Reviewerly/exHarmony huggingface dataset:
Blind restoration of vertical motion blurred image based on point spread function estimation

Blind image restoration has always been a hot and difficult problem in the field of image processing. As the vertical motion blur image has obvious characteristics in the frequency domain, the distance between the center point and the nearest horizontal black strip is automatically estimated by setting thresholds, and the estimated vertical motion point spread function is obtained; the image is blindly restored by the constrained least squares algorithm. Experimental results show that the proposed vertical motion point spread function estimation algorithm has high accuracy, and the restored image has better visual effects and higher PSNR.
Capitolo 13. Usi metalinguistici del lessico di Roma nei testi italiani tra Cinque e Ottocento: materiali per un glossario

This research aims to detect, in Italian writings from the sixteenth to the nineteenth century, some words and phrases that the authors claim to be Roman. The whole documentation is composed of hundreds of examples and it should contribute to the knowledge of the Roman dialect. However, each example needs close investigation to make sure of its actual Roman origin. This essay, which is only the preliminary stage of an ongoing research, presents some interesting texts for such research and provides a provisional glossary (composed of 33 entries).
3D reconstruction of cross-scale narrow space with thin structure

This paper proposes a 3D reconstruction method for the cross-scale narrow space containing thin structure. This method takes monocular image sequences as input and respectively constructs large-scale scene point cloud of main part based on incremental SfM and small-scale thin structure point cloud based on the curve method. The small-scale thin structure point cloud reconstructed individually alleviates the defect of incomplete point cloud from the large-scale scene. By introducing PCA for initial registration, alternatively iterating the closest point algorithm and the global optimization algorithm, we achieve a more precise registration for the thin structure point cloud. Then, a replacement algorithm is used to fuse it with the point cloud of the main part. The experiment containing two scenes verifies the effectiveness of our method, which reconstructs point cloud of the thin structure with high accuracy and retains the overall 3D information of the scene at the same time.
Dealing with contents related to ‘media’ Reviewing the sequencing of textbook learning activities-Focused on achievement standards related to ‘critical acceptance and evaluation of media’-

본 연구에서 ‘매체의 비판적 분석 및 평가’와 관련한 성취기준이 중학교 국어, 고등학교 국어, 독서, 언어와 매체 교과서의 학습 활동으로 구현될 때 계열성을 갖추었는지 분석한 결과, 교과서별로 학습 내용이 반복되나 심화되지 않고, 텍스트 난이도의 학년별 차이가 두드러지지 않으며, 학습자의 흥미와 발달 수준에 맞게 매체 자료가 다양화되지 않음을 발견하였다.As a result of analyzing whether the achievement standards related to Critical Analysis and Evaluation of Media are serialized when middle school Korean, high school Korean, reading, language, and media textbooks are implemented, and the text difficulty level is not noticeable. It was found that media data were not diversified according to the learner s level of interest and development.
Elements of the Automotive Disruption

Disruptive technology-driven developments have greatly changed the nature of customer interaction and the design of the customer experience with new digital touchpoints. Targeted leveraging of data is critical in shaping the shift from a product-centric to a service-centric business model. Connectivity, V2X communication, and the Internet of Things play a key role in the transformation of the automotive industry—especially in the roadmap to fully autonomous driving. But electrification and mobility-as-a-service (MaaS) are also shaping automotive disruption. The publication not only examines the premises and hurdles for the further development of the new forms of mobility but also shows which individual and societal benefits—including road safety, access to mobility, traffic efficiency, improved space utilization, decrease in pollution, reduced transportation costs, service improvement—result from them. Furthermore, the impact of digitalization and networking on the classic areas of the value chain is analyzed and possible use cases and development scenarios of “smart” value creation are discussed. The way manufacturers interact with each other and with customers will change fundamentally—digital ecosystems and underlying network effects play an essential role here. This publication looks at the possible implications of this ecosystem model.
Modeling in nuclear physics: a visual approach to the limitations of the semi-empirical mass formula

Abstract The semi-empirical mass formula (SEMF) is a phenomenological model describing nuclear binding energies with high accuracy. We present different realizations and applications of this model based on 3D-printing and animations. A central new contribution is a visualization not only of the energy landscape provided by the SEMF, but also a comparison with actual experimental data. Our visualization of this difference energy landscape reveals limitations of the phenomenological model, and at the same time shows the special importance of the so-called magic numbers which have been explained in the shell model, introduced much later than the liquid drop model. This provides an excellent opportunity to discuss limits and merits of models in general in the context of physics education.
Educational Management Features of First Soviet Teachers-Innovators N. K. Krupskaya and A. S. Makarenko

