Reviewerly/exHarmony huggingface dataset:
Effective Password Strengthening Techniques

Abstract: Protecting passwords from hackers is a formidable challenge in the current technological landscape, given the myriad of tools available in the hacking domain. This paper delves into an exploration of various password types and lengths, highlighting those that are ill-advised for use. Additionally, it discusses password types that offer comparatively greater security and outlines prevalent cracking techniques employed by hackers. The document further outlines effective measures to enhance password security, incorporating algorithms designed for this purpose. Comprehensive coverage is provided on methods to safeguard passwords from potential attacks. Notably, the paper extends its scope to include insights into the various types of WiFi passwords.
A Survey on: Legal Chain Vault Using Blockchain

Abstract: In the face of escalating crime rates accompanying rapid urbanization, traditional criminal record systems face challenges of tampering and inefficiency. The Paper proposes a blockchain-based solution, leveraging the decentralization and immutability of blockchain to create a secure and transparent network for legal records. By utilizing peer-to-peer networks, this system ensures easy accessibility while safeguarding data integrity through the immutability feature of blockchain. The decentralized nature of the blockchain reduces corruption risks, allowing third parties to monitor tamperproof transactions and promoting objectivity in criminal record management. The blockchain-based approach not only enhances transparency and accountability in criminal record keeping but also facilitates timely access to authentic records for law enforcement authorities. The Paper aims to revolutionize record management, making it more efficient, secure, and conducive to effective law enforcement in evolving urban environments.
Gesture Vocalizer for Assisting Deaf and Dumb

Some individuals possess the innate ability to engage in communication with one another. However, regrettably, not everyone possesses the capacity for speech and hearing. Nonverbal communication, employed within the community of individuals with diverse abilities, serves as a method of interaction. To convey meaning, sign language combines simultaneous combinations of hand forms and movements with body, arm, and hand gestures as well as facial expressions. Those who are unable to speak use sign language to communicate with other people who are also speech-impaired as well as with average people who are familiar with the subtleties of sign language. Sometimes, an interpreter is necessary to translate the intentions behind the signs for those who can speak but may not fully understand sign language. However, consistent access to interpreters for sign language communication is not always feasible, and not everyone can master the intricacies of sign language. Hence, an alternative approach is the implementation of a device known as the Gesture Vocalizer. The Gesture Vocalizer functions as an intermediary. It translates the gestures produced by the person with speech impairment into audio output. The hardware iteration of this Gesture Vocalizer project is an array of microcontroller systems designed to enable inter-system communication between mute, deaf, and visually impaired communities and those who do not face such communication challenges. In this effort, we proposed a gesture vocalizer that is based on sensors and a microcontroller. The design of the gesture vocalizer primarily revolves around a hand glove integrated with a microcontroller system.
A Bibliometric Analysis of the Intellectual Landscape of Mobile Technology and Higher Education Research

Technology has become a crucial part of higher education. Higher education institutions have adapted to embrace technology-enabled classroom practices to meet the rising expectations of a varied student population while continually enhancing their learning experience. Mobile technology in higher education, in particular, has the opportunity to provide access to or improve education at a low cost with a less demanding infrastructure configuration. In the current study, the researchers aimed to better understand the intellectual landscape of mobile technology and higher education through bibliometric analysis of research articles published in the Scopus database. This research included a study of 277 papers published in Scopus-indexed peer-reviewed journals between 2006 and 2023. The citation network, co-citation analysis, and publication patterns were examined to discover influential work in this domain. Bibliometric analysis was used to identify the most notable journals, authors, nations, articles, and topics, followed by thoroughly examining the content of 277 papers in the identified clusters. The four major themes enumerated are—Rise of mobile learning, E-learning—the blended and collaborative way, Mobile Technologies in higher education, and Student Engagement in the times of mobile learning. The paper provides interesting insights into these emerging themes, the study will assist regulators, policymakers, and academic scholars in understanding the fundamentals of mobile technology and higher education and identifying pertinent topics for further research.
Tripartite evolutionary game study on coordination information security in prescription circulation

