Reviewerly/exHarmony huggingface dataset:
The study of children’s drawing as a phenomenon of artistic culture

Background. Currently, there is a contradiction between the high guidelines of psychological and pedagogical science for the creative development of each student in the lessons of art disciplines and the traditional academic orientation of art curricula. It raises questions about the orientation of children to participate in competitions that require mental stress from the child, are held within a time frame for which it is impossible to create a meaningful work worthy of the author who is a subject of culture. The solution to the problem should be sought in referring to the historical experience of art pedagogy, when creativity of children was studied as a phenomenon of artistic culture, while the child was perceived as a participant in the cultural process. Objective. The aim is to reveal the method of studying children's drawing as a phenomenon of artistic culture. This method has been tested and reflected in scientific research and the results of children's artistic creativity during the period between years 1910 and 1920. Methods. Analysis of psychological, pedagogical and cultural literature on the problem, comparative comprehensive analysis of the results of artistic education and methodological documents. Results. The psychological and pedagogical value is shown. The collection of children's drawings formed by A.V. Bakushinsky and his colleagues during their work at the State Academy of Art Sciences (1921–1929), which is now part of the International Collection of Children’s Drawings of the Institute of Art Education and Cultural Studies of RAE, as part of his psychological and pedagogical heritage, is introduced into scientific circulation. The methods of systematization, description, study of children's creativity, analysis and generalization of the results of artistic development from the standpoint of artistic culture, psychology and pedagogy of his time are revealed. Conclusion. The research of A.V. Bakushinsky and his students represents an ideal model of a scientifically based system of aesthetic education aimed at development of an artistic culture of a person capable of creative invention in the field of a chosen area.
Formation of Readiness of Future Teachers for Interactive Interaction in Educational Activities

&lt;p&gt;This article presents the experience of educational activities with future teachers in the format of interactive learning. However, it is perceived by most as an educational activity with the use of appropriate digital gadgets. Meanwhile, the interactive approach is based on the provisions of symbolic interactionism, which emphasizes not only unidirectional contact, but also feedback. This aspect was leveled during the period of distance learning. But for a teacher, the ability to &amp;ldquo;see&amp;rdquo; the audience, hold the attention of students, flexibly respond to emerging situations, etc. is especially important. This is the three&amp;ndash;dimensional vision of the educational process that forms the basis of interactive learning. The academic semester was devoted to mastering this approach, during which students learned variations of communication interaction in a quasi-professional situation. The positive results of this approach in the training of future teachers were confirmed by the data of a survey conducted among future teachers, which clarified the terminological grounds, risk zones, prospects, etc. of interactive learning for modern educational practice.&lt;/p&gt;
Adaptive Randomized Graph Neural Network Based on Markov Diffusion Kernel

Graph neural networks (GNNs) especially Graph convolutional networks (GCNs) GCNs, are popular in graph representation learning. However, GCNs only consider the nearest neighbor, which makes it difficult to expand the node domain, and their performance decline as the number of layers increases due to the over-smoothing problem. Therefore, this paper proposes an Adaptive Randomized Graph Neural Network based on Markov Diffusion Kernel (ARM-net) to overcome these limitations. Firstly, ARM-net designs a random propagation strategy based on Bernoulli distribution; Secondly, an adaptive propagation process based on Markov diffusion kernels is designed to separate feature transformations from propagation, expand the node domain and reduce the risk of model’s over-smoothing; Finally, a graph regularization term is added to enable nodes to find more information useful for their classification results, thereby improve the generalization performance of the model. Experimental results show that the model outperforms several recently proposed semi-supervised classification algorithms in semi-supervised node classification tasks. ARM-net has better performance on multiple data sets. Through experiments, ARM-net solves the over-smoothing problem encountered during GNN propagation to some extent, and the model has better generalization performance.
Style Expansion Without Forgetting for Handwritten Character Recognition

Handwritten character recognition (HCR) is still a challenging task due to diverse writing styles. In existing works, the recognition models for recognizing handwritten characters are usually trained with limited handwriting styles. However, there are always some new styles that are not included in the training sets in practical applications, degrading the recognition performance. Re-training the models with updated training sets containing new styles would increase the computational cost and complexity. In this paper, a new Style Expansion Learning HCR (StyleEL-HCR) problem is formulated to characterize this issue and a novel Reliable Prototype Augmentation (RePA) framework is developed for StyleEL-HCR. The RePA is composed of Soft Knowledge Distillation with replay (SKD), Character Prototype Augmentation (CPA), and Strict Gate Mechanism (SGM). SKD memorizes knowledge from old styles through distillation and replay, CPA learns representative information by memorizing character-representative prototypes and augmenting them in new learning phases to better distinguish different characters when the replay data is limited, and SGM augments the prototypes in a reliable way to improves the reliability of the recognition model. Adopting distillation and replay together with reliable prototype augmentation, RePA has the ability to learn new styles as well as preserve knowledge of old styles. Our experimental results have shown that the RePA can obtain better recognition performance for handwritten English, Chinese and digit characters compared to other methods, particularly when the handwritten Chinese character categories scale is large.
Adaptive Segmentation Network for Scene Text Detection

