Reviewerly/exHarmony huggingface dataset:
RESTful Service based Software Defect Prediction using ML Algorithms

Objectives: To present a suitable RESTful service-based software defect prediction approach that employs Machine Learning (ML) algorithms to identify software defects. Methods: The proposed approach is designed to provide a flexible solution for predicting software defects using various machine-learning techniques. It leverages RESTful web service-based class-level software metrics, including code complexity metrics, size metrics, coupling metrics, and cohesion metrics, and uses these metrics to train various ML models, such as Logistic Regression, Random Forest Classifier, LightGBM, XGBoost, and Support Vector Machines. Findings: We have proposed a correlation co-efficient method for feature selection and reduced it from 98 features to 25 features. With the granularity of class-level metrics of the RESTful service-based Elastic Search Engine’s dataset, we achieved the highest F-measure score of 0.677 using the LightGBM Machine Learning model. The existing work was done using the 10-fold cross-validation and achieved an F-measure of 0.5817 using the Decision Table model. Novelty: Most of the existing works carried out by various researchers using publicly available NASA PROMISE datasets which were generated long ago on legacy programming languages and further no updates were taken into consideration. This could lead to data source bias, meaning the findings and models developed may not be representative of software systems from different domains or industries. The proposed work carried out is using a newly generated RESTful software defects-based dataset and publicly available: Bug Hunter Dataset. The Bug Hunter dataset aims to cover a wide range of projects and software systems from different domains and industries. This diversity allows researchers to develop defect prediction models that are more generalizable and applicable to real-world scenarios and specific organizations or domains. Apart from the original author, as of now, no one used this dataset for software defect prediction. In the proposed work we have used one of the Bug Hunter Datasets called Elastic Search Engine — a RESTful Service-based software. We have applied different feature selection methods and achieved the best results using the Correlation Coefficient technique and achieved the best F-Measure of 0.677 using LightGBM with ahold-out validation approach whereas, in the existing work, the 10-Fold crossvalidation technique was used and achieved 0.5817 as the highest F-measure using the Decision Table machine learning model. There is future scope for working with other Machine Learning Models for exhaustive comparison with the proposed model. Keywords: Software Defect Prediction; Feature Reduction; Correlation Coefficient; Machine Learning; RESTful Service Software; LightGBM; Random Forest; SVM
Analyzing Social Media Adoption for Promoting CSR: A Study of UAE Healthcare Sector

Despite the significant potential presented by social media and its strategic relevance in driving business activities, its adoption continues to pose a considerable challenge, with rates remaining notably low. The primary objective of this study is to examine the factors that influence the application of social media for the promotion of corporate social responsibility (CSR) in the healthcare industry. To achieve this goal, the research employs the analytic hierarchy process (AHP) to establish a hierarchy and assign importance to the elements that shape the utilization of social media in advancing CSR objectives. The results reveal key drivers that impact the integration of social media for CSR promotion in the healthcare industry. These drivers encompass effective CSR communication, differences among social media platforms, the advantages stemming from social media use, stakeholder expectations, and the chosen implementation strategy. Furthermore, these findings hold the potential to provide important guidance for policymakers and pertinent organizations, aiding them in devising targeted strategies grounded in the specific factors of social media adoption that have demonstrated efficacy in CSR promotion.
Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.
Supplemental Material: Petrogenesis of Paleocene–Eocene gabbros in the Gangdese belt: Geochemical tracking of transitioning from oceanic subduction to continental collision related magmatism in southern Tibet

&lt;p&gt;Supplemental Tables S1–S6.&lt;/p&gt;
EXAMINATION OF ANALYSIS METHOD FOR HYDRAULIC MODEL EXPERIMENT UTILIZING RGBD IMAGES AND DUALSPHYSICS

Reconstruction method of real objects into point cloud based on RGB images and Depth images (RGB-D Images) has been adopted in various fields. By utilizing such reconstruction method, it would be possible to efficiently measure the complex shape of objects, such as tetrapods, without directly contacting to them in the real experiment. However, the measurements of three-dimensional (3D) objects have rarely been used in hydrodynamic experiments. In the present study, the methodology was built to carry out hydraulic experiments and numerical simulations focusing on the displacement of complex objects. It was carried out by combining reconstructed 3D point cloud data acquired from Intel RealSense LiDAR Camera L515 (L515) and DualSPHysics. The result of displacement of the objects was investigated by numerical simulation and hydrodynamic experiments.
Penanganan Data Tidak Seimbang Menggunakan Hybrid Method Resampling Pada Algoritma Naive Bayes Untuk Software Defect Prediction

Software defect prediction is software data that is used to identify a software module and can also be used to predict software defects. Before carrying out further trials, it is necessary to carry out special handling, especially by using algorithm models as predictions of software defects with the aim of obtaining information from the device being developed. Therefore, it is necessary to predict software defects using appropriate classification and prediction methods, so that the resulting accuracy results are better. In this study, the naïve Bayes algorithm was used as a classification with a resampling technique approach to handle unbalanced data, including SMOTEENN and SMOTETomek. The best accuracy results in the research conducted were 92.5% on the Nasa Repository PC4 dataset
Transfer and Ensemble Approach for Breast Cancer Detection and Classification Using Deep Learning

Breast cancer is a serious disease that can cause significant health problems for women worldwide. It is crucial to detect and classify breast cancer early stage so that doctors can promptly treat it and aid patients in their recovery. Many investigators have used various deep learning (DL) strategies to detect and classify breast cancer. However, due to the complexity of the problem, relying on a single DL model may not suffice to achieve high accuracy. Therefore, this study suggests a transfer and ensemble deep model for breast cancer detection and classification. The suggested model involves using pre-trained models such as Sequential, Xception, DenseNet201, VGG16, and InceptionResNetV2. The top three models are selected to collaborate and deliver the most accurate results. The proposed DL model was tested on publicly available breast BUSI datasets, demonstrating its superiority over single DL models, achieving an accuracy of 87.9% on the BUSI dataset. Additionally, the model proved to be adapTABLE to different amounts of data, making it potentially valuable in hospitals and clinics.
Low-Resource Neural Machine Translation Improvement Using Data Augmentation Strategies

The development of neural models has greatly improved the performance of machine translation, but these methods require large-scale parallel data, which can be difficult to obtain for low-resource language pairs. To address this issue, this research employs a pre-trained multilingual model and fine-tunes it by using a small bilingual dataset. Additionally, two data-augmentation strategies are proposed to generate new training data: (i) back-translation with the dataset from the source language; (ii) data augmentation via the English pivot language. The proposed approach is applied to the Khmer-Vietnamese machine translation. Experimental results show that our proposed approach outperforms the Google Translator model by 5.3% in terms of BLEU score on a test set of 2,000 Khmer-Vietnamese sentence pairs.
Machine Learning Based Twitter Sentiment Analysis and User Influence

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral.&#x0D; In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic.&#x0D; The proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy.&#x0D; The proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively.
Peer-to-Peer Conferencing using Blockchain, WebRTC and SIP

&#x0D; The owner of the centralized video platform has more control over uploaded content than the content producer does. But the other Blockchain-based decentralized video services are attempting to reduce ad pressure and get rid of middlemen. The article suggests a combination of a safe encryption technique and an access control mechanism created "with technology" to create a successful decentralized video streaming platform built on the Blockchain. Peer-to-peer (P2P) overlays are one of the complicated network applications and services that have been migrated to the Web as a result of the increasing support for Web Real-Time Communication (WebRTC) standard in modern browsers for real-time communications. The expansion of access networks’ bandwidth also makes it possible for end users to start their own content businesses. This paper presents a preliminary proposal of metrics and technologies to move toward a decentralized cooperative architecture for large-scale, real-time live stream content de- livery based on WebRTC, without the requirement of a Content Delivery Network (CDN) infrastructure. The paper takes into account the light of the aforementioned aspects [6].
Hybrid Simulated Annealing: An Efficient Optimization Technique

Genetic Algorithm falls under the category of evolutionary algorithm that follows the principles of natural selection and genetics, where the best adapted individuals in a population are more likely to survive and reproduce, passing on their advantageous traits to their offsprings. Crossover is a crucial operator in genetic algorithms as it allows the genetic material of two or more individuals in the population to combine and create new individuals. Optimizing it can potentially lead to better solutions and faster convergence of the genetic algorithm. The proposed crossover operator gradually changes the alpha value as the search proceeds, similar to the temperature in simulated annealing. The performance of the proposed crossover operator is compared with the simple arithmetic crossover operator. The experiments are conducted using Python and results show that the proposed crossover operator outperforms the simple arithmetic crossover operator. This paper also emphasizes the importance of optimizing genetic operators, particularly crossover operators, to improve the overall performance of genetic algorithms.
Substitutional Validity for Modal Logic

In the substitutional framework, validity is truth under all substitutions of the nonlogical vocabulary. I develop a theory where □ is interpreted as substitutional validity. I show how to prove soundness and completeness for common modal calculi using this definition.
Deep Learning-based Cnn Multi-modal Camera Model Identification for Video Source Identification

There is a high demand for multimedia forensics analysts to locate the original camera of photographs and videos that are being taken nowadays. There has been considerable progress in the technology of identifying the source of data, which has enabled conflict resolutions involving copyright infringements and identifying those responsible for serious offences to be resolved. This study focuses on the issue of identifying the camera model used to acquire video sequences used in this research that is, identifying the type of camera used to capture the video sequence under investigation. For this purpose, we created two distinct CNN-based camera model recognition techniques to be used in an innovative multi-modal setting. The proposed multi-modal methods combine audio and visual information in order to address the identification issue, which is superior to mono-modal methods which use only the visual or audio information from the investigated video to provide the identification information.According to legal standards of admissible evidence and criminal procedure, Forensic Science involves the application of science to the legal aspects of criminal and civil law, primarily during criminal investigations, in line with the standards of admissible evidence and criminal procedure in the law. It is responsible for collecting, preserving, and analyzing scientific evidence in the course of an investigation. It has become a critical part of criminology as a result of the rapid rise in crime rates over the last few decades. Our proposed methods were tested on a well-known dataset known as the Vision dataset, which contains about 2000 video sequences gathered from various devices of varying types. It is conducted experiments on social media platforms such as YouTube and WhatsApp as well as native videos directly obtained from their acquisition devices by the means of their acquisition devices. According to the results of the study, the multimodal approaches suggest that they greatly outperform their mono-modal equivalents in addressing the challenge at hand, constituting an effective approach to address the challenge and offering the possibility of even more difficult circumstances in the future
Twitter Misinformation Discourses About Vaping: Systematic Content Analysis

