scholarly journals Dialect text as a source of information about the life of eastern sloboda inhabitants

Linguistics ◽  
2021 ◽  
pp. 5-14
Author(s):  
Maryna Voloshynova ◽  
Keyword(s):  
Structural Features ◽  
Semantic Features ◽  
Folk Beliefs ◽  
Food And Beverages ◽  
Spiritual Culture ◽  
Food And Drink ◽  
High Level ◽  
Source Of Information

The article is devoted to the analysis of structural features of dialect texts about the life and way of living the eastern Sloboda inhabitants. It has been emphasized that in recent years, scholars have increasingly chosen large volumes of coherent texts as an object of their study, concerning the high level of informativeness, spontaneity and ease. In systematic descriptions, scholars pay attention to the study of linguistic, cognitive and pragmatic parameters of dialect texts, their structural and semantic features. Dialect texts recorded in Ukrainian East Sloboda dialects are grouped into the following thematic groups: texts about food and drink; texts about dishes and kitchen utensils; texts about folk beliefs; texts about life. The analysis focuses on dialect texts-descriptions of food and beverages, which are of great value to dialectologists, ethnologists, historians, as informants during the story describe in detail the ways of cooking various dishes, their recipes, eating traditions, modern and archaic management. It was found that the texts on traditional spiritual culture, which reflect superstitions and folk beliefs in the afterlife, saturate numerous repetitions (so-called identical repetitions). Such speech techniques are usually used by dialect speakers to emphasize the importance of reported events. Repetitions themselves become a means of coherence in the text and ensure its integrity. The conclusions to the article emphasize the importance of further research of East Sloboda dialect texts on their features at the phonetic, lexical, grammatical levels.

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

COMPARATIVE ANALYSIS OF INTERNATIONAL STANDARDS IN THE FIELD OF NON-FINANCIAL REPORTING AND THE PRACTICE OF THEIR APPLICATION

2021 ◽  
Vol 2 (4) ◽  
pp. 146-152
Author(s):  
E. V. ANDRIANOVA ◽  
◽  
P. S. SHCHERBACHENKO ◽  
Keyword(s):  
Financial Reporting ◽  
Financial Statements ◽  
Business Environment ◽  
International Standards ◽  
Positive Trend ◽  
Financial Indicators ◽  
Additional Tool ◽  
Foreign Companies ◽  
High Level ◽  
Source Of Information

This article discusses and analyzes the most popular standards of non-financial reporting, which has a significant impact on the transformation of the business environment. Already, domestic and foreign companies with a high level of responsibility are beginning to publish non-financial statements in addition to financial statements, which is an additional tool for communication with stakeholders and a new source of information about their activities. To date, reports of this type are clearly unregulated, there are no verification standards, however, there is already a positive trend and the active introduction of non-financial indicators in the regular reporting of companies.

scholarly journals A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250782
Author(s):  
Bin Wang ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Semantic Features ◽  
General Object ◽  
High Level ◽  
The Impact

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

scholarly journals Measuring and interpreting cognitive representations of foods and drinks: A procedure for collecting and coding feature listing data

2020 ◽  
Author(s):  
Esther K. Papies ◽  
Betül Tatar ◽  
Mike Keesman ◽  
Maisy Best ◽  
Katharina Lindner ◽  
...  
Keyword(s):  
Semantic Features ◽  
Cognitive Representations ◽  
Specific Category ◽  
Coding Manual ◽  
Research Findings ◽  
Consumption Situation ◽  
Alcoholic Drinks ◽  
Food And Drink ◽  
Independent Features ◽  
Feature Listing

