Reviewerly/exHarmony huggingface dataset:
Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.
Features of the formation of the skills of working with information based on the use of multiple texts in mathematics classes among younger schoolchildren

The study aims to substantiate the need to form the skills of working with information based on the use of multiple texts in mathematics classes among younger schoolchildren. The paper discusses the definitions of the notion of “information”, outlines the specifics of working with tables, diagrams, pictograms. Among the types of information, special emphasis is given to the multiple text, since it serves as a tool for reading literacy assessment. The paper presents the results of an empirical study concerning the level of formation of the skills of working with information among 3rd-grade students according to three criteria: the cognitive criterion, the activity criterion and the evaluation criterion. The main difficulties of younger schoolchildren in the formation of information skills are described. The scientific novelty of the work consists in determining the criteria for the formation of the skills of working with information based on the use of multiple texts among younger schoolchildren and in describing the level characteristics of these criteria. As a result of the study, it has been determined that the multiple text can act as a means of forming the skills of working with information among younger schoolchildren, the difficulties encountered by younger schoolchildren when working with information have been indicated.
Mitigating Technological Anxiety through the Application of Natural Interaction in Mixed Reality Systems

Technology anxiety contributes to an increased cognitive load and reduces user adoption of novel technologies. An illustrative example of this phenomenon is observed in the field of smart homes.To address this issue, we have identified an interaction paradigm known as natural interaction, which enables humans to engage with technology in a way that closely resembles natural human behavior and communication. This approach offers more user-friendly interactions that users are already familiar with, potentially reducing their cognitive load. In this paper, we present a case study of a smart lock system where we implement natural interaction and deploy it on a mixed reality (MR) device. By leveraging advanced features offered by MR head-mounted displays, we recreate the experience of people opening locks in everyday life. We conduct a user study comparing this interaction with traditional WIMP interaction in a mixed-reality environment. Through the analysis of collected data and user feedback, we examine the advantages and limitations of our proposed system.
Digital transformation of higher education: Components and conditions of functioning (on the example of Samara State University of Economics)

The aim of the study is to substantiate the components and conditions of the digital transformation of higher education. The article examines the state of the digital transformation of education on the example of Samara State University of Economics and ways to improve it. The scientific novelty of the study lies in the following hypothesis: a university teacher who is interested in improving his/her competence in general and digital competence in particular will be in demand in the context of the digital transformation of education provided that the following conditions are met: 1) the competent use of information and communication technologies in the educational process; 2) the continuous improvement of digital competence; 3) a developed digital environment of the university. The main results of the study are as follows: a review and an analysis of the problem are conducted; the definitions and components of the digital educational environment and the digital competence of a teacher presented by various authors and teams of scientists are analyzed; the author’s definition of the digital competence of a university teacher is formulated; the advantages of information and communication technologies and ways to increase the level of the digital competence of university teachers are outlined; the experience of creating a digital educational environment of Samara State Economic University is presented.
Ethno-cultural identity as an indicator of the formation of future psychologists’ professional readiness for multicultural interaction

The paper examines the psychological and pedagogical aspects of future psychologists’ readiness for professional activities in the conditions of multiculturalism. The aim of the study is to substantiate the possibility of using ethno-cultural identity types as indicators of future psychologists’ readiness for professional activities in multicultural conditions. Various approaches to the category of readiness in psychology and pedagogy are considered. Professional readiness is understood as an integral personal characteristic that includes awareness of one’s abilities, preparedness and the desire to realize them in professional activities. The scientific novelty lies in elaborating the essence of the notion of “future psychologists’ professional readiness for multicultural interaction”, as well as in identifying the relationship between ethno-cultural identity and the formation of future psychologists’ professional readiness for multicultural interaction. The results of the study showed that that the high level of readiness for professional activities in multicultural conditions is observed only among students with positive ethno-cultural identity. The more pronounced positive ethno-cultural identity is, the higher the level of future psychologists’ professional readiness for multicultural interaction is, which determines the importance of developing this type of identity among specialists working in multicultural conditions. Ethnonigilism is associated with the low level of future psychologists’ readiness for multicultural interaction. Expectations, attitude to another culture and its assessment, confidence in one’s abilities and competencies are important aspects of professional readiness for multicultural interaction, but its strongest indicator is intercultural interest.
Methodological debates as an effective pedagogical technology for the development of universal competencies of future teachers