To further reform the medical and health care system, regulating multi-level treatment and rationalizing the use of medicine, and securing prescription circulation information, this study explores the evolutionary behavior of three players in terms of information security collaboration under the prescription circulation policy, analyzes the evolutionary paths, and examines the influence of key parameters on evolutionary outcomes by constructing a tripartite evolutionary game model consisting of hospitals, retail pharmacies, and healthcare service platforms. The study shows the following: (1) When the information security costs of prescription circulation increase, the willingness of hospitals to promote information collaboration weakens, the probability of control and regulation by healthcare platforms will be enhanced, and the incentive for retail pharmacies to undertake prescription circulation increases and then decreases. (2) The increased profitability of prescription drug sales can cause a decrease in the likelihood of both parties working together to promote information security. Increasing the collaborative space between hospitals and retail pharmacies is conducive to improving information security in the circulation of prescriptions. (3) A bi-directional constraint relationship exists between the circulation and control subjects. The shorter the technology spillover time from the healthcare service platform is, the higher the probability that hospitals and retail pharmacies will maintain the security of prescription information. (4) In the early stages of prescription circulation, the external regulatory action of the healthcare service platform is essential to improve the coordination of information security. Finally, combined with the tripartite evolutionary game model and simulation analysis results, it offers countermeasures and suggestions for the government to realize the prescription circulation information security collaboration.
Multi-step Methods for Machine Learning Models with Web Metrics

The purpose of the article is to outline methods as a multi-step process for developing an information flow with machine learning models that can be used for research, analysis, and training. Web metrics analysis of available web content can provide useful information about user behaviour. The results show that the formulated solutions can be successfully used for different tasks and can be adapted to new technologies and applications.
Intercultural Learning Strategies to Enhance BIPA Students’ Language Skills

Foreign language learning is related to the culture of the target language. Understanding Indonesian culture has an impact on the behavior of BIPA students when communicating with native Indonesian speakers and also strengthens linguistic knowledge. This study used a descriptive qualitative method.
Language Disorders in Children with Down Syndrome

Children with Down syndrome have language impairments that lead to an inability to produce a good language for communication. This problem was found from the results of early observations in the learning process at school. Children with Down syndrome find it difficult to pronounce the correct articu
Document Store Schema Design Alternatives and Their Impact

Smart apps in the twenty-first century can use any data from real-world objects properties, behaviours, and events. Thus, the amount and variety of data, from structured to unstructured, are growing. The integration of data from many sources produces a wide range of variability. NoSQL database popularity has increased due to its flexible schema, easy query interface, and scalability to handle such heterogeneous data. Document oriented NoSQLs are more popular to handle such heterogeneous data with flexible schema and a semi-structured data model. A flexible schema facilitates multiple ways to represent different types of variability that exist in the data collection. However, the impact of choosing different data representations on storage, energy, and performance is unknown. On the other hand, analysing the scope of optimising storage and energy efficiency is recommended for sustainable development. This paper discusses key components of the document store schema, its alternate implementations, and empirically analyse its impact on performance, storage space, and energy requirements. The aim of this paper is to assist developers in choosing an appropriate representation of each schema component for their application in the document stores. Considering its high popularity rating, MongoDB is chosen here for empirical analysis.
Grid Search-Optimized Artificial Neural Network for Heterogeneous Cross-Project Defect Prediction

In software engineering, the defect prediction is a crucial aspect. Every software development process aims for the removal of defects in the beginning. Cross-project Defect Prediction (CPDP) is an important research area in software engineering. CPDP is based on transfer learning. Transfer learnig is based on transfer of knowledge from one project to another project with similatrity in source and target domain to some extent. CPDP aims to identify defects in the target project based on the knowledge transferred from the source project. Due to the limited amount of data, CPDP domain grows rapidly in the upcoming years. However, the selection of suitable target projects is a challenging task in the research. Artificial Neural Network (ANN) is used to design a CPDP model using grid search. Furthermore, the performance of grid search ANN is compared with traditional ANN with default parameter settings. The grid search ANN outperformed the traditional neural network for CPDP. Grid search ANN optimizes hyperparameters through multiple iterations. ANN in combination with optimization algorithms results in an efficient CPDP model. The performance of prediction models is evaluated using AUC metric.
Analysis of News Translation Multi-strategy Fusion Translation Mode

