54 Result(s)
within
Chapter
Yue** Zhang
Information Systems Applications (incl. Internet)
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Chapter and Conference Paper
Boosting COVID-19 Severity Detection with Infection-Aware Contrastive Mixup Classification
This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach,...
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Chapter and Conference Paper
CMC_v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022). In our approach, we employ the win...
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Chapter and Conference Paper
Intelligent Modeling Framework for System of Systems Architecture Based on Knowledge Graph
Enterprise architecture framework such as DoDAF has become an effective method in recent years to describe a system of systems structure and guide its revolution, especially in the military field. However, the...
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Chapter and Conference Paper
Research on Construction Method of SoS Architecture Knowledge Graph
System of systems (SoS) architecture data is the foundation of SoS architecture design, modeling, and evaluation. Traditional methods of architecture data collection generally need the involvement of modelers....
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Chapter and Conference Paper
A Novel Preprocessing Approach with Soft Voting for Hand Gesture Recognition with A-Mode Ultrasound Sensing
To explore the potential of gesture recognition based on the A-mode ultrasound (AUS) interface in human-computer interaction (HCI), according to the characteristics of AUS signal, a novel preprocessing approac...
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Chapter and Conference Paper
HaptMR: Smart Haptic Feedback for Mixed Reality Based on Computer Vision Semantic
This paper focuses on tactile feedback based on semantic analysis using deep learning algorithms on the mobile Mixed Reality (MR) device, called HaptMR. This way, we improve MR’s immersive experience and reach...
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Chapter and Conference Paper
A Local Collaborative Distributed Reinforcement Learning Approach for Resource Allocation in V2X Networks
In this paper, we propose a resource allocation approach for V2X networks based on distributed reinforcement learning with local collaboration. We construct a local collaborative mechanism for sharing informat...
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Chapter and Conference Paper
Automated Honey Document Generation Using Genetic Algorithm
Sensitive data exfiltration attack is one of predominant threats to cybersecurity. The honey document is a type of cyber deception technology to address this issue. Most existing works focus on the honey docum...
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Chapter and Conference Paper
Crowdturfing Detection in Online Review System: A Graph-Based Modeling
With the widespread popularity of online reviews and crowdsourcing, people may publish fake comments on online review system and get paid for crowdsourcing tasks. In order to identify these reviewers, machine ...
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Chapter and Conference Paper
An Adaptive Slice Type Decision Algorithm for HEVC Based on Local Luminance Histogram
Video frame type decision is one of the key factors affecting coding efficiency. Based on the framework of High Efficiency Video Coding (HEVC), an adaptive frame type decision algorithm based on local luminanc...
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Chapter and Conference Paper
Approximated Masked Global Context Network for Skin Lesion Segmentation
The number of skin cancer cases worldwide is increasing by millions every year. A large number of patients bring great pressure to the diagnosis and treatment of skin cancer, it is urgent to apply automatic se...
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Chapter and Conference Paper
A Graph-Based Keyphrase Extraction Model with Three-Way Decision
Keyphrase extraction has been a popular research topic in the field of natural language processing in recent years. But how to extract keyphrases precisely and effectively is still a challenge. The mainstream ...
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Chapter and Conference Paper
Multi-modal Perceptual Adversarial Learning for Longitudinal Prediction of Infant MR Images
Longitudinal magnetic resonance imaging (MRI) is essential in neuroimaging studies of early brain development. However, incomplete data is an inevitable problem in longitudinal studies because of participant a...
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Chapter and Conference Paper
A Literature Review of the Research on Take-Over Situation in Autonomous Driving
In order to understand driver’s response time from automatic driving to manual driving and driving behavior after taking over in the complex traffic environment, and influencing factors of driver’s driving swi...
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Chapter and Conference Paper
Domain Adaptive Neural Sentence Compression by Tree Cutting
Sentence compression has traditionally been tackled as syntactic tree pruning, where rules and statistical features are defined for pruning less relevant words. Recent years have witnessed the rise of neural m...
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Chapter and Conference Paper
Domain Representation for Knowledge Graph Embedding
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore...
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Chapter and Conference Paper
Investigating Lexical and Semantic Cognition by Using Neural Network to Encode and Decode Brain Imaging
The question of how the human brain represents conceptual knowledge has received significant attention in many scientific fields. Over the last decade, there has been increasing interest in the use of deep lea...
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Chapter and Conference Paper
Optimizing Word Embedding for Fine-Grained Sentiment Analysis
Word embeddings have been extensively used for various Natural Language Processing tasks. However, word vectors trained based on corpus context information fail to distinguish words with the same context but d...
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Chapter and Conference Paper
Overview of the NLPCC 2019 Shared Task: Cross-Domain Dependency Parsing
This paper presents an overview of the NLPCC 2019 shared task on cross-domain dependency parsing, including (1) the data annotation process, (2) task settings, (3) methods, results, and analysis of submitted ...
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Chapter and Conference Paper
Learning Domain Invariant Word Representations for Parsing Domain Adaptation
We show that strong domain adaptation results for dependency parsing can be achieved using a conceptually simple method that learns domain-invariant word representations. Lacking labeled resources, dependency ...
