Reviewerly/exHarmony huggingface dataset:
MetricHunter: A software metric dataset generator utilizing SourceMonitor upon public GitHub repositories

Version control systems are pervasively consulted nowadays to obtain software metric datasets. Accordingly, machine learning is applied to predict different aspects of a software including quality monitoring, influence analysis, etc. However, construction of a metric dataset is challenging and the dataset content may affect the success of the learning-based models. In this study, we propose a dataset construction tool, MetricHunter, which is able to produce platform/language specific datasets that can be used for predicting the features of newly created software. The proposed tool is developed by C# programming language utilizing a known metric gathering tool, i.e. SourceMonitor, and the GitHub REST API for public repositories. Thus, one can construct a proper dataset from a graphical user interface by simply specifying the programming language or target platform. The outputs of the tool on a set of repositories are validated by investigating automatically generated attribute values and comparing them with the measurements of metric gathering tools as well as the GitHub metric values.
Method for Arabic text Summarization using statistical features and word2vector approach

Arabic text summarization is a field of natural language processing, and many algorithms for Arabic texts summarization have been developed, but these algorithms face weakness points which generated from the specifications of the Arabic language manipulation.
Fusion Local and Global Aspect-based Sentiment Analysis

Aspect-based sentiment classification is an important task in natural language processing research, and in response to the fact that most studies at this stage ignore the influence of contextual semantic information on the sentiment polarity of aspect words, our model proposed in this paper combines local aspect word feature extraction and global contextual semantic information extraction based on Bi-directional Long Short-Term Memory (BiLSTM), and after a multi-headed attention mechanism to enhance the local aspect word sentiment representation. Comparative experiments were conducted on the restaurant and laptop datasets of the SEMEVAL2014 evaluation task. The experimental results show that the model proposed in this paper achieves good classification results in the aspect-level sentiment analysis task of text reviews. The method provides a new idea for ABSA task development.
Critical success factors of augmented reality technology adoption in technical skill training for manufacturing

The evolution toward smart manufacturing solution is using several key technologies such as the Internet of Things, cloud computing and communication systems while augmented reality has been presented as a core technology for future key research, along with virtual reality. The purpose of this study is to examine the critical success factors of augmented reality technology adoption in skill training for manufacturing sector. The questionnaires were distributed to TVET instructors and 37 sample were collected and analyzed using SPPS. The results show that all four factors have significance influence toward augmented reality technology adoption with the most critical factor is technology readiness, followed by students’ achievement, learning content and instructors’ acceptance. This study recommends exploring students’ participation as an active user of augmented reality as learning tool.
A Case Study on Smishing: An Assessment of Threats against Mobile Devices

Smartphones emerged as a sector of outstanding the growth and development with the advent of smart homes, cities, and everything. These gadgets have been integrated into ordinary human activity. Due to their influence and expansion, these gadgets are now more susceptible to attacks than other devices like desktops or laptops. Smartphones include text messaging capabilities, often known as SMS (Short Text Messages), which attackers use to target consumers. Smishing (SMS) is an assault using text messages to target smartphone users. The main purpose of the case study was to assess vulnerability of various attacks; this study investigates the threats against mobile devices and their many forms of attacks to prevent, lessen, and end these kinds of attacks and suggests various approaches to deal with the country's current problem about the sms attack problems.
XGBoost for Classifying Ethereum Short-term Return Based on Technical Factor

The concept of digital cash has the potential to completely change how people think about money. Digital currency has emerged as a possible alternative for exchanging currency and traditional payment systems, in addition to a popular investment option due to its potential for high returns. One of the three main varieties of digital currency is cryptocurrency that is secured by blockchain technology. Bitcoin, Ethereum, and many other cryptocurrencies exist in crypto markets. Investing in cryptocurrencies still carries risks and uncertainties due to the price volatility. It is thus important to approach such investments with caution and thoroughly research the market and its risks before making investment decisions. This paper presents an application of AI technology for learning the price movement of Ethereum (ETH) which is second only to Bitcoin in market capitalization. Based on the Technical factor, the XGBoost model is constructed for classification of return on Ethereum close price. The technical indicators such as moving averages and relative strength index, together with the Bitcoin price trend are chosen to determine influence on Ethereum price further used for computing the short-term return separate into 3 classes: downtrend, sideway, and uptrend. The model performance is measured by multiclass ROC-AUC, achieving the micro-average ROC-AUC of 0.66 saying the model is reasonably good at predicting the overall trend of ETH price.
Improvements of the Software Quality Assurance Process Towards Issues’ Effectiveness in a Global Software Development Environment.

In the software industry, the software testing activity’s main objective is to guarantee the quality of products in development, achieving more reliability through the software process. Therefore, it is important to assure that the tasks which are part of the quality process, such as issue analysis, test case development and test execution, are performed effectively. In this context, this paper aims to describe an experience account from a global software development environment, through a study performed between 2020 and 2022, during which were developed improvements in the software process quality, due to the existence of many incorrect issue reports, causing frequent correction of test cases and rework in the test process. Those improvements are related to the implementation of a process flow which focus on the communication of test cases requisites, issues discovered and test execution. Through the implementation of this methodology, there was a reduction of 82% of escaped issues to other software phases, around 76% reduction of test cases correction, and a 79% increase of the registered issues’ effectiveness. Finally, to complement this paper’s results, a qualitative survey was made in which 100% of the test team stated that the improvement of the process through activity feedback is important. Therefore, one might conclude that those improvements brought more effectiveness to the issues discovered in a global software development environment, reducing problems related to the process of assuring software quality.
Implementing the Tesseract Method for Information Extraction from Images

Character recognition (CR) has been the subject of substantial research over the past half century and has now reached a level of development that is adequate to build applications driven by technology. Now, the fast expanding computer power makes it possible to execute the existing Optical Character Recognition (OCR) approaches and produces an increasing demand in a wide variety of emergent application domains that call for more advanced methodologies. Scanning the paper and entering the digitized image into a computer system is one of the quickest and easiest ways to save text information. After that, it will be saved on the computer, and if necessary, alterations can be made to it as well. However, recognizing the text in an image that has been recorded is a very difficult challenge to accomplish. As a result, The Tesseract method has been employed to extract text from images, simplifying the process of doing so.
A Multi-Criteria Decision-Making Framework for Software Project Management Tool Selection

Project management tools are critical for managing software projects, decreasing costs, accelerating timelines, and using available resources effectively. However, project managers find it very tedious to choose a suitable software project management (SPM) tool from available tools in the market. The appropriateness of the SPM tool depends on the nature of the software project being developed – an SPM tool suitable for a large-scale software project may not be appropriate for small-scale projects. However, due to the complexities of the software projects and the development conditions, the selection of an SPM tool is often biased by personal preferences or marketing publicities which may lead to inefficiencies in SPM and consequently, lead to higher cost and lower quality of end product. In this work, we present a decision support framework for SPM tool selection. The proposed system develops a set of selection criteria based on an analytic network process (ANP). To demonstrate the internal working of the proposed framework, we put forward an example of the tool selection problem using the proposed system. Furthermore, the proposed system can be used as is for SPM tool selection for other than software projects.
An Autonomous Fake News Recognition System by Semantic Learning and Cognitive Computing

It has been well understood that fake news recognition is a persistent challenge to cognitive computing and autonomous systems in general, and to Artificial Intelligence (AI), machine knowledge learning, and computational linguistics in particular. This work develops an Autonomous Fake News Recognition (AFNR) system by cognitive computing theories underpinned by Intelligent Mathematics (IM) such as concept algebra and semantic algebra. A training-free methodology and a formal algorithm for Differential Sematic Analysis (DSA) are designed in Real-Time Process Algebra (RTPA) and implemented in MATLAB. The AFNR system is implemented in the Anaconda environment with Python, the natural language toolkit (NLTK), and an English parser – Spacy. Compared to the classical data-driven neural network methodologies, AFNR and DSA have demonstrated a significant improvement against the level of accuracy over the randomly selected and large-scale benchmark of a fake news database. The DSA methodology for fake news recognition has enabled autonomous machine knowledge learning and semantic comprehension towards differential and robust semantic analyses for fake news in natural languages. The AFNR system has reached an accuracy level of 70.1%, which over performs the top ranked teams in DataCup’19 with the highest reported accuracy of 55.0%.
Application of Blockchain Technology for Defending the Pandemic: A Bibliometric Analysis

The COVID-19 pandemic had disrupted the health system in the world, and modern technologies are essential for drug delivery, monitoring, sanitation, regulating access controls, and for many more initiatives. Blockchain technology plays a significant role in the development of vibrant health and preventive system against COVID-19. India and the USA lead in the research related to the application of blockchain technology. The leading research organizations and funding agencies are from the Middle East and China. The future researchers can concentrate on these leading institutions for sponsorships and collaborations. Future research can be on the innovative applications of blockchain technology in vaccine delivery, controlling, and education of people.
Stress Contagion Protocols for Human and Autonomous Robot Teams

The objective of this paper is to formalize the stress contagion protocols in first responder teams which respond to life-threatening crises on a daily basis. Such teams include both human and autonomous machine teammates. While the stress contagion is an attribute of human relations, collaboration between the humans under stress and, and the stressed humans and machines, impacts the mission performance. Thus, it is important to model this process and use the modeling to train the first responder teams. This paper proposes a framework for modeling the human-machine communication protocols using the advances in computational multivalued logic. We also separate the formalization of the protocol from the choice of an appropriate model. This separation allows to mitigate the constraints posed by the limited real-world initial data of absence of thereof. The proposed formalization based on multivalued logic is well-supported by the computational and benchmarking tools.
Fairness, Bias and Trust in the Context of Biometric-Enabled Autonomous Decision Support

Developing a trustworthy biometric-enabled autonomous and semi-autonomous system involves computing the amount of bias and fairness of the data upon which the decisions are made. In this paper, we evaluate the performance of a system that performs human users’ face identification in terms of hit and miss rates. We also assess how the usage of data for training biometric systems can lead to unfairness, especially in the context of evolving biometric traits in times of pandemics. In particular, we contrast the performance of normal “unmasked” facial images with their “masked” counterparts. The SpeakingFace and Thermal-Mask dataset is used for assessment, where the Thermal-Mask dataset consists of synthetically applied masks on both the thermal (IR) and visual (RGB) domains. The comparative experiment assesses how fairness metrics such as demographic parity difference (DPD) and equalized odds difference (EOD) can be applied to both masked and normal facial images across the thermal and visual domains. For age, gender, and ethnicity demographic groups, the DPD approaches 0.10%, 0.01%, and 0.01% when the hit rate and miss rate for facial identification approach 100% and 0%, respectively. For masked-thermal facial identification using a simple 2-block convolutional neural network, we obtain a hit rate of 69.96% whereas an 85.82% hit rate is reported for the unmasked alternative. The corresponding age DPD for such hit rate is 49.06% and 34.02% for their respectively masked-thermal and normal-thermal images. We conclude that datasets with inherent biases influence the fairness of a biometric system. The bias manifests itself across the demographic groups (age, gender, and ethnicity), the spectral domain (IR or RGB), and whether synthetic procedures are applied to generate new images. In particular, the error rates among masked facial images are consistently above the unmasked alternative for both visual and thermal domains.
High-Performance Code Compression Using Adaptive Encoding for RISC Processor

In this paper, high-performance code compression using adaptive encoding for RISC processor is implemented. The main intent of reduced instruction set computer (RISC) processor is to utilize the small and optimized instructions. Code compression in RISC processor will encode the optimized data to a certain extent. Initially, the data is moved from one place to another using data bus buffer. Adaptive encoding will compress the lossless data and characterize the lossless data. Basically, encoding in RISC processor will improve the usage of look-up tables and slices. It causes the more utilization of area, so to overcome this problem, adaptive encoding in RISC processor is implemented. From results, it is observed that the total delay of conventional encoding in RISC processor is 21.015 ns and adaptive encoding in RISC processor is 4.128 ns. Logic delay of conventional encoding in RISC processor is 12.843 ns, and adaptive encoding in RISC processor is 3.493 ns. Route delay of conventional encoding in RISC processor is 8.172 ns, and adaptive encoding in RISC processor is 0.725 ns. From the simulation results, it is observed that the total delay, logic delay, route delay, memory usage, number of slices, and number of LUTs are reduced effectively compared with the conventional encoding in RISC processor.
Fuzzy Sets: A Key Towards Hybrid Explainable Artificial Intelligence for Image Understanding

In this paper, we propose a basis for discussing the role of fuzzy sets theory in the context of explainable artificial intelligence. We advocate that combining several frameworks in artificial intelligence, including fuzzy sets theory, adopting a hybrid point of view both for knowledge and data representation and for reasoning, offers opportunities towards explainability. This idea is instantiated on the example of image understanding, expressed as a spatial reasoning problem.
A Specialized Xie-Beni Measure for Clustering with Adaptive Distance

To certify good data partitioning, it is necessary to use an evaluation measure. This measure must take into account the specificity of the modeled partition. For centroid-based fuzzy partitioning, different measures exist. However, none of them takes into account the adaptive distance that some clustering models use. In our study, we extend the Xie-Beni measure, using both the Mahalanobis distance and the Wasserstein distance. The numerical results show the relevance of our new index.
FIDOC: A New Combination of Fuzzy Impulse Noise Detection and Open-Close Filtering