Inspired by deep convolution segmentation algorithms, scene text detectors break the performance ceiling of datasets steadily. However, these methods often encounter threshold selection bottlenecks and have poor performance on text instances with extreme aspect ratios. In this paper, we propose to automatically learn the discriminate segmentation threshold, which distinguishes text pixels from background pixels for segmentation-based scene text detectors and then further reduces the time-consuming manual parameter adjustment. Besides, we design a Global-information Enhanced Feature Pyramid Network (GE-FPN) for capturing text instances with macro size and extreme aspect ratios. Following the GE-FPN, we introduce a cascade optimization structure to further refine the text instances. Finally, together with the proposed threshold learning strategy and text detection structure, we design an Adaptive Segmentation Network (ASNet) for scene text detection. Extensive experiments are carried out to demonstrate that the proposed ASNet can achieve the state-of-the-art performance on four text detection benchmarks, i.e., ICDAR 2015, MSRA-TD500, ICDAR 2017 MLT and CTW1500. The ablation experiments also verify the effectiveness of our contributions.
ANALISIS PERSEPSI MAHASISWA AKUNTANSI TERHADAP ETIKA PROFESI AKUNTAN PADA PERGURUAN TINGGI DI KOTA BATAM

This research aimed to analyze the perceptions of accounting students regarding the ethics of the accounting profession, based on the variables of gender and education. Using quantitative research methodology, primary data were collected through the distribution of questionnaires via Google Forms utilizing the Slovin formula. The population comprised 1,258 accounting students from several universities in Batam city, with a sample size of 303 respondents. The questionnaire results were subjected to validity and reliability testing. Data analysis techniques included classical assumption analysis, linear regression assumptions, determination coefficients, T-tests, and F-tests, using SPSS version 26. The findings indicated that gender had a significant partial impact on the ethics of the accounting profession (t-value 5.375 &gt; t-table 1.967). Education also had a significant partial impact on the ethics of the accounting profession (t-value 9.337 &gt; t-table 1.967). Furthermore, simultaneous testing revealed that both gender and education had a positive and significant influence on the ethics of the accounting profession (F-value 255.550 &gt; F-table 3.025). These results provide valuable insights into the factors that shape the ethical perceptions of accounting students.
Bibliometric Analysis and Mapping of the Benefits and Challenges of Cloud ERP Systems

Enterprise resource planning is an effective tool in achieving management goals.Cloud ERP systems and applications are platform-independent offerings of this management tool in a cloud environment.This study was carried out to make sense of the advantages, difficulties and relationships of the cloud ERP system with scientific studies.For this purpose, the Scopus, Web of Science and Google Scholar databases and the Publish or Perish, WOSviewer and Excel applications were used.Statistical analysis, text mining, word network association, visual mapping and trend analysis were performed.As a result of the analysis, it was found that the total rate of publications produced in the last 3 years was 43%, the most cited work was Springer (20%) and the country was the USA (10%).It was determined that the three most frequently used keywords were 'cloud ERP', 'ERP system' and 'ERP'.A strong correlation was found between 'study' and 'challenge' in text mining.The challenge was closely related to 'SMEs', 'data', 'provider', 'technology', 'literature' and 'cloud environment'.In recent studies, the concept of 'cloud ERP implementation' in SMEs has come to the fore.
An Analysis the Character Education in Student’s Textbook Entitled English Skills for the Future for 10th Grade Publishing by Grafindo Media Pratama

