• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.521-525
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• 2021

An essential area is the creation of a single digital market between the EU and Ukraine through information technology. Purpose: to investigate and analyze civil law relations in the field of adaptation of Ukrainian civil law to civil law regulations of the EU. The object of research: Ukrainian civil law and civil law of the EU. The subject of the study is civil law in the context of adaptation of civil law to the legislation of the EU. The following methods of scientific cognition were used during the research: semantic, historical, comparison, analysis and synthesis, generalization. The results of the study show that the harmonization of the legal system of Ukraine with EU law is caused by several goals: successful integration of Ukraine into the EU, legal reforms based on the positive example of EU countries, promoting access of Ukrainian enterprises to the EU market; attracting foreign investment, increasing the welfare of Ukrainian citizens. The adaptation includes three stages, the final of which is the preparation of an expanded program of harmonization of Ukrainian legislation with EU legislation. In the process of adaptation, it is important to take into account the legal history, tradition, features and mentality of Ukraine and before borrowing legal structures to analyze the feasibility of their application in the Ukrainian legal field.

• Asia Pacific Journal of Corpus Research
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• v.2 no.2
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• pp.17-29
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• 2021

The current study explores small corpora of critiques written by Chinese and non-Chinese university students and how strategies used by these writers compare with high-rated L1 students. Data collection includes three small corpora of student writing; 20 student critiques in 2017, 23 student critiques from 2018, and 23 critiques from the online Michigan MICUSP collection at the University of Michigan. The researchers employ Text Inspector and Lexical Complexity to identify university students' vocabulary knowledge and awareness of syntactic complexity. In addition, WMatrix4® is used to identify and support the comparison of lexical and semantic differences among the three corpora. The findings indicate that gaps between Chinese and non-Chinese writers in the same university classes exist in students' knowledge of grammatical features and interactional metadiscourse. In addition, critiques by Chinese writers are more likely to produce shorter clauses and sentences. In addition, the mean value of complex nominal and coordinate phrases is smaller for Chinese students than for non-Chinese and MICUSP writers. Finally, in terms of lexical bundles, Chinese student writers prefer clausal bundles instead of phrasal bundles, which, according to previous studies, are more often found in texts of skilled writers. The current study's findings suggest incorporating implicit and explicit instruction through the implementation of corpora in language classrooms to advance skills and strategies of all, but particularly of Chinese writers of English.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.163-167
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• 2022

The article explores linguo-stylistic influence markers in essayistic texts. The novelty of this investigation is provided by its perspective. Essayism is looked at as a style of thinking and writing and studied as a holistic philosophical and cultural phenomenon, as a revalent form of comprehension of reality that features non-lasting author's judgements and enhancement of the author's voice in the text. Based on the texts by V. Rosanov, G.K. Chesterton, and D. Dontsov, the remarkable English, Russian, and Ukrainian essay-writers of the first party of the 20th century, the article tracks the typical ontological-and-existentialist correlation at the content, stylistic, and semantic levels. It is observed in terms of the ideas presented in the texts of these publicists and the lexicostylistic markers of the influence on the reader that enable these ideas to implement. The explored poetic syntax, key lexemes, dialogueness, intonational melodics, specific language, free associations, aphoristic nature, verbalization of emotions and feeling in the psycholinguistic form of their expression, stress, heroic elevation, metaphors and evaluative linguistic units in the ontological-and-existentialist aspects contribute to extremely delicate and demanding nature of the essayistic style. They create a "lacework" of unpredictable properties, intellectual illumination, unexpected similarity, metaphorical freshness, sudden discoveries, unmotivated unities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.55-66
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• 2023

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.284-291
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• 2022

