The easy sharing of multimedia content on social media has caused a rapid dissemination of fake news, which threatens society's stability and security.
We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask.
Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency.
The results indicate that the proposed framework has a better capability in mining crucial features for fake news detection.
Finding an optimal set of critical nodes in a complex network has been a long-standing problem in the fields of both artificial intelligence and operations research.
Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking.
Extensive comparison results are given to show its effectiveness in solving CNP.
In this paper, we propose a Cost-Quality Adaptive Active Learning (CQAAL) approach for CNER in Chinese EHRs, which maintains a balance between the annotation quality, labeling costs, and the informativeness of selected instances.
Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems.
Afterwards, both semantic and structure embeddings are combined to measure the relevancy between the terminology and the entity.
Meanwhile, to minimize the computational cost of learning, we propose a joint model including a word segmenter and a loss prediction model.
Entity and relation extraction is the necessary step in structuring medical text.
Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services.
Clinical text structuring is a critical and fundamental task for clinical research.
Based on the proposed model, we also construct a PatientEG dataset with 191, 294 events, 3, 429 distinct entities, and 545, 993 temporal relations using EMRs from Shanghai Shuguang hospital.
In this paper, we propose a Residual Dilated Convolutional Neural Network with Conditional Random Field (RD-CNN-CRF) to solve it.
Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths.
In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities.
Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research.
Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints.