Reviewerly/exHarmony huggingface dataset:
The Impact of Forced Working-From-Home on Code Technical Debt: An Industrial Case Study

Background: The COVID-19 outbreak interrupted regular activities for over a year in many countries and resulted in a radical change in ways of working for software development companies, i.e., most software development companies switched to a forced Working-From-Home (WFH) mode. Aim: Although several studies have analysed different aspects of forced WFH mode, it is unknown whether and to what extent WFH impacted the accumulation of technical debt (TD) when developers have different ways to coordinate and communicate with peers. Method: Using the year 2019 as a baseline, we carried out an industrial case study to analyse the evolution of TD in five components that are part of a large project while WFH. As part of the data collection, we carried out a focus group with developers to explain the different patterns observed from the quantitative data analysis. Results: TD accumulated at a slower pace during WFH as compared with the working-from-office period in four components out of five. These differences were found to be statistically significant. Through a focus group, we have identified different factors that might explain the changes in TD accumulation. One of these factors is responsibility diffusion which seems to explain why TD grows faster during the WFH period in one of the components. Conclusion: The results suggest that when the ways of working change, the change between working from office and working from home does not result in an increased accumulation of TD.
A Mapping Study of Security Vulnerability Detection Approaches for Web Applications

For the last few decades, the number of security vulnerabilities has been increasing with the development of web applications. The domain of Web Applications is evolving. As a result, many empirical studies have been carried out to address different security vulnerabilities. However, an analysis of existing studies is needed before developing new security vulnerability testing techniques. We perform a systematic mapping study documenting state-of-the-art empirical research in web application security vulnerability detection. The aim is to describe a roadmap for synthesizing the documented empirical research. Existing research and literature have been reviewed using a systematic mapping study. Our study reports on work dating from 2001 to 2021. The initial search retrieved 150 papers from the IEEE Xplore and ACM Digital Libraries, of which 76 were added to the study. A classification scheme is derived based on the primary studies. The study demonstrates that vulnerability detection in web applications is an ongoing field of research and that the number of publications is increasing. Our study helps illuminate research areas that need more consideration.
Classification System of Malaria Disease with Hu Moment Invariant and Support Vector Machines

Malaria is an infectious disease caused by a plasmodium parasite transmitted by the female Anopheles mosquito. According to the World Health Organization (WHO) in 2020 there are an estimated 241 million cases of malaria worldwide with an estimated global death stood at 627. 000. The standard method of malaria diagnosis is by conducting microscopic examination or laboratory test and Rapid Diagnostic Test (RDT). Laboratory tests have a high risk of human error whereas RDT has weaknesses in temperature sensitivity, genetic variation, and antigen resistance in the bloodstream. This research offers a classification system of malaria disease by applying the Hu moment invariant and Support vector Machine (SVM) method with 3 types of malaria parasitic objects, namely falciparum, Malaria and vivax. The classification system uses 3 SVM models, namely linear SVM, polynomial SVM and Gaussian SVM with the Falciparum class as a positive data and malaria and vivax as negative data. The best classification outcome is on the Gaussian SVM model with 96.67% sensitivity and 90% specificity. The mean accuracy of the Gaussian SVM model with a 5-fold cross Validation 90 image sample which is divided into 3 classes is 86.66%.
An Exploratory Study on Chatbots

Chatbots are now widely used in our daily life. The Chatbots are software applications, which are being used to simulate human conversation and in other words it can be said that to simulate user and system/computer interactions. These makes the user think that they are chatting with a human being where as they're chatting with the software applications. Chatbots help the humans to get information about the data they required. Chatbots systems reduce the workload and helps us to get more efficient information. Here we have done an exploratory study on the techniques and various platforms used to design and develop the Chatbots. We have found that Artificial Intelligent (AI) systems will use human actions, the system will take the decision at a moment and according to that, obligatory actions will be taken. We have also studies the work flow of the Chatbots and as per working techniques we have classified them. Also we have described the scientific way for evaluating these Chatbots. Use cases are highlighted. The consistent automatic assessment tactic is a beacon lights for resolving the present Chatbots problems.
A new framework for an eKYC system

Identity verification has long been a crucial problem to solve to automate financial operations which requires user authentication and detect fraudulency. Until recently the realization of this task was nearly impossible to do with considerable accuracy, thanks to advancements in machine learning over the past few years we can achieve this task. This paper will discuss a proposed solution for high accuracy, high-performance eKYC system. In an eKYC system, we need to verify our client's identity as per his identity documents with the constraint that he passed a liveness detection test to ensure he is doing the financial operation in person. In our proposed system, verification is done in three main stages, which are: face detection, face verification, and face antispoofing detection. We employed an AI model to perform each task, We employed MTCNN [1] for face detection and FaceNet [12] for face verification. For face antispoofing, we implemented a state-of-the-art model PatchNet [15].
Advantages of Redis in-memory database to efficiently search for healthcare medical supplies using geospatial data

