct a systematic mapping study to investigate the use of service-oriented architecture for the development of automotive software systems. This study aims at providing publication trends, available architectural solutions, core benefits and open challenges in the automotive service-oriented architectures. From an initial set of 341 peer-reviewed publications, we select 28 primary studies, which are classified and analysed using a systematic and comprehensive protocol. Using the extracted data, we provide both quantitative and qualitative analyses using vertical and orthogonal analysis. The results indicate that there has been a significant increase in the number of publications recently, and that the studies focused on defining functionalities and data flows among them. Functional suitability is found to be the most recognised benefit while security, safety and reliability are the most addressed challenges when utilising service-oriented architectures in the automotive domain.
Towards Continuous Systematic Literature Review in Software Engineering

Context: New scientific evidence continuously arises with advances in Software Engineering (SE) research. Conventionally, Systematic Literature Reviews (SLRs) are not updated or updated intermittently, leaving gaps between updates, during which time the SLR may be missing crucial new evidence. Goal: We propose and evaluate a concept and process called Continuous Systematic Literature Review (CSLR) in SE. Method: To elaborate on the CSLR concept and process, we performed a synthesis of evidence by conducting a meta-ethnography, addressing knowledge from varied research areas. Furthermore, we conducted a case study to evaluate the CSLR process. Results: We describe the resulting CSLR process in BPMN format. The case study results provide indications on the importance and preliminary feasibility of applying CSLR in practice to continuously update SLR evidence in SE. Conclusion: The CSLR concept and process provide a feasible and systematic way to continuously incorporate new evidence into SLRs, supporting trustworthy and up-to-date evidence for SLRs in SE.
Technical Analysis of Development of Rooftop Solar Photovoltaic Generation System

Electricity is one of the basic needs in this modern technological era. Currently, the most of electrical energy supply is derived from fossil fuels such as petroleum, coal and natural gas. However, the supply of these energy sources continues decreasing. Renewable energy is one of the solutions to meet the increasing demand for electricity. One of the most promising renewable energy sources is solar photovoltaic (PV). The main purpose of this article is to conduct a technical analysis of the Development of the solar PV generation system at the University of Riau Rectorate Building. To analyze the potential of solar PV generation development, the Software System Advisor Model (SAM) was adopted. The simulation results show that the average global horizontal irradiance (GHI) in the University of Riau Rectorate Building is 4.78 kWh/m^2/day, producing electrical energy up to 13,185 kWh, performance ratio (PR) 79% in one year with capacity factor (CF) of 79% and losses of 21.4%. The total annual electrical energy production was 158,216 kWh. Therefore, it is feasible to build the Rooftop Solar PV generation system with a power of 120 kWp on the roof of University of Riau rectorate building to supply the existing load.
Using COSMIC to measure functional size of software: a Systematic Literature Review

We present a systematic literature review performed to understand and summarize the application of COSMIC which is a functional size measurement (FSM) method, mainly applied for estimating software development effort. The results reveals that it is considered to be suitable for a broader range of application domains, e.g., Web applications, Mobile app. Furthermore, the review shows that a lot has been done also for automating the use of COSMIC as well as for rapidly and early applying the method through some approximations.
Algorithms To Increase Data Reliability In Video Transcription

Currently, the transmission of data in the form of video and audio on computer networks is growing rapidly. Increasing the flow of media data makes it the task of qualitative data processing and analysis. The growth of video data is one of the most pressing issues in the intellectual analysis of the content of this video data and transcription to extract the texts contained in the video files. In the process of video transcription, the process of transferring the data in the video file to the text without errors is followed. The process of video transcription is important for the intellectual analysis of data in hearing-impaired and large- scale video networks. To date, a lot of research has been done on video transcription. Nevertheless, shortcomings in transcription remain sufficient for arbitrary language. This research includes theoretical research on the study of algorithms and models of video transcription, as well as a theoretical analysis of experiments based on them.
Smart Customer Care: Scraping Social Media to Predict Customer Satisfaction in Egypt Using Machine Learning Models

This paper proposes the utilization of posts from social media to extract and analyze customer opinions and sentiments towards any specified topic in Egypt. Summarized statistics and sentiment values are then displayed to the consumer (companies such as Vodafone, WE etc..) through both an attractive and functional user interface. Text, location, and time of thousands of posts are scrapped, stored, preprocessed, then managed through topic modelling to infer all the hidden themes and delivered to a Recurrent Neural Network (RNN) to output whether the topic was positive or negative. Topic modelling was implemented using the BERT architecture and AraBert word embedding. Sentiment analysis model training was conducted on approximately 4000 rows of processed data and made use of Arabic glove embedding to speed up sentiment and word pattern recognition. Five models were experimented on: LSTM, GRU, CNN, LSTM + CNN and GRU + CNN. Overall, the GRU was the model with the best results, concluding with an accuracy of (86.19%), loss of (0.3349) and an F1-score of (0.858) when validating through the test data.
API Deprecation: A Systematic Mapping Study

Application Programming Interfaces (APIs) are the prevalent interaction method for software modules, components, and systems. As systems and APIs evolve, an API element may be marked as deprecated, indicating that its use is disapproved or that the feature will be removed in an upcoming version. Consequently, deprecation is a means of communication between developers and, ideally, complemented by further documentation, including suggestions for the developers of the API’s clients.API deprecation is a relatively young research area that recently gained traction among researchers. To identify the current state of research as well as to identify open research areas, a meta-study that assesses scientific studies is necessary. Therefore, this paper presents a systematic mapping study on API deprecation to classify the state of the art and identify gaps in the research field. We identified and mapped 36 primary studies into a classification scheme comprising general and API-specific categories.We identified five major gaps in previous research on API deprecation as opportunities for future studies: studying remote APIs, investigating a broader range of static APIs, joining suppliers’ and clients’ views, including humans in studies, and avoiding deprecation.
Regularity or Anomaly? On The Use of Anomaly Detection for Fine-Grained JIT Defect Prediction

Fine-grained just-in-time defect prediction aims at identifying likely defective files within new commits. Popular techniques are based on supervised learning, where machine learning algorithms are fed with historical data. One of the limitations of these techniques is concerned with the use of imbalanced data that only contain a few defective samples to enable a proper learning phase. To overcome this problem, recent work has shown that anomaly detection can be used as an alternative. With our study, we aim at assessing how anomaly detection can be employed for the problem of fine-grained just-in-time defect prediction. We conduct an empirical investigation on 32 open-source projects, designing and evaluating three anomaly detection methods for fine-grained just-in-time defect prediction. Our results do not show significant advantages that justify the benefit of anomaly detection over machine learning approaches.