Search
Full database 2,043 resources
-
Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.
-
In a context of a new transnational division of labour, temporary international labour mobility is on the rise in Europe. In particular, recent decades have seen considerably more women seeking work experience abroad. Observers have been concerned with how such mobility is related to individualization, and in particular how it may challenge collective institutions, communities and families. The aim of this study is to explore such issues among women and men with international work experience. Using data from European Social Survey, the paper investigates previously mobile workers in terms of their current working and living conditions. Across genders, we consider different forms of individualization that may be associated with transnational labour mobility. While both women and men with transnational work experience generally feature strong strategic individualization, this is most pronounced among men. Hence, men's mobility is among other things associated with increased autonomy in working life, while – in contrast to women – it does not seem to hamper their integration in the sphere of social reproduction.
-
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.
-
The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing complex temporal dependencies. In addition, existing unsupervised domain adaptation methods for time series data are designed to align marginal distribution between source and target domains. However, existing UDA methods (e.g. R-DANN Purushotham et al. (2017), VRADA Purushotham et al. (2017), CoDATS Wilson et al. (2020)) neglect the conditional distribution discrepancy between two domains, leading to misclassification of the target domain. Therefore, to learn domain-invariant representations by capturing the temporal dependencies and to reduce the conditional distribution discrepancy between two domains, a novel Attentive Recurrent Adversarial Domain Adaptation with Top-k time series pseudo-labeling method called ARADA-TK is proposed in this paper. In the experiments, our proposed method was compared with the state-of-the-art UDA methods (R-DANN, VRADA and CoDATS). Experimental results on four benchmark datasets revealed that ARADA-TK achieves superior classification accuracy when it is compared to the competing methods.
-
Stock movement prediction is one of the most challenging problems in time series analysis due to the stochastic nature of financial markets. In recent years, a plethora of statistical methods and machine learning algorithms were proposed for stock movement prediction. Specifically, deep learning models are increasingly applied for the prediction of stock movement. The success of deep learning models relies on the assumption that massive training data are available. However, this assumption is impractical for stock movement prediction. In stock markets, a large number of stocks do not have enough historical data, especially for the companies which underwent initial public offering in recent years. In these situations, the accuracy of deep learning models to predict the stock movement could be affected. To address this problem, in this paper, we propose novel instance-based deep transfer learning models with attention mechanism. In the experiments, we compare our proposed methods with state-of-the-art prediction models. Experimental results on three public datasets reveal that our proposed methods significantly improve the performance of deep learning models when limited training data are available.
-
This review article is among the first to examine the new junket regulations in the Macau gaming industry. Particular emphasis is on the legal and regulatory framework governing the junket activity of gaming promoters and their associates. The recent changes to Macau gaming laws have resulted in stronger licensing requirements for local junket participants and precipitated the collapse of the VIP room system in casinos. Furthermore, this article highlights the policy and managerial implications of the current junket environment for the gaming industry in Macau and possibly other regional gaming jurisdictions. The effects of the new legal environment for Macau junkets could also provide insights into the implementation of similar legislation in other jurisdictions.
Explore
USJ Theses and Dissertations
- Doctorate Theses (58)
- Master Dissertations (1,048)
Academic Units
- Domingos Lam Centre for Research in Education (1)
- Faculty of Arts and Humanities (262)
- Faculty of Business and Law (194)
- Faculty of Health Sciences (40)
- Faculty of Religious Studies and Philosophy (92)
- Institute for Data Engineering and Sciences (29)
- Institute of Science and Environment (128)
- Library (3)
- Macau Ricci Institute (17)
- School of Education (186)
Resource type
- Blog Post (3)
- Book (67)
- Book Section (124)
- Conference Paper (133)
- Document (4)
- Encyclopedia Article (1)
- Film (1)
- Journal Article (419)
- Magazine Article (19)
- Manuscript (1)
- Newspaper Article (34)
- Preprint (4)
- Presentation (61)
- Radio Broadcast (5)
- Report (62)
- Thesis (1,102)
- TV Broadcast (1)
- Web Page (2)
United Nations SDGs
- 01 - No Poverty (1)
- 02 - Zero Hunger (1)
- 03 - Good Health and Well-being (33)
- 04 - Quality Education (17)
- 05 - Gender Equality (1)
- 07 - Affordable and Clean Energy (3)
- 08 - Decent Work and Economic Growth (6)
- 09 - Industry, Innovation and Infrastructure (23)
- 10 - Reduced Inequalities (1)
- 11 - Sustainable Cities and Communities (9)
- 12 - Responsable Consumption and Production (4)
- 13 - Climate Action (5)
- 14 - Life Below Water (19)
- 15 - Life on Land (4)
- 16 - Peace, Justice and Strong Institutions (2)
Cooperation
Student Research and Output
Publication year
- Between 1900 and 1999 (13)
- Between 2000 and 2024 (2,016)
- Unknown (14)