Reviewerly/exHarmony huggingface dataset:
A Coupled 3d Morphological Reconstruction Approach for Surface Microcrack in Si<sub>3</sub>n<sub>4</sub> Ceramic Bearing Roller Based on Adaptive Nano Feature Extraction &amp; Multiscale Depth Fusion

Abstract To extract the fuzzy contour features, tiny depth features of surface microcracks in the Si 3 N 4 ceramic bearings roller. An adaptive nano feature extraction and multiscale deep fusion coupling method is proposed, to sufficiently reconstruct the three‐dimensional morphology characteristics of surface microcracks. Construct an adaptive nano feature extraction method, form the surface microcrack image scale space and the Gaussian difference pyramid function equation, realize the detection and matching of global feature points. The sparse point cloud is obtained. Through polar‐line correction, depth estimation, and fusion of feature points on the surface microcracks image, a multiscale depth fusion matching cost pixel function is established to realize a dense point cloud reconstruction of surface microcracks. The reconstruction results show that the highest value of the local convex surface reconstructed by the dense point cloud reaches 1183 nm, and the lowest local concave surface is accurate to 296 nm. Compared with the measurement results of the confocal platform, the relative error of the reconstruction result is 24.6%. The overall feature‐matching rate of the reconstruction reaches 93.3%. It provides a theoretical basis for the study of surface microcrack propagation mechanism and the prediction of bearing life.
Information and communication technology use by students with disabilities in higher education during the COVID-19 pandemic

Recently, due to the spread of COVID-19, the implementation of remote learning has been increasing worldwide. This study aims to analyze the difficulties and convenience of using information and communication technology (ICT) for students with disabilities and changes in their perceptions of ICT use after the completion of courses for each form of remote learning. The survey included 122 students with disabilities and 314 students without disabilities via a web-based questionnaire. The questionnaire consisted of four situations, categorized according to the type of remote classes. We conducted an analysis of variance using a two-factor mixed design with disability (non-paired: two levels) × situations (paired: four levels) regarding perceptions of resistance toward ICT and self-rated comprehension as the dependent variables. Results show that students with disabilities were more positive about using ICT than students without disabilities in many items. However, before the courses that required the use of relatively new application software, such as web conferencing systems, students with disabilities showed significantly higher levels of resistance and lower levels of self-assessed comprehension. Further, a comparison of the amount of change in perceptions before and after the course reveals that students with disabilities showed significantly more improvement in negative items before the course. These results suggest the importance of providing opportunities for students with disability to learn how to use ICT and understand its convenience in an environment similar to an actual classroom, given the rapid changes occurring in ICT.
Mortality prediction on unsupervised and semi-supervised clusters of medical intensive care unit patients based on MIMIC-II database

The study aimed to propose a framework for identifying patient clusters in medical intensive care units (MICUs) based on the Medical Information Mart for Intensive Care II (MIMIC-II) database. The suggested framework makes use of the survival outcomes and physiological information available in the dataset and is hence called a semi-supervised approach. Five neural networks were trained on the clusters identified using the proposed approach to determine whether the proposed framework could improve the predictive accuracy of the deep learning models. This study utilized data from the MIMIC-II database, which is a publicly available database that contains information on patients admitted to intensive care units. The clusters underlying the MICU patient population were identified using unsupervised and semi-supervised K-means clustering. Mortality in the resulting clusters was predicted using five deep learning-based survival models and the performance of these models was compared using two metrics. Three clusters (cluster 1, n = 1304; cluster 2, n = 474; cluster 3, n = 1079) were identified using unsupervised K-means, and another three clusters (cluster 1, n = 479; cluster 2, n = 1492; cluster 3, n = 886) were identified using semi-supervised K-means clustering. Experimental results demonstrate that, in general, the performance of deep learning models was better on semi-supervised clusters obtained by combining Cox proportional hazards (Cox-PH) model-based feature selection and K-means compared to unsupervised clusters. In the present study, it was observed that deep learning-based survival models tend to perform better on clusters that are identified in a semi-supervised fashion. This approach helps to extract more meaningful patterns and associations between different clinical features and patient outcomes.
Speed Reading Boosts Elementary Students’ Learning Outcomes in Offline Learning

