Ming Xu

Never Ignore the Significance of Different Anomalies: A Cost-Sensitive Algorithm Based on Loss Function for Anomaly Detection

2015

In our daily life, anomalies are everywhere in various application domains. Most anomalies may cause huge losses if we fail to detect them in advance. A lot of researches on this field have been carried …

A universal compression strategy using sorting transformation

2019 · International Journal of Computational Science and Engineering

Although traditional universal compression algorithms can effectively utilise repetition located in a slide window, they cannot take their own advantages for some message source in which similar messages are distributed uniformly. In this paper, we …

FIFL: A Fair Incentive Mechanism for Federated Learning

2021

Federated learning is a novel machine learning framework that enables multiple devices to collaboratively train high-performance models while preserving data privacy. Federated learning is a kind of crowdsourcing computing, where a task publisher shares profit …

A Mobile Application-Classifying Method Based on a Graph Attention Network from Encrypted Network Traffic

2023 · Electronics

Classifying mobile applications from encrypted network traffic is a common and basic requirement in network security and network management. Existing works classify mobile applications from flows, based on which application fingerprints and classifiers are created. …

Context-Dependent Anomaly Action Recognition

2025

We explore the problem of unsupervised anomaly action recognition, focusing on identifying abnormal human behavior that takes into account the surrounding context. Unlike conventional approaches that rely solely on observing the human action, we recognize …