Mengchu Zhou
Biographic Data
| ID | 10054209 |
|---|---|
| NAME | Mengchu Zhou |
| GIVEN NAMES | Mengchu |
| FAMILY NAME | Zhou |
| SIGNATURE | ZHOU M |
| AFFILIATIONS | New Jersey Institute of Technology |
| ORCID | 0000-0002-5408-8752 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 14 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A Subspace-Based Method for Facial Image Editing
In the realm of computational social systems, the ability to edit facial attributes accurately plays a crucial role in enhancing user experience on social media platforms and virtual environments. However, we face significant challenges in isolated attribute manipulation and balancing the tradeoff between editing fidelity and facial identity preservation. Here, this article presents a novel approach to constructing an orthogonal decomposition sub…
Detecting k -Vertex Cuts in Sparse Networks via a Fast Local Search Approach
The k -vertex cut ( k -VC) problem belongs to the family of the critical node detection problems, which aims to find a minimum subset of vertices whose removal decomposes a graph into at least k connected components. It is an important NP-hard problem with various real-world applications, e.g., vulnerability assessment, carbon emissions tracking, epidemic control, drug design, emergency response, network security, and social network analysis. In …
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…
Dynamic Dependence and Hedging of Stock Markets
To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China …
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, …
Loss Aversion Robust Optimization Model Under Distribution and Mean Return Ambiguity
From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown. Then, under the premise that the mean returns of risky assets belong to an ellipsoidal uncertainty…
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…
Information-Utilization-Method-Assisted Multimodal Multiobjective Optimization and Application to Credit Card Fraud Detection
Different from multiobjective optimization problems (MOPs), multimodal MOPs (MMOPs) focus on both decision and objective spaces rather than only objective one. Thus, finding a good Pareto front approximation and finding the maximal number of equivalent Pareto optimal solutions for each objective vector in the Pareto front are two core tasks for them. Although some multimodal multiobjective evolutionary algorithms have been proposed to handle them…
A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems
As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long-term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold-start …
Aspect-Based Sentiment Analysis
Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This artic…
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…
Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation
In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or…
Security and Trust in Blockchains
As a new promising distributed technology, blockchains have been widely applied since its inception. Its decentralization feature reduces the reliance on the trusted authorities and third parties. It can well solve the problem of data being tampered and increase data sharing. However, a blockchain system faces various security and trust issues, such as attacks against consensus mechanisms and propagation processes, which may make it store malicio…
No prominent works on this page.
Aspect-Based Sentiment Analysis
Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This artic…
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…
Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation
In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or…
Security and Trust in Blockchains
As a new promising distributed technology, blockchains have been widely applied since its inception. Its decentralization feature reduces the reliance on the trusted authorities and third parties. It can well solve the problem of data being tampered and increase data sharing. However, a blockchain system faces various security and trust issues, such as attacks against consensus mechanisms and propagation processes, which may make it store malicio…
Information-Utilization-Method-Assisted Multimodal Multiobjective Optimization and Application to Credit Card Fraud Detection
Different from multiobjective optimization problems (MOPs), multimodal MOPs (MMOPs) focus on both decision and objective spaces rather than only objective one. Thus, finding a good Pareto front approximation and finding the maximal number of equivalent Pareto optimal solutions for each objective vector in the Pareto front are two core tasks for them. Although some multimodal multiobjective evolutionary algorithms have been proposed to handle them…
A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems
As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long-term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold-start …
Loss Aversion Robust Optimization Model Under Distribution and Mean Return Ambiguity
From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown. Then, under the premise that the mean returns of risky assets belong to an ellipsoidal uncertainty…
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…
Detecting k -Vertex Cuts in Sparse Networks via a Fast Local Search Approach
The k -vertex cut ( k -VC) problem belongs to the family of the critical node detection problems, which aims to find a minimum subset of vertices whose removal decomposes a graph into at least k connected components. It is an important NP-hard problem with various real-world applications, e.g., vulnerability assessment, carbon emissions tracking, epidemic control, drug design, emergency response, network security, and social network analysis. In …
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…
Dynamic Dependence and Hedging of Stock Markets
To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China …
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, …
A Subspace-Based Method for Facial Image Editing
In the realm of computational social systems, the ability to edit facial attributes accurately plays a crucial role in enhancing user experience on social media platforms and virtual environments. However, we face significant challenges in isolated attribute manipulation and balancing the tradeoff between editing fidelity and facial identity preservation. Here, this article presents a novel approach to constructing an orthogonal decomposition sub…
Computer Science (14 works) · Artificial Intelligence (8 works) · Machine learning (8 works) · Mathematics (8 works) · Computer security (4 works) · Data mining (4 works) · Imbalanced Data Classification Techniques (4 works) · Algorithm (3 works) · Credit card (3 works) · Credit card fraud (3 works)