Reviewerly/exHarmony huggingface dataset:
Ultrasound Assistant Device for the Blind

In modern society, the issue of creating an accessible environment for people with disabilities, and in particular the visually impaired and the blind, is becoming increasingly acute. Every year, millions of people around the world are visually impaired or completely deprived of it. This problem is becoming more and more serious every year. Modern technologies make it possible to create both extremely technological devices and relatively simple ones to help these people. One of the most promising areas is the creation of rangefinder devices that in one way or another inform the user about the presence of obstacles and thus provide an additional source of information about the environment. This work is devoted to the creation of a prototype assistant - device to help in orientation in space for the blind, as well as its possible technical implementation and solving related problems in detecting the distance to obstacles.
Investigating Benefits Realization of Information Security Policies for Palestine Higher Education: An SEM – ANN Approach

In essence, Information Security Management (ISM) is a real-life situation based on its characteristics; confidentiality, integrity, and availability. To generate knowledge based on eight key concepts of ISM supported by popular management theory that uses the core principles of planning, organizing, leading and controlling (PLOC). The research will investigate benefits realization for IS Policies (ISP) by integrating Cranfield Benefits Management Model CBMM. A conceptual research model will be developed from extant academic literature, standards, and the examination of interrelationships between the objectives and practices, and refined via survey data from managers and security professionals based in Palestine higher education institutions. A benefits realization plan BRP is used to define the benefits of the overall ISM work and responsibilities for their realization, measurement and reporting for the ISP realization. Building on these previous efforts, the purpose of this study is to investigate the conceptual theoretical model to recognize benefits realization in the ISP based on the many guidelines and standards available in both academia and practice. Such a conceptual model could provide educational institutions with a favoured benefits realization approach to ISP and future research activities for Palestine higher education institutions. In the context of applied research, the research will use a quantitative method research paradigm using a grounded theory that employs quantitative data. The model used in this research was analysed in two steps. In the first step, Structural Equation Modelling (SEM) was used to determine significant determinants that affect the adoption of PLOC in the ISP realization. In the second step, a Neural Network Model ANN was applied to validate the findings in step 1 and establish the relative importance of each determinant to the adoption of ISP realization. The expected achievable benefits attributes from ISP implementation may include the following; It is beneficiary, gain, attributable and discernible.
An Evaluation of Mathematical Models and Stability Analysis of Learning Based on Reaction Kinetics

Human cognition and consciousness are perhaps the most confounding mystery. Somehow it has a linkage to the process of learning and storage of short-term and long-term memory in the form of knowledge. This paper examines a brief background of early models in learning presented by Atkinson and Shriffrin (1965) and related stochastic models utilizing probability functions. Each of these learning models capture certain facets of the learning process but are ineffective in describing the physical basis in which learning occurs. For this reason, this paper explores analogous mathematical models based on reaction kinetics that have been shown to represent chemical reactions found in nature. Six learning models are presented of unitary, binary, reversible binary, reversible binary with mass action, and enzyme learning model reactions with and without decay. Preliminary analysis of time series plots, phase line diagrams, and phase plane plots were conducted to illustrate equilibrium conditions and stability of the models. Each model is examined in terms of its limitations in the philosophy and inability to capture certain elements that are understood about the learning process. Finally, this paper concludes that the feasibility of understanding behavior such as stability through the tools of applied mathematics and thereby illuminating certain layers of human cognition and learning is a useful tool in examining the suitability of a possible deterministic model that could describe the learning process. Further analysis with empirical data would validate the suitability of the presented models.
Pedagogical conditions for forming the readiness of future specialists in computer technologies to professional activity

The study is devoted to the actual problem of training and professional self-improvement of future specialists in computer technologies in institutions of higher education. An analysis of pedagogical conditions for the professional self-improvement of future computer technology specialists, which contribute to the formation of the readiness of future computer technology specialists for professional activity, was carried out, the essence of the concept of «Pedagogical conditions» was clarified. The work considers pedagogical strategies, aspects and approaches aimed at developing the competencies of future specialists, including technical, communicative, analytical and creative skills. The structural components, criteria, methods, means, and approaches are determined, the methodological approaches of forming the readiness of future computer technology specialists for professional self-improvement are substantiated.&#x0D; The results of the study can contribute to the improvement of educational programs, methods and approaches in the training of future specialists in computer technologies that meet the requirements of the labor market and the technological challenges of today.
Analysis of Product Review Sentiment Analysis using Improved Machine Learning Techniques

