Guanjun Liu
Biographic Data
| ID | 9870055 |
|---|---|
| NAME | Guanjun Liu |
| GIVEN NAMES | Guanjun |
| FAMILY NAME | Liu |
| SIGNATURE | LIU G |
| AFFILIATIONS | Tongji University |
| ORCID | 0000-0002-7523-4827 |
| VERIFIED | Yes |
| TOTAL WORKS | 15 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 15 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
TSF-Net
Despite significant advances in machine learning for financial forecasting, prevailing approaches face a fundamental modeling challenge in simultaneously capturing long-term stability and short-term agility in stock return prediction. Current methodologies tend to treat these temporal characteristics as mutually exclusive, where models demonstrate strong performance in identifying persistent patterns, they often suppress short-term fluctuations a…
GDB-Tr
Query-based trip recommendation is an important task in location-based services (LBS), which aims to provide users with a sequence of points of interest (POIs) based on their queries. In trip recommendation, the effect of the visited POIs on the following decisions of users, called a forward effect, is mined by the existing studies. However, the effect of the following POIs on the previously visited ones, called a reverse effect, receives no atte…
Detecting Information Leakage Against Chinese Wall Policy Based on the Unfolding Technique of Colored Petri Nets
Information leakage easily occurs in large-scale information interactions and brings harm to individuals, enterprises, and society. As a well-known security policy, Chinese Wall (CW) provides a security guideline, which combines mandatory and discretionary access control to avoid information leakage. Colored Petri nets (CPNs) are a widely used formal method, well suited for verification of CW policy due to the capability of characterizing the con…
Automated Graph Contrastive Learning Based on Node-Level and Edge-Level Learnable Augmentation
Graph neural networks (GNNs) are capable of modeling graph data using various types of nodes and edges, and thus can be widely used in the fields of recommender systems and bioinformatics. However, most existing graph neural network models do not alleviate the problem of incomplete raw graph data and the need for extensive labels. This leads to increased labor costs and produces suboptimal or even incorrect results. In this article, we propose an…
A Heterogeneous Graph Neural Network With Attribute Enhancement and Structure-Aware Attention
Heterogeneous information network (HIN) has been applied in a wide variety of graph analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to cast the meta-paths aside, since it solves the problem of structural information loss caused by artificially designed meta-paths. However, existing meta-path-free HGNNs fail to take into account that most node types in many HINs have no attributes, and they cannot make full…
Nus
Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new undersampling scheme, called a clustering-based noisy-sample-removed undersampling scheme (NUS) for i…
The Adaptation of Concept Drift
With the rapid development of Internet industry, the learning methods based on data stream have attracted more and more attention owing to their great application value in many industries such as banking, insurance, and telecom industry. In the process of learning from stream data, one of the most significant challenges is how to adapt to the so-called concept drift which means that the data stream distribution changes over time in unpredictable …
Asa-GNN
Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods either leverage original features only or require manual feature engineering so that they show a weak ability to learn discriminative representations from transaction data. Moreover, criminals often commit fraud by imitating cardholders’; behaviors, …
Petri-Net-Based Model Checking for Privacy-Critical Multiagent Systems
Computation tree logic of knowledge (CTLK) can be used to specify many properties related to privacy of multiagent systems (MASs). Our previous work defined knowledge-oriented Petri nets (KPNs) to formally describe both the interacting/collaborating process of multiagents and their epistemic evolutions. Our KPN-based verification of CTLK required to obtain all reachable states, the transition relation of all states, and the equivalence relations …
Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors
With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as their transactional representations, while they fail to reveal the hidden fraudulent behaviors. In this work, we propose a novel model to extract transactional behaviors of users and learn new transactio…
Robustness Verification of Swish Neural Networks Embedded in Autonomous Driving Systems
With the applications of deep learning in safety-critical domains such as autonomous driving systems gaining ground, it demands rigorous verification to guarantee the safety and reliability of corresponding systems. As the intelligent component in such systems, neural networks (NNs) must be robust in that their outputs are not affected by minor perturbation to inputs. Many research studies have shown that formal methods are effective ways to the …
A New Method for Measuring the Behavioral Consistency Degree of WF-Net Systems
How to guarantee the consistency between different systems has become a bone of contention in model-driven software development. Behavioral relations are a prominent technique/method to analyze the consistency degree of two workflow systems. There are four kinds of behavioral relations, i.e., strict order relation, exclusiveness relation, interleaving order relation, and inverse order relation. An activity in a module may be in different behavior…
Fall risks and the related factors for the homebound older people with dementia
Purpose Falls are a major public health problem, especially for older people. This research aimed to provide a direct illustration of fall risks among the homebound older people with dementia in China, and to identify the risk factors associated with it. Methods In 2020, a questionnaire-based field survey was used to assess 1,042 people aged over 60 years in Ningbo, Eastern China. The Morse Fall Risk Scale's result was employed as the dependent v…
Improved TrAdaBoost and its Application to Transaction Fraud Detection
AdaBoost is a boosting-based machine learning method under the assumption that the data in training and testing sets have the same distribution and input feature space. It increases the weights of those instances that are wrongly classified in a training process. However, the assumption does not hold in many real-world data sets. Therefore, AdaBoost is extended to transfer AdaBoost (TrAdaBoost) that can effectively transfer knowledge from one dom…
Deep Representation Learning With Full Center Loss for Credit Card Fraud Detection
Credit card fraud detection is an important study in the current era of mobile payment. Improving the performance of a fraud detection model and keeping its stability are very challenging because users' payment behaviors and criminals' fraud behaviors are often changing. In this article, we focus on obtaining deep feature representations of legal and fraud transactions from the aspect of the loss function of a deep neural network. Our purpose is …
No prominent works on this page.