While there has been substantial analysis of social media content deemed to spread misinformation about electronic nicotine delivery systems use, the strategic use of misinformation accusations to undermine opposing views has received limited attention.This study aims to fill this gap by analyzing how social media users discuss the topic of misinformation related to electronic nicotine delivery systems, notably vaping products. Additionally, this study identifies and analyzes the actors commonly blamed for spreading such misinformation and how these claims support both the provaping and antivaping narratives.Using Twitter's (subsequently rebranded as X) academic application programming interface, we collected tweets referencing #vape and #vaping and keywords associated with fake news and misinformation. This study uses systematic content analysis to analyze the tweets and identify common themes and actors who discuss or possibly spread misinformation.This study found that provape users dominate the platform regarding discussions about misinformation about vaping, with provaping tweets being more frequent and having higher overall user engagement. The most common narrative for provape tweets surrounds the conversation of vaping being perceived as safe. On the other hand, the most common topic from the antivape narrative is that vaping is indeed harmful. This study also points to a general distrust in authority figures, with news outlets, public health authorities, and political actors regularly accused of spreading misinformation, with both placing blame. However, specific actors differ depending on their positionalities. The vast number of accusations from provaping advocates is found to shape what is considered misinformation and works to silence other narratives. Additionally, allegations against reliable and proven sources, such as public health authorities, work to discredit assessments about the health impacts, which is detrimental to public health overall for both provaping and antivaping advocates.We conclude that the spread of misinformation and the accusations of misinformation dissemination using terms such as "fact check," "misinformation," "fake news," and "disinformation" have become weaponized and co-opted by provaping actors to delegitimize criticisms about vaping and to increase confusion about the potential health risks. The study discusses the mixed types of impact of vaping on public health for both smokers and nonsmokers. Additionally, we discuss the implications for effective health education and communication about vaping and how misinformation claims can affect evidence-based discourse on Twitter as well as informed vaping decisions.
Machine Learning Methodology for the Recognition of Unsolicited Mail Communications

It is very demanding to classify and label data in machine learning algorithms manually. This works by analysing the probability of different words occurring in legit and spam mails and classifying them accordingly. This research used the notion of applying few labelled data to extrapolate and work on unlabelled data to attain high-accuracy classifiers. Many organizations, students and workplaces have recently used emails to communicate about current affairs, update people on events, etc. Unfortunately, spammers always find their way to exploit recipients by either flooding them with spam emails or sending spam messages periodically. Researchers started applying machine learning techniques to messages to detect if they were spam or not. This was met by the spammers fabricating standard mails. Most people regard spam mails as annoying emails repeatedly used for advertising products and brand promotion. Many such emails are blocked before they get into the user's inbox, but the spam menace is still prevalent. Some other spam mails pose a significant risk to users, like identity theft, containing malware and viruses used to hack people's gadgets, and spam that facilitates fraud. There is a need to create robust spam filters that can efficiently and correctly classify any incoming mail as either spam or not.
PairEE: A Novel Pairing-Scoring Approach for Better Overlapping Event Extraction

Event Extraction (EE) is a fundamental task and has achieved much success in the past few years. However, the overlap between the event elements has been largely ignored in most previous studies. In this paper, we propose a novel pairing-scoring approach to better solve the overlapping problem. In particular, we firstly extract all event triggers and arguments simultaneously. Then we cast the acquisition of the complete event as an assembly task. By pairing and scoring all possible event triggers and arguments, we group event elements into different events to get the list of complete events. In addition, we design a type-aware fusion layer to improve the extraction of event elements by explicitly exploiting predefined event type and argument role label information. Experiments on the FewFC demonstrate that our proposed model can significantly improve the performance of overlapping EE.
Implementation of Finite State Automata in Graphic Design Class Process Online

The development of technology is growing rapidly, the number of increasingly sophisticated inventions can also help humans in carrying out their activities, one of which is online classes, especially graphic design from various official organizers such as from coursera, udemy, and other organizers. Online graphic design classes are currently in great demand by the general public, especially at the school level, some have to pay a certain nominal, there are also free ones to access the class, besides that the classes offered are starting to vary, ranging from the beginner level to the advanced level also with certain software such as Adobe Photoshop, Figma, and other design applications and also with different systems, online or offline, graphic design is much needed, especially in the world of work such as printing, advertising and other fields. In this study, it was designed by using the FSA (Finite State Automata) method with the TYPE of NFA (Non-deterministic Finite Automata) to help students in the process of online graphic design classes starting from registration on the website to getting a class certificate so that students do not feel confused about what to do after registering, and is expected to make students understand in the process of online graphic design classes, The results of using FSA with nfa type can help students to better understand the flow of the registration process for online graphic design classes, with the creation of an FSA with this type of NFA is also expected to help students in registering online to get a certificate because they have attended an online graphic design class. Keywords : desain grafis, FSA, NFA, corel, adobe.