Reviewerly/exHarmony huggingface dataset:
Power supply station equipment status monitoring and evaluation system based on wireless network technology

With the continuous development of the power industry and the acceleration of the process of intelligence, monitoring and analyzing the status of power supply equipment is crucial for ensuring power supply and safety. Traditional power monitoring systems still have certain problems in data transmission. Using wireless network technology (WNT) to detect and analyze the status of power supply station equipment can not only achieve real-time monitoring and remote control, improve equipment operation and management efficiency, but also achieve automated monitoring and analysis of equipment status, providing technical support for the intelligence of power supply station equipment. In order to verify the effectiveness of the WNT-based power supply station equipment status monitoring and analysis system, a comparative experiment was conducted with traditional wired network monitoring methods. The final experimental results show that the average feedback time for testing the WNT power supply station equipment status monitoring and analysis system is 7.71 seconds, which is 38.22 seconds shorter than traditional methods. The average fault location accuracy is 95.98%, which is 35.91% higher than traditional methods. The average management efficiency is 92.88%, which is 34.71% higher than traditional methods. Through testing data, it can be concluded that the power supply station equipment status monitoring and analysis system based on WNT has greater advantages than traditional power equipment monitoring methods.
The deep past in the virtual present: developing an interdisciplinary approach towards understanding the psychological foundations of palaeolithic cave art

Abstract Virtual Reality (VR) has vast potential for developing systematic, interdisciplinary studies to understand ephemeral behaviours in the archaeological record, such as the emergence and development of visual culture. Upper Palaeolithic cave art forms the most robust record for investigating this and the methods of its production, themes, and temporal and spatial changes have been researched extensively, but without consensus over its functions or meanings. More compelling arguments draw from visual psychology and posit that the immersive, dark conditions of caves elicited particular psychological responses, resulting in the perception—and depiction—of animals on suggestive features of cave walls. Our research developed and piloted a novel VR experiment that allowed participants to perceive 3D models of cave walls, with the Palaeolithic art digitally removed, from El Castillo cave (Cantabria, Spain). Results indicate that modern participants’ visual attention corresponded to the same topographic features of cave walls utilised by Palaeolithic artists, and that they perceived such features as resembling animals. Although preliminary, our results support the hypothesis that pareidolia—a product of our cognitive evolution—was a key mechanism in Palaeolithic art making, and demonstrates the potential of interdisciplinary VR research for understanding the evolution of art, and demonstrate the potential efficacy of the methodology.
Examining the Factors Affecting Reading Readiness of Kindergarten Students

Reading is one of the core literacy skills of every individual and early reading for children has a great impact on future learning pathways and the success of school life. The purpose of this study was to investigate the reading readiness of kindergarten students in Myanmar and examine factors affecting reading readiness. A quantitative survey method was used in this study with a total of 222 kindergarten students from seven schools in Yangon and Rakhine selected using the multistage cluster sampling method. The Myanmar Reading Assessment Test (Cronbach’s alpha of 0.925) for kindergarten was used to administer the reading readiness skills of the students. In addition, demographic factors related to students and parents’ involvement in their children’s learning were collected using a self-administered questionnaire. The statistical analysis methods used were descriptive statistics, percentiles, t test, and Analysis of Variance (ANOVA). According to the results, there were five reader groups: below basic (25.7%), basic (6.8%), satisfactory (11.3%), proficient (16.7%), and advanced (39.6%) levels. The results of the statistical analysis concluded that no preschool experiences, lower socioeconomic status, the region in which they live, and unsupportive parents were the main factors affecting reading readiness. Among these factors, preschool experience is the only solution that can be tackled to improve reading readiness than other factors. However, most children in Myanmar cannot yet access preschool programmes adequately. Therefore, the government, NGOs, INGOs, CSOs, CBOs, and other organisations should ensure that every child has the opportunity to attend preschool regardless of geographical barriers.
Universal method using a pre-deformed reference subset to eliminate the interpolation bias in digital image correlation

