Meng Wang

Understanding formal specifications through good examples

2018

Formal specifications of software applications are hard to understand, even for domain experts. Because a formal specification is abstract, reading it does not immediately convey the expected behaviour of the software. Carefully chosen examples of …

A Novel Intelligent Tutoring System For Learning Programming

2020

The goal of this paper is to propose the concept, structure and implementation of a novel intelligent tutoring system designed for beginners in C language and Python. The system is implemented by adding the functions …

Joint Item Recommendation and Attribute Inference

2020

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item …

MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

2020

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to …

Spinner: Automated Dynamic Command Subsystem Perturbation

2021 · arXiv (Cornell University)

Injection attacks have been a major threat to web applications. Despite the significant effort in thwarting injection attacks, protection against injection attacks remains challenging due to the sophisticated attacks that exploit the existing protection techniques' …

Method for Predicting Compound Properties in Drug Development Based on Machine Learning

2021

Data on drugs, targets and indications were collected from the database, and gene expression profile data processed by 1309 small drug molecules were collected from the connectivity map. The biclustering algorithm was used to cluster …

Information-Enhanced Hierarchical Self-Attention Network for Multiturn Dialog Generation

2022 · IEEE Transactions on Computational Social Systems

Transformer structure has shown promising results in multiturn dialog generation. The self-attention mechanism can learn global dependencies but ignores local information, limiting the model’s ability to model context information. In this article, we propose an …

How to Retrain Recommender System?

2020

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since …

Reliable Federated Disentangling Network for Non-IID Domain Feature

2023 · arXiv (Cornell University)

Federated learning (FL), as an effective decentralized distributed learning approach, enables multiple institutions to jointly train a model without sharing their local data. However, the domain feature shift caused by different acquisition devices/clients substantially degrades …

Mixed Attention Network for Cross-domain Sequential Recommendation

2023 · arXiv (Cornell University)

In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which …

Application decision model of blockchain technology in construction supply chain

2024 · Research Square

Abstract With the advent of the global digital era, the new generation of information technology represented by blockchain has gradually matured and penetrated into various industries, triggering a new round of technological innovation and industrial …

Single-pulse two-qubit gates for Rydberg atoms with noncyclic geometric control

2024 · Physical Review A

Arrays of neutral atoms have emerged as promising platforms for quantum computing. The realization of high-fidelity two-qubit gates with robustness is currently a significantly important task for large-scale operations. In this paper, we present a …

Hierarchical Label-Enhanced Contrastive Learning for Chinese NER

2025 · IEEE Transactions on Neural Networks and Learning Systems

Recently, character-word lattice structures have achieved promising results for Chinese named entity recognition (NER), reducing word segmentation errors and increasing word boundary information for character sequences. However, constructing the lattice structure is complex and time-consuming, …

AQE-RF: An Adaptive Quantifier Extension and Rule-Filtering Graph Network for Logical Reasoning of Text

2025 · IEEE Transactions on Neural Networks and Learning Systems

Logical reasoning of text requires neural models to possess strong contextual comprehension and logical reasoning ability to draw conclusions from limited information. To improve the logical reasoning capabilities of pretrained language models (PLMs), existing approaches …

AXIS: Explainable Time Series Anomaly Detection with Large Language Models

2025 · arXiv (Cornell University)

Time-series anomaly detection (TSAD) increasingly demands explanations that articulate not only if an anomaly occurred, but also what pattern it exhibits and why it is anomalous. Leveraging the impressive explanatory capabilities of Large Language Models …

Token-guided multimodal prognosis in hepatocellular carcinoma: a framework steered by tumour–stroma ratio

2026 · Gut

BACKGROUND: The tumour-stroma ratio (TSR) is a potential prognostic indicator, yet hindered by quantification challenges and conflicting reports. OBJECTIVE: To determine whether TSR follows a non-linear prognostic pattern and to develop an artificial intelligence (AI)-powered …

Neural Graph Collaborative Filtering

2019

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's …

A Neural Influence Diffusion Model for Social Recommendation

2019

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the …

Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction …

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

2020 · arXiv (Cornell University)

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on …

LightGCN

2020

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on …

Bias and Debias in Recommender System: A Survey and Future Directions

2022 · ACM Transactions on Information Systems

While recent years have witnessed a rapid growth of research papers on recommender system (RS) , most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior …

Reinforced negative sampling over knowledge graph for recommendation

2020 · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)

National Research Foundation (NRF) Singapore under International Research Centre in Singapore Funding Initiative

Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

2021

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors …

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant …

A Review-aware Graph Contrastive Learning Framework for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing …