Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations

Anomaly detection in sequential data has been studied for a long time because of its potential in various applications, such as detecting abnormal system behaviors from log data. Although many approaches can achieve good performance on anomalous sequence detection, how to identify the anomalous entries in sequences is still challenging due to a lack of information at the entry-level. In this work, we propose a novel framework called CFDet for fine-grained anomalous entry detection. CFDet leverages the idea of interpretable machine learning. Given a sequence that is detected as anomalous, we can consider anomalous entry detection as an interpretable machine learning task because identifying anomalous entries in the sequence is to provide an interpretation to the detection result. We make use of the deep support vector data description (Deep SVDD) approach to detect anomalous sequences and propose a novel counterfactual interpretation-based approach to identify anomalous entries in the sequences. Experimental results on three datasets show that CFDet can correctly detect anomalous entries

Factive and nonfactive mental state attribution

Factive mental states, such as knowing or being aware, can only link an agent to the truth; by contrast, nonfactive states, such as believing or thinking, can link an agent to either truths or falsehoods. Researchers of mental state attribution often draw a sharp line between the capacity to attribute accurate states of mind and the capacity to attribute inaccurate or “reality-incongruent” states of mind, such as false belief. This article argues that the contrast that really matters for mental state attribution does not divide accurate from inaccurate states, but factive from nonfactive ones