This paper investigates the role of outliers in literature-based knowledge discovery. It shows that detecting interesting outliers which appear in the literature on a given phenomenon can help the expert to find implicit relationships among concepts of different domains

The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error.

A software birthmark means inherent characteristics that can be used to identify a program. In this paper, we propose a birthmark technique based on object traces of Java programs.

Many computer users rely on the Windows Update tool to keep hardware drivers current. Unfortunately, Microsoft does not monitor each and every piece of hardware on our systems as we would like to believe

See original here:
Update All Hardware Drivers with Ma-Config

The most common approach to automatic summarization and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets, which can be used for both semantic description and event detection within sports videos