According to diagnostic criteria, a patient can find clinics he needs depending on the symptoms of disease. The paper shows an effective solution that allows rapid access to information about clinic using Redis in memory database technology. In this paper using Redis help us to collect a wide array of geospatial capabilities finding the best way and build out this type of functionality. Redis has the capability to store by Redis intelligent optimized systems in their native format, and update and serve them with minimal computing infrastructure needed to implement these algorithms at scale. Also, our approach is to study the effect of the databases on system’s working speed, comparing Redis and MS SQL.
Development of Mobile Butterfly Application-Based Augmented Reality as an Alternative Learning Medium During Post COVID-19

Indonesia experienced a COVID-19 pandemic starting in 2020. Limited mobility impacts various sectors, including tourism. Alian Butterfly Park is one of those affected. Alternatives were made to attract visitors, such as developing Augmented Reality applications to educate visitors about the new normal. Previous research developed a similar application, namely butterfly education with Augmented Reality technology, but its implementation still uses markers. It makes the user have to visit in person. This research improves the previous application. The improvement such as adding the interaction in AR butterfly life cycle, a virtual tour to collect butterflies, and the AR object is developed markerless. The application is developed using the agile development method. The application that has been developed is tested on the target user of the application. Testing is done by giving a black box testing. The test method is carried out by the black box testing method on 3 students. From the tests carried out, from a total of 25 test scenarios, there are 23 successful test scenarios and 2 test scenarios that fail due to bugs. The success value of the entire test scenario is 92% and the bugs found are 156 only affect the appearance of the application and do not affect the application critically. In addition, 2 scenarios are successful but are given special note due to environmental factors that can affect the results of the test.
A Syntax-based BSGCN Model for Chinese Implicit Sentiment Analysis with Multi-classification

There are two types of sentiment analysis about Chinese sentences: explicit sentiment analysis and implicit sentiment analysis. Implicit sentiment, unlike explicit sentiment, lacks a well-defined sentiment vocabulary. Currently, the majority of relevant research has focused on extracting implicit emotion via word analysis, ignoring the role of syntactic structures and relationships between words in the analysis of implicit emotions in Chinese. In this paper, we use a graph convolutional neural network (GCN) to analyze the syntactic structure of implicit sentiment texts, then combine it with a Bidirectional Encoder Representations from Transformers (BERT) to extract contextual information to create the BSGCN, a multi-classification Chinese implicit sentiment analysis model, that can classify implicit sentiment Chinese sentences into five types: happiness, sadness, disgust, surprise, and neutral. In the experiment based on the dataset SMP-ECISA, the accuracy of the Chinese implicit sentiment analysis model proposed in this paper was 82.1%, which is a significant improvement over existing models.
Information Extraction from Arabic Medications Leaflets

Making information in electronic documents easily accessible has been a major concern over the past years. There has been increasing interest in gleaning information from unstructured text and presenting it as structured data using information extraction (IE). Since Arabic has seen major growth in web content, mainly unstructured text, the need for IE from Arabic documents has gained importance. The processing capacity needed for IE far exceeds human ability to extract knowledge manually. The medical field is one such area, where awareness of health issues makes the task of automating medical informatics crucial for better access to medical knowledge. Thus, work on extracting information from medical documents has increased rapidly. In this paper we address the issue of IE from Arabic drug leaflets. We use a combination of rule-based, machine learning and deep learning methods and employ a suit of tools that account for the particularities of Arabic to extract information from Arabic drug package inserts to make this information available in structured form and thus better accessible to regular users and health care providers. A prototype system that utilizes the IE results was developed with useful functionality such as alerting to possible Adverse Drug Reactions (ADR) and finding drug alternatives.
Recommender Diagnosis System with Fuzzy Logic in Cloud Environment

