57 Result(s)
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Chapter and Conference Paper
A Multimodal Text Block Segmentation Framework for Photo Translation
Nowadays, with the vigorous development of OCR (Optical Character Recognition) and machine translation, photo translation technology brings great convenience to people’s life and study. However, when translati...
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Chapter and Conference Paper
BiblioEngine: An AI-Empowered Platform for Disease Genetic Knowledge Mining
Recent decades have seen significant advancements in contemporary genetic research with the aid of artificial intelligence (AI) techniques. However, researchers lack a comprehensive platform for fully exploiti...
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Chapter and Conference Paper
Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large a...
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Chapter and Conference Paper
End-to-End Multilingual Text Recognition Based on Byte Modeling
Nowadays, multilingual text recognition is more and more widely used in computer vision. However, in practical applications, the independent modeling of each language cannot make full use of the information be...
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Chapter and Conference Paper
Vision-Language Adaptive Mutual Decoder for OOV-STR
Recent works have shown huge success of deep learning models for common in vocabulary (IV) scene text recognition. However, in real-world scenarios, out-of-vocabulary (OOV) words are of great importance and SO...
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Chapter and Conference Paper
EmoKnow: Emotion- and Knowledge-Oriented Model for COVID-19 Fake News Detection
Content-based methods are inadequate for detecting fake news related to COVID-19 due to the complexity of this domain. Some studies integrate the social context information of the news to improve performance. ...
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Chapter and Conference Paper
ParaNet:Parallel Networks with Pre-trained Models for Text Classification
The application of linguistic knowledge derived from pre-trained language models has demonstrated considerable potential in text classification tasks. Despite this, effectively learning the distance between sa...
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Chapter and Conference Paper
A Contextual Information-Augmented Probabilistic Case-Based Reasoning Model for Knowledge Graph Reasoning
Knowledge Graph Reasoning (KGR) is one effective method to improve incompleteness and sparsity problems, which infers new knowledge based on existing knowledge. Although the probabilistic case-based reasoning ...
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Chapter and Conference Paper
Discriminative Graph-Level Anomaly Detection via Dual-Students-Teacher Model
Different from the current node-level anomaly detection task, the goal of graph-level anomaly detection is to find abnormal graphs that significantly differ from others in a graph set. Due to the scarcity of r...
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Chapter and Conference Paper
Improved AODV Routing Protocol Based on Multi-objective Simulated Annealing Algorithm
Ad Hoc network is a kind of common wireless mobile communication network. Unlike cellular mobile networks and wireless local area networks, Ad Hoc networks do not require preset base stations and are suitable ...
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Chapter and Conference Paper
PD-SRS: Personalized Diversity for a Fair Session-Based Recommendation System
Session-based Recommender Systems (SRSs), which aim to recommend users’ next action based on their current and historical sessions, play a significant role in many real-world online services. The existing sess...
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Chapter and Conference Paper
KIR: A Knowledge-Enhanced Interpretable Recommendation Method
Recommendation System (RS) is of great significance for screening adequate information and improving the efficiency of information acquisition. The existing recommendation methods can improve the accuracy of t...
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Chapter and Conference Paper
Data Analytics Research in Nonprofit Organisations: A Bibliometric Analysis
Profitable organisations that applied data analytics have obtained a double-digit improvement in reducing costs, predicting demands, and enhancing decision-making. However, in nonprofit organisations (NPOs), a...
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Chapter and Conference Paper
Spine-Rib Segmentation and Labeling via Hierarchical Matching and Rib-Guided Registration
Accurate segmentation and labeling of spine-rib are of great importance for clinical spine and rib diagnosis and treatment. In clinical applications, the spine-rib segmentation and labeling are often challengi...
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Chapter and Conference Paper
CorLab-Net: Anatomical Dependency-Aware Point-Cloud Learning for Automatic Labeling of Coronary Arteries
Automatic coronary artery labeling is essential yet challenging step in coronary artery disease diagnosis for clinician. Previous methods typically overlooked rich relationships with heart chamber and also mor...
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Chapter and Conference Paper
VertNet: Accurate Vertebra Localization and Identification Network from CT Images
Accurate localization and identification of vertebrae from CT images is a fundamental step in clinical spine diagnosis and treatment. Previous methods have made various attempts in this task; however, they fai...
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Chapter and Conference Paper
Fast Rational Lanczos Method for the Toeplitz Symmetric Positive Semidefinite Matrix Functions
In this paper, we use the rational Lanczos method to approximate Toeplitz matrix functions, in which the matrices are symmetric positive semidefinite (SPSD). In order to reduce the computational cost, we use t...
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Chapter and Conference Paper
Multi-scale Segmentation Network for Rib Fracture Classification from CT Images
As the most common thoracic trauma, rib fracture classification is essential for clinical evaluation and treatment planning. However, it is challenging for manual identification and classification, due to the ...
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Chapter and Conference Paper
Hierarchical Phenoty** and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms
The cells and their spatial patterns in the tumor microenvironment (TME) play a key role in tumor evolution, and yet the latter remains an understudied topic in computational pathology. This study, to the best...
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Chapter and Conference Paper
EN-DIVINE: An Enhanced Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
Knowledge Graphs (KGs) are often incomplete and sparse. Knowledge graph reasoning aims at completing the KG by predicting missing paths between entities. The reinforcement learning (RL) based method is one of...
