AIMC Topic: Semantics

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Decoding Interaction Patterns from the Chemical Sequence of Polymers Using Neural Networks.

ACS macro letters
The relation between chemical sequences and the properties of polymers is considered using artificial neural networks with a low-dimensional bottleneck layer of neurons. These encoder-decoder architectures may compress the input information into a me...

A practical approach towards causality mining in clinical text using active transfer learning.

Journal of biomedical informatics
OBJECTIVE: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defin...

Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation.

IEEE journal of biomedical and health informatics
Accurate segmentation of the polyps from colonoscopy images provides useful information for the diagnosis and treatment of colorectal cancer. Despite deep learning methods advance automatic polyp segmentation, their performance often degrades when ap...

Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training.

IEEE transactions on medical imaging
Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly ...

Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer.

Sensors (Basel, Switzerland)
With the development of blockchain technologies, many Ponzi schemes disguise themselves under the veil of smart contracts. The Ponzi scheme contracts cause serious financial losses, which has a bad effect on the blockchain. Existing Ponzi scheme cont...

A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding.

Computational intelligence and neuroscience
The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attri...

Unsupervised cross-lingual model transfer for named entity recognition with contextualized word representations.

PloS one
Named entity recognition (NER) is one fundamental task in the natural language processing (NLP) community. Supervised neural network models based on contextualized word representations can achieve highly-competitive performance, which requires a larg...

Graph Attention Feature Fusion Network for ALS Point Cloud Classification.

Sensors (Basel, Switzerland)
Classification is a fundamental task for airborne laser scanning (ALS) point cloud processing and applications. This task is challenging due to outdoor scenes with high complexity and point clouds with irregular distribution. Many existing methods ba...

ConAnomaly: Content-Based Anomaly Detection for System Logs.

Sensors (Basel, Switzerland)
Enterprise systems typically produce a large number of logs to record runtime states and important events. Log anomaly detection is efficient for business management and system maintenance. Most existing log-based anomaly detection methods use log pa...

Depth Estimation from Light Field Geometry Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the bal...