Research relevance: educational institutions are the main lever of power in organizing ideological and educational work with the younger generation. In connection with the current situation in the country, the Kyrgyz society is facing the urgent issue of educating the younger generation. Research objectives: to identify the features of the educational management of the first Soviet teachers, innovators N. K. Krupskaya and A. S. Makarenko and their contribution to the organization of effective educational work in accordance with the regulations in the country. Research materials and methods: analysis of historical data and educational works by N. K. Krupskaya and A. S. Makarenko about pedagogical management of youth. Research results: pedagogical management of A. S. Makarenko differed from the educational activities of N. K. Krupskaya in that it was built on democratic principles. Conclusions: according to preliminary research results of scientific and methodological works, it can be concluded that pedagogical management by A. S. Makarenko was based on democratic principles and management by N. K. Krupskaya on the worldview of authoritarian government.
Oasiscreen

research-article Oasiscreen Share on Author: Jieyuan Huang University of Arts and Design Linz, Austria University of Arts and Design Linz, AustriaSearch about this author Authors Info & Claims ARTECH 2021: 10th International Conference on Digital and Interactive ArtsOctober 2021 Article No.: 133Pages 1–3https://doi.org/10.1145/3483529.3483787Online:20 February 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Spatial Charge Sensitivity in a Multimode Superconducting Qubit

Understanding and suppressing sources of decoherence is a leading challenge in building practical quantum computers. In superconducting qubits, low frequency charge noise is a well-known decoherence mechanism that is effectively suppressed in the transmon qubit. Devices with multiple charge-sensitive modes can exhibit more complex behaviours, which can be exploited to study charge fluctuations in superconducting qubits. Here we characterise charge-sensitivity in a superconducting qubit with two transmon-like modes, each of which is sensitive to multiple charge-parity configurations and charge-offset biases. Using Ramsey interferometry, we observe sensitivity to four charge-parity configurations and track two independent charge-offset drifts over hour timescales. We provide a predictive theory for charge sensitivity in such multi-mode qubits which agrees with our results. Finally, we demonstrate the utility of a multi-mode qubit as a charge detector by spatially tracking local-charge drift.
Sentiment Visualization of Covid-19 Vaccine Based On Naive Bayes Analysis

COVID-19 is one of the topics that is being discussed intensively. The virus which was declared a global pandemic on March 11 by WHO caused around 2.09 million Indonesians to be infected with the COVID-19 virus. To overcome this, the government carried out a vaccination program. The data taken for this study is public opinion about the COVID-19 vaccine written on Twitter. The number of opinions written on Twitter requires classification according to the sentiments they have, whether they tend to be negative opinions or positive opinions using lexicon-based The idea of this research is to classify the covid vaccination dataset using the naive Bayes classifier method and visualization using word cloud. Crawling to obtain the dataset from Twitter, text pre-processing and labelling to determine the positive and negative classes, TFIDF feature extraction, data splitting with a percentage of 80% for train data and 20% for data testing, and finally classification using nave Bayes are the stages in this research The system's sentiment analysis research yielded significant results, the accuracy value is 73.1%, the precision value is 73% and the recall value is 83%.
Reconhecimento online de gestos dinâmicos para ambientes interacionais multicâmeras

This work proposes an online dynamic gesture recognizer that can be used in an interactive multicamera environment. The proposal consists of reconstructing the three-dimensional skeleton of a user present in the scene, using a temporal segmentation model to segment the skeleton sequences that contain gestures and classifying them into one of the possible classes. Thus, the main contributions in this work are three-fold: (i) a model able to segmenting a temporal flow of 3D skeletons as being gestures or non-gestures; (ii) a model that allows classifying a sequence of 3D skeletons movements as belonging to one of the possible gesture classes; and (iii) a system that unites the segmentation and classification models, in order to allow the recognition of online gestures in a multicamera interactive environment. To evaluate the proposal, two gesture datasets were acquired in two distinct interactional spaces. After the experiments, the proposed spotting model obtained an average accuracy of 82% over the test dataset, and the classifier model obtained 72.40%. The two models together reached a Jaccard index of 0.76. Considering the processing time, each observation required an average time of 70 ms when executed in GPU. Thus, the proposed solution is considered effective in online dynamic gesture recognition for multicamera environments.
Kannada Dialect Identification from Case-Based Word Utterances Using Gradient Boosting Algorithm