The study aims to test the effectiveness of organizing methodological debates for the development of universal competencies of future teachers. The paper reveals the didactic potential of methodological debates, describes the features of their preparation and conduct. The experience of conducting methodological debates in the process of training students in the education program 44.03.05 “Pedagogical education”, program specialization “Mathematics, computer science” is presented. The scientific novelty of the study lies in formulating the notion of “methodological debates”, shedding light on their capabilities for the development of universal competencies of future teachers. As a result, a step-by-step methodology for conducting methodological debates for the development of universal competencies of future teachers has been developed; the functions of the main participants in methodological debates have been described; the criteria and indicators for assessing the quality of performance of the participants in methodological debates have been determined; the author’s variant of conducting methodological debates on the topic “Functional literacy of students – is it a relevant educational result?” with a description of students’ activities at each organizational stage has been proposed.
DIGITAL TUNING BASED WHEELCHAIR MOVEMENTS CONTROL FOR PHYSICALLY CHALLENGED PERSON

This paper describes the design of a smart, motorized, voice controlled wheelchair using embedded system. Proposed design supports voice activation system for physically differently abled persons incorporating manual operation. This paper represents the “Voice-controlled Wheel chair” for the physically differently abled person where the voice command controls the movements of the wheelchair. The voice command is given through a cellular device having Bluetooth and the command is transferred and converted to string by the BT Voice Control for Arduino and is transferred to the Bluetooth Module SR-04connected to the Arduino board for the control of the Wheelchair. For example, when the user says “Go” then chair will move in forward direction and when he says “Back”, then the chair will move in backward direction and similarly “Left”, “Right” for rotating it in left and right directions respectively and “Stop” for making it stop. This system was designed and developed to save cost, time and energy of the patient. Ultrasonic sensor is also made a part of the design and it helps to detect obstacles lying ahead in the way of the wheelchair that can hinder the passage of the wheelchair.
A SURVEY A HYBRID MODEL FOR CENTRAL BANK DIGITAL CURRENCY BASED ON BLOCKCHAIN

With the development of blockchain technology, research on digital currency is gaining popularity, especially Central Bank Digital Currency (CBDC), which is essential to the growth of a nation's economy. However, CBDC requires a more complicated system than other crypto currencies do. a stronger focus on supervision and controllable decentralization Therefore, the technical foundation that is in accordance with economic ecology, efficient consensus methods, and network architecture that conserves computer resources are essential elements of CBDC. In this study, we propose a modularity network-enabled hybrid blockchain system for CBDC. Common digital currencies are recorded using the account system, especially for numerous small payment transactions, whereas digital assets and smart contracts with major value fluctuations and low liquidity are recorded using the Unspent Transaction Output (UTXO) scheme. To boost the concurrency of this structured network, a sliced data storage solution is developed, and a modular blockchain design is recommended. The DPOS-BFT technique is enhanced based on the block chain’s CBDC supervision mode, which condenses the original algorithm's two rounds of consensus into a single round. The effectiveness of this approach in speeding up consensus and transaction processing is shown in three simulated studies on scheme, network, and consensus.
Implications of regulatory policy for building secure agile software in Nigeria: A grounded theory

Abstract Nigeria is ranked second worldwide, after India, in reported incidences of cyberattacks. Attackers usually exploit vulnerabilities in software which may not have adequately considered security features during the development process. Agile methods have the potential to increase productivity and ensure faster delivery of software, although they tend to neglect non‐functional requirements such as security. The implementation of government policies, such as the Nigeria Data Protection Regulation (NDPR) Act 2019, impacts the security activities carried out by agile teams. Despite its significance, there is a paucity of research on security issues especially in the Agile Software Development (ASD) domain. To address this gap, a grounded theory study was conducted with 15 agile software practitioners in Nigeria. Based on our analysis of the interview transcripts, we developed a grounded theory of the security challenges confronting agile practitioners. The four challenges identified were (a) a lack of collaboration between security and agile teams; (b) the tendency to use foreign software hosting companies; (c) a poor cybersecurity culture; and (d) the high cost of building secure agile software. We used these challenges to identify gaps within the existing secure ASD and found a lack of indigenous software hosting companies in Nigeria. Our study also revealed tensions between the Nigerian regulatory environment and agile software developers' compliance. While practitioners acknowledged the government's efforts, there were concerns about the practicality of implementing such legislation. We recommend government action to increase awareness of local software hosting companies' capabilities, and closer collaboration between agile and security teams. Thus, the novel contribution of this article is the development of the policy adherence challenges (PAC) model.
Improved Nighttime Traffic Detection Using Day-To-Night Image Transfer

