Reviewerly/exHarmony huggingface dataset:
Folklore work: legality of the use of the term

The purpose of the article is to provide essential justifications for the validity of the definition of “folklore work”. The main sources were the works of philosophers, culturologists, art historians, and folklorists in the field of definitional analysis of the “work of art” category and directly folklore texts. The research methodology includes a structural and functional approach to the analysis of the features of the existence of artistic texts, a phenomenological and anthropological interpretation of folklore artifacts with the inclusion of elements of textual analysis. The question of terminology in the Humanities is quite acute. The use of terms without an exact designation of their subject field can lead not only to discrepancies, but also to significant errors in understanding the essence of phenomena. Moreover, this may lead to claims that the use of certain terms is incorrect. This is the case with the term “folklore work”, which is used in scientific terms. A number of studies, primarily of art criticism, deny the validity of its application on the grounds that the status of stability of works of art contradicts the variant character of folklore texts. This contrast between constancy and variability is explained by a somewhat simplified view of the phenomena being compared. The basis of the folklore work is avantext, which generates equal variants, but performs the function of a stable image structure. It should be interpreted quite broadly: as a kind of conditional matrix within which elements vary without changing the cultural meaning. This is why folklore works are recognizable, preserved and recreated in each case. This variation, in turn, expands the set framework, filling the tradition with relevant content, enriching it with new artistic images, etc. A similar situation occurs with the author's works of art, which for a long time included improvisational fragments. It is the execution and perception of a literary text (and not just the fact that it is fixed in a sign system) that turns it into a work itself, with the inevitable variability. Of course, these changes are not as obvious as in the case of folklore works, but they do not exclude them. Thus, any work of art does not act as a constant system, but as a phenomenon that is quite mobile in the dynamics of cultural activity. In addition, it should be remembered that a work of art is always the result of a specific activity for the production of artifacts. In this case, any created artifact acts as a work of art or, more generally, a work of culture. Therefore, the creation of an artifact in the sphere of folklore culture is the same artistic production as creativity in the sphere of author's musical culture, literature, painting, etc. The use of the term “folklore work” is not only quite justified, but also corresponds to the essential structural, functional and semantic characteristics of this phenomenon.
Education as an event: event nature of the university corporativity

In the article the conceptual issues of the educational philosophy are presented. The aim of the research is to explore the specific character of contemporary education ontology in the whole and the particularities of the university education as a part of it. In addition authors make an attempt to consider the education being as its betweenness in the world, to identify its historical and philosophical transformations closely connected with traditionally obtained corporative university form. Originally, the university was founded as a corporation, but state-of-the-art conditions of its functioning forced the development of its cultural model and structure. Authors stand on the position that the classical ontological university particularities - the wholeness of a being and the absoluteness of criteria - at the moment are displaced by the ontological eventness. Event ontology supposed to obtain ordinary disunification, plurality and conditions singularity. The university educational ontology transformation leads to the corporate culture ontology changing processes. A contemporary university corporate culture has event nature and it is supposed to illuminate itself in the postclassical cultural university identity. The main methods of the investigation are the philosophical and cultural critics (critical analysis) and the comparative research of the classical university education (presented in the unity of its being) and its current condition status represented by the event existence. The novelty of the authors' position is in argumentation of ontological term “event” applied to the contemporary university and relative to it, also event ontology based, corporativity. Eventness of the current cultural processes, including educational ones, is an element, deconstructing being in its entity and wholeness. Being in existent modernity obtains the features of eclecticism, fragmentarity, as a consequence, the key characteristics development of the social institutes is required. The classical university in its students' and professors' unity of scientific investigations has to expand its functions in accordance with the educational market demand. Essentially, a university transforms into a business corporation with the high standard of culture. The authors' position is based on the deconstructive processes within the present day philosophical thinking, distinguishing the classical scope clarity. The position mentioned above give a possibility to consider the modification of ontology of being in entity of its particularities, the education being and the university being alongside its corporate culture. The position explored can be identified as event ontology and it is the result of liquid modernity requirements challenging a contemporary university. The classical university model and the unipolar culture are displaced by the multiple identity and the complementarity of corporate and academic cultures as a vital condition of the university existence.
A Hybrid Technique to Increase Throughput of the Streaming Spectrum Sensor