Sentiment analysis has emerged as a crucial task in the era of big data and social media. Understanding the sentiments expressed in product reviews is vital for businesses to gauge customer satisfaction and make informed decisions. This research paper presents a design simulation and assessment of product review sentiment analysis using improved machine learning techniques. The aim is to develop a robust sentiment analysis model that outperforms existing approaches in accuracy and efficiency. We propose a novel methodology that combines advanced feature extraction, sentiment classification algorithms, and model optimization techniques. The introduction provides an overview of the importance of sentiment analysis in the context of product reviews and the challenges faced by conventional methods. It also outlines the objectives and scope of this research. The related works section presents a comprehensive review of existing literature and highlights the limitations of current approaches. The proposed methodology section describes the technical details of our enhanced machine learning approach and the reasoning behind the selected techniques. In the analysis of sample results, we evaluate the performance of our proposed model on a diverse dataset of product reviews. We present the accuracy, precision, recall, and F1-score metrics, along with a comparison to baseline models and state-of-the-art sentiment analysis systems. Furthermore, we discuss the model's robustness in handling various types of products and reviews.&#x0D; Our research demonstrates significant improvements in sentiment analysis accuracy compared to traditional methods. We introduce tables and graphs to illustrate the model's performance in different scenarios and identify its strengths and weaknesses. The paper concludes by discussing the implications of our findings, potential applications in industry, and directions for future research. Overall, this research contributes to the advancement of sentiment analysis techniques and provides a valuable resource for businesses aiming to enhance their understanding of customer sentiments through product reviews.
HISTORY OF FORMATION AND DEVELOPMENT OF THE CONCEPT OF DIGITAL LITERATURE

The article discusses the content, genesis of a new literary phenomenon – the concept of digital literature and the current state of digital literature in the modern literary process, its main features. This paper analyzes not only the issues related to the history of formation and development of the concept of digital literature, but also the factors that contribute to the spread of both digital literature and traditional literature in the global network. The relevance of this research work is due to the lack of a fundamental study of digital literature in Kazakh literary studies. Only a few articles related to the concept of digital literature are devoted to the study of specific digital genres recognized as digital works. Currently, there is a lack of voluminous academic papers devoted to the problems of digital literature. Despite the fact that digital literature has become the subject of study in many disciplines, such as sociology, literary criticism, folklore, cultural studies, a universal methodological apparatus for studying digital literature, taking into account its features, has not yet been formed. The paper defines the main stages of the process of formation of digital literature associated with the historical change in the functionality of the Internet, the intensive development of devices and programs of information technology. In addition, various approaches to the definition of the concept of “digital literature” are taken as a basis and literary and theoretical analysis of the concept of “digital literature” is carried out. It is concluded that a new creative phenomenon – digital literature, which includes the unique properties of traditional literature and its own characteristics as a new phenomenon in literary studies, should become the object of large-scale scientific research.
Examining the Moderating Effect of Knowledge Sharing in the Relationship between Innovation Management Dimensions and Organizational Sustainability of Deposit Money Banks in Nigeria

The study examined how knowledge sharing moderated the relationship between innovation management dimensions and sustainability of deposits money banks in Nigera with particular emphasis to deposit money banks in Makurdi metropolis. The objectives were based on the effect of product, process, marketing and organizational innovation on sustainability, and the moderation of knowledge sharing between innovation management and sustainability. Quantitative research design was used. Cross-sectional survey was deployed and 5-point Likert standardized questionnaire utilized to collect quantitative data from 300 participants drawn from 934 staff across 12 deposit money banks. Multiple regression was done with the aid of Statistical Package for Social Sciences (SPSS V23). Findings revaled that product (β=.071,p&lt;0.5), process(β=-.070,p&lt;0.5), marketing(β=.047,p&lt;0.5), and organizational innovation (β=.957,p&lt;0.5) had significant effect on organizational sustainability. Also, knowledge sharing was found to have moderated significantly the effect of innovation management on sustainability in the deposit money banks studied. It was concluded that innovation management dimensions were key drivers of organizational sustainability, and knowledge sharing key moderator between the product, process, marketing and organizational innovation on sustainability in the deposit money banks. The study conclude amongst other things that the management of the banks and similar organizations should ensure the effective management of innovation dimensions, and encourage sharing of knowledge within their organizational members.
Dijkstra's shortest path in sleeping model

