Reviewerly/exHarmony huggingface dataset:
Classification of latent profiles and influencing factors of career activity experiences of 2nd grade students in middle school

Objectives This study used data from the 4th(2019) to 5th(2020) years from the 4th to 5th years of the Busan Education Longitudinal Study (BELS) Elementary School panel to classify latent classes according to their career experience, identify factors that affect career maturity, learning participation, and academic self-efficacy.&#x0D; Methods The latent classes were classified into four based on the presence or absence of seven career activities, including ‘counseling with homeroom teachers’, ‘counseling with private education workers’, ‘counseling with parents’, ‘conducting career-related tests’, ‘attending senior school entrance briefing’, and ‘collecting media use information’. The four latent classes were named ‘primary teacher-dependent group’, ‘active career search group’, ‘career teacher-dependent group’, and ‘career information collection group’, reflecting the characteristics of each type related to career activity experience.&#x0D; Results High career maturity, learning participation, and academic self-efficacy were found in the “active career search group” and “career information collection group” with high parental communication. However, while the “active career search group” showed low academic achievement, the “career information collection group” showed high results in parental attachment, self-esteem, and academic achievement. In the case of students with high anxiety and depression, it was found that they were likely to be included in the “career information search group” or the “career teacher-dependent group”.&#x0D; Conclusions This study is meaningful in that it classified latent classes by dividing career activity experiences by type, and analyzed the impact of each latent class on psychological factors related to learning.
Metallogeny and Emplacement Conditions of Continental Terminal 3 (Ct&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;) Iron Formations of the Niamey Region (Western Niger)

This study aims to characterize the different lithofacies of the Ct3 formation in the Niamey region, and to determine the distribution of major and trace elements, in order to highlight the conditions for the establishment of iron mineralization. A lithological column, synthesizing sections of selected outcrops in the vicinity of Niamey, was produced. The chemical compositions of the selected samples were determined by X-ray fluorescence (XRF) spectrometry. Microscopic analysis of the thin sections determined the gœthitic nature of the oolitic iron ore. The oolites show a quartz, limonitic or gœthitic nucleus. Sometimes the nucleus is absent. From a morphoscopic point of view, two types of oolites have been distinguished: spherical-shaped and ellipsoidal-shaped oolites. The oolites are either contiguous or disseminated, as the case may be, in a limonitic to goethitic cement or in a fine sandstone matrix. The larger oolites (pisolites) are relatively friable. They reflect the influence of a relatively turbulent to submerged environment. The hardground of the iron mineralized horizons are covered by quartz grains. They are indicative of a submerged or emergent environment. X-ray fluorescence analysis shows high Fe2O3 contents (50% to 80%) and variable contents of major elements SiO2, Al2O3, TiO2, MnO, MgO, CaO, K2O and P2O5 associated with certain trace elements such as Th, U, V, Y, Zn, Zr and As. The results of the study are an important tool for decision-makers to adopt effective prevention/remediation measures for groundwater contamination in the Continental terminal aquifer systems.
Analisis Principal Component Analysis (PCA) dalam Penentuan Faktor Kepuasan Pengunjung terhadap Layanan Perpustakaan Digilib

Maximum management of the library, especially improving services to users is a must that needs to be evaluated by the library manager. Visitor satisfaction is difficult to measure because it relates to the high and low expectations of visitors to the service they want. The purpose of this study is to investigate the most important factors or components in aspects of visitor satisfaction. So that it can increase satisfaction appropriately so that the library can carry out the right strategy to maintain and provide the best quality service. The subjects of this study were students with 2019 – 2022 status. 120 samples were taken using a random sampling technique. The collected data will be analyzed using the Principal Component Analysis (PCA) method. The PCA analysis process was carried out in several stages, namely the Bartlett's test, testing the KMO value, testing the correlation matrix, PCA analysis, and interpreting the PCA results. Findings from PCA analysis by reducing 12 correlated variables into 2 factors, namely reference services and facilities and infrastructure. The results of this study indicate that the facilities and infrastructure factor have a greater cumulative percentage of variance compared to the reference service factor, which is equal to 51,387%.
Language-based Photo Color Adjustment for Graphic Designs

