Reviewerly/exHarmony huggingface dataset:
Research on fusion modeling based on privacy computing technology

In order to solve the problem of data islands, the power system needs to integrate data from other industries for crossdomain fusion modeling. This paper focuses on privacy computing technology, combined with the power industry's big data capabilities, and proposes a research method for cross-domain modeling. And practice this method from the three links of privacy computing technology architecture, federated learning fusion modeling, and industry model application, and finally summarize its innovative advantages and value enhancement points.
A Sentiment Analysis of Blockchain Technology in the Financial (FinTech) Sector by Machine Learning and Deep Learning Models

&lt;p&gt;Blockchain is a cutting-edge technology which is a new topic in Fintech. Financial industry wants to use the advantages of blockchain to improve their services. Financial sector should analyze the adoption of this technology before implementing in their system. In the adoption process, this sector has to explore customers’ attitudes regarding blockchain technology. &lt;/p&gt; &lt;p&gt;This study helps the banking sector to understand the view of their clients towards blockchain technology. To conduct this research, Twitter sentiment analysis is deployed. To do so, firstly relevant tweets are collected. Then, machine learning and deep learning models are deployed. These learning models are SVM, LSTM, CNN, and Ensemble models. The highest accuracy in this study is 87.95% which is achieved in the Ensemble model. This research shows mostly people have a neutral attitude about having blockchain technology in banks. A potential reason for this would be a lack of knowledge about this technology.&lt;/p&gt;
Improved Drug Traceability Code Detection Algorithm Based on YOLOv5

Abstract In order to enhance the storage efficiency of drug traceability code information on the blockchain and improve the extraction capability of drug traceability codes on drug packaging, a detection algorithm based on an enhanced version of YOLOv5 is proposed for the drug production and transportation scenario. The proposed algorithm introduces the SPD-Conv module into the backbone network, thereby enhancing the network’s ability to extract detailed feature information. Additionally, the CA attention mechanism is incorporated into the Neck of the network, providing the network with superior feature fusion capabilities. Furthermore, the activation function in the network is replaced with the LeakyReLU activation function, reducing computational requirements during training and inference. This replacement improves the model’s test accuracy and detection speed. Experimental evaluation, conducted on a self-built dataset, demonstrates the effectiveness of the improved YOLOv5 model. The results indicate a compression of model parameters from 6.14M to 2.71M, an increase in mAP@.5 from 82.4% to 93.7%, and a boost in detection speed from 32FPS to 51FPS. These findings establish the superior performance of the enhanced model compared to the original version.
Low illumination image enhancement based on attention mechanism and global illumination estimation

Abstract Aiming at the problems of insufficient or excessive local brightness enhancement, color distortion, and excessive noise in the existing low-light image enhancement algorithms, a low-light image enhancement method combining attention mechanism and global illumination estimation is proposed. First, the illumination distribution map of low illumination image is obtained through the illumination distribution estimation network coupled with the attention gate mechanism. Then, the weight of the light distribution map is learned in the feature attention module. Finally, the image details are fused by the detail reconstruction module to create improved image. According to the experimental results, this method may effectively improve image brightness, contrast, and color in subjective visual effects while also enhancing objective evaluation indicators like PSNR, SSIM, and MSE when compared to some conventional methods.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Double Anonymous • Conference submission management system : Morressier • Number of submissions received : 118 • Number of submissions sent for review : 101 • Number of submissions accepted : 40 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 33.9 • Average number of reviews per paper : 2 • Total number of reviewers involved : 23 • Contact person for queries: Name: Bimastyaji Surya Ramadan Email : bimastyaji@live.undip.ac.id Affiliation : Universitas Diponegoro
Competency Development of Early Childhood Teacher in the 21st Century

The goal of this study is to help early childhood educators in the province of Roi Et develop their management skills for early childhood learning in the 21st century. Examples include the 100 early childhood Teachers in the province of Roi Et, research study materials like the Competency Manual on Learning Management and the 21st Century Early Childhood Competency, and tests. Mean and standard deviations are two statistics that were utilized to analyze the data. The findings indicated that early childhood teachers&amp;rsquo; competency development involves five areas: content, technology, knowledge creation, communication, and creativity. The results were obtained utilizing percentage and t-test statistically ready-made programs. Curriculum, learning experience management, media use, innovation and technology in 21st century learning management, and measurement make up the four areas of the 21st century early childhood teacher competency development in learning management.
Test-Trace strategy for disease control and management: South Africa’s control measures to contain the spread of COVID-19