Feature listing is a novel method to study people’s rich, multifaceted cognitive representations of food and drink objects. In other words, it helps researchers understand the content activated in memory when people think about foods or drinks. Feature listing is an easy-to-administer method that has traditionally been used to study the semantic features of conceptual representation in cognitive science. We have recently adapted it for examining the representations of food and alcoholic and non-alcoholic drinks. Here, we describe a procedure for collecting feature listing data for food or drink objects, and for systematically coding the features produced by participants to enable quantitative analyses. First, we provide an overview of the feature listing method along with detailed instructions to participants. Then, we describe a systematic procedure for coding the wide variety of features that participants list in such tasks, using a total of 44 hierarchically-organised feature categories. We first present a general overview of the coding procedure followed by a systematic overview of the categories used, including categories of consumption situation features, non-consumption features, and situation-independent features. We then provide an extensive coding manual describing the categories and subcategories in detail, offering detailed criteria for coding a feature as belonging to a specific category along with examples and disambiguation procedures. This manual should allow researchers to systematically collect and code responses in feature listing tasks for foods and drinks, and increase the reproducibility of research findings involving feature listing.

Knowledge Disclosed in Patent Documents as Source of Information to Address Emergencies: A Strategy to Achieve Technological Developments Addressing COVID-19

2021 ◽  
Vol 15 ◽  
Author(s):  
Tatiana Duque Martins ◽  
Diéricon Sousa Cordeiro
Keyword(s):  
Emergency Situation ◽  
Daily Basis ◽  
Accurate Information ◽  
Technological Information ◽  
The World ◽  
Technological Developments ◽  
A Cell ◽  
Patent Documents ◽  
High Level ◽  
Source Of Information

Background: Face COVID-19 pandemic, a need for accurate information on SARS-CoV-2 virus is urgent and scientific reports have been published on daily basis to enable effective technologies to fight the disease progression. However, at the first moments of Pandemic, no information on the matter was known and technologies to fight the Pandemic were not readily available. However, searches in patent databases, if strategically designed, can offer quick responses to new pandemics. Objective: Aiming to provide existing information in patent documents useful to develop technologies addressing COVID-19, considering the emergency situation the world was facing and the knowledge of COVID-19 available until April, 2020, this work presents an analysis of the main characteristics of the technological information in patent documents worldwide, related to coronaviruses and the severe acute respiratory syndrome (SARS). Method: Regions of concentration of such technologies, the number of available documents and their technological fields are disclosed in three approaches: 1) a wide search, retrieving technologies on SARS or coronaviruses; 2) a targeted search, retrieving documents additionally referring to Angiotensin converting enzyme (ACE2), which is used by SARS-CoV-2 to enter a cell and 3) a punctual search, which retrieved patents disclosing aspects related to SARS-CoV-2 available at that time. Results and Conclusion: Results evidence the high-level technology involved in these developments and a monopoly tendency of such technologies, evidencing that it is possible to find answers to new problems in patent documents.

Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

2022 ◽  
Vol 40 (1) ◽  
pp. 1-29
Author(s):  
Siqing Li ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Bolin Ding ◽  
Ji-Rong Wen
Keyword(s):  
Peer Review ◽  
Prediction Models ◽  
Citation Count ◽  
Specific Aspect ◽  
Semantic Features ◽  
Predictive Capacity ◽  
Topic Distribution ◽  
Real World Datasets ◽  
Data Signal ◽  
High Level

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models. Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.

scholarly journals Object Detection Based on Region Decomposition and Assembly

2019 ◽  
Vol 33 ◽  
pp. 8094-8101 ◽  
Author(s):  
Seung-Hwan Bae
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Performance Improvement ◽  
Semantic Relations ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Multi Scale ◽  
Object Proposals ◽  
Object Region ◽  
High Level

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results. However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals. In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection.In the proposed R-DAD, we first decompose an object region into multiple small regions. To capture an entire appearance and part details of the object jointly, we extract CNN features within the whole object region and decomposed regions. We then learn the semantic relations between the object and its parts by combining the multi-region features stage by stage with region assembly blocks, and use the combined and high-level semantic features for the object classification and localization. In addition, for more accurate region proposals, we propose a multi-scale proposal layer that can generate object proposals of various scales. We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors.

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