In previous work, there has been significant progress on edge device RF feature detection using a hardware algorithm known as the streaming spectrum sensor (StSS). Unlike other spectrum sensor implementations, the StSS can function on edge devices due to its low memory requirements. While the StSS accomplishes its objective of low memory detection, it sacrifices detection throughput. This work investigates a hybrid technique to balance memory and throughput.
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 March 2023; 128 (2): 181–182. doi: https://doi.org/10.1352/1944-7558-128.2.181 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Building an Optimal Convolutional Neural Network Model for Solving Complex Character Recognition Problems

The purpose of the study is aimed at developing a lighter architecture of a convolutional neural network model that will cope with the narrowly focused task of recognizing complex characters better than large-scale and well-known ones. As the source data, the characters of the Japanese language are used, consisting of two syllabic alphabets: hiragana and katakana, which are the most complex, since their writing style is characterized by a large number of features and similarity of characters, which greatly complicates the task of their classification and recognition. The author's model of a convolutional neural network is designed in the article, consisting of four convolutional layers, three layers of subdiscretization and three layers of exclusion. The developed model was compared with one of the most popular models of the EfficientNetBO neural network from the point of view of their architecture and the results of work on the same data. To implement its own convolutional neural network model, the classic Keras + Tensorflow bundle was used, since these libraries provide the most convenient tools for working in the field of machine learning. The result of the conducted research is the developed technology of fast and accurate recognition of complex symbols based on a convolutional neural network, which can become the basis of a software product in the field of computer vision.
A measurement system toward to skill analysis of wall painting using a roller brush

Abstract In this study, we describe a measurement system aiming to skill analysis of wall painting work using a roller brush. Our proposed measurement system mainly comprises an RGB-D sensor and a roller brush with sensors attached. To achieve our requirements in understanding roller operation, we developed an algorithm that is suitable to estimate the roller part pose with high accuracy. We also show a method to generate a swept map that can be used for both visualization and evaluation. In the proof experiment, a dataset for actual painting work was collected using the proposed measurement system. Then, the dataset was analyzed, and the quality of the work was quantitatively evaluated by comparing skilled and unskilled persons.
Võro South Estonian1

This chapter is an overview of Võro South Estonian. It contains a general typological profile and information on demographics, geography, and variation. The phonology of Võro is examined in detail. The morphology section covers the properties of nominals (number, case, possession), verbs (tense, mood, aspect, person), and other parts of speech. The syntactic section includes information, for example, about basic clause types, possession, comparison, argument indexing and noun phrase flagging, negation, complex sentences (relative, complement, supporting), and questions. Each point is illustrated with authentic glossed, segmented, and translated examples from a corpus. The chapter also contains a section on word-formation and information about the lexicon. The chapter concludes with an annotated text sample and a list of references.
Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations

Voice conversion (VC) consists of digitally altering the voice of an individual to manipulate part of its content, primarily its identity, while maintaining the rest unchanged. Research in neural VC has accomplished considerable breakthroughs with the capacity to falsify a voice identity using a small amount of data with a highly realistic rendering. This paper goes beyond voice identity manipulation and presents an original neural architecture that allows the manipulation of voice attributes (e.g., gender and age). The proposed architecture is inspired by the fader network, transferring the same ideas to voice manipulation. The information conveyed by the speech signal is disentangled into interpretative voice attributes by means of minimizing adversarial loss to make the encoded information mutually independent while preserving the capacity to generate a speech signal from the disentangled codes. During inference for voice conversion, the disentangled voice attributes can be manipulated and the speech signal can be generated accordingly. For experimental evaluation, the proposed method is applied to the task of voice gender conversion using the freely available VCTK dataset. Quantitative measurements of mutual information between the variables of speaker identity and speaker gender show that the proposed architecture can learn gender-independent representation of speakers. Additional measurements of speaker recognition indicate that speaker identity can be recognized accurately from the gender-independent representation. Finally, a subjective experiment conducted on the task of voice gender manipulation shows that the proposed architecture can convert voice gender with very high efficiency and good naturalness.
Gaussian Integer Solutions of the Diophantine Equation x^4+y^4=z^3 for x≠ y