Dialects or accents constitute the grammatical variations along with phonological and lexical changes those are commonly observed in the usage of a language with minor and subtle differences. Dialectal variations existing among dialects are mainly due to unique speaking patterns followed among the group of speakers. The dialect processing systems are essential in the development of automatic speech recognition systems (ASRs) for regional and resource-constrained languages in the country like India. Since India is with rich diversity in languages. In this paper, a language-dependent dialect identification system is proposed for Kannada language from words especially with the Kannada language-specific case (Vibhakthi Prathyayas) information. Special morphological operations that exist in the Kannada language in terms of various cases commonly called as a grammatical function of a noun or pronoun. These word utterances are used for the classification of five dialects of Kannada. This is a novel idea to use the smaller word utterances that consist of dialect-specific information representing the unique characteristics. In this paper, case-based word utterance dataset is prepared by considering five Kannada dialects from Kannada Dialect Speech Corpus (KDSC). Dynamic and static prosodic features are extracted to capture dialectal variations. Addition to these features, spectral MFCC features are also considered for evaluation of differences among dialects from these word-level units. Initially, multi-class Support vector machine (SVM) technique is used and later effective extreme gradient boosting (XGB) ensemble algorithms are used for the development of an automatic Kannada dialect recognition system. The research findings have demonstrated the words with case information convey dialect specific linguistic cues effectively. The combination of dynamic and static prosodic cues has a significant effect on the characterization of dialects along with spectral features.
Development of Programming Interface for Integrating Assembly Language with Python

The paper analyzes the programming interface for using the Assembler language inserts in the C language, considers the use of C functions in the Python language and describes the use of the programming interface for integrating the Assembler language with Python. The library for integration of Assembly language with Python has been developed. Examples of using functions implemented using PyXAsm are given.
Assessment of Satisfaction Levels of Learning Foreign Languages in a Digital Format for Students / Course Attendees

The relevance of this study is due to the need to adapt the process of teaching foreign languages at high school to the conditions of a forced transition to an exclusively distance form of education. The purpose of this study is to identify ways to optimize the organization of the educational process, its didactic and methodological support. The leading method in considering this problem is a survey (double sample questionnaire and regular conversations with students). The article analyzes the changed learning conditions and the consequences they caused, the results of the survey are presented and some proposals for reorganizing the educational process in the context of the ever-increasing digitalization of education are considered.
Using chemical experiment in teaching at High schools: Situation and solutions applied for General and Non-Metallic Chemistry parts

This study investigates the situation of applying the experiment-based teaching in General Chemistry and Non-Metallic Chemistry at High schools. The frequency, experimental types (hand-on; modeling; etc.), learning methods (project-based; inquiry-based learning; etc.), and the difficulties and obstacles when they use hand-on experiments were taken into account. The results of a survey with 150 chemistry teachers in 112 high schools in 19 Northern provinces show the facts that the hands-on experiments in teaching chemistry were rarely used and some main reasons were indicated. Based on analyzing the survey results as well as surveyors’ proposals and desires, the solutions based on using experiment kits, fun experiments, experiments using household ingredients were introduced to increase the level and effectiveness of using hand-on experiments.
Geodiversity Assessment to Regional Scale. A Case Study

Geodiversity is the variety of earth materials, forms and processes that constitute and shape the Earth planet. Therefore, geodiversity constitutes an essential part of developing nature-based solutions and must be adequately characterized. This research aims to evaluate the geodiversity of Mainland Ecuador by estimating a Geodiversity Index, which is the sum of five partial indexes (Geological, Geomorphological, Paleontological, Pedological, and Mineral occurrences), in a 25 × 25 km grid. In addition, present and analyze the cartography of the geodiversity index obtained. The methodology focuses on: i) the collection, preparation and integration of the base cartographic data, ii) application of an international methodology for the assessment and cartography of geodiversity (estimation of geodiversity index), and iii) analysis and interpretation of the results (mapping of the partial index and final index). The cartography developed generally illustrates the distribution of the geological diversity existing in Ecuador. Specifically, the ranges of the indices reached in the geodiversity map, including the medium, high, and very high categories, corresponding to 47.52% of the extension of Ecuador using the Pereira method and to 52.91% using the procedure proposed in this study. These special categories are located mainly along with the Andes Mountain range and in the newly formed mountain ranges in the coastal zone. In addition, these categories defined in the geodiversity map coincide, spatially, with the main initiatives of geoparks and geo-routes catalogued in the country. In general, the maps generated and the estimated indices represent relevant tools in territorial planning and, mainly, future geoconservation strategies.
Creating a Framework for Remote and Hybrid Learning Amid COVID 19