Historical data from comparable projects can serve as benchmarking data for an ongoing project's planning during the project scoping phase. As project owners typically store substantial amounts of data generated throughout project life cycles in digitized databases, they can capture appropriate data to support various project planning activities by accessing digital databases. One of the most important work tasks in this process is identifying one or more past projects comparable to a new project. The uniqueness and complexity of construction projects along with unorganized data, impede the reliable identification of comparable past projects. A project scope document provides the preliminary overview of a project in terms of the extent of the project and project requirements. However, narratives and free-formatted descriptions of project scopes are a significant and time-consuming barrier if a human needs to review them and determine similar projects. This study proposes an Artificial Intelligence-driven model for analyzing project scope descriptions and evaluating project similarity using natural language processing (NLP) techniques. The proposed algorithm can intelligently a) extract major work activities from unstructured descriptions held in a database and b) quantify similarities by considering the semantic features of texts representing work activities. The proposed model enhances historical comparable project identification by systematically analyzing project scopes.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.355-369
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• 2023

Since the widespread adoption of deep-learning and related distributed representation, there have been substantial advancements in part-of-speech (POS) tagging for many languages. When training word representations, morphology and shape are typically ignored, as these representations rely primarily on collecting syntactic and semantic aspects of words. However, for tasks like POS tagging, notably in morphologically rich and resource-limited language environments, the intra-word information is essential. In this study, we introduce a deep neural network (DNN) for POS tagging that learns character-level word representations and combines them with general word representations. Using the proposed approach and omitting hand-crafted features, we achieve 90.47%, 80.16%, and 79.32% accuracy on our own dataset for three morphologically rich languages: Uyghur, Uzbek, and Kyrgyz. The experimental results reveal that the presented character-based strategy greatly improves POS tagging performance for several morphologically rich languages (MRL) where character information is significant. Furthermore, when compared to the previously reported state-of-the-art POS tagging results for Turkish on the METU Turkish Treebank dataset, the proposed approach improved on the prior work slightly. As a result, the experimental results indicate that character-based representations outperform word-level representations for MRL performance. Our technique is also robust towards the-out-of-vocabulary issues and performs better on manually edited text.

• Journal of Fashion Business
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• v.27 no.5
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• pp.78-92
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• 2023

Fashion trends are actively disseminated through social media, which influences both their propagation and consumption. This study explored how users perceive subversive basic fashion in social media videos, by examining the associated concepts and characteristics. In addition, the factors contributing to the style's social media dissemination were identified and its distinctive features were analyzed. Through text mining analysis, 80 keywords were selected for semantic network and CONCOR analysis. TF-IDF and N-gram results indicate that subversive basic fashion involves transformative design techniques such as cutting or layering garments, emphasizing the body with thin fabrics, and creating bold visual effects. Topic modeling suggests that this fashion forms a subculture that resists mainstream norms, seeking individuality by creatively transforming the existing garments. CONCOR analysis categorized the style into six groups: forward-thinking unconventional fashion, bold and unique style, creative reworking, item utilization and combination, pursuit of easy and convenient fashion, and contemporary sensibility. Consumer actions, linked to social media, were shown to involve easily transforming and pursuing personalized styles. Furthermore, creating new styles through the existing clothing is seen as an economic and creative activity that fosters network formation and interaction. This study is significant as it addresses language expression limitations and subjectivity issues in fashion image analysis, revealing factors contributing to content reproduction through user-perceived design concepts and social media-conveyed fashion characteristics.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.30-33
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• 2023

The recent scholarly focus has been directed towards the expeditious and accurate detection of salient objects, a task that poses considerable challenges for resource-limited edge devices due to the high computational demands of existing models. To mitigate this issue, some contemporary research has favored inference speed at the expense of accuracy. In an effort to reconcile the intrinsic trade-off between accuracy and computational efficiency, we present novel model for salient object detection. Our model incorporate group-wise attentive module within the decoder of the encoder-decoder framework, with the aim of minimizing computational overhead while preserving detection accuracy. Additionally, the proposed architectural design employs attention mechanisms to generate boundary information and semantic features pertinent to the salient objects. Through various experimentation across five distinct datasets, we have empirically substantiated that our proposed models achieve performance metrics comparable to those of computationally intensive state-of-the-art models, yet with a marked reduction in computational complexity.