The investigation of determining solutions for the Diophantine equation over the Gaussian integer ring for the specific case of is discussed. The discussion includes various preliminary results later used to build the resolvent theory of the Diophantine equation studied. Our findings show the existence of infinitely many solutions. Since the analytical method used here is based on simple algebraic properties, it can be easily generalized to study the behavior and the conditions for the existence of solutions to other Diophantine equations, allowing a deeper understanding, even when no general solution is known.
NITS-IQA Database: A New Image Quality Assessment Database

This paper describes a newly-created image database termed as the NITS-IQA database for image quality assessment (IQA). In spite of recently developed IQA databases, which contain a collection of a huge number of images and type of distortions, there is still a lack of new distortion and use of real natural images taken by the camera. The NITS-IQA database contains total 414 images, including 405 distorted images (nine types of distortion with five levels in each of the distortion type) and nine original images. In this paper, a detailed step by step description of the database development along with the procedure of the subjective test experiment is explained. The subjective test experiment is carried out in order to obtain the individual opinion score of the quality of the images presented before them. The mean opinion score (MOS) is obtained from the individual opinion score. In this paper, the Pearson, Spearman and Kendall rank correlation between a state-of-the-art IQA technique and the MOS are analyzed and presented.
Continuous Prediction of Web User Visual Attention on Short Span Windows Based on Gaze Data Analytics

Understanding users' visual attention on websites is paramount to enhance the browsing experience, such as providing emergent information or dynamically adapting Web interfaces. Existing approaches to accomplish these challenges are generally based on the computation of salience maps of static Web interfaces, while websites increasingly become more dynamic and interactive. This paper proposes a method and provides a proof-of-concept to predict user's visual attention on specific regions of a website with dynamic components. This method predicts the regions of a user's visual attention without requiring a constant recording of the current layout of the website, but rather by knowing the structure it presented in a past period. To address this challenge, the concept of visit intention is introduced in this paper, defined as the probability that a user, while browsing, will fixate their gaze on a specific region of the website in the next period. Our approach uses the gaze patterns of a population that browsed a specific website, captured via an eye-tracker device, to aid personalized prediction models built with individual visual kinetics features. We show experimentally that it is possible to conduct such a prediction through multilabel classification models using a small number of users, obtaining an average area under curve of 84.3%, and an average accuracy of 79%. Furthermore, the user's visual kinetics features are consistently selected in every set of a cross-validation evaluation.
Survey on Non-Invasive Blood Group Detection Using Image Processing

Detecting the blood group of people is necessary, before performing a transfusion of blood in an urgent situation or when checking a person’s blood group for donation. Presently, the tests are conducted manually by the lab personnel in the laboratory. This takes time and consequences in human error when determining blood type. This may result in loss of life or seriously affect people’s lives. To overcome this, we propose a non-invasive model using image processing and machine learning algorithms. Image processing is useful in various ways for researchers to succeed in their goals, especially in medical fields. This system displays the corresponding blood type that is A+, A-, B+, B-, AB+, AB-, O+, and O- in a short time. Image matching techniques like ORB are used to extract features from an image. It involves signal extraction processes, spectroscopic information, feature calculation processes, and Photoplethysmographic (PPG) signal. The features extracted are given to a machine learning model to detect the blood type of a person in a short period. This method does not require mixing any antigens like A, B, and O with blood samples. This method does not use any blood samples, does not require any chemicals, and reduces human error.
Improvement of Flick Input Interface Using Hand Tracking in VR Environments

Recently, Virtual Reality (VR) technology is becoming widespread. In particular, techniques of hand tracking has enabled us to reflect movements of fingers in the real space upon the VR environments without wearing any special devices. Along with that, methods for freehand text input, especially flick input which is widely used in mobile devices, has been studied. However, the flick input in VR environments has a problem of poor input accuracy. In this study, we propose a method for improving the flick input interface using hand tracking in input accuracy and operability. Since one of the previous studies suggests that some users occasionally fail to notice the visual feedback, we introduce three types of feedback, namely visual, auditory and tactile feedback, to the system so that the user can easily notice it. Furthermore, we focus on the fact that failure of hand tracking often make input accuracy and operability worse, and propose a novel key layout suitable for flick input in VR environments that prevents the failure of hand tracking. Experimental results show that three types of feedback introduced in this study improve subjective evaluation of the user, and the proposed layout is effective in improving both input accuracy and subjective evaluation of the user.
Technological Structure Analysis of Digital Healthcare: Fractional Assignment Vs. Disrecte Assignment