Adjusting the photo color to associate with some design elements is an essential way for a graphic design to effectively deliver its message and make it aesthetically pleasing. However, existing tools and previous works face a dilemma between the ease of use and level of expressiveness. To this end, we introduce an interactive language-based approach for photo recoloring, which provides an intuitive system that can assist both experts and novices on graphic design. Given a graphic design containing a photo that needs to be recolored, our model can predict the source colors and the target regions, and then recolor the target regions with the source colors based on the given language-based instruction. The multi-granularity of the instruction allows diverse user intentions. The proposed novel task faces several unique challenges, including: 1) color accuracy for recoloring with exactly the same color from the target design element as specified by the user; 2) multi-granularity instructions for parsing instructions correctly to generate a specific result or multiple plausible ones; and 3) locality for recoloring in semantically meaningful local regions to preserve original image semantics. To address these challenges, we propose a model called LangRecol with two main components: the language-based source color prediction module and the semantic-palette-based photo recoloring module. We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training. We evaluate our model via extensive experiments and user studies. We also discuss several practical applications, showing the effectiveness and practicality of our approach. Please find the code and data at https://zhenwwang.github.io/langrecol.
SlowFast Based Real-Time Human Motion Recognition with Action Localization

Artificial intelligence is increasingly being applied in the field of video analysis, particularly in the area of public safety where video surveillance equipment such as closed-circuit television (CCTV) is used and automated analysis of video information is required. However, various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging. Video analysis technology applies object classification, detection, and relationship analysis to continuous 2D frame data, and the various meanings within the video are thus analyzed based on the extracted basic data. Motion recognition is key in this analysis. Motion recognition is a challenging field that analyzes human body movements, requiring the interpretation of complex movements of human joints and the relationships between various objects. The deep learning-based human skeleton detection algorithm is a representative motion recognition algorithm. Recently, motion analysis models such as the SlowFast network algorithm, have also been developed with excellent performance. However, these models do not operate properly in most wide-angle video environments outdoors, displaying low response speed, as expected from motion classification extraction in environments associated with high-resolution images. The proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure methods. The input data are preprocessed through object tracking and background removal using YOLO and DeepSORT. A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit structure. Based on the confusion matrix, accuracies of 70.16% and 70.74% were obtained for the existing SlowFast and proposed model, respectively, indicating a 0.58% increase in accuracy. Comparing detection, based on behavioral classification, the existing SlowFast detected 2,341,164 cases, whereas the proposed model detected 3,119,323 cases, which is an increase of 33.23%.
Blockchain-Based Invoice Management System for Electricity Trading

Aiming at the problems of information sharing, low trust of multiple parties and complex traceability process in practice, the blockchain-based electricity trading invoice management system is proposed by using the technical characteristics of blockchain such as decentralization, tamper-proof and timestamp. The system uses P2P network technology to design blockchain network topology, guarantees efficient and convenient invoice related operations based on smart contracts, uses cryptography technology to realize user privacy protection, and combines fault-tolerant ideas with multi-node backup to enhance system reliability. Blockchain-based electricity trading invoices have the characteristics of full information closed-loop traceability and tamper-proof, which can effectively improve the efficiency of finance department, reduce the difficulty of invoice supervision, increase the security of user information, realize data sharing and enhance the trust among electricity trading participants. It is of great significance for power enterprises to grasp the opportunity of digital transformation to realize service upgrading, reduce costs, improve invoice certification management efficiency and reduce tax risks.
A Study on the Composition of Discussion Tasks for Foreign Undergraduates in the Digital Literacy Environment

This study proposes a detailed structure of Korean debate assignments that can be utilized in a digital literacy environment. First, it examined the relationship between learners’ collaborative problem-solving skills and their debate abilities, the components of debate abilities, and learners’ cognitive characteristics required for surveying debate materials. It then presented an example of a debate assignment structure that can be used in actual classrooms. The usefulness of the debate assignments examined in this study are as follows. Firstly, it promotes learners’ debate activities. An amicable environment is formed where learners with limited background knowledge or passive attitudes can easily access debate topics, facilitating debate activities. Secondly, it enhances learners’ digital literacy through exploratory reading activities of digital materials. In particular, debate activities become more active during the process of sharing, selecting, and organizing information. Thirdly, it contributes to improving foreign language learners’ academic performance through digital literacy problem-solving. This study differs from previous studies in that it actively incorporates the changed learning environment into Korean language education and discusses in detail the process of foreign language learners creating the content of discussions by activating expanded background knowledge based on digital literacy.
Silicon Photonic Neural Network Accelerators: Opportunities and Challenges