Image noise can be viewed as unwanted disturbances in a digital image that should be removed or reduced before further processing and analysis. Impulsive noise, also known as impulse noise, is a very disruptive type of noise, characterized by abrupt variations in brightness in a subset of the image pixels. Impulsive noise commonly occurs during image acquisition and transmission. To mitigate its effects, various impulsive noise reduction methods have been proposed by the image processing community. In contrast to classical filters such as the median filter, most current impulsive noise reduction techniques implement a two-step approach that consists of a noise detection phase to identify noisy pixels and a filtering phase to reduce the amount of noise in the presumably corrupted pixels. The approach presented in this paper is also along this line. To be more precise, we draw on the principles of two state-of-the-art impulsive noise reduction methods, namely the adaptive fuzzy transform based image filter (ATIF) and the improved fuzzy mathematical morphology open-close filter (i-FMMOCS), in order to propose a new method for general impulsive noise reduction.
Mutual Effect Between Language Use and Media Technology via Digital Communication

Over the years, technology has revolutionized our globe and daily lives. Additionally, it has created amazing tools and resources, putting useful information at our fingertips so much that we have had new quicker ways to communicate, especially through social networking or digital platforms. And this study aims to investigate ways of English used in some famous American universities’ Facebook pages to find out several newly developed linguistic features based on theoretical viewpoints of Bodomo (2010) and Crystal (2006, 2011) with the support of a qualitative approach. The results show that innovative linguistic features are developed into some trends of styles, grammar use, pragmatics, and vocabulary due to two groups of factors, namely face-to-face communication habits and socio-technical affordances. Moreover, this research makes an effortful contribution to reflecting the mutual effect in the rapid development of English and media technology that are becoming more and more dominant and attract large numbers of new users every day.
A Multitask Deep Learning Approach for Staples and Wound Segmentation in Abdominal Post-surgical Images

Deep learning techniques provide a powerful and versatile tool in different areas, such as object segmentation in medical images. In this paper, we propose a network based on the U-Net architecture to perform the segmentation of wounds and staples in abdominal surgery images. Moreover, since both tasks are highly interdependent, we propose a multitask architecture that allows to simultaneously obtain, in the same network evaluation, the masks with the staples and wound location of the image. When performing this multitasking, it is necessary to formulate a global loss function that linearly combines the losses of both partial tasks. This is why the study also involves the GradNorm algorithm to determine which weight is associated to each loss function during each training step. The main conclusion of the study is that multitask segmentation offers superior performance compared to segmenting by separate tasks.
The Breach is Dead, Long Live the Breach: A Spatial Temporal Study of Healthcare Data Breaches

In recent years, a growing number of healthcare organizations have been affected by data breaches. In healthcare entities, preventing data security incidents mainly requires a comprehensive understanding of the context and the factors contributing to such threats. Utilizing publicly available federal data, we examine, from a spatio-temporal perspective, the factors and the context associated to data breaches targeting healthcare sector in the United States (US) between 2009 and 2021. We shed light on healthcare data breaches that occurred during the Covid19 Pandemic era to gain a better understanding of the impact of the global context on data breach incidents. In our findings, the hacking and IT incidents are the most common types of breaches targeting specifically Healthcare providers entities. Victim healthcare organizations are more frequent in Texas, California and Florida. Over the course of years, Emails and Network servers have become the main targeted breach locations. By understanding and identifying factors related to data breach incidents, healthcare IT security professionals can mitigate the risk of leaks.
Root Cause Analysis with Fuzzy Cognitive Maps and Correlation Coefficient

Production line calibration is a critical industrial task that requires thoroughly planned actions. Even tiny deviations from the optimal settings can cause dramatic deficiencies. Automated Root Cause Analysis can be employed to suggest the actions that result in faulty states, and therefore, to resolve situations and prevent recurrence. This work presents a methodology for Root Cause Analysis focused on the calibration process of a valve block in an elevator system. The causalities (weighted interconnections) between oil flow control (actions) and system velocity (output) are estimated using Pearson Correlation. The produced weight matrix is evaluated by exploiting expert knowledge. An FCM model for Root Cause Analysis is developed to study the system behavior and explore the root causes of deficiencies. The proposed approach eliminates the need for labeled root causes. Results support the efficiency of the proposed FCM model for correcting the sub-optimal configurations; the proposed approach seems to work even when the calibration actions are unknown.