Reviewerly/exHarmony huggingface dataset:
Developing a Unique Identifier of Smallest Possible Ballot Area that Create One-One-Match with Voting Unit Identifier for Efficient Election Management in Jos South LGA Plateau State, Nigeria

The in-efficient approach adopted in managing election data by the Election Management Body (EMB), coupled with none-application of Geographic Information System (GIS) resulted into series of election fraud, particularly in the 2023 general election and the recent off-season governorship elections in Kogi, Bayelsa and Imo States in November 11th, 2023. That is why this article aimed at using GIS in developing unique identifier for the smallest possible ballot area that create one-one-match with voting unit identifier, in order to effectively manage election process. That is why, developing a unique identifier of smallest possible ballot area that create one-one-match with voting unit identifier for efficient election management is so generic, particularly when establishing a geo-spatial election database. A Geo-Relational Database Management System (GRDBMS) on MySQL format was developed after a detailed study data needs assessment which provides a ‘strong entity relation’ table. The result at prompt of ArcGIS using ‘Polling unit’ as the Strong Entity (SE) revealed in tabular format showing one-one-match voter. That is, each voter is designated a specific ballot point, name and address geo-coded and other bio-metric characteristics in spread sheet. This indeed enable the Independent National Electoral Commission (INEC) to effectively plan for election and be the owner and custodian of election fundamental datasets for election planning processes.
BJWT-EHR: A novel JWT based Blockchain System for Electronic Health Records

Since most transactions in the world now take place digitally, it is crucial to maintain data integrity, share data securely, and provide quick access to it. Our reliance on computers in our daily lives is growing, and new data will inevitably be generated to meet this need. Providing for safe data exchange is becoming increasingly crucial every day. Maintaining and safeguarding the security of this data is crucial. Recently, the term "blockchain" has been used extensively for data security. A decentralized digital ledger called blockchain is in charge of preserving data integrity and facilitating safe data sharing. Blockchain transfers data by the principle of transparency, records data one way, and prevents reverse engineering. According to reports, blockchain is the general term for the technology that promotes common data sharing of these recorded data and reduces access times, essential for preserving data security. There are several risks to patients' privacy when information from medical records is stolen or altered. The damage caused by malicious people with this information causes serious harm to both the patient and the financing institution. Therefore, cloud-based EHR (electronic health records) is becoming a focal point in many areas. Storing patient data, ensuring data accuracy, protecting data confidentiality, and ensuring fast data transmission in the cloud system is a significant concerns in the healthcare industry. In this study, the layered architecture structure is also addressed in addition to the blockchain for sharing health data with the help of cloud service providers while providing data access control and auditing for EHR records. To enter this layered architectural system, new solutions are proposed by applying JWT (JSON web token) and hashing function of patient information. This proposed method manages and controls the patient's health records electronically. Therefore, these technologies used in the blockchain platform open new doors to more secure and sustainable solutions.
FORMATION IN SENIOR SCHOOL STUDENTS OF THE IDEA ABOUT PHYSICAL PICTURE OF THE WORLD IN THE PROCESS OF LEARNING PHYSICS

The article highlights the ways of forming the ideas of senior school students about the physical picture of the world, in particular, an in-depth study of the history of formation of physical pictures of the world, biographies and scientific heritage of physicists, creators of these pictures of the world; selection of elements available for students to study modern physical picture of the world and their introduction to the content of physics education; acquaintance with the methods of scientific knowledge in the process of learning physics, coverage of epistemological aspects of physics. Formation of students' ideas about the physical picture of the world should be based on the basic concepts formed in the process of learning physics, and targeted help provided by a teacher to students during this formation. For the teacher, one of the most important tasks of the physics course is the task of forming students' understanding of the unity of nature, forming the idea that the object of the physical picture of the world is the material world. The important role is given to the motivational aspect of forming an idea about a physical picture of the world: students must have a need for acquisition of knowledge, obtaining quality education, understanding the usefulness of physical knowledge regardless of choosing a future profession, the ability to distinguish between facts and assessments, compare assessment conclusions, formulate and justify own position. It is shown that systematic and purposeful activity of a teacher on the formation of senior school students' ideas about the physical picture of the world is most effectively carried out in general lessons, in which the teacher in the joint educational activity with the students allocates the main physical theories and ideas, laws and regularities, establishes cause and effect connections between phenomena.
TRADITIONAL, DISTANCE, BLENDED LEARNING IN HIGHER SCHOOL IN THE CONTEXT OF COMFORT AND EFFECTIVENESS

