Reviewerly/exHarmony huggingface dataset:
FORMATION OF DIGITAL COMPETENCE OF PEDAGOUGE IN THE PROCCESS OF THEIR PROFESSIONAL TRAINING

An important task of education in the XXI century is the formation of digital competence of the teacher of the institution of higher education, directly due to the rapid development of an information oriented society.Teacher for successful professional educational activities It is necessary to master the latest computer information technology.Free possession of them provides a high level of professional culture of the teacher of the institution of higher education, makes it possible to apply them for a full-time or remote day or absentee educational process.The article proposes the following definition of the concept of «digital competence»: the component of the professional culture of the teacher, based on personal abilities and acquired in the process of professional development of skills and skills aimed at the effective and high-quality organization of the educational process with the free and competent use of innovative computer, mobile and telecommunication technologies To prepare specialists in accordance with the requirements of digital society.Also identified the leading place of digital competence in the professional culture of the teacher and its components.Significant work on the creation of framework documents in the field of education and employment is carried out by international organizations, including the European Research Center of the European Commission, which organized the Working Group, developed and presented a framework for a citizen's digital competence.In the process of continuing this design, a frame of digital competence for teachers is presented.She identified the main forms and methods of developing the digital competence of the teacher and applicants for higher educational institutions, as well as the principles of creating a digital educational environment in such establishments.Frame of digital competence for society and the Digital Competence Frame for teachers use most European countries to view and create appropriate training programs, continuously improve the digital competence of teachers and supporting employment opportunities for future specialists.
PERF: Performant, Explicit Radiance Fields

We present a novel way of approaching image-based 3D reconstruction based on radiance fields. The problem of volumetric reconstruction is formulated as a non-linear least-squares problem and solved explicitly without the use of neural networks. This enables the use of solvers with a higher rate of convergence than what is typically used for neural networks, and fewer iterations are required until convergence. The volume is represented using a grid of voxels, with the scene surrounded by a hierarchy of environment maps. This makes it possible to get clean reconstructions of 360° scenes where the foreground and background is separated. A number of synthetic and real scenes from well-known benchmark-suites are successfully reconstructed with quality on par with state-of-the-art methods, but at significantly reduced reconstruction times.
Quadrant-based contour features for accelerated shape retrieval system

Abstract Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image retrieval systems. The proposed method is an extended version of our previous study which uses contour information of shapes. The previous study calculated the center of mass (CoM) of the shape. By taking the CoM as origin, we created imaginary vectors in every angular direction. From each vector, we extracted three features which are the number of intersections between vector and contour, average distance of intersection points to CoM, and standard deviation of these points. In this method, we extract novel features and decrease the size of the feature set to decrease the computation time. We divide the shape into quadrants and represent each quadrant by nine features. Each shape image is represented by a 4x9 feature vector. We tested the proposed method on MPEG-7 and ETH-80 datasets and compared it with the state-of-art. According to the results, our method decreased the computation time dramatically while giving a state-of-art level retrieval accuracy.
Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques

Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes. The care of DFU patients can be substantially improved through self-management, in order to achieve early-diagnosis, ulcer prevention, and complications management in existing ulcers. In this paper, we investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers: noise reduction and super-resolution. In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoencoders (CNN-SAE). CNN-SAE was tested on RGB images, induced with Gaussian noise. The latter scenario involves the deployment of four deep learning super-resolution models. The performance of all models, for both scenarios, was evaluated in terms of execution time and perceived quality. Results indicate that applied techniques consist a viable and easy to implement alternative that should be used by any system designed for DFU monitoring.
An efficient framework for the similarity prediction with query recommendation in E‐learning system

Summary A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.
Phase retrieval for objects in rain based on a combination of variational image decomposition and variational mode decomposition in FPP