Improved TrAdaBoost and its Application to Transaction Fraud Detection
AdaBoost is a boosting-based machine learning method under the assumption that the data in training and testing sets have the same distribution and input feature space. It increases the weights of those instances that are wrongly classified in a training process. However, the assumption does not hold in many real-world data sets. Therefore, AdaBoost is extended to transfer AdaBoost (TrAdaBoost) that can effectively transfer knowledge from one dom…
Deep Representation Learning With Full Center Loss for Credit Card Fraud Detection
Credit card fraud detection is an important study in the current era of mobile payment. Improving the performance of a fraud detection model and keeping its stability are very challenging because users' payment behaviors and criminals' fraud behaviors are often changing. In this article, we focus on obtaining deep feature representations of legal and fraud transactions from the aspect of the loss function of a deep neural network. Our purpose is …
A New Method for Measuring the Behavioral Consistency Degree of WF-Net Systems
How to guarantee the consistency between different systems has become a bone of contention in model-driven software development. Behavioral relations are a prominent technique/method to analyze the consistency degree of two workflow systems. There are four kinds of behavioral relations, i.e., strict order relation, exclusiveness relation, interleaving order relation, and inverse order relation. An activity in a module may be in different behavior…
Fall risks and the related factors for the homebound older people with dementia
Purpose Falls are a major public health problem, especially for older people. This research aimed to provide a direct illustration of fall risks among the homebound older people with dementia in China, and to identify the risk factors associated with it. Methods In 2020, a questionnaire-based field survey was used to assess 1,042 people aged over 60 years in Ningbo, Eastern China. The Morse Fall Risk Scale's result was employed as the dependent v…
Petri-Net-Based Model Checking for Privacy-Critical Multiagent Systems
Computation tree logic of knowledge (CTLK) can be used to specify many properties related to privacy of multiagent systems (MASs). Our previous work defined knowledge-oriented Petri nets (KPNs) to formally describe both the interacting/collaborating process of multiagents and their epistemic evolutions. Our KPN-based verification of CTLK required to obtain all reachable states, the transition relation of all states, and the equivalence relations …
Time-Aware Attention-Based Gated Network for Credit Card Fraud Detection by Extracting Transactional Behaviors
With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as their transactional representations, while they fail to reveal the hidden fraudulent behaviors. In this work, we propose a novel model to extract transactional behaviors of users and learn new transactio…
Robustness Verification of Swish Neural Networks Embedded in Autonomous Driving Systems
With the applications of deep learning in safety-critical domains such as autonomous driving systems gaining ground, it demands rigorous verification to guarantee the safety and reliability of corresponding systems. As the intelligent component in such systems, neural networks (NNs) must be robust in that their outputs are not affected by minor perturbation to inputs. Many research studies have shown that formal methods are effective ways to the …
A Heterogeneous Graph Neural Network With Attribute Enhancement and Structure-Aware Attention
Heterogeneous information network (HIN) has been applied in a wide variety of graph analysis tasks. At present, it is a trend of heterogeneous graph neural networks (HGNNs) to cast the meta-paths aside, since it solves the problem of structural information loss caused by artificially designed meta-paths. However, existing meta-path-free HGNNs fail to take into account that most node types in many HINs have no attributes, and they cannot make full…
Nus
Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new undersampling scheme, called a clustering-based noisy-sample-removed undersampling scheme (NUS) for i…
The Adaptation of Concept Drift
With the rapid development of Internet industry, the learning methods based on data stream have attracted more and more attention owing to their great application value in many industries such as banking, insurance, and telecom industry. In the process of learning from stream data, one of the most significant challenges is how to adapt to the so-called concept drift which means that the data stream distribution changes over time in unpredictable …
Asa-GNN
Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods either leverage original features only or require manual feature engineering so that they show a weak ability to learn discriminative representations from transaction data. Moreover, criminals often commit fraud by imitating cardholders’; behaviors, …
Detecting Information Leakage Against Chinese Wall Policy Based on the Unfolding Technique of Colored Petri Nets
Information leakage easily occurs in large-scale information interactions and brings harm to individuals, enterprises, and society. As a well-known security policy, Chinese Wall (CW) provides a security guideline, which combines mandatory and discretionary access control to avoid information leakage. Colored Petri nets (CPNs) are a widely used formal method, well suited for verification of CW policy due to the capability of characterizing the con…
Automated Graph Contrastive Learning Based on Node-Level and Edge-Level Learnable Augmentation
Graph neural networks (GNNs) are capable of modeling graph data using various types of nodes and edges, and thus can be widely used in the fields of recommender systems and bioinformatics. However, most existing graph neural network models do not alleviate the problem of incomplete raw graph data and the need for extensive labels. This leads to increased labor costs and produces suboptimal or even incorrect results. In this article, we propose an…
TSF-Net
Despite significant advances in machine learning for financial forecasting, prevailing approaches face a fundamental modeling challenge in simultaneously capturing long-term stability and short-term agility in stock return prediction. Current methodologies tend to treat these temporal characteristics as mutually exclusive, where models demonstrate strong performance in identifying persistent patterns, they often suppress short-term fluctuations a…
GDB-Tr
Query-based trip recommendation is an important task in location-based services (LBS), which aims to provide users with a sequence of points of interest (POIs) based on their queries. In trip recommendation, the effect of the visited POIs on the following decisions of users, called a forward effect, is mined by the existing studies. However, the effect of the following POIs on the previously visited ones, called a reverse effect, receives no atte…
Computer Science (12 works) · Artificial Intelligence (11 works) · Machine learning (7 works) · Data mining (6 works) · Computer security (5 works) · Imbalanced Data Classification Techniques (5 works) · Mathematics (5 works) · Credit card (4 works) · Credit card fraud (4 works) · Financial Distress and Bankruptcy Prediction (4 works)