The article focuses the problem of using different learning models organization at the present time: traditional (classroom/face-to-face), distance and blended learning. Learning models organization were evaluated as a process (comfort indicators): psychological comfort; motivation of the educational/professional activity of all participants in the educational process; as a result (effectiveness indicators): effectiveness of knowledge acquisition, formation and development of skills; effectiveness of students' development personal and professional. The author included 1826 respondents in the anonymous online survey Google Forms during the Covid-19 pandemic in Ukraine (2020-2021), including: 140 PhD students, 172 university teachers, 1514 Bachelor and Master students (1071 in 2020, 443 in 2021); used IBM SPSS Statistics 23 for statistical analyses. Cronbach's alpha varies between 0,813-0,876. Spearman's rank correlation (0,440-0,667) indicate the existence of a medium-level direct relationship between indicators of learning models organization. Respondents believe that traditional face-to-face learning has the best effectiveness for acquiring knowledge, forming the skills of students, for personal and professional development. Learning effectiveness decreases with an increase in the online component. Teachers have maximal psychological comfort in the traditional face-to-face learning, students - blended and distance learning (survey of PhD students) or traditional and blended learning (survey of Bachelor and Master students). The motivation activity (learning/teaching) increases for all respondents in the direction: distance → blended → traditional face-to-face learning. The t-test was calculated for 168 cases, of which 104 cases (62%) did not have statistically significant differences (p≤0,05) between groups of respondents. The analysis shows that in most cases there are no statistically significant differences in the comparison of individual samples on the same basis (gender, specialty, form of education).
Enhancing Agile Software Development Sustainability Through the Integration of User Experience and Gamification

Abstract This article provides a rich discussion on how the sustainability of agile development processes can be enhanced. In particular, we focus on a recently developed framework, named GLUX, that integrates Lean UX into Scrum. GLUX’s main goal is to facilitate a seamless integration between agile and user experience (UX) by using gamification to motivate agile teams to adopt a user-centered mindset and carry out UX activities collaboratively throughout the development process. Our role as software researchers is to contribute towards improving software sustainability and provide the software engineering community with the tools and techniques that will improve the human, economic, and environmental sustainability of software development. We found that GLUX addresses human sustainability by empowering self-sufficient, problem-focused teams, building a motivating and engaging environment, and developing team cooperation. Economic sustainability is addressed by minimizing UX debt and using gamification techniques to direct the focus of the behavior and mindset of agile teams towards value creation. Finally, environmental sustainability is promoted by encouraging agile teams to build a minimum viable product (MVP).
Streamlined data analysis in Python

Python is a particularly appealing language to carry out data analysis, owing in part to its user-friendly character as well as its access to well maintained and powerful libraries like NumPy and SciPy. Still, for the purpose of analyzing data in a lattice QCD context, some desirable functionality is missing from these libraries. Moreover, scripting languages tend to be slower than compiled ones. To help address these points we present the AnalysisToolbox, a collection of Python modules to facilitate lattice QCD data analysis. Some highlighted features include general-purpose jackknife and bootstrap routines; modules for reading in and storing gauge configurations; a module to carry out hadron resonance gas model calculations; and convenience wrappers for SciPy integration, curve fitting, and splines. These features are sped up behind the scenes using parallelization and just-in-time compilation.
Meme Kanseri Sınıflandırması İçin Yeni Bir Yöntem: Ultrason Görüntülerinde Sinyal İşleme Temelli Bir Yaklaşım

Breast cancer, a leading cause of mortality among women worldwide, the importance of accurate and efficient diagnostic methods is emphasized. This study contributes to the literature on breast cancer classification, particularly using breast ultrasound images, with a new method using a signal processing approach. It introduces a novel approach by combining features extracted from signals obtained from breast ultrasound images with signals from Variational Mode Decomposition (VMD) sub-bands. The results demonstrate that utilizing features from both preprocessed raw data and VMD sub-band signals can effectively distinguish benign and malignant breast ultrasound images. Classification performance varied depending on the algorithms and data used. According to the numerical results, the highest classification performance was achieved through the study with balanced data using the artificial neural network method, with an area under the curve value of 0.9971 and an accuracy value of 0.9821.
Recognition of Bangladeshi Sign Language (BdSL) Words using Deep Convolutional Neural Networks (DCNNs)