This study investigates the effectiveness of speed reading as a means to improve elementary students' reading skills during offline learning periods.The sample consisted of 20 third-grade students, and a one group pretest-posttest experimental design was employed.The results showed that before using the speed reading method, students' learning outcomes were very low (70.80%),low (10.08%), and moderate (0%).However, after using the speed reading method, the students' learning outcomes significantly improved, with 88.95% achieving high levels and only 4.45% in the very low category.Therefore, the findings suggest that speed reading is a useful approach to enhance elementary students' reading skills during offline learning periods, which has significant implications for education policy and teaching practices.
Vocalist's Concert-Stage Artistry as an Art Course Phenomenon

The author of the article analyzed the invariant interpretation of the essence of the concept "artistry" given by representatives of various science branches (psychology, pedagogy, art history, etc.). Having considered the functional orientation of the vocalist's concert and stage artistry, it becomes possible to recognize it as a phenomenon of art course phenomenon. It has been proven that the concert and stage artistry of a vocalist should be studied precisely from the standpoint of art history, and not only from the standpoint of psychology, pedagogy, and other fields of science. According to the existing paradigm, it is the researchers of art course studies who have the tools to determine the essence of this phenomenon, since they have a deep understanding of the characteristic features of the individual physiology of the vocalist's vocal apparatus. The physiology of the vocal apparatus affects the timbral and aesthetic coloring of the sound; builds the individual reflection in the process of transforming the singer into an imaginary artistic image of a musical work; creates "manners" of sound creation taking into account the logic of building melodic and rhythmic lines in flexible intonation and phrasal development; clarifies the specifics of the effect of emotogenic conditions on the work of the vocal apparatus, etc. The article describes the essence of the concept of "concert and stage artistry of a vocalist" from the point of view of art history. It has been found that the specified art phenomenon reflects the specificity of the internal transformation into an artistic image of a musical work and a universal system of communicative and regulatory stage movements aimed at convincingly strengthening the process of conveying the necessary information to listeners/spectators during a public performance. The mechanisms of manifestation of the vocalist's individual type of concert and stage artistry (irrational and expressive) are revealed from the standpoint of the psychological theory of empathy. The specifics of the definition of the "communicative core" of irrational and expressive types of the vocalist's concert and stage artistry are described, taking into account the individual features of his performance style.
Deep learning method for enhancing luminescence image resolution

Luminescence imaging is a fast and non-destructive method to spatially resolve non-uniform electrical properties of solar cells. The spatial resolution of these images determines the smallest identifiable features. The higher the spatial resolution of an image, the better the capability to detect small defects. However, high-resolution cameras with high near-infrared light sensitivity that are suited to luminescence imaging of crystalline silicon are often expensive. In this study, we present a method, based on deep learning, that enhances the spatial resolution of luminescence images computationally with minimal cost. We also demonstrate the ability to overcome noise which is inevitable in any imaging system. This approach provides a simple and promising path toward reducing the cost of luminescence imaging systems and enhancing the capability of existing systems.
Improving the Simulation of a Mobile Robot by imitations of Ultrasonic Sensors

This article presents imitating the operation of an ultrasonic sensor in a 3D simulation of the Flowcode program to improve the performance and localization of a mobile robot.The main task will be to compare different methods of measuring distance using a simulation model of the sensor and determine the most profitable placement of these sensors to ensure trouble-free passage of obstacles without collisions.
HMDO : Markerless multi-view hand manipulation capture with deformable objects

We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). With our built multi-view capture system, it captures the deformable interactions with multiple perspectives, various object shapes, and diverse interactive forms. Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, the visual features between the hand and the object are entangled, and the reconstruction of the interaction area deformation is difficult. To tackle this challenge, we propose a method to annotate our captured data. Our key idea is to collaborate with estimated hand features to guide the object global pose estimation, and then optimize the deformation process of the object by analyzing the relationship between the hand and the object. Through comprehensive evaluation, the proposed method can reconstruct interactive motions of hands and deformable objects with high quality. HMDO currently consists of 21600 frames over 12 sequences. In the future, this dataset could boost the research of learning-based reconstruction of deformable interaction scenes.
Obstacle Detector for Blind People Using IOT