Recommendation systems are now used in a wide range in many fields. In the medical field, recommendation systems have a great stature to both doctors and patients for its accurate prediction. It can reduce the time and efforts spent by doctors and patients. The present work introduces a simple and effective methodology for medical recommendation system based on fuzzy logic. Fuzzy logic is an important method to be used based on fuzzy input data. The input data for each patient are not the same, on which recommendation can differ. This work aims to develop techniques for handling the patient data to urge accurate lifestyle recommendations to the patient. Fuzzy logic is utilized to form different recommendations for the patient like lifestyle recommendations, medicine recommendations, and sports recommendations based on different patient factors like age, gender and patient diseases. After evaluating the system its efficiency reached 94%. This Experiment is the final module in a four modules recommendation system. The first one is responsible for diagnosing chest diseases using ECG signals. The second one makes diagnosis using X-ray images. The third is utilizing the security of the whole system through encryption when sending user data over the cloud.
Comparative Analysis of Radial Basis Method with Backpropagation for Signature Identification

TSignatures are one of the most frequently used biometric authentication systems, they cannot be stolen or loaned to others. Signature recognition can be done by eye, but signature identification using this method can be fooled by someone's exper-tise in forging signatures. The absence of special characteristics of a signature that makes it difficult for people to forge it. Human physical limitations (fatigue, inaccuracy and impaired vision) can affect the interpretation of a signature. Therefore, automatic signature identification can assist forensic experts in processing and analyzing signatures. This study compares the backpropagation and radial basis artificial neural network methods. The data used are 96 images, consisting of 84 training images and 12 test images. Feature extraction used is Fast Fourier Transform, which is scanned horizontally and vertically. The backpropagation architecture uses traingdx learning, the variable rate of learning is 0.1, the ratio of increasing the learning rate is 1.6, the ratio is decreasing the learning rate is 0.5, and the momentum is 0.3. The first hidden layer is 48, the second hidden layer is 24 and the output is 12. The identification accuracy using the backpropagation method is 91.67%. The architecture of the radial basis method uses a data spread width of 1, epoh 10.000, goal 1e-2. The identification accuracy using the radial basis method is 66.67%. This research proves the backpropagation method is more accurate than the radial basis, if it is used to identify biometric systems with signature specimens.
A Critical Survey on Arabic Named Entity Recognition and Diacritization Systems

Language technologies are considered a subdivision of the Artificial intelligence (AI) field, which sheds light on how toolkits are programmed to simulate the natural language of humans. Over the last decennia, there has been a unique advancement in the Natura Language Processing (NLP) field, namely regarding the Arabic language. Arabic is the language spoken by almost two billion Muslims worldwide and is one of the six officially acknowledged languages by the UN organization. This paper is dedicated to a survey on three cutting-edge toolkits utilized to process and analyze the Arabic language: Cameltools, Farasa, and Madamira. This paper presents a background on the challenges that have confronted Arabic Natura Language Processing (ANLP), predominantly concerning diacritization, and Named Entity Recognition (NER) systems. Next, it illustrates what are the main components of Cameltools, Farasa, and Madamira. After that, the evaluation processes of the three toolkits shall be presented and their results will be compared. Finally, the paper shall present observations based on the previous comparison. The survey reveals that Camel is the best since it has been inspired by the designs of the best toolkits provided in the field. Farasa outpaces Madamira in all comparisons regarding ANER and Arabic diacritization.
Arabic Sentences Semantic Similarity Based on Word Embedding

Natural language processing pays significant attention to semantic textual similarity. It's useful in a variety of NLP-applications, including information retrieval, plagiarism detection, data extraction, and machine translation. Sentence similarity in the Arabic language has not been investigated deeply because of the lack of Arabic language resources. Moreover, it's critical to calculate the degree of similarity between Arabic sentences accurately. The method for determining the semantic similarity of Arabic sentences is suggested in this research. The strategy suggested uses word embedding to measure the similarity between words. Moreover, more than one similarity measure is combined to calculate the final similarity. Furthermore, due to the lack of Arabic resources, a new dataset for evaluating similarity techniques has been constructed. The new dataset is available for public use. An experiment have been conducted to show the efficiency of the strategy suggested. Two datasets are used to compare other approaches. Experiments reveal that the proposed methods outperform alternative approaches to measuring sentence similarity in the Arabic language.
The Impact of Parameters Optimization in Software Prediction Models

Several studies have raised concerns about the performance of estimation techniques if employed with default parameters provided by specific development toolkits, e.g., Weka. In this paper, we evaluate the impact of parameter optimization with nine different estimation techniques in the Software Development Effort Estimation (SDEE) and Software Fault Prediction (SFP) domains to provide more generic findings of the impact of parameter optimization. To this aim, we employ three datasets from the domain of SDEE (China, Maxwell, Nasa) and three different regression-based datasets from the SFP domain (Ant, Xalan, Xerces). Regarding parameter optimization, we consider four optimization algorithms from different families: Grid Search and Random Search, Simulated Annealing, and Bayesian Optimization. The estimation techniques are: Support Vector Machine, Random Forest, Classification and Regression Tree, Neural Networks, Averaged Neural Networks, k-Nearest Neighbor, Partial Least Square, MultiLayer Perceptron, and Gradient Boosting Machine. Results reveal that, with both SDEE and SFP datasets, seven out of nine estimation techniques require optimization/configuration of at least one parameter. In majority of the cases, the parameters of the employed estimation techniques are sensitive to the optimization of specific types of data. Moreover, not all the parameters need to be optimized as some of them are not sensitive to optimization.
Detecting Phishing Websites with Non-Parametric Machine Learning