Before COVID-19, schools in India were based on the traditional methods of education (i.e., they followed the traditional set up of face-to-face lectures in a classroom). Although many schools had started blended learning, a lot of them were stuck with old procedures. The outbreak of a deadly disease called COVID-19 caused by a corona virus shook the entire world. The situation challenged the education system across India and forced schools to shift to an online mode of teaching. This chapter outlines the pedagogical approaches adopted for the integration of e-learning/virtual learning in schools across India. With e-learning, children can access information without being restricted to knowledge of distance, time, and space. The chapter also analyses the opportunities and challenges of virtual learning in a time of crisis.
Weighted Knowledge Based Knowledge Distillation

We proposed a novel controlling method of knowledge transfer to increase the efficiency of knowledge distillation. Specifically, combining KL divergence and predictive accuracy defines new extended indicator, which improves the efficiency of knowledge representation. Based on this representation, each knowledge is segmented by the extended indicator. We differentiate the weights for each sector so that the loss function can be weighted according to the sector. The results of our experiments successfully demonstrated that the proposed method much further improves the accuracy on CIFAR100, Cub-200-2011, and Tiny ImageNet data for ResNet, MobileNet, and WRN models, compared to the results of HKD and DML. We also showed that this approach is also promising to be plugged in not only the conventional knowledge distillation but also the collaborative knowledge distillation to improve the performance.
A Hybrid System for Intelligent Waste Measurement

The technological growing progress increases the demand for various goods, which generates significantly a greater amount of wastes. Properly management of the waste is a very important aspect. To increase the effectiveness and the accuracy of this process, it is necessary to implement a relevant system for their control and monitoring. The National Regulation of the Minister of Environment from 14th of December 2016 “on recycling levels, preparation for reuse and recovery by other methods of other municipal waste fractions” indicates that in following years they must meet the increasing recycling rates. Eventually until 2020 they should attain the level of 70%. To achieve that, it is necessary to improve Waste Management System. The only possible, relatively simple and low-cost solution, which would improve management of waste, is an efficient and precise solution for weighing and cataloguing the type of the collected waste from a given location. In this article, authors show a hybrid system for the intelligent waste measurement.
Perinatal sex hormones induce male-biased microglial mitochondrial dysfunction

Using medium altitude footage in various image based pedestrian detection and tracking tasks have several advantages as opposed to low altitude footage. For instance, much larger covered area, or weaker perspective distortions and occlusions. Furthermore, it does not pose privacy issues. However, it also has a disadvantage that is the irregular camera motion. Such motions can be observed with some ground level footage as well. Some state-of-the-art multi-object tracking (MOT) algorithms, which excel at pedestrian tracking, handle these implicitly, however this is insufficient in the case of medium altitude footage. The problem is further enhanced in online real-time tracking where frames may need to be skipped. Although there are explicit frame registration methods developed for wide area motion imagery (WAMI), they operate on assumptions that are not true in medium altitude footage such as the background is mostly stationary. We show that by augmenting the pedestrian tracking algorithms with a simple visual object tracking (VOT) based frame registration method their accuracy on medium altitude footage can be substantially improved without hindering the execution speed. Moreover, we show that the algorithms’ execution speed can be significantly increased by better utilization of the resources. To evaluate these, we introduce helicopter video sequences with MOT annotation.
Multiple GANs guided by self-attention mechanism for automatic cardiac image segmentation

In this paper, we propose a cross-modal unsupervised domain adaptive cardiac substructure automatic segmentation method to address the problem of a sharp decline in performance of a neural network trained in a specific imaging domain to migrate to another target domain that lacks annotations. The proposed multiple generative adversarial networks guided by self-attention framework is called GANSA for short. GANSA eliminates the differences in image appearance and features between different domains (MRI and CT) through GANs, and uses cyclic consistency loss to ensure that the features of the image itself remain unchanged. We use the self-attention mechanism to control GANs, which ensures the details of the generated image, and pays attention to the connection of long-distance information of the image, in order to complete the cross-modal cardiac segmentation task. The proposed framework is trained and tested on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset. Experimental results show that our method outperforms the state-of-the-art method.
Scalable Attribute Extraction at Instacart

Structured attribute information extracted from natural text inputs has been extensively exploited in e-commerce to help improve the customer experience. For example, attributes can be extracted from the product catalog data such as product name & product descriptions; similarly, attributes can also be extracted from user queries to the search engine. Having these attribute information available can help greatly boost relevance of many different functionalities such as search, recommendation, and ads. However, with the huge space of product categories and extensive details in the product information, how to extract attribute information from text with high accuracy and high efficiency becomes an extremely challenging problem.