@inproceedings {43077,
	title = {Multimedia datasets: challenges and future possibilities},
	journal = {International conference on multimedia modeling},
	year = {2023},
	doi = {https://doi.org/10.1007/978-3-031-27818-1_58},
	author = {Nguyen, Thu and Stor{\r a}s, Andrea and Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {nguyen2023principal,
	title = {Principal Components Analysis Based Imputation for Logistic Regression},
	journal = {International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
	year = {2023},
	pages = {28{\textendash}36},
	publisher = {{Springer},
	author = {Thuong HT {Nguyen}},
	editor = {Le, Bao and Nguyen, Phuc and Linh GH {Tran} and Nguyen, Thu and Binh T {Nguyen}}
}
@inproceedings {43291,
	title = {ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset},
	journal = {International Conference on Multimedia Modeling (MMM)},
	volume = {13833},
	year = {2023},
	pages = {502 - 514},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people{\textquoteright}s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called\&nbsp;ScopeSense\&nbsp;from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.},
	isbn = {978-3-031-27076-5},
	issn = {0302-9743},
	doi = {10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2.pdfhttps://link.springer.com/10.1007/978-3-031-27077-2_39https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2_39},
	author = {Riegler, Michael and Thambawita, Vajira and Chatterjee, Ayan and Nguyen, Thu and Hicks, Steven and Telle-Hansen, Vibeke and Svein Arne {Pettersen} and Johansen, Dag and Jain, Ramesh and Halvorsen, P{\r a}l},
	editor = {Dang-Nguyen, Duc-Tien and Gurrin, Cathal and Larson, Martha and Alan F. {Smeaton} and Rudinac, Stevan and Dao, Minh-Son and Trattner, Christoph and Chen, Phoebe}
}
@article {28274,
	title = {ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks},
	journal = {Information Sciences},
	year = {2022},
	publisher = {arXiv},
	author = {Duy H. M. {Nguyen} and Duy M. {Nguyen} and Mai T. N. {Truong} and Nguyen, Thu and Khanh T. {Tran} and Nguyen A. {Triet} and Pham T. {Bao} and Binh T. {Nguyen}}
}
@article {NGUYEN2022108082,
	title = {DPER: Direct Parameter Estimation for Randomly missing data},
	journal = {Knowledge-Based Systems},
	volume = {240},
	year = {2022},
	pages = {108082},
	publisher = { Elsevier},
	abstract = {{Parameter estimation is an important problem with applications in discriminant analysis, hypothesis testing, etc. Yet, when there are missing values in the data sets, commonly used imputation-based techniques are usually needed before further parameter estimation since works in direct parameter estimation exists in only limited settings. Unfortunately, such two-step procedures (imputation-parameter estimation) can be computationally expensive. Therefore, it motivates us to propose novel algorithms that directly find the maximum likelihood estimates (MLEs) for an arbitrary one-class/multiple-class randomly missing data set under some mild assumptions. Furthermore, due to the direct computation, our algorithms do not require multiple iterations through the data, thus promising to be less time-consuming while maintaining superior estimation performance than state-of-the-art methods under comparisons. We validate these claims by empirical results on various data sets of different sizes.},
	keywords = {MLEs, parameter estimation, Randomly missing data},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2021.108082},
	url = {https://www.sciencedirect.com/science/article/pii/S0950705121011540},
	author = {Nguyen, Thu and Khoi Minh {Nguyen-Duy} and Duy Ho Minh {Nguyen} and Binh T. {Nguyen} and Bruce Alan {Wade}}
}
@misc {tu2022finnet,
	title = {FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural Network},
	year = {2022},
	publisher = {arXiv},
	author = {Son N. T. {Tu} and Nguyen, Thu}
}
@inproceedings {nguyen2022parallel,
	title = {Parallel feature selection based on the trace ratio criterion},
	journal = {International Joint Conference on Neural Networks (IJCNN)},
	year = {2022},
	publisher = {IEEE},
	author = {Nguyen, Thu and Thanh Nhan {Phan} and Van Nhuong {Nguyen} and Thanh Binh {Nguyen} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {nguyen2021tatl,
	title = {TATL: Task Agnostic Transfer Learning for Skin Attributes Detection},
	journal = {Medical Image Analysis},
	year = {2022},
	publisher = {Medical Image Analysis},
	author = {Duy MH {Nguyen} and Nguyen, Thu and Vu, Huong and Pham, Quang and Nguyen, Manh-Duy and Binh T {Nguyen} and Sonntag, Daniel}
}
@inproceedings {28276,
	title = {Unequal Covariance Awareness for Fisher Discriminant Analysis and Its Variants in Classification},
	journal = {2022 International Joint Conference on Neural Networks (IJCNN)},
	year = {2022},
	pages = {1-8},
	publisher = {IEEE},
	author = {Nguyen, Thu and Le, Quang and Tu, Son and Binh Thanh {Nguyen}}
}
@article {28183,
	title = {EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data},
	journal = {Information Sciences},
	year = {2021},
	publisher = {Elsevier},
	abstract = {The problem of monotone missing data has been broadly studied during the last two decades and has many applications in various fields such as\&nbsp;bioinformatics\&nbsp;or statistics. Commonly used imputation techniques require multiple iterations through the data before yielding convergence. Moreover, those approaches may introduce extra noises and biases to the subsequent modeling. In this work, we derive exact formulas and propose a novel algorithm to compute the\&nbsp;maximum likelihood estimators\&nbsp;(MLEs) of a multiple class, monotone missing dataset when all the\&nbsp;covariance matrices\&nbsp;of all categories are assumed to be equal, namely Efficient Parameter Estimation for Multiple Class Monotone Missing Data (EPEM). We then illustrate an application of our proposed methods in\&nbsp;Linear Discriminant Analysis\&nbsp;(LDA). As the computation is exact, our EPEM algorithm does not require multiple iterations through the data as other imputation approaches, thus promising to handle much less time-consuming than other methods. This effectiveness was validated by empirical results when EPEM reduced the error rates significantly and required a short computation time compared to several imputation-based approaches. We also release all codes and data of our experiments in a GitHub repository to contribute to the research community related to this problem.\&nbsp;},
	author = {Nguyen, Thu},
	editor = {Nguyen, Duy}
}
@inproceedings {28273,
	title = {Deep Matrix Tri-Factorization:Mining Vertex-wise Interactions in Multi-Space Attributed Graphs},
	journal = {SIAM International Conference on Data Mining},
	year = {2020},
	author = {He, Yi},
	editor = {Chen, Sheng and Nguyen, Thu and Wade, Bruce and Wu, Xindong}
}
