Dialog is a core building block of human natural language interactions.It contains multiparty utterances used to convey information from one party to another in a dynamic and evolving manner.The ability to compare dialogs is beneficial in many real world use cases, such as conversation analytics for contact center calls and virtual agent design.We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity.Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants.We evaluate this new approach and compare it to existing document similarity measures on two publicly available datasets.The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.

We present a novel approach for detecting malicious user activity in databases. Specifically, we propose a new machine learning algorithm for detecting attacks such as a stolen user account or illegal use by a user. Our algorithm relies on two main components that examine the consistency of a user's activity and compare it with activity patterns learned from past access. The first component tests for self-consistency, to determine whether the actions performed by a user are consistent with previous patterns. This engine is based on a probabilistic model that we developed to capture a user's normal behavior. The second component checks for global-consistency, to determine whether a user's actions are consistent with the past actions of similar users. We test our algorithm on access data from SQL databases. Experimental results show that we can keep false positive rates while retaining the overall accuracy level. An outlier detection engine based on the presented methods is now included in the standard offering of IBM InfoSphere Guardium1 with positive user feedback.