Reviewerly/exHarmony huggingface dataset:
Blockchain in Supply Chain Management

Blockchain technology has been the focus of much attention and discussion recently. Its unique characteristics, such as immutability, transparency, and decentralization, create a great potential solution for many industries. Especially the automotive sector, which is known for its complex supply chains, large amounts of data, and wide-reaching infrastructure, can profit in many aspects by incorporating blockchain technology. To illustrate the advantages and potential of blockchain in supply chain management, this chapter includes a case study of one of the leading corporations in the automotive industry that has been increasingly committed to adopting this technology. The analysis uses a set of strategic analysis tools frequently used by managers for strategic planning to highlight the benefits and challenges of this approach. Besides contributing to the literature on blockchain and supply chain management, this chapter offers valuable insights for managers, namely in the automotive sector, who are considering adopting blockchain technology in their operations and processes.
Examining the Performance Implications of Design Patterns in Front-end Web Development: A Preliminar Comparative Study with React and Vue.js

With the increasing use of design patterns as well as front-end libraries and frameworks in web development, understanding the computational trade-offs associated with their adoption is essential. In this sense, this paper discusses the preliminary research results focusing on the performance implications of design patterns using React and Vue.js. Through a computational experiment, we analyzed a web application’s memory consumption, CPU usage, rendering time, and response time with five design patterns implemented. The findings contribute to a better understanding of the trade-offs associated with design pattern adoption and provide valuable insights for developers and organizations seeking to optimize the performance of their web applications.
Enhancing the Skill of Grade 5 Students to Correctly Pronounce Alphabet Using the Phonetic Method at Thammislam Foundation School in Bangkok, Thailand

&#x0D; &#x0D; &#x0D; &#x0D; There are various a variety of problems that students must confront with that become barriers to the improvement of their English skills, based to research on how to teach English as a foreign language in Thailand. One of them is the students lack of inspire in learning English and their pronunciation when speaking and when pronunciation of letters of the alphabet. A few children even struggle to write their names using the alphabet because they always use their native tongue. The purpose of this research is to improve student learning outcomes by showing students how to remember the alphabet phonetic method by using words starting with each letter in the alphabet. The Thammislam Foundation School's 27 students were part of this study who participated in this research. Furthermore, this study aims to enhance the English language outcomes for learning. The steps taken by the author include creating a stimulating educational setting, implementing English learning materials easier to understand, and including students more in each lesson so that they ought to participate and increase more confidence in speaking English.&#x0D; &#x0D; &#x0D; &#x0D;
The Application of MATLAB in Mathematics: An Overview

This research paper aims to provide an overview of the application of MATLAB in mathematics. MATLAB is a widely used software tool in the field of mathematics, providing a user-friendly environment for numerical computations and visualization of data. The paper covers various areas of mathematics in which MATLAB can be applied, including linear algebra, calculus, differential equations, and statistics. The paper also highlights the advantages of using MATLAB in mathematical research, such as its ability to handle large data sets and its ability to perform complex numerical computations efficiently. The paper concludes with a discussion on the future of MATLAB in mathematical research and its potential to revolutionize the field.
Structural Relationship on Factors Influencing Digital Literacy of College Students

In the era of rapid development of science and electronic technology, cultivating students’ digital literacy has become one of the key goals. This study aims to explore the causal relationship between students’ cognitive engagement, behavioral engagement, emotional engagement, and digital literacy. For this purpose, 454 college students participated in the survey held in March 2022. According to the results of the structural equation model, (1) students’ cognitive engagement significantly influenced emotional engagement, behavioral engagement, and digital literacy. (2) Students’ cognitive and behavioral engagement is related to their digital literacy. (3) Emotional engagement could not predict digital literacy. (4) Behavioral engagement moderated the relationship between cognitive engagement and digital literacy. This study emphasizes socio-emotional ability, which critically explores the value of online information between meaningful information and fake news. In conclusion, higher education should be designed to cultivate each dimension by clearly recognizing the sub-dimensions of digital literacy.
Modeling Human Frailty with a Smart Home-Based Approximation of Entropy

The global population is aging. These older adults face changes in health that result in increasing frailty. Digital biomarkers created by passive, continuous sensors offer an early indicator of impending frailty that can be used to delay or reverse frailty. Building on the notion of a human as a complex system, we introduce and compare three methods to model and estimate the complexity of indoor human behavior. Each method offers potential benefits for estimating human frailty. We introduce a formalization of the approaches, extend their use for arbitrary-size sensor suites, and demonstrate how they can be used to visualize and calculate a person's behavioral complexity based on smart home data collected continuously for an older adult subject.
Learning Experience Using Nursing Skills Videos of Undergraduate Nursing Students

Purpose: The purpose of this study is to explore students’ experiences with the usefulness and limitations of learning through nursing skill videos and to identify strengths and weaknesses based on production methods. Methods: A qualitative content analysis study was conducted with 15 second-year nursing students who used nursing skill videos as supplementary materials for their fundamentals of nursing laboratory class. Data collection was done through face-to-face interviews. The initial interview question was “How was your learning experience of watching nursing skill videos?”. Results: Through in-depth content analysis, 12 categories and 21 subcategories were established within 4 domains as follows: 1) usefulness of video learning, 2) limitations of video learning, 3) need for improvement in video learning, and 4) strengths and weaknesses depending on production methods. Conclusion: Students found nursing skill videos useful for visually understanding procedures and facilitating self-directed learning. However, there is a need for detailed explanations that take into consideration the level of students who are new to nursing.
Implementation of Bluetooth Based Cargobot Using Controlled Arduino Robocar

As the interaction between human and machine interfaces is increasing greatly, which reduces the gap between machines and humans, thereby making standardized living much easier. A Robocar is an Arduino-controlled car that works on Arduino. It is essentially an open architecture prototype. The Cargobot: Bluetooth-controlled Arduino Robocar performs various functions to give a strong and flexible Robocar, Moreover, lowering human labor. Developing a flexible and intelligent vehicle is crucial, which is controlled by an Arduino, to support the physical distribution of goods. At a specific distance, the Wi-Fi module Technology seeks to exchange data wirelessly.
PENGGUNAAN KIALO EDU UNTUK MENGEMBANGKAN IDE MENULIS ESAI ARGUMENTATIF : PERSEPSI MAHASISWA

This study aims to explore students' perceptions of the use of Kialo Edu in developing ideas for writing argumentative essays. The participants of this study were thirteen of sixth semester students of the Pontianak Foreign Language Institute who enrolled essay writing class. The methodology employed was a descriptive study with a three-scale closed questionnaire and semi-structured interviews as data collection instruments. The findings indicated that after using Kialo Edu for three times, students showed positive perceptions in developing ideas for writing argumentative essays. 5 of the 7 statements on the questionnaire received a 100% "agree" response. The positive response from the interview also supports this result. Referring to this result, the answer to the question has been found that the students' perception of the use of Kialo Edu is positive in helping them develop ideas for writing argumentative essays. In the other words Kialo Edu can be recommended as a platform that can be used in the classroom to develop ideas on essays that require argument elaboration, such as argumentative essays.
Loyal Consumers: Efforts to Improve and Maintain Consumer Databases Using Youtube Optimization Techniques

This study tries to test the ranking model on youtube search. Youtube brings up search results based on the number of visits and video recommendations available. The method used in this research begins by collecting 10 keywords that appear in a youtube search. Of the 10 results will be used as the image file name to be used. The 10 image files are then turned into a video. The link generated from the upload process is then registered with a free ping service on the internet. For testing, day parameters are used, namely 1 day, 2 days and 3 days. Within 3 days the video has appeared on the first page of a Google search with keywords that match the names of the image files.
Characteristics of drug users admitted in Lido Rehabilitation Center during 2022

The increasing trend of illicit drug use leaves a huge concern for health and behavioral changes, including in Indonesia. Rehabilitation is a series of treatments designed to help them recover from addiction and its impacts comprehensively. The present study aimed to explore the demographic characteristic and drug use profiles of clients admitted in Lido Rehabilitation Center, the national referral center for drug use treatment in Indonesia, during 2022. A survey study was carried out quantitatively using a 2022 database of client admission in February 2023. Descriptive analysis was performed to present the data as appropriate. The overall total of drug users was 592 clients (569 males, 23 females). The demographic characteristics were dominated by young adults (79.90%), voluntary (74.70%), Muslims (86.80%), high-school-to-higher graduates (74.70%), and employers (61.80%). The drug mostly used was methamphetamines (69.80%) with duration being less than 10 years (75.50%) and addiction level being mild-to-moderate (92.80%). The main reasons for starting drug use were peer/partner pressure (37.18%) and curiosity (29.92%). From this study, we can see that the trend of methamphetamine use in productive ages is still dominating in 2022. Further study is needed to evaluate the outcomes of the treatment they have received during the rehabilitation program.
Mobile Robot Navigation System Based on Ultrasonic Sensors

