Quoc Viet Hung Nguyen
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View article: EXPLAINABLE ARTIFICIAL INTELLIGENCE IN ADDITIVE MANUFACTURING: A SYSTEMATIC REVIEW ON METHOD CONVERGENCE AND ASSESSMENT OF STANDARDIZATION GAPS
EXPLAINABLE ARTIFICIAL INTELLIGENCE IN ADDITIVE MANUFACTURING: A SYSTEMATIC REVIEW ON METHOD CONVERGENCE AND ASSESSMENT OF STANDARDIZATION GAPS Open
Additive manufacturing has revolutionized production capabilities across industries, yet quality assurance and process optimization remain significant challenges due to complex, multi-parameter interactions. Explainable Artificial Intellig…
View article: Digital Twins: Enhancing Personalized Experience Through Multiple and Dynamic Persona Across Customer Phygital Journey
Digital Twins: Enhancing Personalized Experience Through Multiple and Dynamic Persona Across Customer Phygital Journey Open
This study explores how phygital profiling and dynamic personas enhance personalized customer experiences across the evolving customer journey. Drawing on Foursquare data from 27,148 users and over 2 million check-ins across four global ci…
View article: Scalable and effective negative sample generation for hyperedge prediction
Scalable and effective negative sample generation for hyperedge prediction Open
Hypergraphs have demonstrated their superiority in modeling complex systems compared to traditional graphs by directly capturing the interactions among multiple entities. Hyperedge prediction, which aims to predict unobserved potential hyp…
View article: M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation Open
Sequential recommendation systems aim to predict users' next preferences based on their interaction histories, but existing approaches face critical limitations in efficiency and multi-scale pattern recognition. While Transformer-based met…
View article: Multi-agents based User Values Mining for Recommendation
Multi-agents based User Values Mining for Recommendation Open
Recommender systems have rapidly evolved and become integral to many online services. However, existing systems sometimes produce unstable and unsatisfactory recommendations that fail to align with users' fundamental and long-term preferen…
View article: The effect of recycled waste polystyrene plastic aggregate on the engineering properties of lightweight composites
The effect of recycled waste polystyrene plastic aggregate on the engineering properties of lightweight composites Open
The substantial generation of polystyrene waste from the food industry poses significant environmental andhuman health challenges. This study addresses these issues by using recycled lightweight aggregates (RLWA)from waste polystyrene plas…
View article: Robust federated contrastive recommender system against targeted model poisoning attack
Robust federated contrastive recommender system against targeted model poisoning attack Open
Federated recommender systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two dilemmas. First, …
View article: Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions?
Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions? Open
Short term Load Forecasting (STLF) plays an important role in traditional and modern power systems. Most STLF models predominantly exploit temporal dependencies from historical data to predict future consumption. Nowadays, with the widespr…
View article: Model-Free Counterfactual Subset Selection at Scale
Model-Free Counterfactual Subset Selection at Scale Open
Ensuring transparency in AI decision-making requires interpretable explanations, particularly at the instance level. Counterfactual explanations are a powerful tool for this purpose, but existing techniques frequently depend on synthetic e…
View article: Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts Open
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Temporal Graph Neural Networks (STGNNs) have established thems…
View article: Certified Unlearning for Federated Recommendation
Certified Unlearning for Federated Recommendation Open
Recommendation systems play a crucial role in providing web-based suggestion utilities by leveraging user behavior, preferences, and interests. In the context of privacy concerns and the proliferation of handheld devices, federated recomme…
View article: Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting
Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting Open
Among various spatio-temporal prediction tasks, epidemic forecasting plays a critical role in public health management. Recent studies have demonstrated the strong potential of spatio-temporal graph neural networks (STGNNs) in extracting h…
View article: Tackling Data Heterogeneity in Federated Time Series Forecasting
Tackling Data Heterogeneity in Federated Time Series Forecasting Open
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Although substantial progress has been made in time series…
View article: On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective Open
Content-based Recommender Systems (CRSs) play a crucial role in shaping user experiences in e-commerce, online advertising, and personalized recommendations. However, due to the vast amount of categorical features, the embedding tables use…
View article: Harnessing Large Language Models for Group POI Recommendations
Harnessing Large Language Models for Group POI Recommendations Open
The rapid proliferation of Location-Based Social Networks (LBSNs) has underscored the importance of Point-of-Interest (POI) recommendation systems in enhancing user experiences. While individual POI recommendation methods leverage users' c…
View article: Scalable and Effective Negative Sample Generation for Hyperedge Prediction
Scalable and Effective Negative Sample Generation for Hyperedge Prediction Open
Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commerce systems. Traditional methods often face difficulties in …
View article: Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era Open
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides…
View article: CTFacTomo: Reconstructing 3D Spatial Structures of RNA Tomography Transcriptomes by Collapsed Tensor Factorization
CTFacTomo: Reconstructing 3D Spatial Structures of RNA Tomography Transcriptomes by Collapsed Tensor Factorization Open
Cells are organized to form three-dimensional structures of complex tissues. To map the complete 3D organization of a tissue, technologies based on tissue microdissections provide deep bulk RNA sequencing of orthographically arranged cryos…
View article: Privacy-preserving explainable AI: a survey
Privacy-preserving explainable AI: a survey Open
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preservin…
View article: Multi-turn Response Selection with Commonsense-enhanced Language Models
Multi-turn Response Selection with Commonsense-enhanced Language Models Open
As a branch of advanced artificial intelligence, dialogue systems are prospering. Multi-turn response selection is a general research problem in dialogue systems. With the assistance of background information and pre-trained language model…
View article: Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures Open
Recommender systems have become an integral part of online services due to their ability to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning a…
View article: Heterogeneous Hypergraph Embedding for Recommendation Systems
Heterogeneous Hypergraph Embedding for Recommendation Systems Open
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate …
View article: A Contrastive Learning and Graph-based Approach for Missing Modalities in Multimodal Federated Learning
A Contrastive Learning and Graph-based Approach for Missing Modalities in Multimodal Federated Learning Open
Federated Learning has emerged as a decentralized method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while keeping their data st…
View article: A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems Open
Since the creation of the Web, recommender systems (RSs) have been an indispensable mechanism in information filtering. State-of-the-art RSs primarily depend on categorical features, which ecoded by embedding vectors, resulting in excessiv…