With the rapid development of convolutional neural networks (CNNs), real-time traffic monitoring, which benefits both traffic optimization and management, has been widely applied with smart cameras on streets. However, compared with the daytime traffic detection, nighttime traffic detection is limited by labeling annotations and is thus unstable, inaccurate, and inefficient in practice. To address this issue, this study proposes to use the augmented nighttime traffic images generated by cycle generative adversarial networks (CycleGAN) for better nighttime detection performance. Using CycleGAN, transferred nighttime traffic images are generated by using the daytime traffic images, as both share the same annotations of traffic instances in the daytime. For comparison, different learning rates and crop sizes are adopted for day-to-night traffic image transfer. The previously proposed detection network, dense traffic detection network (DTDNet), is adopted to train the prepared image data set. The indicators of mean average precision (mAP), precision, and recall are adopted for training performance evaluation and comparison. Based on the visualization results, CycleGAN, with a learning rate of 2e-5 and a crop size of 64, has better performance on day-to-night traffic image transfer with our proposed image data set. Considering the indicators of training performance, DTDNet, with 60% of transferred nighttime images and 40% of the original images, has better accuracy in four categories. Overall, this study provides a possible solution for addressing the issue of training data limitation in nighttime traffic detection and demonstrates the potential of GAN-based data augmentation in the transportation domain.
The Deep Learning Generative Adversarial Random Neural Network in data marketplaces: The digital creative

Generative Adversarial Networks (GANs) have been proposed as a method to generate multiple replicas from an original version combining a Discriminator and a Generator. The main applications of GANs have been the casual generation of audio and video content. GANs, as a neural method that generates populations of individuals, have emulated genetic algorithms based on biologically inspired operators such as mutation, crossover and selection. This article presents the Deep Learning Generative Adversarial Random Neural Network (RNN) with the same features and functionality as a GAN. Furthermore, the presented algorithm is proposed for an application, the Digital Creative, that generates tradeable replicas in a Data Marketplace, such as 1D functions or audio, 2D and 3D images and video content. The RNN Generator creates individuals mapped from a latent space while the GAN Discriminator evaluates them based on the true data distribution. The performance of the Deep Learning Generative Adversarial RNN has been assessed against several input vectors with different dimensions, in addition to 1D functions and 2D images. The presented results are successful: the learning objective of the RNN Generator creates tradeable replicas at low error, whereas the RNN Discriminator learning target identifies unfit individuals.
First Learning Steps to Recognize Faces in the Noise

A UNet-type encoder-decoder inpainting network is applied to weaken the protection strength of selectively encrypted face samples. Based on visual assessment, FaceQNet quality, and ArcFace recognition accuracy the strategy is shown to be successful, however, to a different extent depending on the original protection strength. For almost cryptographic strength, inpainting does not cause a practically relevant protection weakening, while for lower original protection strength inpainting almost removes the protection entirely.
Usability Evaluation of Flip Applications Using Cognitive Walkthrough and Think Aloud Evaluation Methods

The Flip application is a digital financial service that makes it easy for users to transfer between national and international banks without administrative fees, there are also digital wallet services such as purchasing credit, electricity, and others. Creating a good user experience, it takes an evaluation of the usability of the Flip application, to find out the level of user satisfaction and identify problems. Therefore, it is necessary to evaluate usability using cognitive walkthrough and think-aloud methods by performing task scenarios and assessments from the user’s perspective on the Flip application. The test was carried out on 100 respondents inthe Pekanbaru city area. Based on the test results, the effectiveness rate was 92.3% and the efficiency level was 0.22 goals/sec as well as the satisfaction level based on the method used value which can be seen from the problems caused are not included in the fatal category, and the positive response of respondents regarding the evaluation. This shows that the Flip application is very good but still needs improvement to be used more optimally. Recommendations are given as a reference in developing and improving applications in overcoming problems from the test results. Recommendations are interface improvements including appearance, indication, function, and operation of the Flip application.
The Effect of Learning Facilities and the Role of Lecturers on Interest in Hybrid Learning: A Case Study at Binus University