News, as an important transmission medium, can accurately and rapidly transmit the latest events in the world to the audience timely and accurately, while its most important function is to disseminate information and convey opinions. Through the textual characteristics of news and the specific characteristics of Chinese news in English translation, by listing a large number of news and news translation cases, this paper will discuss the characteristics of news translation, as well as the commonly used translation strategies and the specific translation methods of Chinese news translation. It also analyses the feasibility of combining the domesticating and foreignizing strategies and translation methods in previous news translations according to the cases. It is expected that by using case studies of Chinese news translation and publicity in English to find out how to get the optimal Chinese news translation in English, in order guide better Chinese news translation in English in the future.
macOS Management Settings and Reinstallation Process

In this chapter, we delve into the various aspects of macOS functionality, with a special focus on customizing the operating system to suit individual user preferences. We begin by exploring how users can tailor macOS settings at the operating system level to optimize their overall experience.
Applications and Utilities Introduction

In this chapter, we will delve into the usability of applications and utilities within macOS so that you can have a comprehensive understanding of their functions and features. Our discussion will begin with an exploration of the various types of user accounts and file systems (application and formatting) that are supported on Mac devices, which are essential for effectively managing macOS.
An Autoscaling System Based on Predicting the Demand for Resources and Responding to Failure in Forecasting

In recent years, the convergence of edge computing and sensor technologies has become a pivotal frontier revolutionizing real-time data processing. In particular, the practice of data acquisition—which encompasses the collection of sensory information in the form of images and videos, followed by their transmission to a remote cloud infrastructure for subsequent analysis—has witnessed a notable surge in adoption. However, to ensure seamless real-time processing irrespective of the data volume being conveyed or the frequency of incoming requests, it is vital to proactively locate resources within the cloud infrastructure specifically tailored to data-processing tasks. Many studies have focused on the proactive prediction of resource demands through the use of deep learning algorithms, generating considerable interest in real-time data processing. Nonetheless, an inherent risk arises when relying solely on predictive resource allocation, as it can heighten the susceptibility to system failure. In this study, a framework that includes algorithms that periodically monitor resource requirements and dynamically adjust resource provisioning to match the actual demand is proposed. Under experimental conditions with the Bitbrains dataset, setting the network throughput to 300 kB/s and with a threshold of 80%, the proposed system provides a 99% performance improvement in terms of the autoscaling algorithm and requires only 0.43 ms of additional computational overhead compared to relying on a simple prediction model alone.
Reduced CPU Workload for Human Pose Detection with the Aid of a Low-Resolution Infrared Array Sensor on Embedded Systems

Modern embedded systems have achieved relatively high processing power. They can be used for edge computing and computer vision, where data are collected and processed locally, without the need for network communication for decision-making and data analysis purposes. Face detection, face recognition, and pose detection algorithms can be executed with acceptable performance on embedded systems and are used for home security and monitoring. However, popular machine learning frameworks, such as MediaPipe, require relatively high usage of CPU while running, even when idle with no subject in the scene. Combined with the still present false detections, this wastes CPU time, elevates the power consumption and overall system temperature, and generates unnecessary data. In this study, a low-cost low-resolution infrared thermal sensor array was used to control the execution of MediaPipe’s pose detection algorithm using single-board computers, which only runs when the thermal camera detects a possible subject in its field of view. A lightweight algorithm with several filtering layers was developed, which allowed the effective detection and isolation of a person in the thermal image. The resulting hybrid computer vision proved effective in reducing the average CPU workload, especially in environments with low activity, almost eliminating MediaPipe’s false detections, and reaching up to 30% power saving in the best-case scenario.
Using the Tinkercad environment when studying the theme "Control" in an informatics course at the level of basic general education

The article discusses the author's methodology for teaching the theme "Control" of an informatics course of basic general education based on the Tinkercad environment. The place of this theme in the Work Program of Basic General Education in the subject "Informatics" is highlighted. The main advantages of using the Tinkercad environment are described. Two options for covering this theme are considered, depending on the availability of robotic equipment at the school. For each option, thematic planning is provided, and the main features of conducting lessons are highlighted. The types of activities of the teacher and students at each stage of the theme's lessons are considered. For all classes, projects are proposed, each of which covers the stages of prototyping and programming. The proposed projects have varying complexity, but at the same time they are interconnected, which makes it possible to build a holistic methodology for studying the topic. All projects are aimed at developing students' subject and meta-subject results for each subsection of the "Control" theme. Sources are provided in which project code is available for downloading to students' personal accounts on the Tinkercad platform.
Influence of Normal Aging and Multisensory Data Fusion on Cybersickness and Postural Adaptation in Immersive Virtual Reality