The high measurement accuracy of the digital image correlation (DIC) method is derived from the sub-pixel registration algorithm, which interpolates the intensities at the sub-pixel position in the image. The displacement error caused by the interpolation is a systematic bias in the DIC method, known as the sinusoidal bias in the sub-pixel translation experiment. Although the interpolation bias has been well researched, there is a lack of a universal method to eliminate interpolation bias. In this work, we propose a universal method to eliminate the interpolation bias using a pre-deformed reference subset; pixel points in the pre-deformed subset are deviated from the integer-pixel location. The purpose of the adjustment is to set the deformed pixel points at a specific position, so that the interpolation bias of all deformed pixel points cancels each other out, close to zero. The adjustment of the pre-deformed reference subset is related with the subset size and subset deformation. Numerical experiments including DIC challenge data and a real uniaxial tensile test were conducted to verify the effectiveness and universality of the proposed method, contributing to improved measurement accuracy. Considering the effect of pixel point location on the interpolation bias, this work proposes a universal method to eliminate the interpolation bias and provides a perspective to study DIC errors.
Geochemical studies of the gold mineralization of Kodiarani-(Doko permit). Prefecture of Siguiri, Administrative region of Kankan, Republic of Guinea

West Africa is now a major player in world gold production with numerous gold deposits hosted in Birimien green belts (2.2-2.0 Ga). However, these belts and their geological formations remain unexplored, as does their evolution during the Eburnean orogeny. This is why, after acquiring its exploration Licence, TOROGOLD focused its work on Air Core drilling activities in the Doko-Kodiarani Licence in north-eastern Guinea. During these activities, geochemical studies will be carried out on rock samples taken from the six (6) boreholes pre-selected for this purpose. The aim of this research work is to carry out a geological study in order to determine the chemical associations between the mineralization, the nature of the host rock and others for future work. To achieve this objective, we used a number of methods, including a statistical approach, field reconnaissance, and macroscopic and lithogeochemical analyses. Geochemical studies reveal that the rocks studied are meta-sediments. They belong to the schist groups. Their protholites are moderately altered (60 &lt; CIA &lt; 80) and the source is thought to be andesitic and basaltic, emplaced in a geotectonic oceanic island arc. As for the various AC drilling sections, our observations reveal that the gold mineralization is found in two types of deposit: disseminated, present in all lithology’s, and vein-type, resulting from the filling of fracture zones, associated with the highest concentrations. We noted a strong discontinuity of mineralization in these veins, due to a tectonic phase that may have played an important role in the remobilization of gold in these structures.
Science Learning Strategies at Secondary Level Schools in Nepal

This study explores the strategies for teaching and learning science at secondary-level public schools in Nepal. A descriptive quantitative research design was adopted and non-random convenient sampling techniques were adopted in this study. An observation form and a closed-ended survey questionnaire were used for the data collection. It was found that the majority of science teachers used lecture and demonstration methods and 55% of teacher select the teaching methods on the basis of the subject matters rather than the student’s interest and classroom environment. Only 2 schools out of 11 were conduct science exhibition program and students enjoyed to participation science exhibition. Also, students used internet websites and applications including YouTube, and Facebook for learning. It is recommended that the use of student-centered approaches while teaching and learning science at secondary public schools in Nepal will have positive impact.
Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks

Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification.
On Sherds, Vessels, and Pragmatics: Reaction to Feathers

Abstract Feathers addresses the dual challenges of inferring original vessel counts from sherds and inference to use life from reconstructed vessels. His solution assumes the validity of sherd assemblages as units of observation that considerable research invalidates and overlooks methods that estimate original vessels from sherds. Feathers also doubts that use life can be inferred for reconstructed vessels. Although not a focus of my article, the larger study from which it derived addresses this matter in detail that strongly warrants vessel size as use-life measure. Of course we must be pragmatic in quantifying pottery assemblages, but first we must identify valid units of observation, and only then attend to pragmatics.
Crowd Counting via De-background Multicolumn Dynamic Convolutional Neural Network

