Graph Intention Embedding Neural Network for tag-aware recommendation.

Tag-aware recommender systems leverage the vast amount of available tag records to depict user profiles and item attributes precisely. Recently, many researchers have made efforts to improve the performance of tag-aware recommender systems by using deep neural networks. However, these approaches still have two key limitations that influence their ability to achieve more satisfactory results.

HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network.

Generative adversarial network (GAN) has achieved remarkable success in generating high-quality synthetic data by learning the underlying distributions of target data. Recent efforts have been devoted to utilizing optimal transport (OT) to tackle the gradient vanishing and instability issues in GAN. They use the Wasserstein distance as a metric to measure the discrepancy between the generator distribution and the real data distribution.

A Multi-perspective Model for Protein-Ligand-Binding Affinity Prediction.

Gathering information from multi-perspective graphs is an essential issue for many applications especially for protein-ligand-binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance.

Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic.

People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via different online social media networks such as Twitter. Due to strict restrictions to reduce the spread of the novel virus, many people are forced to stay at home, which significantly impacts people's mental health.

Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks.

Federated learning (FL) has recently emerged as a striking framework for allowing machine and deep learning models with thousands of participants to have distributed training to preserve the privacy of users' data. Federated learning comes with the pros of allowing all participants the possibility of creating robust models even in the absence of sufficient training data. Recently, smartphone usage has increased significantly due to its portability and ability to perform many daily life tasks.

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media.

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying.

Causal Optimal Transport for Treatment Effect Estimation.

Treatment effect estimation helps answer questions, such as whether a specific treatment affects the outcome of interest. One fundamental issue in this research is to alleviate the treatment assignment bias among those treated units and controlled units. Classical causal inference methods resort to the propensity score estimation, which unfortunately tends to be misspecified when only limited overlapping exists between the treated and the controlled units.

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19.

The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables.

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention.

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records.

Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia.

The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioral, and physical health problems with significant consequences, both personal and social costs included.

Deep learning for misinformation detection on online social networks: a survey and new perspectives.

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation.

Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals.

Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix.

Aim: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals.

CAME: Content- and Context-Aware Music Embedding for Recommendation.

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions.

Big data analytics for preventive medicine.

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost.

Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection.

Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function.

Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.

Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins.

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data.

The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.

Efficient Brain Tumor Segmentation With Multiscale Two-Pathway-Group Conventional Neural Networks.

Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features.

On Deep Learning for Trust-Aware Recommendations in Social Networks.

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations.

[Detection of hypermethylation of p16 gene in plasma DNA from lung cancer patients].

Objective: To detect hyper methylation of p16 gene in plasma DNA from patients with lung cancer, and to assess its potential as a malignant marker.

Methods: Using a modified semi-nested methylation-specific PCR (MSP), the status of methylation of the p16 was investigated in plasma DNA from 137 lung cancer patients and 112 matched tumor tissues.

Results: Hypermethylation of the p16 was present in 75.

[Hypermethylation of p16 gene in clinical specimens of patients with lung cancer].

Objective: To evaluate aberrant methylation of the p16 promoter as a useful biomarker of lung cancer.

Methods: A modified methylation-specific semi-nested PCR was performed to detect p16 hypermethylation in the matched samples of tumor tissue, blood plasma and sputum derived from 51 cases of lung cancer patients.

Results: Hypermethylation of p16 promoter was demonstrated in 84.