This chapter provides a critical examination of South Africa’s COVID-19 testing and contact tracing response, including emerging successes and weaknesses. Lessons emerging from South Africa’s COVID-19 testing and contact tracing strategy highlight the importance of rapid roll-out of community screening and testing across the country, robust implementation of public health measures, and leveraging existing healthcare services such as the deployment of existing Community Health Workers. The absence of a standardised national information system to support contact tracing efforts, long turnaround times and backlogs in testing, and low uptake of digital solutions such as the COVIDConnect and COVID Alert Apps, were noted as some of the challenges of the process. New considerations to take into account in planning for any future outbreaks include ways to enhance intersectoral approaches and integrate with other community surveillance systems, how to rationalise health workforce redistribution during surge periods by prioritising cases, and the use of technology solutions to manage data.
Setting Up a dbt Project

As with any new technical project, time must be spent on the setup and configuration to establish a solid foundation that will enable the contributors to the project to be successful. dbt is no different in this respect, and there is more than one path available to you for successfully setting up a new project.
To Review Is to Be

Received 16 August 2023DOI:https://doi.org/10.1103/PRXEnergy.2.030001© 2023 American Physical Society
The Effect of Dietary Preferences on Academic Performance Among Kindergarten-Aged Children

This study examines the impact of kindergarten children's dietary preferences on their cognitive performance and social relationships, with a focus on traditional foods versus processed foods. The research demonstrates that a child's choice of breakfast can significantly affect their cognitive abilities, including memory and concentration, as well as their interpersonal relationships. Traditional foods like salads and olive oil are found to increase cognitive abilities, while processed foods like hot dogs and chocolate sandwiches can be detrimental to academic performance and social behavior. The study utilized puzzle games and card games to evaluate cognitive abilities and found that a processed diet can lead to decreased focus, hyperactivity, and impaired social relationships. QEEG analysis contributes to the existing body of evidence indicating that a traditional diet can enhance academic performance. The study highlights the importance of educating parents about the advantages of a traditional diet and encourages them to prepare breakfast together with their children using traditional foods like olive oil and salad to enhance academic performance and social relationships. The findings offer valuable insights into the importance of a traditional diet for young children's education and highlight the need for greater attention to be given to diet as an important factor in children's academic success.
Influence High-Performance Work Systems and Knowledge Management Process on Open Innovation, The Mediator Effect Knowledge Worker Productivity

The purpose of this study is to investigate the impact of high- performance work system (HPWS) on open innovation capability of firms via mediating role of knowledge management process (KMP). It attempts to deepen insight on the moderating mechanism of knowledge workers' productivity (KWP) in enhancing the effects of KMC on Open innovation. The paper utilized structural equation modelling (SEM) and cross sectional design to test hypotheses in the proposed research model with data collected from 248 participants in 70 small and medium businesses in Jordan (SEMs).The findings indicate that KMP mediates HPWS effects on open innovation. In addition, the findings indicate that KWP mediator KMP effects on open innovation
Can Business Intelligence Promote the Radical and Incremental Innovation? Knowledge Management and Agility Organization in a Moderated Mediation Model

This paper investigates the relationship between business intelligence (BI) and the radical and incremental innovation of Jordanian industrial firms through the mediating role of knowledge management capability and the moderating role of organizational agility. Design/methodology/approach: A self-administered questionnaire was used to collect data from 314 respondents in the industrial sector. Analyzing the data was performed using SmartPLS using a bootstrapping method. It was observed that knowledge management capability had a mediating effect and organizational agility had a moderating effect Findings : Due to the statistical significance of the relationships between the constructs, the proposed moderated-mediation model was accepted. According to the data analysis, business intelligence is positively correlated with innovation capability, and knowledge management plays a mediating role. A moderating relationship is found between knowledge management and radical or incremental innovation in terms of organization agility. As a result of these results, BI and KMC are key components of enhancing radical and incremental innovation in the context of organizational agility Research limitations/implications: There are methodological limitations that affect the generalization of the results of this cross-sectional study, as well as the fact that the results are restricted to one sector Originality/value :In this study, BI, KMC, organizational agility, and radical and incremental innovation were integrated into a theoretical model. The concept of moderated mediation is tested in the industrial sector in this paper, adding new theoretical and practical contributions to the innovation literature. Differentiating this study from other studies, it focuses on BI and KMC's role in enhancing organizational advantages in the Jordanian industrial sector through integral and incremental innovation
Differences Between Single and Sequential Pictorial Storytelling