The current state-of-the-art density map-based crowd counting methods have focused on designing convolution neural network (CNN)-based models to exploit multiscale features to handle crowd shape change due to perspective distortion. However, the significant concerns with such approaches are using static kernels and being not adaptive to input data. Again, the multiscale features should be more attentive towards background minimization. Hence, this chapter proposes a de-background multicolumn dynamic CNN for crowd counting to address the issues. The proposed model can handle crowd shape change due to perspective distortion and learn to minimize the background influence while doing crowd counting. Two benchmark crowd counting datasets, Mall and UCSD, are used to show the model’s effectiveness. In addition to this, a separate ablation study has been conducted to show the effect of individual modules of the proposed model.
COVID-19 contact tracing at work in Belgium - how tracers tweak guidelines for the better

Abstract Background When conducting COVID-19 contact tracing, pre-defined criteria allow differentiating high-risk contacts (HRC) from low-risk contacts (LRC). Our study aimed to evaluate whether contact tracers in Belgium followed these criteria in practice and whether their deviations improved the infection risk assessment. Method We conducted a retrospective cohort study in Belgium, through an anonymous online survey, sent to 111,763 workers by email. First, we evaluated the concordance between the guideline-based classification of HRC or LRC and the tracer’s classification. We computed positive and negative agreements between both. Second, we used a multivariate Poisson regression to calculate the risk ratio (RR) of testing positive depending on the risk classification by the contact tracer and by the guideline-based risk classification. Results For our first research question, we included 1105 participants. The positive agreement between the guideline-based classification in HRC or LRC and the tracer’s classification was 0.53 (95% CI 0.49–0.57) and the negative agreement 0.70 (95% CI: 0.67–0.72). The type of contact tracer (occupational doctors, internal tracer, general practitioner, other) did not significantly influence the results. For the second research question, we included 589 participants. The RR of testing positive after an HRC compared to an LRC was 3.10 (95% CI: 2.71–3.56) when classified by the contact tracer and 2.24 (95% CI: 1.94–2.60) when classified by the guideline-based criteria. Conclusion Our study indicates that contact tracers did not apply pre-defined criteria for classifying high and low risk contacts. Risk stratification by contact tracers predicts who is at risk of infection better than guidelines only. This result indicates that a knowledgeable tracer can target testing better than a general guideline, asking for a debate on how to adapt the guidelines.
Characterizing the role of human behavior in the effectiveness of contact-tracing applications

Although numerous countries relied on contact-tracing (CT) applications as an epidemic control measure against the COVID-19 pandemic, the debate around their effectiveness is still open. Most studies indicate that very high levels of adoption are required to stop disease progression, placing the main interest of policymakers in promoting app adherence. However, other factors of human behavior, like delays in adherence or heterogeneous compliance, are often disregarded.To characterize the impact of human behavior on the effectiveness of CT apps we propose a multilayer network model reflecting the co-evolution of an epidemic outbreak and the app adoption dynamics over a synthetic population generated from survey data. The model was initialized to produce epidemic outbreaks resembling the first wave of the COVID-19 pandemic and was used to explore the impact of different changes in behavioral features in peak incidence and maximal prevalence.The results corroborate the relevance of the number of users for the effectiveness of CT apps but also highlight the need for early adoption and, at least, moderate levels of compliance, which are factors often not considered by most policymakers.The insight obtained was used to identify a bottleneck in the implementation of several apps, such as the Spanish CT app, where we hypothesize that a simplification of the reporting system could result in increased effectiveness through a rise in the levels of compliance.
Problems and Prospects of Implementing Assessment of the Level of Maturity of Cyber Security Processes of Critical Infrastructure Objects of the Energy Sector of Ukraine in Accordance with the NIST Cybersecurity Framework