This study identifies the technology structure of the digital healthcare field using patent data and compares the characteristics between techniques in the technical structure analysis. 2,064 patents obtained from USPTO, and analyzed by LDA to extract 15 technology topics, and documents classified as Doc2vec performed 15 technology groups through Spherical K-means clustering. As a result, LDA derived component technology of digital healthcare such as implantable medical devices, but Doc2ve-based clustering was confirmed promising technology of digital healthcare, including blockchain, telehealth service. This study are expected to be useful for establishing technology strategies and commercializing technologies for digital healthcare companies in the future.
Photovoltaic power forecast for the next 24 hours with an analytical model and a FFNN model

Photovoltaic (PV) self-consumption installations have increased by 101.84% in Spain, from 2020 to 2021. In this context, developing PV generation forecasting tools can contribute to increase the PV self-consumption ratio, boosting the use of renewable energies. On this matter, this paper presents two PV generation forecasting models for the next 24 hours. These are an analytical model developed in OpenModelica software and a model based on artificial intelligence (AI), specifically a feedforward neural network (FFNN). Both models use measured meteorological data obtained from a weather station 3km from the PV installation. This work analyses how the use of different data as input information affects the prediction of the FFNN. It was found that adding a time vector as an input of the FFNN improves the prediction, thus compensating the fact that it is not a recurrent network. Furthermore, the behaviour of both models has been compared. Both the analytical model and the FFNN obtain a correlation coefficient r of 0.941 and 0.94, respectively. Even so, the MAE and RMSE metrics highlight how the analytical model has a higher error and level of dispersion.
An Analytical Study of Translating Hate Speech Tweets from English into Arabic

Social media, nowadays, is considered a mirror in which all human beings can see their true identity, a sensor to measure used to see the pulse of society. It is a weapon used by individual and politicians alike to attack each other. In this paper, the researcher aims to investigate and analysis the translation of ten hateful tweets by students in the second stage. The students were asked to implement Vinay and Darbelnet (1995)’ procedures of translation. The results of this investigation shed some lights on the problems the students encounter, and over solutions to be followed. Keywords: Social Media, Twitter, Hateful Tweets, discrimination, Translation Procedure.
Phono-strategic language preference for Andalusian Spanish (L2) over German (L1) in lisping bilinguals

This paper investigates the preference for Andalusian Spanish (second language, L2) over German (first language, L1) in bilinguals who have a lisp and do not encounter a feeling of belonging to their L1 society due to reactions such a divergent pronunciation provokes. This issue is novel due to its interdisciplinary nature within this unique language combination, and it draws particularly on the fields of speech (pathology), language preference, communication, bilingualism, phonetics, psycholinguistics and sociolinguistics. To test the hypothesis ‘some bilinguals who have a lisp prefer to use Andalusian Spanish (their L2) over German (their L1) to avoid the noticeability of their speech divergence’, within the boundaries of the present exploratory case study, interviews with participants were recorded. It was found that participants sacrifice the usage of their L1 in favour of their L2 in an attempt to vanish their phonetic divergence. Such a finding is important because in Andalusian Spanish the pronunciation of the /s/ is significantly reduced during the conversation flow on account of many of the language’s specific phonetic conventions.
AUGMENTED REALITY TECHNOLOGY FOR TEACHING -LEARNING OF SCIENCE IN SCHOOLS

Science education today is committed to producing human resources that have superior competencies and can compete in the global era. This is in accordance with the 21st-century education development paradigm, where learning outcomes of science are directed at critical thinking and problem-solving skills, creativity and innovation, collaboration and communication skills, and skill at using media, information, technology, and communication. Hence, it is believed that the adoption of Augmented Reality (AR) in science learning could improve interest, motivation and ultimately helps students to provide both conceptual and practical experiences in and around the subject of science. The purpose of this paper is to present a view of Augmented Reality (AR) technology as a significant tool for learning and teaching various subjects in general and science in particular. The review showed that Augmented Reality (AR) technology provides many affordances for Science learning; however, it is not ready for total integration into science classes. The paper also provides practical suggestions for activities enriched with Augmented Reality (AR) in science skills and recommended applications. This paper also offers various implications of AR technology around the teaching-learning process.