What differentiates narration with sequential images from narration with single images? Pictorial narration can take different forms, depending on how many images are used to tell a story. This paper questions common usages of the notions single images and sequential images , in order to better understand the differences between them for visual narration. It highlights the specific potential of sequential images regarding storytelling and notes what kinds of inference a spectator needs to undertake to correctly understand such pictorial narratives. The aim is to gain a more thorough understanding of a specific kind of two-dimensional pictorial narration: narration with sequential images.
Using mineral chemistry in determining the formation conditions and tectonic setting of Dalfard quartz-diorites (northwest of Jiroft-Kerman Province)

Using mineral chemistry in determining the formation conditions and tectonic setting of Dalfard quartz-diorites (northwest of Jiroft-Kerman Province)
Enhancing Health and Wellness: Deep Learning Powered Exercise Recognition and Tracking

Abstract: This project aims to develop a React-based web application that enables users to track, analyze, manage, and schedule their workout routines. Additionally, the application incorporates a machine learning model that utilizes the user’s web camera to track their movements and count reps, sets, and calories burned. This innovative feature provides users with a more accurate representation of their workout progress and empowers them to make data-driven decisions regarding their fitness goals. To achieve this, we utilized various technologies such as React for the frontend, Node.js for the backend, and MongoDB for the database. We also integrated TensorFlow.js, an open-source machine learning library, to build the ML model that powers the application’s tracking and analysis capabilities. Furthermore, we designed the user interface using Bootstrap with Vanilla CSS to ensure a responsive and user-friendly experience. Through this project, our primary objective was to address the limitations of existing workout tracking applications that heavily rely on subjective and often inaccurate user input. Our solution offers a more objective and efficient approach to tracking fitness progress. We firmly believe that this technology has the potential to revolutionize the fitness industry. Overall, our project showcases the power of machine learning and its ability to enhance people’s lives in meaningful ways.
A new generation of paper

Ynvisible has made headlines with the development of a new type of interactive and seamless human-machine interface based on printed e-paper displays. In OPE journal, the company with headquarters in Vancouver, Canada, provides an update on its activities and innovations.
Attention-Based Human Age Estimation from Face Images to Enhance Public Security

Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, tests are conducted on different attention modules, namely CBAM, SENet, and Self-attention, implemented with a convolutional neural network. The focus is on developing a lightweight model that requires a low number of parameters. A merged dataset and other cutting-edge datasets are used to test the proposed model’s performance. In addition, transfer learning is used alongside the scratch CNN model to achieve optimal performance more efficiently. Experimental results on different aging face databases show the remarkable advantages of the proposed attention-based CNN model over the conventional CNN model by attaining the lowest mean absolute error and the lowest number of parameters with a better cumulative score.
Forecasting and Mapping of Vulnerable Areas Floods and Droughts in Rice Crops Impacts of Climate Change In 2022 in East Java Province

The impact of climate change has a major impact on the agricultural sector, especially rice crops. The consequences of climate change include floods and droughts which can reduce production. In 2021, East Java was affected by floods in an area of ??14,158.31 hectares of rice plants and 9,195.39 hectares of drought affected by drought. Information on forecasting and mapping of floods and droughts in rice crops is needed. This study aims to obtain forecasts of affected areas and mapping of flood and drought prone areas in certain space and time units. The study uses historical data on flood and drought events for the last 10 years in East Java and analyzed using time series forecasting models and hazard forecasts and interpreted in maps using QGIS software. The flood forecasting model is obtained Log (Y) = 1.657 + 0.406 Log (X) R2 0.74 with a forecast number of flood events in 2022 covering an area of ??11,837.66 Ha. The drought forecasting model obtained Log (Y) = 0.977 + 0.647 Log (X) R2 0.72 with a forecast number of drought events in 2022 covering an area of ??5815.20 Ha. The forecast mapping for the highly flood-prone areas in East Java is Bojonegoro and Gresik regencies, while the flood-prone categories are Tuban, Lamongan, and Lumajang regencies. For mapping areas, the category of very drought-prone areas is Bojonegoro and Lamongan Districts, while the drought-prone category is Trenggalek Regency. Flood and Drought Forecasting and Mapping can be used as initial information on alertness and as a basis for preparing anticipatory, mitigation and adaptation strategies in order to minimize loss of agricultural production.
A Social and Psychological Portrait of the Personality of a Fraudster