Character education is everything that is the teacher, who is capable of influencing the character of the learner. The teacher helps shape the disposition of learners. It's exemplary how a teacher behaves, how a teacher talks or conveys material, how teachers tolerate, and various other related things. The aim of this research is to found the character education in the English Textbook of 2013 curriculum entitled “English for the Future for Grade X of Senior High School Students published by Grafindo Media Pratama”. This researcher use the methodology Qualitative content analysis in this research. Qualitative content analysis goes beyond counting words or extracting objective content from texts to examine meanings, themes and patterns that may be manifest or latent in a particular text. The result showed that character education found in the English textbook of 2013 curriculum entitled English for the Future for tenth grade of Senior High school Students Published by Grafindo Media Pratama. The researcher found there is 18 character education in this book, those, religious approximately 7.3% found the this book, honest around 3.3%, tolerance about 0.6%, discipline approximately 12.1% , hardwork 12.67%, creative 13,3%, independent 0.6%, democratic 4.67%, curiosity 12%, nationalism 4%, patriotism 2.67%, rewarding achievement 2.67%, friendly/communicate 6%, love of peace 0.67%, love to read 6.67%, caring the environment 2%, social care 6.67%, and responsibility 9.33%. The second question is about how the character education were delivery in this book. The researcher found there is 82.14% character education were deliver implicitly in this book, while the character education the deliver explicitly is about 17.86%.
Model Leeching: An Extraction Attack Targeting LLMs

Model Leeching is a novel extraction attack targeting Large Language Models (LLMs), capable of distilling task-specific knowledge from a target LLM into a reduced parameter model. We demonstrate the effectiveness of our attack by extracting task capability from ChatGPT-3.5-Turbo, achieving 73% Exact Match (EM) similarity, and SQuAD EM and F1 accuracy scores of 75% and 87%, respectively for only $50 in API cost. We further demonstrate the feasibility of adversarial attack transferability from an extracted model extracted via Model Leeching to perform ML attack staging against a target LLM, resulting in an 11% increase to attack success rate when applied to ChatGPT-3.5-Turbo.
Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model

In this paper, the single input rule modules (SIRMs) dynamically connected fuzzy inference model is employed to design a path following controller for a unicycle wheeled mobile robot(WMR).The SIRMs model is mainly used to reduce the number of fuzzy inference rules and consequently reduce the processing time of the conventional Fuzzy Logic Control (FLC) schemes. The adopted path following strategy uses two control unites working in parallel to drive the WMR to follow a path composed of a succession of discrete waypoints. The first unit is a heading controller charged of generatingthe angular velocity command and thesecond one is a linear velocity controller charged of the generation the speed control signal.For the headingcontroller, all input variables are assigned with two fuzzy inference modules: a SIRM and a dynamic importancedegree (DID). The structure of the SIRMs based linear velocitycontrolunitwas modified to simplify the designand to fulfill the requirements of the path following strategy. It contains two SIRM modules andonecommonDID. To ensure the stability of the WMR'smotion, the SIRMs and the DIDs in the two control units were designedin such a manner that the control of the robot's orientationgets higher priority over the control of the WMRspeed. The use of the SIRM fuzzy inference model allows the online automatic adjustment of priority orders of thedifferent control actions. No optimizationof the controller parameters is required since thatall base values areset to 0 and all the breadthsare set to 1 for the DID modules. This means that all the input items play equal rolesin the control of the WMR.The structure of the proposedpath following controller is simple and the inferencerules are intuitively understandable because they are inspired from the knowledge of a human expert. Numericalsimulations conducted in the MATLABenvironment show that the proposed SIRMs based controller outperforms apath followingcontroller that uses the conventional FLC scheme.
Polynomial-time Solver of Tridiagonal QUBO and QUDO problems with Tensor Networks

We present an algorithm for solving tridiagonal Quadratic Unconstrained Binary Optimization (QUBO) problems and Quadratic Unconstrained Discrete Optimization (QUDO) problems with one-neighbor interactions using the quantum-inspired technology of tensor networks. Our method is based on the simulation of a quantum state to which we will apply an imaginary time evolution and perform a series of partial traces to obtain the state of maximum amplitude, since it will be the optimal state. We will also deal with the degenerate case and check the polynomial complexity of the algorithm.
Improved Object Detection by Utilizing the Image Stream

If we evaluate any deep learning-based object detection architecture on consecutive camera frames, one of the problems is that flickering can occur. Even the tiniest changes in the object placed on the image plane can destabilize the detection performance, not to mention the upcoming occlusions in the sensorial perspective of a dynamic scene. In an online methodology, namely in a driving assist (autonomous driving) scenario, utilizing the temporal information can be one of the tricks against the mentioned problems. The focus of this article is to demonstrate that the information from past images can be fed to an object detection model as integrated recurrent layers in an independent multiscale sense. We based our idea on the convolutional Gated Recurrent Unit (GRU) by Mitsubishi Research [1], where they applied it in the Single Shot Detector (SSD) architecture right before the detection layer. This proved to extract information from the consecutive frames and improve the network’s precision. Currently, we are adapting the You Only Look Once V3 (YOLOv3) architecture [2] with an extension to capture multi-scale information flow. For the YOLOv3 model’s training purposes, we used the driving-oriented KITTI object detection dataset. We have measured the change in precision and inference time to the base model’s capabilities to evaluate the efficiency of the recurrent extension.
A Detail Geometry Learning Network for High-Fidelity Face Reconstruction