In a world where effective communication is fundamental, individuals who are Deaf and Dumb (D&amp;D) often face unique challenges due to their primary mode of communication—sign language. Despite the interpreters' invaluable roles, their lack of availability causes communication difficulties for the D&amp;D individuals. This study explores whether the field of Human-Computer Interaction (HCI) could be a potential solution. The primary objective is to assist D&amp;D individuals with computer applications that could act as mediators to bridge the communication gap between them and the wider hearing population. To ensure their independent communication, we propose an automated system that could detect specific Bangla Sign Language (BdSL) words, addressing a critical gap in the sign language detection and recognition literature. Our approach leverages deep learning and transfer learning principles to convert webcam-captured hand gestures into textual representations in real-time. The model's development and assessment rest upon 992 images created by the authors, categorized into ten distinct classes representing various BdSL words. Our findings show the DenseNet201 and ResNet50-V2 models achieve promising training and testing accuracies of 99% and 93%, respectively. Doi: 10.28991/ESJ-2023-07-06-019 Full Text: PDF
Long Short Term Memory Based Sign Language Detection System

Sign language recognition plays a crucial role in facilitating communication and inclusivity for individuals with hearing impairments. This research paper proposed a novel approach for detecting sign language gestures using Long Short-Term Memory (LSTM) networks. By leveraging the sequential data processing capabilities of LSTM networks and with the use of feature engineering an accurate model has been developed to predict sign language gestures. The paper extends in-depth discussions into data collection, data pre-processing and feature engineering techniques used to increase the efficiency of the LSTM model. Keras API for TensorFlow was used for creating a sequential model. The paper also presents a comparative study regarding the change in accuracy resulting from the change in the size of the LSTM layer and the dropout layer ratio. The highest-performing model with an accuracy of 91.28 percent was used for testing the performance of the model in real-life applications.
A User-Centric Self-Sovereign Identity-Based Authentication Framework for Decentralized Data Management in Healthcare Settings

Establishing identities and privacy in the modern digital landscape faces critical challenges, such as counterfeit identities, identity theft, and data breaches. These issues have significant implications for the healthcare system. To address these concerns, access control and Self-Sovereign Identity (SSI) play vital roles in creating a privacy-conscious ecosystem. SSI enables user-centric management of identification credentials and electronic health records (EHR), allowing users to validate their credentials without relying on central or external servers. The proposed work is an innovative approach to identifying various stakeholders in the healthcare system. By utilizing cryptographic proofs, the proposed method instills trust in the stakeholder's identity. Moreover, the proposed work successfully mitigates various security attacks. The demonstration of this work leverages the Hyperledger Aries framework and a test blockchain network called Von-network.
Vulnerability Assessment on Cross-site scripting attack in a simulated E-commerce platform using BeEF and XSStrike

The risk of cyberattacks increases along with the quantity of e-commerce websites. One of the frequent attacks that target and threaten said websites is cross-site scripting (XSS) attack. They take use of flaws in online applications to insert harmful code into a website, which can be exploited to steal confidential data or impair the performance of the website. XSS attacks can have significant effects on end users as well as the companies in charge of maintaining the websites. Websites can be edited or defaced, users can be tricked into giving up their information and login credentials, and websites or applications can even be taken over. This study is conducted to gauge the susceptibility of e-commerce websites to XSS attacks, enumerate as many vulnerabilities as our testing will allow us to find, and glean recommendations from the results to help improve the security of such websites. Additionally, this study also aims to demonstrate the severity of XSS attacks and their effects on the end-users and organizations affected by such attacks. Penetration testing using the freely available open-source XSS tools BeEF and XSStrike are used to identify potential vulnerabilities in the two websites created and provided by the researchers’ structure, features, and functionality.
SLIDEX: Sliding Window Extension for Image Processing

With the rising need for efficient image processing in emerging applications such as Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR), many existing solutions do not meet their performance and energy efficiency requirements or are domain-specific and lack generality. In this work, we introduce SLIDEX, a novel ISA extension that leverages Sliding Window Processing (SWP) to bridge this gap in the image processing domain. SWP is a novel SIMD model that exposes to the programmer and natively manipulates vector registers as groups of overlapped windows of pixels to exploit the sliding-window dataflow found in convolutions and other stencil operations. SWP amplifies the available Data Level Parallelism (DLP) and reduces memory and register file accesses. We evaluated SLIDEX benefits in the critical image processing task of a state-of-the-art visual localization system widely used in AD and AR/VR. SLIDEX obtains an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 1.2\times$</tex> overall speedup and 22% energy reduction.
Automatic Code Generation for High-Performance Graph Algorithms

