VLAD: Task-agnostic VAE-based lifelong anomaly detection.

Lifelong learning represents an emerging machine learning paradigm that aims at designing new methods providing accurate analyses in complex and dynamic real-world environments. Although a significant amount of research has been conducted in image classification and reinforcement learning, very limited work has been done to solve lifelong anomaly detection problems. In this context, a successful method has to detect anomalies while adapting to changing environments and preserving knowledge to avoid catastrophic forgetting. While state-of-the-art online anomaly detection methods are able to detect anomalies and adapt to a changing environment, they are not designed to preserve past knowledge. On the other hand, while lifelong learning methods are focused on adapting to changing environments and preserving knowledge, they are not tailored for detecting anomalies, and often require task labels or task boundaries which are not available in task-agnostic lifelong anomaly detection scenarios. This paper proposes VLAD, a novel VAE-based Lifelong Anomaly Detection method addressing all these challenges simultaneously in complex task-agnostic scenarios. VLAD leverages the combination of lifelong change point detection and an effective model update strategy supported by experience replay with a hierarchical memory maintained by means of consolidation and summarization. An extensive quantitative evaluation showcases the merit of the proposed method in a variety of applied settings. VLAD outperforms state-of-the-art methods for anomaly detection, presenting increased robustness and performance in complex lifelong settings.

Gaze aversion in conversational settings: An investigation based on mock job interview.

We report the results of an empirical study on gaze aversion during dyadic human-to-human conversation in an interview setting. To address various methodological challenges in assessing gaze-to-face contact, we followed an approach where the experiment was conducted twice, each time with a different set of interviewees. In one of them the interviewer's gaze was tracked with an eye tracker, and in the other the interviewee's gaze was tracked. The gaze sequences obtained in both experiments were analyzed and modeled as Discrete-Time Markov Chains. The results show that the interviewer made more frequent and longer gaze contacts compared to the interviewee. Also, the interviewer made mostly diagonal gaze aversions, whereas the interviewee made sideways aversions (left or right). We discuss the relevance of this research for Human-Robot Interaction, and discuss some future research problems.