In this paper, we propose a Detail Geometry Learning Network (DGLN) approach to investigate the problem of self-supervised high-fidelity face reconstruction from monocular images. Unlike existing methods that rely on detail generators to generate “pseudo-details” where most of the reconstructed detail geometries are inconsistent with real faces. Our DGLN can ensure face personalization and also correctly learn more local face details. Specifically, our method includes two stages: the personalization stage and the detailization stage. In the personalization stage, we design a multi-perception interaction module (MPIM) to adaptively calibrate the weighted responses by interacting with information from different receptive fields to extract distinguishable and reliable features. To further enhance the geometric detail information, in the detailization stage, we develop a multi-resolution refinement network module (MrNet) to estimate the refined displacement map with features from different layers and different domains (i.e. coarse displacement images and RGB images). Finally, we design a novel normal smoothing loss that improves the reconstructed details and realisticity. Extensive experiments demonstrate the superiority of our method over previous work.
DWA: Differential Wavelet Amplifier for Image Super-Resolution

This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR). DWA invigorates an approach recently receiving less attention, namely Discrete Wavelet Transformation (DWT). DWT enables an efficient image representation for SR and reduces the spatial area of its input by a factor of 4, the overall model size, and computation cost, framing it as an attractive approach for sustainable ML. Our proposed DWA model improves wavelet-based SR models by leveraging the difference between two convolutional filters to refine relevant feature extraction in the wavelet domain, emphasizing local contrasts and suppressing common noise in the input signals. We show its effectiveness by integrating it into existing SR models, e.g., DWSR and MWCNN, and demonstrate a clear improvement in classical SR tasks. Moreover, DWA enables a direct application of DWSR and MWCNN to input image space, reducing the DWT representation channel-wise since it omits traditional DWT.
Relative Intrinsic Dimensionality Is Intrinsic to Learning

High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule of thumb than a reliable property as high dimensionality alone is neither necessary nor sufficient for successful learning. Here, we introduce a new notion of the intrinsic dimension of a data distribution, which precisely captures the separability properties of the data. For this intrinsic dimension, the rule of thumb above becomes a law: high intrinsic dimension guarantees highly separable data. We extend this notion to that of the relative intrinsic dimension of two data distributions, which we show provides both upper and lower bounds on the probability of successfully learning and generalising in a binary classification problem
Color-Dependent Prediction Stability of Popular CNN Image Classification Architectures

The ImageNet-1k dataset has been a major contributor to the development of novel CNN-based image classification architectures over the past 10 years. This has led to the advent of a number of models, pre-trained on this dataset, that form a popular basis for creating custom image classifiers by means of transfer learning. A corollary of this process is that whatever weaknesses and biases the original model possesses, the derived model will also have. Some of these have already been extensively covered, but color sensitivity has so far been understudied. This paper explores the prediction stability of several popular CNN architectures when input images are subjected to hue or saturation shifts. We show that even small shifts in image hue can alter a model’s initial prediction, with larger shifts introducing changes up to 60% and 40% of the time for AlexNet and VGG16 respectively. For all models considered, saturation changes have less impact. To illustrate the issue being inherited by models obtained through transfer learning, we confirm that EmoNet, a model derived from AlexNet, exhibits similar behavior. By further comparing a same architecture trained separately on ImageNet-1k, Places365 and Stylized ImageNet, we confirm that the issue is shared across datasets. Finally, we propose a new preprocessing data augmentation to alleviate this problem.
NEW TRACEABILITY APPROACH USING SWARM INTELLIGENCE FOR MILITARY BLOCKCHAIN

Current military supply chain management is complex and complicated. Activities such as information, and knowledge sharing among involved parties are prone to cybercriminal. Protection of such private and confidential documents are very important. Therefore, a military supply chain derives a critical need for decentralized and digitize transactions in the ledger. This study is proposing a new traceability chain algorithm for military shipment using blockchain. The development of this traceability chain algorithm is based on algorithm development methodology. The new traceability chain algorithm is expected to trace product movement along with the blockchain network. In addition, it is also believed that this study will provide positive results for defense shipment. Future work is to broaden the scope to other military areas such as threat intelligence.