The Dijkstra&apos;s algorithm is a classic algorithm to find the shortest path between nodes in a graph. However, in distributed computing environments, the algorithm faces challenges due to the dynamic and intermittent character of the network. The sleeping model is a technique used to conserve energy in distributed computing environments by allowing nodes to alternate between active and sleeping states. In this paper, we presented a version of Dijkstra&apos;s algorithm designed specifically for the sleeping model, providing a novel implementation to improve its energy efficiency and scalability. We compared the performance of the sleeping model implementation with the traditional implementation of the Dijkstra&apos;s algorithm in a congest model.
Metaheuristic-based PID controller approach for solar tracking

With modernization and industrialization, the demand-supply gap for energy is constantly growing. This scenario has led to the development of renewable energy sources, which are clean, safe and boundless. Among renewable resources, the solar energy is considered to be the best option for energy harvesting schemes because of its abundance availability. However, the full consumption of solar energy using single axis sun tracking system is still a challenging task. Hence this paper focuses on the development of new metaheuristic based dual-axis solar tracking system which can accurately track the sun with respect to azimuthal and tilt angle. The movement of solar tracker DC motor is precisely controlled by the optimized Proportional-Integral-Derivative (PID) controller. For the optimum tuning of PID parameters, three metaheuristic algorithms named Whale optimization algorithm (WOA), Ant colony optimization (ACO), and Genetic algorithm (GA) have been employed. The simulation results shows that the proposed system performs better with WOA as compared to ACO, and GA, as it demonstrates a faster convergence rate and better time domain specifications.
Deep Learning Based Abnormal Behavior Detection for Elderly Healthcare Using Consumer Network Cameras

Abnormal behavior has become the leading cause of injuries among the elderly in the modern society. Elderly anomaly is a widespread concern in both academic and industrial fields. Detection of abnormal behaviors using deep learning techniques has been pervasively investigated due to the flourishing of powerful edge computing and the decreasing costs of consumer electronics, such as commercial vision sensors. This paper intentionally designs an innovative architecture to detect the elderly typical abnormal behaviors: falls and tumbles, aggression and wandering, using the consumer network cameras, which are configured in the residential areas, healthcare centers, to name a few. Different from the conventional abnormal behavior detection methods, which suffer the issues of improper model generalization and lack of the spatial and temporal coherence, we design a deep learning model that robustly extracts the skeleton joints, detects and classifies the abnormal behaviors while considering the spatial and temporal context. It should be noted that the images are captured with the network cameras rigidly fixed at the typical elderly activity spots. The group of captured images will be fed to the local server, equipped with GPUs, for online monitoring and alarming. Our method is extensively evaluated on the elderly anomaly detection platform. It achieves the competitive performance with mAP greater than 85%.
The Special Theory of Probability

In the range theory, knowledge of objective import is the basis for statements of probability. The range theory provides a satisfactory explanation of the law of large numbers, and it clarifies the notion of likelihood. Statements about probability are associated with essential premises; pride of place is given to the assumption of independence among cases. Two types of factors determine probability: nomological and ontological. Nomological statements concern generally valid laws; ontological statements have singular meaning. It is our ignorance of ontological relations which makes outcomes seem uncertain. An event is called random, if more precise ontological determinants (withheld from our knowledge) are constraints on which an event depends. The notion of a normal distribution is introduced, by the example of series of draws from an urn. Different procedures for the draws are introduced to illustrate hypernormal and hyponormal dispersion for distributions. Hyponormal dispersion occurs when successive cases are not independent.
Penggunaan media video dalam meningkatkan kualitas mengajar guru di sekolah

This article analyzes the needs of teachers in an effort to improve the quality of teacher teaching by using technology and information through video media. Learning video media is a form of technology that is indispensable for teachers to support the successful implementation of effective and efficient learning. This is because learning video media can increase students' attention and interest in learning. The purpose of this study is to increase the use of instructional video media in the teaching and learning process and to improve the teaching quality of teachers in schools. The method used is descriptive quantitative method using quantitative data analysis techniques. The data collection technique used is a survey. Based on the data obtained, the results of this study indicate that 90% of teachers have difficulty in making learning media. In addition, 75% of teachers have limitations in the use of IT.
Perancangan Website Masyarakat Peduli Sampah Kelurahan Ratu Sima