Covid-19 has brought many changes in all fields, including the world of education. Since the Indonesian government banned face-to-face learning (Onsite learning), Universities held online learning. At the beginning of the change, there were so many obstacles experienced. Bina Nusantara University also held online learning. In the even semester of the 2022 academic year, hybrid classes begin. This research aims to determine the effect of learning facilities and the role of lecturers on interest in learning in a hybrid student of the Japanese study program at Binus University. This study used the quantitative method by distributing questionnaires to students in semesters 1 and 3 in odd semesters in 2022. This research is categorized as ex post facto research by conducting systematic empirical investigations. From the results of simultaneous testing of the associative hypothesis as the elaboration of the problem formulation, learning facilities and the role of the lecturer simultaneously influence the interest in learning in a hybrid variable by 80%.
An Early Detection of Ovarian Cancer and The Accurate Spreading Range in Human Body by using Deep Medical Learning Model

Current methods of ovarian cancer screening include physical examination, ultrasound, and blood tests to detect the cancer antagonist (CA-125). All of these are subject to appropriate restrictions. That is, the correct diagnosis of cancer by biopsy depends on the expertise of the doctor performing the test. In many cases, other disorders are also mistaken for ovarian cancer. Ultrasound also does not help differentiate tumors as malignant or benign. Although antibody tests are accurate, they do not help diagnose the disease in its early stages. Also, CA-125 antagonist levels may also increase due to growth or inflammation of the uterus. Therefore, even if all these methods are combined, it is not possible to detect the disease at an early stage. In this paper an innovation model to identify the ovarian cancer and its spreading range by using the deep medical learning model. Ovarian cancer often goes undiagnosed until it spreads to the pelvis and abdomen. But if these are detected early, these malignant tumors can be surgically removed so that they do not spread to other parts of the body. When this diagnostic approach is combined with chemotherapy for cancer, patients’ quality of life and well-being improves
Enhancing Charity Systems by Leveraging Blockchain Technology and Machine Learning

The biggest obstacle that prohibits relatively wealthy individuals and organizations from donating to charities is a lack of trust, transparency, and security. It is not inconceivable to think the donated funds might not achieve the intended purpose or reach the expected audience–whether it is for cancer patients, environmental conservatism, social services, nationalized healthcare, or so on. This belief is amplified by the stories of fraudulent charities and middlemen defrauding generous donors under the pretext of social causes. Our research paper aims at designing a charity system that uses blockchain to store transaction records for charities. This solves the issues of trust, transparency, and security that plague current charity platforms. Further, we have leveraged machine learning to develop an enhanced recommendation system of charities to donors, on the basis of a calculated accountability score (which is a signature of the transparency for that charity) and donor interests.
Analyzing Programming Competency: A Student Perspective in Python Programming

Computer programming is a skill that every information technology (IT) student must know and have. Computer programming is delivered using different programming languages. A programming language is a set of instructions to build computer programs. The College of Information and Communications Technology (CICT) of Bulacan State University (BulSU) offers different programming languages to its IT students from first- to fourth-year level. One of the languages students must take is Python programming. Python programming language is a scripting language used to develop web applications, data analytics, machine learning, and more. This study focuses on determining the students' programming competency in Python programming using a paired sample t-test. To deliver its objective, a concurrent triangulation design of mixed methods research was utilized to gather quantitative and qualitative data at the same time from fourth-year IT students of BulSU-CICT. Using paired sample ttests to calculate and interpret the quantitative data gathered, this study found that there is a significant difference between the before and after performance of the students in programming (P<0.00l), specifically in Python programming. Additionally, a manual analysis was delivered on the thoughts of the students upon completing the course. It is highlighted that students learned a lot, and their expectations were met after taking the course. From what this study discovered, course offerings of other programming languages may also be subjected to analyze the need for course syllabus revisions to fit what the student needs based on their performance and thoughts.