Silicon photonic neural network accelerators (SPNNAs) offer chip-scale and light-speed computation and communication to boost AI inferencing and training performance. In this invited paper, we discuss some of the benefits and challenges of implementing SPNNAs.
Distribution of Time-Bin Entangled Photons through a 7.7 km Hollow-Core Fiber

We distribute entangled time-bin qubits, separated by 200 picoseconds, through a low-latency 7.7 km hollow-core fiber. Due to dispersion, these time bins cannot be resolved when sent through commercial silica fiber of the same length.
Image Restoration Using Optoelectronic Coherent Ising Machine

We propose an optoelectronic Coherent Ising Machine implemented using off-the-shelf optical telecommunications devices for image denoising for the first time. Our hardware implementation yields clean images with a pixel error rate of 3%.
Knowledge on heart disease of the person with ischemic heart disease

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Instruments that allow us to measure the users' knowledge about their health can be useful in clinical practice to allow adjusting interventions, quantifying health gains and developing cost-effective interventions. Purpose To determine knowledge about heart disease among the population with diagnosed ischemic heart disease. Methods Cross-sectional, descriptive study using a sequential sample of patients with ischemic heart disease admitted between May 2nd and July 30th, 2022 at the cardiology service of the Hospital Centre of our city. A self-administered questionnaire was developed and applied to 165 patients, encompassing several dimensions: sociodemographic characteristics, clinical history, lifestyle, number of hospitalizations due to heart disease and characterization of the current admission. An instrument was used to determine knowledge about heart disease (Questionário de Avaliação de Conhecimentos em Doença Cardíaca: QACDC - Questionnaire for the Assessment of Knowledge in Heart Disease), the Medical Term Recognition Test (METER) to assess literacy and the Hospital Anxiety and Depression Scale (HADS). Patients were assessed during their stay at the hospital unit where they had been admitted due to an acute illness or for elective procedures. Using, preferably, parametric tests, we studied the association between the average score obtained in the QAC-DC and the variables studied. Results Women, the less educated and those with inadequate literacy levels were the groups with the lowest levels of knowledge. Regarding the areas of knowledge, the least recognized cardiovascular risk factors were the family history of ischemic heart disease (recognized by 86%), diabetes mellitus (recognized by 73%) and gender (marked by 19% of respondents). The most frequently identified symptom of heart disease was chest discomfort (92%), followed by difficulty breathing or shortness of breath (84%), while jaw, neck or interscapular discomfort was only recognized by 54% of respondents. Symptoms of stroke and acute myocardial infarction were confused by 88% of the sample. Conclusions There are important gaps in some areas of knowledge about heart disease among patients with ischemic heart disease. These gaps are particularly important in certain patient subgroups. Health intervention strategies must be sensitive to these data. The QAC-DC knowledge assessment instrument proved to be reliable, valid and applicable in clinical practice.
Statistical mechanics of continual learning: Variational principle and mean-field potential

An obstacle to artificial general intelligence is set by continual learning of multiple tasks of a different nature. Recently, various heuristic tricks, both from machine learning and from neuroscience angles, were proposed, but they lack a unified theory foundation. Here, we focus on continual learning in single-layered and multilayered neural networks of binary weights. A variational Bayesian learning setting is thus proposed in which the neural networks are trained in a field-space, rather than a gradient-ill-defined discrete-weight space, and furthermore, weight uncertainty is naturally incorporated, and it modulates synaptic resources among tasks. From a physics perspective, we translate variational continual learning into a Franz-Parisi thermodynamic potential framework, where previous task knowledge serves as a prior probability and a reference as well. We thus interpret the continual learning of the binary perceptron in a teacher-student setting as a Franz-Parisi potential computation. The learning performance can then be analytically studied with mean-field order parameters, whose predictions coincide with numerical experiments using stochastic gradient descent methods. Based on the variational principle and Gaussian field approximation of internal preactivations in hidden layers, we also derive the learning algorithm considering weight uncertainty, which solves the continual learning with binary weights using multilayered neural networks, and performs better than the currently available metaplasticity algorithm in which binary synapses bear hidden continuous states and the synaptic plasticity is modulated by a heuristic regularization function. Our proposed principled frameworks also connect to elastic weight consolidation, weight-uncertainty modulated learning, and neuroscience-inspired metaplasticity, providing a theoretically grounded method for real-world multitask learning with deep networks.
Temperaments of the Scale