As far as we know, there is no paper reported to retrieve the phase of an object in rain by the fringe projection profilometry (FPP) method. The fringe projection pattern taken in rain contains much rain noise, which makes it difficult to accurately retrieve the phase of the object. In this paper, we focus on the phase retrieval of the object in rain by the FPP method. We first decompose the original fringe projection pattern into a series of band-limited intrinsic mode functions by the two-dimensional variational mode decomposition (2D-VMD) method. Then we screen out fringe-associated modes adaptively based on mutual information and reconstruct the fringe projection pattern. Next, we decompose the reconstructed fringe projection pattern by the TGV-Hilbert-BM3D variational model to obtain the de-rained fringe component. Finally, we use the Fourier transform method, phase unwrapping method, and carrier-removal method to obtain the unwrapped phase. We test the proposed method on three fringe projection patterns taken in simulated rain weather, and we compare our proposed method with the phase-shifting method, windowed Fourier method, morphological operation-based bidimensional empirical mode decomposition method, 2D-VMD method, and the TGV-Hilbert-BM3D method. The experimental results demonstrate that, for the first time to our knowledge, our method can effectively retrieve the phase of an object in rain from a single fringe projection pattern.
Investigating Motivations and Patient Profiles for Personalization of Health Applications for Behaviour Change

Personalization is a key aspect when developing applications targeting health behaviour change. However, the use of personalized mobile interventions for lifestyle behaviour is still in its infancy. Based on our former research on mobile applications to support cardiac patients in health behaviour change, we identified four key motivations to enhance the personalization offered in applications targeting health behaviour change. In this paper, we propose a mixed-methods approach, using both qualitative and quantitative data collected in prior studies, to apply personalization in the design of health applications. Our approach consists of five steps: 1) collecting data for personalization, 2) detecting patient profiles using clustering methods, 3) understanding patient profiles using a graphical representation, 4) describing patient profiles using personas, and 5) personalizing a health application according to patient profiles. One of the major strengths of our approach is that it combines established HCI techniques such as personas and data visualization techniques with methods from big data analytics and artificial intelligence to identify ways to personalize health applications. We conclude by presenting future directions to apply personalization in the domain of health technologies.
An Online Japanese Teaching Mode Based on Virtual Reality

With the close cultural exchanges between China and Japan, the demand and quality of Japanese talents are gradually increasing. Traditional Japanese teaching modes and measures cannot meet the basic requirements. With the rapid development of network multimedia technology and virtual reality technology, in the process of Japanese teaching, virtual reality technology is used to reform the traditional teaching mode and to improve the quality of Japanese teaching. Based on virtual reality technology, this paper studies the online Japanese teaching mode, analyses the characteristics of Japanese teaching, combines modern information technology to assist teaching, comprehensively improves the quality of Japanese teaching, and improves the traditional Japanese teaching mode. By describing the concept, basic features, and components of the AR system in detail and by introducing the virtual construction method based on the geometric model, this paper improves the traditional Japanese teaching mode and establishes the online Japanese teaching mode. Finally, the online Japanese teaching mode for one semester is applied in colleges and universities to analyze whether this mode can improve students’ interest in Japanese learning and the effect of Japanese learning. The results show that 59% of the students using this mode expressed an increased interest in Japanese learning, while only 25.8% of the students not using this mode showed an increased interest in Japanese learning. 72% of students indicated that they could improve their oral Japanese performance after using this model, while 45% of students who did not use this model could improve their oral Japanese performance.
A privacy-preserving decentralized randomized block-coordinate subgradient algorithm over time-varying networks

This study considers a constrained huge-scale optimization problem over networks where the objective is to minimize the sum of nonsmooth local loss functions. To solve this problem, many optimization algorithms have been proposed by employing (sub)gradient descent methods to handle high-dimensional data, but the computation of the entire sub(gradient) becomes a computational bottleneck. To reduce the computational burden of each agent and preserve privacy of data in time-varying networks, we propose a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm over time-varying networks, in which the coordinates of the subgradient vector is randomly chosen to update the optimized parameter and the partially homomorphic cryptography is used to protect the privacy of data. Further, we prove that our algorithm is convergent asymptotically. Moreover, the rates of convergence are also established by choosing appropriate step sizes, i.e., O(logK/K) under local strong convexity and O(logK/K) under local convexity, in which K represents the number of iterations. Meanwhile, we show that the privacy of data can be protected by the proposed algorithm. The results of experiments demonstrate the computational benefit of our algorithm on two real-world datasets. The theoretical results are also verified by different experiments.
A survey on stereo matching and semantic matching