Immersive Virtual Reality (VR) systems are expanding as sensorimotor readaptation tools for older adults. However, this purpose may be challenged by cybersickness occurrences possibly caused by sensory conflicts. This study aims to analyze the effects of aging and multisensory data fusion processes in the brain on cybersickness and the adaptation of postural responses when exposed to immersive VR. Methods: We repeatedly exposed 75 participants, aged 21 to 86, to immersive VR while recording the trajectory of their Center of Pressure (CoP). Participants rated their cybersickness after the first and fifth exposure. Results: The repeated exposures increased cybersickness and allowed for a decrease in postural responses from the second repetition, i.e., increased stability. We did not find any significant correlation between biological age and cybersickness scores. On the contrary, even if some postural responses are age-dependent, a significant postural adaptation occurred independently of age. The CoP trajectory length in the anteroposterior axis and mean velocity were the postural parameters the most affected by age and repetition. Conclusions: This study suggests that cybersickness and postural adaptation to immersive VR are not age-dependent and that cybersickness is unrelated to a deficit in postural adaptation or age. Age does not seem to influence the properties of multisensory data fusion.
Evaluating the effectiveness of decomposed Halstead Metrics in software fault prediction

The occurrence of faults in software systems represents an inevitable predicament. Testing is the most common means to detect such faults; however, exhaustive testing is not feasible for any nontrivial system. Software fault prediction (SFP), which identifies software components that are more prone to errors, seeks to supplement the testing process. Thus, testing efforts can be focused on such modules. Various approaches exist for SFP, with machine learning (ML) emerging as the prevailing methodology. ML-based SFP relies on a wide range of metrics, ranging from file-level and class-level to method-level and even line-level metrics. More granularized metrics are expected to possess a higher degree of micro-level coverage of the code. The Halstead metric suite offers coverage at the line level and has been extensively employed across diverse domains such as fault prediction, quality assessment, and similarity approximation for the past three decades. In this article, we propose to decompose Halstead base metrics and evaluate their fault prediction capability. The Halstead base metrics consist of operators and operands. In the context of the Java language, we partition operators into five distinct categories, i.e. , assignment operators, arithmetic operators, logical operators, relational operators, and all other types of operators. Similarly, operands are classified into two classes: constants and variables. For the purpose of empirical evaluation, two experiments were designed. In the first experiment, the Halstead base metrics were used along with McCabe, Lines of Code (LoC), and Halstead-derived metrics as predictors. In the second experiment, decomposed Halstead base metrics were used along with McCabe, LoC, and Halstead-derived metrics. Five public datasets were selected for the experiments. The ML classifiers used included logistic regression, naïve Bayes, decision tree, multilayer perceptron, random forest, and support vector machines. The ML classifiers’ effectiveness was assessed through metrics such as accuracy, F-measure, and AUC. Accuracy saw an enhancement from 0.82 to 0.97, while F-measure exhibited improvement from 0.81 to 0.99. Correspondingly, the AUC value advanced from 0.79 to 0.99. These findings highlight the superior performance of decomposed Halstead metrics, as opposed to the original Halstead base metrics, in predicting faults across all datasets.
Uso y análisis de la metodología de diseño para la construcción de herramientas web educativas

The aim of this mixed research is to establish the stages of the Design Methodology for the Construction of Educational Web Tools (MDCHWE) and analyze its implementation through the creation of a web application for the field of statistics. The results indicate that the simulator of the web application positively influences the assimilation of knowledge about arithmetic progression (depreciation), motivation and learning during the COVID-19 post-pandemic. The participants are 27 students studying Applied Geography at the National Autonomous University of Mexico (NAUM). This research recommends the use of the MDCHWE methodology to organize and implement the educational web applications. According to the students of the UNAM, the educational web application facilitates learning in the distance modality through the presentation of the mathematical procedure step by step. Also, this technological tool is easy to use. In conclusion, educators and educational institutions can use the MDCHWE methodology to innovate the school activities through the creation of educational web applications.