Against the background of various forms of fraud, including domestic fraud, gaining rapid momentum in the Russian Federation, the question of establishing a typical portrait of a fraudster is becoming more and more urgent. This is necessary not only to increase the effectiveness of work on the detection and prevention of fraud by law enforcement offi cers, but also to carry out preventive psychological work with the population, especially with potential victims of such crimes from among socially vulnerable segments of the population. As a result of the research, the author identifi es a number of cognitive properties, needmotivational attitudes, emotional-volitional features, as well as character traits inherent in most scammers.
AMAX-AR: A Way to Maximize Augmented Reality

Augmented reality intention will rise exponentially because it amplifies its authority to have valued and remold challenges. AR will advance as the cover interface between engines and humans, bridging the digital and right humanities. While difficulties in planting dwell, presenting institutions, analogous as Amazon, Google, Facebook, Mayo Clinic, the U.S. Navy, and General Electric formerly dealt with AR and observed a considerable strike on caliber. AR operations are formerly guided and posted in shops and across the valuation train, and their composition and reach will exclusively rise. AR operations give a kind of X-ray delusion, discovering interior features that would exist subtle to experience. AR gets aimed at a wide consumer cult that has generally lived aimed for smartphones, holding the upper hand over their plainness and ubiquity. For farther smart guests, pots use tablets, which give huge defenses, better plates, and lower processing authority. In 2013, Bujak et al. (Comput Educ 68:536–544, 2013) refocused on ‘augmented reality (AR) is just starting to scrape the look in informative operations.’ This paper deals with our operation AMAX-AR which will help in the business and education sphere by giving a three-dimensional view of the arrangements or plates which can be more understood in three-dimensional rather than two-dimensional. Also tapping the generated model will give the accurate labels of the model. This operation will be accessible on both Android-grounded biases as well as Windows-grounded biases that have a camera or web camera access.
Audience Targeting—Identify Gap in Audience Targeting for Storage Revenue YTY

This research attempts to explore the gap in the requirement and the available options for the audience with reference to AP Systems Ltd. The data was collected from ERP forms of AP storage systems and a few clients. The form questions were prepared and uploaded to ERP systems. In this ERP system, all the sellers and marketing individuals have access who puts data from the beginning to the end, from getting any customer source or probable buyer and focused on gaps in different companies as well as opportunities for win (Revenue). Results indicated the region-wise and year-wise trend of opportunities that can be utilized to forecast data storage. A model is proposed to handle the challenge of data storage on a private cloud. The proposed system will use a lesser database to store the keys, which is generated dynamically and can be recycled after the request is completely processed. This article becomes useful for analyzing the trend as it considers five years of data for the study. This also helps in segmenting the market and strategically positioning the firms. This study is generally limited to firms’ internal and exclusive functions but not to the overall market focus. In this article, we are trying to correlate the outcome with marketing strategy formulation.
User Experience Evaluation for Pre-primary Children Using an Augmented Reality Animal-Themed Phonics System

With smart phones being ubiquitous in every household, people are increasingly relying on educational applications to simplify the learning process of children. This research is focused on creating a child-friendly framework to evaluate the user experience of preschool children aged between three to five years through an augmented reality (AR) application. Despite the rapid growth and importance of AR applications in various fields, providing a positive user experience for pre-primary students has proven to be a challenge. AR applications have yet to be fully implemented for academic purposes. The aim of our work is to introduce an application that enhances phonics teaching methods and encourages students to engage actively in learning activities. Creating effective AR experiences for children between the ages of three to five can be a difficult task. Designers must take into account the cognitive and motor skill limitations of children in this age group. Understanding the target audience is crucial to developing an educational application that is both entertaining and appealing to students and teachers alike. To address this challenge, we established an evaluation framework for testing AR applications focused on phonics, using animal-themed content. This framework was developed using various techniques such as interviews, literature research, and persona creation. As a result of this process, we formulated and also evaluated the effectiveness of the theoretical framework for assessing the user experience of young learners.