Detecting Semantic Anomalies

Detecting of Semantic Anomalies Using fNIRS

LMF-based approach for detecting semantic anomalies in electronic dictionaries

Dictionaries are used for learning and disseminating natural languages. This important role implies that it is necessary to perform the operations of creating, enriching and updating carefully. Even in electronic versions, dictionaries may contain anomalies notably when the used acquisition system is not efficient. Several researches have been made in recent years in order to perform the detect...

Detecting Anomalies and Intruders

Brittleness is a well-known problem in expert systems where a conclusion can be made, which human common sense would recognise as impossible e.g. that a male is pregnant. We have extended previous work on prudent expert systems to enable an expert system to recognise when a case is outside its range of experience. We have also used the same technique to detect new patterns of network traffic, s...

Detecting Patterns of Anomalies

An anomaly is an observation that does not conform to the expected normal behavior. With the ever increasing amount of data being collected universally, automatic surveillance systems are becoming more popular and are increasingly using data mining methods to detect patterns of anomalies. Detecting anomalies can provide useful and actionable information in a variety of real-world scenarios. For...

Detecting Floor Anomalies

When a robot moves about a 2D world such as a planar surface, it is important that obstacles to the robot's motions be detected. This classical problem of \obstacle detection" has proven to be di cult. Many researchers have formulated this problem as being the process of determining where a robot cannot move due to the presence of obstacles. An alternative approach presented here is to determin...