In this paper authors propose the mobile robot and its navigation system development. This navigation system is based on two ultrasonic sensors HC-SR04. The device driver is developed. And the experimental results are presented.
An ECC-Based Lightweight CPABE Scheme with Attribute Revocation

Data storage on cloud servers has become a common practice across businesses. Data security and accessibility in cloud environments are jeopardized when stored on unreliable cloud servers, making it necessary to convert user information into encrypted text that is difficult to understand and analyze, even if it is compromised. The novel encryption method known as Ciphertext Policy Attribute Based Encryption (CPABE) can be used to provide fine-grained access control and privacy in a cloud environment. To avoid the costly bilinear pairings, several researchers suggested CPABE approaches based on the elliptic curve cryptosystem (ECC). But there is a key-escrow concern with these systems. Hence, in this paper, we propose a multi-authority lightweight CPABE methodology. In addition, time and geographical attributes are also taken into consideration to enhance fine-grained access control. Our proposed solution also solves the issue related to dynamic attribute revocation by relegating repeated and tedious tasks to proxy servers. The detailed performance analysis demonstrates that the suggested method outperforms current schemes.
Verifiable Secret Image Sharing with Cheater Identification

As digital communication has grown in a variety of industries where data must be secure and confidential, secret image transmission has grown in popularity. It will be a severe issue if false data is produced in the medicine, military, or diplomacy sectors. The three actors in conventional secret image sharing are the dealer, the participants, and the combiner. There is a chance that the Combiner will fabricate information, a participant will alter the data and submit a manipulated share, or someone will pose as a dealer and provide false information. To prevent data tampering, all actors in the secret image-sharing process should be verified and authenticated during transmission. This issue introduces a new area of study on secret sharing known as verification of secret sharing scheme, which addresses cheating activities by implementing various verification which includes dealer verification, cheating detection, and combiner verification techniques. This paper implements a secret image sharing (SIS) scheme with the ability to detect the cheater. Bitwise OR secret sharing scheme is used to share the secrets, and for verification one-way hash function, image hashing, and XOR operations are used. Encryption of the secret is done using the entropy of the image as the key. The dealer assigns shadow shares and shadow ID’s to the participants, which are then reconstructed by the combiner to create the original image. Participants validate the combiner’s authenticity, and the combiner validates the integrity of the shares before reconstruction. After the construction, the participants once again verify if the secret constructed is true or false data.
HGL_GEO: Finer-grained IPv6 geolocation algorithm based on hypergraph learning

IP geolocation is necessary for applications such as location-aware ad recommendation, traceability, and fraud detection. Presently, graph neural network-based approaches focus on geolocation information transfer of neighboring nodes of the target IP node. However, terminal hosts in real physical proximity in tier networks are often not directly connected, graph learning tasks ignore geographic knowledge from more distant connecting relationships. To address the limitations of existing work, we propose a new HyperGraph Neural Network (hyperGNN)-based IPv6 geolocation framework HGL_GEO (HyperGraph Learning Geolocation), by modeling a set of IP host groups with strong geographic-location correlation using hyper-edges, and incorporating IP knowledge and connectivity relationships into the hypergraph. The target IP nodes then learn geolocation knowledge in the hypergraph convolution, thus enabling IPv6-based geolocation tasks. The final experimental results on a 10-month test data collection from three real regions (Shanghai, New York, and Tokyo) show that the median error distance ranges from 5.837 km to 8.52 km, and that the average error distance ranges from 7.012 km to 9.359 km, compared with those of TNN, MLP_Geo, and GWS_Geo IP geolocation algorithms and the GCN and GAT. The median error distance of HGL_GEO is at least 10.12% less and the average error distance is at least 3.3% less than those of deep learning models.
Reservoir computing with optofluidic nonlinearity and short-term memory

We report a novel Reservoir Computing (RC) processor exploiting nonlinearity and memory effects in a thin liquid film cladding integrated with a Si-Photonic device. We report the multiphysics processes resulting in a Thermocapillary (TC) effect that serve as a source of nonlinear response and a short-term memory at the same time.
Evaluating the Explainability of Machine Learning Classifiers: A case study of Species Distribution Modeling in the Amazon

Machine Learning Models are widely used in Computational Ecology. They can be applied for Species Distribution Modeling, which aims to determine the probability of occurrence of a species, given the environmental conditions. However, for ecologists, these models are considered as "black boxes", since basic Machine Learning knowledge is necessary to interpret them. Thus, in this work four Explainable Artificial Intelligence techniques - Local Interpretable Model-Agnostic Explanation (LIME), SHapley Additive exPlanations (SHAP), BreakDown and Partial Dependence Plots - were evaluated to the Random Forests classifier for Coragyps atratus in the Amazon Basin region. It was found that the SHapley Additive exPlanations technique and Partial Dependence Plots are able to improve the explainability of the model.
THE NATURE OF THE RELATIONSHIP BETWEEN VALUES-BASED AND SEMANTIC ORIENTATIONS WITH PROFESSIONAL SELF-IMPLEMENTATION OF ENGINEERING AND TECHNICAL EMPLOYEES

Abstract. The urgency of the research problem on the nature of the connection of values-based and semantic orientations with the professional self-realization of engineering and technical employees has been considered. The scientific works of domestic and foreign authors concerning the research of values-based and semantic orientations and professional self-realization have been analyzed. The expediency of using psychodiagnostic tools to study the nature of the relationship of values-based and semantic orientations with the professional self-realization of engineers and technicians has been substantiated, and mathematical and statistical processing of the results of empirical research has been carried out. Empirical data which testify to the existence of connections of values-based and semantic orientations with professional self-realization of engineering and technical employees have been given, namely: positive correlation between the indicators of professional self-fulfillment and job satisfaction, positive correlation between the indicators of professional self-fulfillment and instrumental values, positive correlation between the indicators of professional self-fulfillment and meaningful life values, positive correlation between the indicators of professional self-fulfillment and the following scales: "Pleasant leisure time, rest", "Material well-being", "Beauty search and enjoyment", "Help and charity to other people", "Love", "Cognition of the new in the world, in nature, in a person", "High social status and management of people", "Recognition and respect for people", "Social activity to achieve positive changes in society", "Communication" and "Health" in engineering and technical employees; negative correlation between the indicators of internal professional self-fulfillment and terminal values of engineering and technical employees. Given the results of the study, it can be asserted about the importance of taking into account the values-based and semantic orientations and professional self-realization in the psychological support of engineering and technical employees.
Pragmatic Deixis in King Charles III’s Inauguration Speech

Deixis in English is a phenomenon of understanding words and phrases by considering contexts and actual speech action. Second learners can employ the pragmatic deixis in various literary works or other media dimensions to comprehend the Meaning of words and utterances. Hence, this study attempted to know the types of deixis in King Charles III Inaugural speech. The researchers obtained the transcript from the New York Times and analysed it using descriptive research to explore and explain the deixis types in the speech in more detail. The writers used Cruse’s (2000) theory to indicate the five types of deixis. The data revealed that all types of deixis occurred in speech, including person deixis, spatial deixis, temporal deixis, discourse deixis, and social deixis. The type of deixis that appears most often is person deixis, with a percentage of 71%. Meanwhile, the deixis that appears the least is social deixis, with a percentage of only 3%. The spatial and discourse deixis each have a percentage of 9%, followed by temporal deixis with a percentage of 8%. The result of this study is likely to enrich second learners of English's knowledge and vocabulary of pragmatic studies, primarily the phenomenon of deixis.
Optimized YOLOv7 for Small Target Detection in Aerial Images Captured by Drone

It is challenging to detect small targets in aerial images captured by drones due to variations in target sizes and occlusions arising from the surrounding environment. This study proposes an optimized object detection algorithm based on YOLOv7 to address the above-mentioned challenges. The proposed method comprises the design of a Genetic Kmeans (1- IoU) clustering algorithm to obtain customized anchor boxes that more significantly apply to the dataset. Moreover, the SPPFCSPC_group structure is optimized using group convolutions to reduce model parameters. The fusion of Spatial Pyramid Pooling-Fast (SPPF) and Cross Stage Partial (CSP) structures leads to increased detection accuracy and enhanced multi-scale feature fusion network. Furthermore, a Detect Head is incorporated into the classification phase for more accurate position and class predictions. According to experimental findings, the optimized YOLOv7 algorithm performs quite well on the VisDrone2019 dataset in terms of detection accuracy. Compared with the original YOLOv7 algorithm, the optimized version shows a 0.18% increase in the Average Precision (AP), a reduction of 5.7 M model parameters, and a 1.12 Frames Per Second (FPS) improvement in the frame rate. With the above-described enhancements in AP and parameter reduction, the precision of small target detection and the real-time detection speed are increased notably. In general, the optimized YOLOv7 algorithm offers superior accuracy and real-time capability, thus making it well-suited for small target detection tasks in real-time drone aerial photography.