Xiao Wang

E-learning recommendation framework based on deep learning

2017

In the paper, considering the limitation of effective method in E-learning area, a recommendation framework for E-Learning based on deep learning is proposed. Our model is based on deep learning, which has strong capability to …

An Adaptive Federated Control Strategy for Participant Selection in Multi-Client Collaboration

2021 · 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI)

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client …

Using Handle system to provide persistent identifiers for diploma

2022 · 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE)

One of the essential factors for recruiters in a selection process is study background. It is, therefore, crucial to verify the authenticity of diplomas. Currently, it's easy to make a fake diploma with some software. …

Geometric Disentangled Collaborative Filtering

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

Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present …

Partial Conditioning for Inference of Many-Normal-Means with Hölder Constraints

2023 · arXiv (Cornell University)

Inferential models have been proposed for valid and efficient prior-free probabilistic inference. As it gradually gained popularity, this theory is subject to further developments for practically challenging problems. This paper considers the many-normal-means problem with …

Reproducibility, Replicability, and Insights into Dense Multi-Representation Retrieval Models: from ColBERT to Col*

2023

Dense multi-representation retrieval models, exemplified as ColBERT, estimate the relevance between a query and a document based on the similarity of their contextualised token-level embeddings. Indeed, by using contextualised token embeddings, dense retrieval, conducted as …

RESCU-SQL: Oblivious Querying for the Zero Trust Cloud

2023 · Proceedings of the VLDB Endowment

Cloud service providers offer robust infrastructure for rent to organizations of all kinds. High stakes applications, such as the ones in defense and healthcare, are turning to the public cloud for a cost-effective, geographically distributed, …

Can Small Language Models be Good Reasoners for Sequential Recommendation?

2024 · arXiv (Cornell University)

Large language models (LLMs) open up new horizons for sequential recommendations, owing to their remarkable language comprehension and generation capabilities. However, there are still numerous challenges that should be addressed to successfully implement sequential recommendations …

Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm

2024 · Mathematics

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data …

Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms

2024 · arXiv (Cornell University)

Human Action Recognition (HAR) stands as a pivotal research domain in both computer vision and artificial intelligence, with RGB cameras dominating as the preferred tool for investigation and innovation in this field. However, in real-world …

P4: Plug-and-Play Discrete Prompting for Large Language Models Personalization

2024

Empowering Large Language Models (LLMs) with distinct human-like personality traits has become an innovative task for developing advanced dialog systems.Although LLMs demonstrate impressive capabilities in following instructions, directly prompting them to exhibit certain personalities through …

Stain Normalization of Histopathological Images Based on Deep Learning: A Review

2025 · Diagnostics

Histopathological images stained with hematoxylin and eosin (H&E) are crucial for cancer diagnosis and prognosis. However, color variations caused by differences in tissue preparation and scanning devices can lead to data distribution discrepancies, adversely affecting …

Interpreting and Unifying Graph Neural Networks with An Optimization Framework

2021

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has been demonstrated effective is the most fundamental part of GNNs. Although …