Zongxing Xie

TL;DR: The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework and indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.

TL;DR: A distributed multiagent intrusion detection system (IDS) architecture is proposed, which attempts to provide an accurate and lightweight solution to network intrusion detection by tackling issues associated with the design of a distributed multi agent system, such as poor system scalability and the requirements of excessive processing power and memory storage.

TL;DR: C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements.

TL;DR: Experimental results have shown that the PCC lightweight anomaly detection classifier outperforms other existing anomaly detection algorithms such as the KNN and LOF classifiers.

TL;DR: The fully automatic process via the combination of distance-based and rule-based data mining techniques can greatly reduce the number of negative instances and the feature dimension to facilitate the final event detection, without pruning away any positive (event) testing instance along the process.