The system utilizes IoT, echolocation, picture handling, man-made brainpower, and route framework innovation to distinguish between close and far snags for the client. If the visually impaired individual falls or has another issue, the framework will send an admonition to the assigned individual. It distinguishes the object before this with a specific reach. At the point when the item is recognized a signal sound is given to the client as a sign. When they hear this sound they can know a snag before them.
A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.
pytorch-widedeep: A flexible package for multimodal deep learning

In recent years datasets have grown in size and diversity, combining different data types.Multimodal machine learning projects involving tabular data, images and/or text are gaining popularity (e.g.Garg et al. ( 2022)).Traditional approaches involved independent feature generation from every data type and their combination in the later stage before passing them to an algorithm for classification or regression.
MS-SST: Single Image Reconstruction-Based Stain-Style Transfer for Multi-Domain Hematoxylin &amp; Eosin Stained Pathology Images

In digital pathology, pathological tissue images that are obtained using scanners are analyzed and diseases are diagnosed. One crucial aspect of this process is the staining of the tissue slides. However, differences appear in the staining color even when using the same staining protocol owing to various factors such as different facilities, hospitals, and scanning equipment. Many stain style normalization studies have been conducted to solve this problem. In this study, we propose a model named multi-domain single image reconstruction-based stain-style transfer. The proposed model is trained using a reconstruction-based learning framework, which can efficiently reduce the complexity and training time compared with that associated with the GAN objective. We randomly extracted stained tissue image patches from the CAMELYON17 and Mitos-Atypia-14 datasets and demonstrated an effective stain-style translation. Our study reveals that it is possible to perform translation among multiple domains using a single training image per domain. Furthermore, we experimentally demonstrated that translation among color temperature domains was possible in the natural image domain. Our code is publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jwkweon/MS-SST</uri> .
Graph learning considering dynamic structure and random structure

Graph data is an important data type for representing the relationships between individuals, and many research works are conducted based on graph data. In the real-world, graph data usually contain rich time information, giving rise to the concept of dynamic graphs, which contain the temporal evolution information. However, current dynamic graph methods tend to focus on capturing low-order neighborhood information (first- or second-order) and rarely consider higher-order global information, due to the constraints imposed by dynamic graph loading. This can limit their ability to gain a good global sense of the field. To address this challenge, we propose a novel method considers Dynamic Structure and Random Structure, called DSRS, which introduces the classic random walk technique to mine high-order structural information. Specifically, we introduce the node2vec algorithm as a pre-train model to perform random walks on the graph for initial embeddings generation, and then utilize contrastive loss to align the temporal node embeddings focusing on dynamic structure with the random node embeddings focusing on high-order structure, constraining the model to maintain balance between the two different structural information. We performed experiments on link prediction using various real-world dynamic graphs, and the results showed a maximum improvement of 4.34% compared to existing temporal graph learning methods. Furthermore, we conducted experiments to test the sensitivity of our parameters, as well as ablation experiments, which both confirmed the effectiveness of our proposed feature pre-training and parameter fine-tuning methods.
A Study on the Improvement of South Korea’s Volunteer Fire Brigade System through the Case of the US

Research on the development of volunteer firefighter systems has highlighted limitations of the South Korean system. These are categorized by a lack of community disaster safety service activities, disaster response cooperation networks, and support to revitalize volunteer firefighters. The US has 676,900 (65%) volunteer firefighters, and their qualifications are similar to full-time firefighters. The National Volunteer Fire Council is self-reliant, and plays practical roles for volunteer firefighters. Various grant programs for volunteer fire brigades are active. Based on the US case, implications for developing Korea’s system include recruiting young talent for sustainability, improving practical education and training systems, strengthening support for volunteer firefighters’ activities, and developing centralized standardization and support programs.
RF Sign: Signature Anticounterfeiting Real-Time Monitoring System Based on Single Tag

Signatures are one of the most important means to ensure the authenticity of documents and are commonly used in life and work. In identifying imitation handwriting, it is easy to make mistakes that cannot correctly identify and evaluate different writing characteristics. In this paper, from the perspective of dynamic handwriting detection, we propose RF sign, a signature anticounterfeiting real-time monitoring model, which achieves passive recognition of signature behavior using only a single antenna with a single tag. The RF sign identifies different users by extracting fine-grained reflection features from the original RF signal. We introduced a dynamic time regularization and neural network technique for similarity calculation and signature recognition matching to achieve template matching and classification. We compiled a real-time signature handwriting detection system. The system effectively identifies the person’s signature by checking real-time spatial and temporal information. Comprehensive experiments show that the recognition accuracy of my signature can reach over 93% and is robust to input location, environmental changes, and user diversity.
Intelligent chatbot interaction system capable for sentimental analysis using hybrid machine learning algorithms

Chatbots actively help the human user through digital conversation through NLP based on artificial intelligence (AI). It can be pre-trained to understand the user's queries and produce an immediate response in NLP. The user input of the chatbot is any format like voice, text, sentiments, etc. Many research works have been implemented. The issues of existing works are that during the digital conversation, it does not accurately identify the user's requirement, it may go irrelevant to the user's query, and also, primarily, it is voice-based and faces hitches in the analysis of the user's intention, unable to track the context in long-conversation. Therefore, for understanding the context, sentimental calculations are essential. This paper proposed to make the immediate response of users in the chatbot by using Bi-directional Recurrent Neural Network with a Fuzzy Naïve Bayes classifier (BRNN-FNB). This paper aims to build chatbot models with AI-based sentimental analysis, which helps humans to perform accurate interactions. The working concept of chatbots is based on two forms of artificial intelligence domains: machine learning and natural language processing. It may be used in many applications like digital marketing, education, and online forums. The accuracy rate of the proposed work BRNN-FNB got 93% using the Seq-to-Seq technique. And also, the accuracy rate of the proposed work BRNN-FNB without using Seq-to-Seq got 92%.
Graph parameters, implicit representations and factorial properties

How to efficiently represent a graph in computer memory is a fundamental data structuring question. In the present paper, we address this question from a combinatorial point of view. A representation of an n-vertex graph G is called implicit if it assigns to each vertex of G a binary code of length O(log⁡n) so that the adjacency of two vertices is a function of their codes. A necessary condition for a hereditary class X of graphs to admit an implicit representation is that X has at most factorial speed of growth. This condition, however, is not sufficient, as was recently shown in [19]. Several sufficient conditions for the existence of implicit representations deal with boundedness of some parameters, such as degeneracy or clique-width. In the present paper, we analyze more graph parameters and prove a number of new results related to implicit representation and factorial properties.
Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models

Deep Neural Network (DNN) models are becoming ubiquitous in a variety of contemporary domains such as Autonomous Vehicles, Smart cities and Healthcare. They help drones to navigate, identify suspicious activities from safety cameras, and perform diagnostics over medical imaging. Fast DNN inferencing close to the data source is enabled by a growing class of accelerated edge devices such as NVIDIA Jetson and Google Coral which host low-power Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) along with ARM CPUs in a compact form-factor to offer a superior performance-to-energy ratio. E.g., the NVIDIA Jetson AGX Xavier kit has a 512-core Volta GPU, an 8-core ARM CPU and 32GB LPDDR4x memory, that operates within 65W of power, costs US999 and is smaller than a paperback novel. Recently, there has been a push towards training DNN models on the edge [2]. This is driven by the massive growth in data collected from edge devices in Cyber-Physical Systems (CPS) and Internet of Things (IoT), the need to refresh the models periodically, the bandwidth constraints in moving all this data to Cloud data centers for training, and a heightened emphasis on privacy by retaining data on the edge. This has led to techniques like federated and geo-distributed learning that train DNN models locally on data on an edge device and aggregate them centrally. In this abstract, we summarise and highlight key results from our full paper [5].
Echoes of Intelligence

We are now in the presence of a new medium disguised as good old text, but that text has been generated by an LLM, without authorial intention—an aspect that, if known beforehand, completely changes the expectations and response a human should have from a piece of text. Should our interpretation capabilities be engaged? If yes, under what conditions? The rules of the language game should be spelled out; they should not be passed over in silence.