Paul C. Davis

NuActiv: recognizing unseen new activities using semantic attribute-based learning

We study the problem of how to recognize a new human activity when we have never seen any training example of that activity before. Recognizing human activities is an essential element for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. A previously unseen activity class cannot be recognized if there were no training samples in the dataset. Even if all of the activities can be enumerated in advance, labeled samples are often time consuming and expensive to get, as they require huge effort from human annotators or experts. In this paper, we present NuActiv, an activity recognition system that can recognize a human activity even when there are no training data for that activity class. Firstly, we designed a new representation of activities using semantic attributes, where each attribute is a human readable term that describes a basic element or an inherent characteristic of an activity. Secondly, based on this representation, a two-layer zero-shot learning algorithm is developed for activity recognition. Finally, to reinforce recognition accuracy using minimal user feedback, we developed an active learning algorithm for activity recognition. Our approach is evaluated on two datasets, including a 10-exercise-activity dataset we collected, and a public dataset of 34 daily life activities. Experimental results show that using semantic attribute-based learning, NuActiv can generalize knowledge to recognize unseen new activities. Our approach achieved up to 79% accuracy in unseen activity recognition.

Towards zero-shot learning for human activity recognition using semantic attribute sequence model

Understanding human activities is important for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. In this paper, we present a new zero-shot learning framework for human activity recognition that can recognize an unseen new activity even when there are no training samples of that activity in the dataset. We propose a semantic attribute sequence model that takes into account both the hierarchical and sequential nature of activity data. Evaluation on datasets in two activity domains show that the proposed zero-shot learning approach achieves 70-75% precision and recall recognizing unseen new activities, and outperforms supervised learning with limited labeled data for the new classes.

A semantic framework for personalized ad recommendation based on advanced textual analysis

In this paper we present a hybrid recommendation system that combines ontological knowledge with content-extracted linguistic information, derived from pre-trained lexical graphs, in order to produce high quality, personalized recommendations. In the described approach, such recommendations are exemplified in an advertising scenario. We propose a distributed system architecture that uses semantic knowledge, based on terminologically enriched domain ontologies, to learn ontological user profiles and consequently infer recommendations through fuzzy semantic reasoning. A real world user study demonstrates the improvements attained in providing user-relevant recommendations with the aid of semantic profiles.