Graph problems are common across many fields, from scientific computing to social sciences. Despite their importance and the attention received, implementing graph algorithms effectively on modern computing systems remains a challenging task that requires significant programming effort and generally results in customized implementations. Current computing and memory hierarchies are not architected for irregular computations, resulting performance that is far from the theoretical architectural peak. In this paper, we propose a compiler framework to simplify the development of graph algorihtm implementations that can achieve high performance on modern computing systems. We provide a high-level domain specific language (DSL) to represent graph algorithms through sparse linear algebra expressions and graph primitives including semiring and masking. The compiler leverages the semantics information expressed through the DSL during the optimization and code transformation passes, resulting in more efficient IR passed to the compiler backend. In particular, we introduce an Index Tree Dialect that preserves the semantic information of the graph algorithm to perform high-level, domain-specific optimizations, including workspace transformation, two-phase computation, and automatic parallelization. We demonstrate that this work outperforms state-of-the-art graph libraries LAGraph by up to 3.7 × speedup in semiring operations, 2.19 ×speedup in an important sparse computational kernel, and 9.05 × speedup in graph processing algorithms.
Improved Zeroing Neural Network for Solving Time-Variant Linear Equation with Harmonic Noise

Time-variant linear equation (TVLE) is commonly involved in the science and engineering. Zeroing neural network (ZNN) and its variants have been developed and exhibited remarkable performance (e.g., noise rejection and finite-time convergence) on solving the TVLE. In this paper, we delve into a more in-depth investigation by enhancing the ZNN model for solving TVLE in the presence of harmonic noise. Such a model enables calculation error tend to zero, thereby facilitating the identification of the exact TVLE solution. To verify the supremacy of this enhanced ZNN model, we conduct a rigorous evaluation, assessing its properties such as convergence and robustness through various illustrative examples.
One-Qubit Transverse-Field Ising Model and Variational Quantum Algorithms

We give a first introduction to combinatorial optimization with the Ising model and its quantum counterpart. We start investigating the basics of the transverse-field Ising model in the case of one qubit. We give an example of a neural network variational ansatz for the ground state.
Qubit Maps

We consider quantum feature mapping in qubit states with entanglement. We introduce tensor notations for qubits in $$\mathtt { TensorFlow}$$ . We review an experiment with IBM quantum computers with memristors. We deepen the mathematical aspects of quantum feature maps and support vector machines.
Quantum Reservoir Computing

We study models including random interferometers and trainable layers.We discuss different strategies for defining a cost function by using first and second derivatives of the characteristic function.We consider circuits with multiple interferometers and phase modulators.
Variational Algorithms, Quantum Approximate Optimization Algorithm, and Neural Network Quantum States with Two Qubits

We describe different variational ansatzes and study entanglement in the ground state of the two-qubit transverse-field Ising model.
Asymmetric Loss Based on Image Properties for Deep Learning-Based Image Restoration

Deep learning techniques have significantly improved image restoration tasks in recent years. As a crucial component of deep learning, the loss function plays a key role in network optimization and performance enhancement. However, the currently prevalent loss functions assign equal weight to each pixel point during loss calculation, which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics fully. To address this issue, this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery task. This novel loss function can adjust the weight of the reconstruction loss based on the grey value of different pixel points, thereby effectively optimizing the network training by differentially utilizing the grey information from the original image. Specifically, we calculate a weight factor for each pixel point based on its grey value and combine it with the reconstruction loss to create a new loss function. This ensures that pixel points with smaller grey values receive greater attention, improving network recovery. In order to verify the effectiveness of the proposed asymmetric loss function, we conducted experimental tests in the image super-resolution task. The experimental results show that the model with the introduction of asymmetric loss weights improves all the indexes of the processing results without increasing the training time. In the typical super-resolution network SRCNN, by introducing asymmetric weights, it is possible to improve the peak signal-to-noise ratio (PSNR) by up to about 0.5%, the structural similarity index (SSIM) by up to about 0.3%, and reduce the root-mean-square error (RMSE) by up to about 1.7% with essentially no increase in training time. In addition, we also further tested the performance of the proposed method in the denoising task to verify the potential applicability of the method in the image restoration task.