This paper reviews the development of stereo matching and semantic matching in the field of image correspondence. The existed matching methods of these two kinds of matching problems are discussed and summarized. Since 2014, technologies based on data-driven and deep learning have played an important role in these two types of matching problems, which accelerates the development of image correspondence technology. This paper discusses stereo matching from three perspectives: local stereo matching, global stereo matching, and stereo matching based on neural networks. Besides, this paper divides semantic matching methods into two categories: parametric semantic matching and nonparametric semantic matching. By reviewing and tracking the research development of these two matching problems, this paper provides good navigation for people who are new to the image correspondence field.
A novel self-directed learning framework for cluster ensemble

This study looked into the problems that occur when a clustering or cluster ensemble model with unsupervised or semi-supervised learning is used in a real-world setting. However, validating the obtained results is a challenging task. Therefore, we propose a self-directed learning (SDL) framework for cluster ensemble, which improves the traditional ensemble framework by assisting the consensus function in achieving the highest assessment of clustering performance. The SDL is built on two models: predicting test set labels (PTL) and detecting best results (DBR). The PTL model assists in predicting the test set labels; the PTL is based on consistently ensembling the outcomes until it obtains satisfactory results. The DBR identifies the correct answers for each data object when a single model provides several outcomes for a single dataset. To highlight the power of our proposed framework, we consider multiple performance measurements; one of them is termed as correction ratio (CR). We compare SDL with several cluster ensemble models. The results show that the proposed framework outperforms other models with 18% accuracy on average and achieves similar results for other performance indicators.
A platform for health emergency warning and wandering behaviour detection supporting people with Intellectual Disability

People with intellectual disabilities (ID) encounter several problems in their daily life, regarding the interaction with their environment, the communication with their caregivers and their mobility. Mobile and modern smartwatch applications have great potential to provide early warning for medical emergencies, such as fever, oxygen saturation decline and, recently, Covid-19 disease. Furthermore, such apps can also track their position, if necessary, and even automatically detect wandering behaviour. In this paper, we propose a platform that will be able to a) automatically recognize health emergency situations and/or detection of wandering behaviour based on smartwatch/smartphone sensors, b) provide appropriate notifications to both user and caregiver when needed. The final system is currently developed within a Greek national research project and a pilot implementation will be evaluated by a Greek assistive center for people with ID.
Indicator mineral analyses of stream-sediment samples using automated mineralogy and mineral chemistry: Applicability to exploration in covered terranes in eastern Alaska, USA

In the past two decades, significant research efforts have been devoted to porphyry copper indicator mineral (PCIM) identification and mineral chemistry to assist exploration. Such studies are important in the Yukon-Tanana upland region of eastern Alaska because well-established geochemical exploration techniques, such as the geochemistry of stream-sediment samples, are less effective in the search for mineral resources owing to cover materials that subdue geochemical signatures. The unglaciated Yukon-Tanana upland region is well-endowed with mineral resources, including porphyry Cu-Mo-Au deposits at Taurus, Bluff, Dennison, and Oreo. Using automated scanning electron microscope (SEM) techniques, numerous PCIMs have been identified in rocks and stream-sediment samples that would likely not have been observed using visual binocular microscope methods. Sulfide minerals such as chalcopyrite, chalcocite, covellite, bornite, molybdenite, and pyrrhotite were identified in rock samples, and more than half of the stream-sediment samples from drainages containing known porphyry mineral occurrences contain chalcopyrite with or without bornite. Many drainages without known occurrences also contain these minerals, which suggests potential for additional occurrences. Svanbergite, an aluminum phosphate sulfate (APS) mineral [SrAl3(PO4)(SO4)(OH)6], was identified in some of the altered or mineralized rock samples from Taurus, and in stream drainages containing porphyry occurrences and may represent the best indicator mineral for Taurus-like porphyry deposits. Apatite chemistry has great potential as a tool for assessing the presence of porphyry mineralization. Grains from rock and stream-sediment samples that contain high Cl (>∼0.4 wt%), Fe (>0.4 wt%), and Mn (>2000 ppm) clearly distinguish apatite from other sources (metamorphic, unmineralized igneous rocks). This study is one of the first to document new streamlined indicator mineral sample collection and processing methods and to combine automated SEM techniques and chemistry of minerals derived from both bedrock and surficial (stream) sediments as they apply to porphyry exploration.
Fast rates of minimum error entropy with heavy-tailed noise