Seeing the importance of an information managed by the kelurahan part, which called as the community who cares about garbage where it is very difficult for people to get information about waste in the Ratu Sima subdistrict. To address this, a website must be created that is both effective and capable of assisting in the management of garbage and information from the Ratu Sima Village. The website was created using the CodeIgniter 3 framework with MySQLI as the database management system. To test how the website works, this system is examined using the black box method. The website was successfully developed according to the concept, and all functionalities can run effectively, as evidenced by the results of the User Acceptance Test (UAT), which scored "Very Good" with an 87 percent score.
Road anomaly detection using self-supervised label generator with vertical disparity maps

Autonomous vehicles and robotic wheelchairs based real world applications need the automated recognition of roads, route holes, and other irregularities. Traditional techniques of image processing are responsible for the inaccurate identification of abnormalities, which ultimately leads to poor performance. As a result, the primary emphasis of this work is focused on the design of a road anomaly detection system, which is based on a Self-Supervised Label Generator (SSLG) and vertical disparity maps (VDM). Here, VDM is used to identify the regular and irregular boundaries of road by using both depth map image. In addition, an anomaly map generator is used to identify the anomalies presented in the road. Finally, supervised machine learning based SSLG approach is used to label the drivable area, unknown area, and anomaly region. According to the findings of the simulations, the performance of the proposed technique resulted in better performance, when compared to the existing methods for all performance metrics.
OpenCitations and SCOSS: one year of support

At the end of 2019, OpenCitations was selected by &lt;strong&gt;The Global Sustainability Coalition for Open Science Service (SCOSS)&lt;/strong&gt; for presentation to the international scholarly community for crowd-sourced sustainability funding, along with the Public Knowledge Project (PKP) and the Directory of Open Access Books (DOAB). Since 2017, SCOSS has been helping identify non-commercial services essential to Open Science, and making recommendatio
Parasitisation prediction in malarial cell images using transfer learning- driven vision transformers

The World Malaria Report 2022 stated that nearly 247 million cases of malaria and 619,000 deaths were reported annually as of 2021. This emphasizes on the tremendous impact the disease has, especially in third-world countries. Through efficient and timely diagnosis, the disease could be accurately identified and suitable lines-of-treatment could be initiated. The requirement for systematic diagnosis benchmarked the arrival of the computer-aided diagnosis landscape through imaging and Machine Learning (ML) techniques. Deep learning-based backbones are particularly efficient for image classification and disease detection. Through this paper, we propose a Deep Neural Network (DNN) backbone VGG16 for extracting the local and global features of blood smear cell images marked with the Giemsa stain, followed by a Vision Transformer (ViT) neural network architecture for classifying the coloured malarial cell images into the parasitized and uninfected classes of images based on the extracted features. This novel approach returned 97.72% classification accuracy with leading sensitivity, specificity, precision, recall, and area under curve metrics.
Identification of fake currency

Currency fraud now possess a serious risk to both people&apos;s livelihoods and the nation&apos;s economy. Due to im-provements, in printing and scanning technology, currency duplication is still a growing worry for governments. Although banks have many note-detecting systems; but are expensive and frequently wrong. This problem has the potential to be considerably solved by the disciplines of machine vision, neural networks, and image processing. Although there are fake cash detectors, they are only used in banks and corporate offices, leaving the general public and small companies at risk. In order To detect and invalidate fraudulent Indian currency, we will develop a software-based system in this project using cutting-edge image processing and computer vision techniques. First, we will analyze the many security character-istics of Indian currency. This currency authentication system was created entirely in the Jupyter Notebook environment using the Python programming language.
Varieties of Numeric Probability

Every probability must have the definite content of a judgment as its object: a propositional content which is either true or false. The pursuit of a method to transpose or compare probabilities by way of logical argument is largely futile. Probability is not transposed by simple inference: the same probability must be attributed to two premises, only if neither may be established in absence of the other. However, the probability derived from two ranges which are very small and nearly adjacent may be represented in numeric terms by Bayes’s rule. Suppose there are two ranges which stand in definite ratio; then say there are gaps in the ranges, so that parts of the ranges remain. The ratio of the residual part-ranges may be shown to follow Bayes’s rule. Yet Bayes’s rule is applied frequently in inappropriate situations, where probabilities are combined more or less arbitrarily. That can lead to meaningless or incorrect statements of probability.