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Article
Safety Assessment and Risk Management of Urban Arterial Traffic Flow Based on Artificial Driving and Intelligent Network Connection: An Overview
As the problems with managing traffic in cities get worse, this paper looks at a way to make it easier to judge safety and handle risks in the flow of traffic on major roads in cities. By combining artificial ...
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Chapter and Conference Paper
Research on Automatic Recognition Method of Action State of High-Voltage Switch Based on Multi-source Information Fusion
This paper studies a multi-level automatic recognition method for the action state of high-voltage switch based on multi-source information fusion technology in substation. Introduce the current different meth...
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Chapter and Conference Paper
Research on Working State Evaluation Method of Transformer Based on KPI Assessment Index System
The multi-level evaluation method for fuzzy comprehensive evaluation of transformer operation state based on the key performance indicator (KPI) is studied. Firstly, the KPI method is used to build the key eva...
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Chapter and Conference Paper
Heterophily-Based Graph Neural Network for Imbalanced Classification
Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node classification. However, in real-world scenarios, data often exhibits an imbalanced, sometimes highly-skewed...
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Chapter and Conference Paper
A Structural-Clustering Based Active Learning for Graph Neural Networks
In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these applications is the underutilization of important structural informatio...
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Chapter and Conference Paper
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training suffers from daunting robust o...
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Chapter and Conference Paper
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks
A number of approaches for anomaly detection on attributed networks have been proposed. However, most of them suffer from two major limitations: (1) they rely on unsupervised approaches which are intrinsically...
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Article
Cluster-preserving sampling algorithm for large-scale graphs
Graph sampling is a very effective method to deal with scalability issues when analyzing large-scale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using ex...
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Article
Open AccessResGCN: attention-based deep residual modeling for anomaly detection on attributed networks
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three m...
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Chapter and Conference Paper
Co-Attention Based Multi-contextual Fake News Detection
recent years, the propagation of fake news on social media has emerged as a major challenge. Several approaches have been proposed to detect fake news on social media using the content of the microblo...
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Chapter and Conference Paper
Semantic-Based Few-Shot Classification by Psychometric Learning
Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a single and unique class asso...
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Article
Evaluation and forecast method of coordination degree between urban traffic planning and land use
Under the background of the integration of multiple regulations, there is an urgent need for the coordinated optimization of transportation planning and land use integration in the aspects of urban overall pla...
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Article
Open AccessSparse evolutionary deep learning with over one million artificial neurons on commodity hardware
Artificial neural networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-incr...
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Chapter and Conference Paper
On Generalization of Graph Autoencoders with Adversarial Training
Adversarial training is an approach for increasing model’s resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature representations that generalize ...
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Article
Open AccessExceptional spatio-temporal behavior mining through Bayesian non-parametric modeling
Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an importan...
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Article
Open Accessstruc2gauss: Structural role preserving network embedding via Gaussian embedding
Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art ...
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Article
Open AccessEEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of a...
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Chapter and Conference Paper
Research on the Visualization Technology of the Secondary Circuit of Intelligent Substation Based on CIM/G
With the wide application of IEC 61850, the actual electrical connection of the cable in the intelligent substation has been replaced by the other media such as the optical fiber, which directly causes the inv...
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Chapter and Conference Paper
Learning Structural Genetic Information via Graph Neural Embedding
Learning continuous vector representations of genes has been proved to be conducive for many bioinformatics tasks as it can incorporate information of various sources including gene interactions and gene-disea...
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Chapter and Conference Paper
\( LogRank^+ \) : A Novel Approach to Support Business Process Event Log Sampling
Massive amounts of business process event logs are collected and stored by modern information systems. Numerous process discovery approaches have been proposed to extract descriptive process models from such e...