In this paper, we investigate the performance of minimum error entropy (MEE) from a theoretical viewpoint. Owing to resistance of heavy-tailed noise or outliers, as an alternative to traditional robust empirical risk minimization schemes, MEE has drawn particular attention over the last decades and has been successfully used in machine learning. The purpose of this paper is to conduct refined learning theory analysis of MEE and establish its improved rates of convergence without the light-tailed noise. It shows that a new comparison theorem not only characterizes the regression calibration properties of MEE, but also refines the variance of analysis of learning theory.
Detection of Proper Orientation of Face Mask Using Deep Neural Network

The pandemic caused by the coronavirus resulted in health protocols regulations to prevent the disease's spread. The World Health Organization recommended using facemasks as a preliminary measure that governments around the world could use help prevent the outbreak. The study's goal is to implement computer vision for facial detection with faces wearing a face mask. The study aims to: (1) capture facial features of masked faces using a Web Camera; and (2) detect a face with proper face mask orientation using deep neural network and mobile net model. (3) For the system to detect whether a person is appropriately wearing a face mask following IATF and WHO standards. The study only allowed one person to face the camera while wearing Standard Surgical Face Masks and being within eye level of the camera if ambient lighting was available. Face Mask Detection, with the Raspberry Pi, has checked the proper face mask orientation for 50 different individuals. The Confusion Matrix Testing determined the following values: 80 percent for the FMD's Accuracy, 62.5 percent for the FMD's Precision, 71.4 percent for the FMD's Recall, Sensitivity, and True Positive Rate, 66.6 percent for the F1 Score, and 16.6 percent for the False Positive Rate.
Quantum Circuit Optimization for Solving Discrete Logarithm of Binary Elliptic Curves Obeying the Nearest-Neighbor Constrained

In this paper, we consider the optimization of the quantum circuit for discrete logarithm of binary elliptic curves under a constrained connectivity, focusing on the resource expenditure and the optimal design for quantum operations such as the addition, binary shift, multiplication, squaring, inversion, and division included in the point addition on binary elliptic curves. Based on the space-efficient quantum Karatsuba multiplication, the number of CNOTs in the circuits of inversion and division has been reduced with the help of the Steiner tree problem reduction. The optimized size of the CNOTs is related to the minimum degree of the connected graph.
Adversarial deep features for weakly supervised document image keyword spotting

Keyword spotting (KWS) aims to retrieve all instances of particular keywords in a document. Modern approaches exploit the representational power of convolutional networks (CNN) to produce discriminative word image representations able to perform in challenging multi-writer conditions. However, they require lots of training data while their adaptivity to unknown document collections when little or no annotations exist is uncertain. To this end, we utilize a CNN to extract intermediate layer deep features. We then combine adversarial learning with spatial transformer networks to obtain discriminative deformations of the feature space leading to compact deep feature representations which alleviate the adaptation of the proposed KWS system into weakly supervised manuscripts. Numerical experiments of the adaptation of deep features from a low resource document collection (GW) to a much more diverse target dataset (IAM) where little annotations exist to fine-tune the original model are on par with the state of the art.
Choreographic Infrastructuring for Design Things

The aim of this study is to contribute knowledge on the implementation of a new multimodal choreography-informed method, Choreopattern, which was developed for participatory educational design. This paper presents an in-depth analysis of Choreopattern applied in a teacher education workshop on curriculum design with participants from the dance education and teacher education sectors. We shed light on the interactions that arise when Choreopattern is implemented in a workshop and how the method provides infrastructure for the configuration of genuine participation in a Thing, a sociomaterial assembly in which participants align around a shared object of concern—that is, the course content and form of a teacher education program. Drawing on choreography, we provide an extended understanding of alignment in a Thing that involves spatial, mental and emotional alignment, presenting Choreopattern as a design method that equally values emotions and rationality.