We have seen that once the problem of how many notes to include in the musical scale has been decided (12 was the choice), we must make corrections so that it closes exactly at the octave. In the previous section it was suggested to correct the last note in the circle of fifthsCircle of fifths (the correction results in the fourth), so that it closes on the eighth. This correction puts all the burden (Pythagorean comma) on one note and makes playing that note in combination with others quite painful. This and other solutions to this problem are referred to as temperaments of the musical scale.
Convolutional Self-attention Guided Graph Neural Network for Few-Shot Action Recognition

The goal of few-shot action recognition is to recognize unseen action classes with only a few labeled videos. In this paper, we propose Convolutional Self-Attention Guided Graph Neural Network (CSA-GNN) for few-shot action recognition. First, for each video, we extract features of video frames sampled from the video and obtain a sequence of feature vectors. Then, a convolutional self-attention function is applied to the sequences to capture long-term temporal dependencies. Finally, a graph neural network is utilized to predict the distance between two sequences of feature vectors explicitly, which approximates the distance between the corresponding videos. By this means, we effectively learn the distance between the support and query videos without estimating their temporal alignment. The proposed method is evaluated on four action recognition datasets and achieves state-of-the-art results in the experiments.
PECA-Net: Pyramidal Attention Convolution Residual Network for Architectural Heritage Images Classification

Heritage buildings have essential cultural and economic values, with constant change and destruction. Existing technologies that classify architectural heritage images can promote the recording and protection of heritage buildings. However, the background information of architectural heritage images is complex and changeable, making it difficult to extract the key feature information. In order to address the above problem, this paper proposes a pyramidal attention convolution residual network (PECA-Net) based on ResNet18, in which pyramidal convolution (PyConv) and attention mechanism have been adopted. PyConv is introduced into residual blocks instead of standard convolution, which can extract detailed information on different scales without increasing the parameter space of the model. Then, we propose a dual-pooling channel attention mechanism (DP-ECA) that effectively improves the ability to extract key information. Compared with the original ResNet18, experimental results show that the accuracy of our model is increased by 1.86%, and the parameters and FLOPs (floating point operations) are reduced by 0.48M and 0.28G respectively.
Revolutionizing Journal Publishing: Unleashing The Power Of Web-Based Chatbot Development With Dialogflow And Natural Language Processing

The chatbot is a manifestation of Artificial Intelligence that can replace humans in engaging in conversational interactions and providing information services to users or visitors. Chatbots generally interact through text messages, voice, images, and other mediums. In this study, a web-based chatbot application was developed for a journal website called the Journal of Multidisciplinary Issues, aiming to relieve site administrators from having to repeatedly answer the same visitor questions. The development of the chatbot application followed the Rapid Application Development method, involving several stages including planning, design, system development, implementation, and testing. The application was built using Google Dialogflow services, employing Natural Language Processing techniques, along with the integration feature called Dialogflow Messenger, which offers a wide range of response options for journal websites utilizing Open Journal Systems. The chatbot application was evaluated using Alpha and Closed Beta Testing methodologies, which yielded promising results. The chatbot demonstrated the ability to provide appropriate responses after being tested with various question scenarios, achieving a score of 85.25% in the questionnaire-based assessment, which aimed to gauge visitor perspectives on the developed chatbot application. Future research endeavors should focus on integrating the chatbot with popular social media platforms such as Facebook, WhatsApp, or LINE.
Towards smart contract distributed directory based on the uniform description language

A Smart Contract (SC) is a piece of code executed on the blockchain to automatically trigger transactions upon the occurrence of predefined events. Due to the intrinsic features regarding traceability and data immutability, many companies started using blockchain Smart Contracts to perform collaborative processes. Despite their promising features, there is a lack of Smart Contacts management platforms that enable blockchain participants to describe and publish their smart contacts or “search and match” already deployed ones. In this paper, a new Distributed Smart Directory (DSD) where providers can publish their SCs description is proposed. The SCs descriptions include metadata covering functional, and non-functional properties of the SC. Hence, users can find SCs according to their non-functional preferences, needs, and constraints. The proposed DSD is an extension of the ebXML directory. It was fully implemented on-chain. The SCs descriptions are generated based on the Uniform Description language for SC (UDL-SC). The proposed solution is implemented on the Ethereum blockchain. It was then tested and evaluated.