Existing problems regarding compliance with requirements in the field of cyber security of Ukraine's critical infrastructure in the energy sector, prospects for the implementation of the NIST Cybersecurity Framework and ways to solve the identified problems are described. The Ukrainian legislative framework was analyzed and compared with European and American practices, and the issue of compliance with the requirements of the legislation of various jurisdictions was determined.
Social Media Cyberbullying Detection on Political Violence from Bangla Texts Using Machine Learning Algorithm

When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.
Education in the palm of your hand: My best Application as an Educational Tool at Bina Sarana Informatika University

This research discusses the utilization of the "MyBest" application as an educational tool in supporting education at Bina Sarana Informatika University. In the era of evolving technology, mobile applications have become an effective means of delivering information and stimulating student participation in actual and relevant issues and sustainability. The purpose of this research is to explore the potential of the "MyBest" application as an educational tool that can provide easy access, increase understanding, and encourage positive actions related to sustainability. This research involves developing the "MyBest" application as an interactive educational tool, relevant content, and features that stimulate student participation and engagement in sustainability challenges and programs. The application is implemented at Bina Sarana Informatika University with the participation of students from various disciplines. The results of this study will include increased student awareness of actual and relevant issues and the importance of sustainable action. The "MyBest" application is expected to provide a deeper understanding of practical ways to support sustainability in everyday life. In addition, this application can also stimulate student participation in programs on campus.
Improved Steganography for IoT Network Node Data Security Promoting Secure Data Transmission using Generative Adversarial Networks

An internet of things (IoT) is an intelligent environment such as homes and smart cities of our country, and IoT improves the new technology implementation for home automation. The problem with security in IoT-based devices is that data transmission and signal passing are easily hacked using encryption and decryption methods. The old technology of the Steganography method does not improve the data hidden in images because encryption and decryption use a 1-bit 0.05-bit store, and low ranges hide the information in images, so that information hides out of the size and bits of the image. The hackers easily hack the hide information pixel by pixel or bit by bit in images. So, need for a proposed system, new technology, or methods. The suggested solution improves data concealment in photos by combining CNN’s deep learning techniques with steganography. The secret information these photographs convey can be shared without drawing hackers’ notice. The data is encrypted before being embedded in the image to increase its security. Steganography messages are frequently encrypted using more conventional methods first, after which the encrypted message is added to the cover image in some manner. The previous algorithm of SFNET algorithm architecture has been divided by segment, the segment based on width, height, and depth changes based improve performances. Existing systems of SFNET and SRNET are compared to the fractal net algorithm to improve the performance of 3 to 1 % of the proposed system.
Using two threshold for mining erasable itemset on dynamic incremental database

Traditional data mining is often applied in static databases and bath processing. In fact, databases are often changed, bath processing is not suitable because it consumes more time to mine in the whole database. Therefore, mining in dynamic databases attracted many researchers where mining erasable itemsets (EIs) in incremental databases is one of interesting areas. Recent years, there are some publications developed for updated EIs in dynamic databases but they consume more time to rescan the original database. In this paper we propose an algorithm for updating EIs using two minimum thresholds to avoid rescanning databases, we also use new data structure to efficient process incremental databases.
A Diffusion Model with A FFT for Image Inpainting

Diffusion models for image inpainting have been the subject of growing research interest in recent years. However, generating content that is consistent with the original images, especially for complex images with intricate details and structural information, remains a significant challenge. In this paper, we propose a diffusion model with an FFT (FFT-DM) to generate content that matches missing region texture and semantics to inpaint damaged images. Specifically, FFT-DM contains two components: a Denoising Diffusion Probabilistic Model (DDPM) and a Convolutional Neural Network (CNN). The DDPM is used to extract global features and generate image prior while the CNN captures more fine-grained details and predicts the parameters in the reverse process of the diffusion model. Notably, we integrate a Fast Fourier Transform (FFT) into the diffusion model to enhance the perception ability and improve the efficiency of the model. Extensive experiments demonstrate that FFT-DM outperforms current state-of-the-art inpainting approaches in terms of qualitative and quantitative analysis.