Phishing is a popular cybercrime, particularly with the growth of technology, in which they employ email and fake websites. To identify phishing websites, this paper employed three common non-parametric machine algorithms: K-NN, SVM, and Decision Tree. By utilizing GridSearch, K-NN and SVM has similar result, while Decision Tree performed the lowest. K-NN is more favorable in this research, since the number of features is less than the amount of training data.
Improving BERT Classification Performance on Short Queries About UNIX Commands Using an Additional Round of Fine-Tuning on Related Data

One of the great advantages of machine learning as a whole is its ability to assist a human with digesting extremely large sets of data, and helping them to learn useful information that otherwise would have been significantly more difficult to piece together, and improvements to ML models often can result in improvements in this ability. To that end, this paper presents an evaluation of the relative performances of differing versions of Bidirectional Encoder Representations from Transformers (BERT) on the task of classifying a dataset of titles from posts scraped from two UNIX-related Q&A forum websites into classes based on what command each post is most likely about. The differing versions of BERT were each first fine-tuned on a different dataset from the post titles in order to try to improve the accuracy and precision of the model’s classification abilities through the introduction of relevant yet longer, more detailed, and more information-rich information. Additionally, the performances of these models are compared to that of the Heterogeneous Graph Attention Network (HGAT). The novel contributions of this paper are a real-world-use comparison between HGAT and BERT, the production of a novel dataset, and the presentation of supporting evidence for the value of relevance and length of text in pretraining for short-text classification.
Improving The Performance of Semantic Text Similarity Tasks on Short Text Pairs

Training semantic similarity model to detect duplicate text pairs is a challenging task as almost all of datasets are imbalanced, by data nature positive samples are fewer than negative samples, this issue can easily lead to model bias. Using traditional pairwise loss functions like pairwise binary cross entropy or Contrastive loss on imbalanced data may lead to model bias, however triplet loss showed improved performance compared to other loss functions. In triplet loss-based models data is fed to the model as follow: anchor sentence, positive sentence and negative sentence. The original data is permutated to follow the input structure. The default structure of training samples data is 363,861 training samples (90% of the data) distributed as 134,336 positive samples and 229,524 negative samples. The triplet structured data helped to generate much larger amount of balanced training samples 456,219. The test results showed higher accuracy and f1 scores in testing. We fine-tunned RoBERTa pre trained model using Triplet loss approach, testing showed better results. The best model scored 89.51 F1 score, and 91.45 Accuracy compared to 86.74 F1 score and 87.45 Accuracy in the second-best Contrastive loss-based BERT model.
A Revised Survey of Paraphrasing Generation Approaches and Tools for Arabic

Due to the technological advancement and progress in NLP and text editing tools, there is an increasing demand for the paraphrasing practice. This demand has motivated researchers because numerous NLP applications are associated with it, including information retrieval, query answering, essay authenticity, text summarization, etc. This paper is a survey of several computational approaches for paraphrasing generation since the task of generating or identifying semantic equivalence for different linguistic elements is an essential part of NLP. It surveys a revised account including the most recent approaches to paraphrase generation up to Universal Networking Language Systems. The research specifically examines paraphrasing generation for Arabic using transformational rules. This is achieved by testing a paraphrasing on-line tool for Arabic and attempting an analysis and evaluation of its paraphrasing practice. The free online selected tool is Paraphrase Tool.com which is used to paraphrase more than one hundred languages, including Arabic. The resulting output is evaluated using BLEU to determine the accuracy of the rendered paraphrase and its semantic equivalence to the original source text.
A Comparative Study of Large Automata Distributed Processing

We enter bigdata domain when we face data that are so much large that they cannot fit in one machine, or the processing cannot fit in one machine RAM, or would last. We focus our study on large NFAs. We describe, implement and compare two solutions of NFA determinization. These novel solutions are based on two different distributed and parallel programing paradigms, namely MapReduce and BSP/Pregel. Running examples are provided with details on execution. This contribution belongs to the first stages of our main target consisting of a language of a high level for big and distributed graph programming.