Davis, Randall



THink: Inferring Cognitive Status from Subtle Behaviors

AI Magazine

The digital clock drawing test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.


Continuous Body and Hand Gesture Recognition for Natural Human-Computer Interaction: Extended Abstract

AAAI Conferences

We present a new approach to gesture recognition that tracks body and hands simultaneously and recognizes gestures continuously from an unsegmented and unbounded input stream. Our system estimates 3D coordinates of upper body joints and classifies the appearance of hands into a set of canonical shapes. A novel multi-layered filtering technique with a temporal sliding window is developed to enable online sequence labeling and segmentation. Experimental results on the NATOPS dataset show the effectiveness of the approach. We also report on our recent work on multimodal gesture recognition and deep-hierarchical sequence representation learning that achieve the state-of-the-art performances on several real-world datasets.


One-Class Conditional Random Fields for Sequential Anomaly Detection

AAAI Conferences

Sequential anomaly detection is a challenging problem due to the one-class nature of the data (i.e., data is collected from only one class) and the temporal dependence in sequential data. We present One-Class Conditional Random Fields (OCCRF) for sequential anomaly detection that learn from a one-class dataset and capture the temporal dependence structure, in an unsupervised fashion. We propose a hinge loss in a regularized risk minimization framework that maximizes the margin between each sequence being classified as "normal" and "abnormal." This allows our model to accept most (but not all) of the training data as normal, yet keeps the solution space tight. Experimental results on a number of real-world datasets show our model outperforming several baselines. We also report an exploratory study on detecting abnormal organizational behavior in enterprise social networks.


A Visual Approach to Sketched Symbol Recognition

AAAI Conferences

There is increasing interest in building systems that can automatically interpret hand-drawn sketches. However, many challenges remain in terms of recognition accuracy, robustness to different drawing styles, and ability to generalize across multiple domains. To address these challenges, we propose a new approach to sketched symbol recognition that focuses on the visual appearance of the symbols. This allows us to better handle the range of visual and stroke-level variations found in freehand drawings. We also present a new symbol classifier that is computationally efficient and invariant to rotation and local deformations. We show that our method exceeds state-of-the-art performance on all three domains we evaluated, including handwritten digits, PowerPoint shapes, and electrical circuit symbols.


Combining Speech and Sketch to Interpret Unconstrained Descriptions of Mechanical Devices

AAAI Conferences

Mechanical design tools would be considerably more useful if we could interact with them in the way that human designers communicate design ideas to one another, i.e., using crude sketches and informal speech. Those crude sketches frequently contain pen strokes of two different sorts, one type portraying device structure, the other denoting gestures, such as arrows used to indicate motion. We report here on techniques we developed that use information from both sketch and speech to distinguish gesture strokes from non-gestures -- a critical first step in understanding a sketch of a device. We collected and analyzed unconstrained device descriptions, which revealed six common types of gestures. Guided by this knowledge, we developed a classifier that uses both sketch and speech features to distinguish gesture strokes from non-gestures. Experiments with our techniques indicate that the sketch and speech modalities alone produce equivalent classification accuracy, but combining them produces higher accuracy.


Reports on the 2004 AAAI Fall Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence presented its 2004 Fall Symposium Series Friday through Sunday, October 22-24 at the Hyatt Regency Crystal City in Arlington, Virginia, adjacent to Washington, DC. The symposium series was preceded by a one-day AI funding seminar. The topics of the eight symposia in the 2004 Fall Symposia Series were: (1) Achieving Human-Level Intelligence through Integrated Systems and Research; (2) Artificial Multiagent Learning; (3) Compositional Connectionism in Cognitive Science; (4) Dialogue Systems for Health Communications; (5) The Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence; (6) Making Pen-Based Interaction Intelligent and Natural; (7) Real- Life Reinforcement Learning; and (8) Style and Meaning in Language, Art, Music, and Design.


Reports on the 2004 AAAI Fall Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence presented its 2004 Fall Symposium Series Friday through Sunday, October 22-24 at the Hyatt Regency Crystal City in Arlington, Virginia, adjacent to Washington, DC. The symposium series was preceded by a one-day AI funding seminar. The topics of the eight symposia in the 2004 Fall Symposia Series were: (1) Achieving Human-Level Intelligence through Integrated Systems and Research; (2) Artificial Multiagent Learning; (3) Compositional Connectionism in Cognitive Science; (4) Dialogue Systems for Health Communications; (5) The Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence; (6) Making Pen-Based Interaction Intelligent and Natural; (7) Real- Life Reinforcement Learning; and (8) Style and Meaning in Language, Art, Music, and Design.


The 2002 AAAI Spring Symposium Series

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.


The 2002 AAAI Spring Symposium Series

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.