Diameter, Split, Bicriteria clustering, Complexity, Polynomial algorithm, Divisive hierarchical clustering,

Effective detection, recognition, interpretation, and analysis of human physiological and behavioral characteristics are of fundamental importance in the design and development of intelligent human computer interaction (HCI) systems. This paper illustrates the issues and challenges in the design of such systems through two real examples, emotion recognition and face detection. In particular, we focus on audiovisual based bimodal emotion recognition, face detection in crowded scene, and facial fiducial points detection. The integration of these systems is expected to produce more robust and stronger performance, and provide more natural and friendly manmachine interaction.

Eye tracking technology can show how people focus their attention and emotionally react to their surroundings. In this study, wearable eye tracker was used to conduct eye movement experiments in realistic environment. For signal processing of the data, a finite impulse response (FIR) filter was chosen, and an eye movement data set was created. First, 26 features were chosen by a machine learning algorithm for emotion recognition, and the average rate of recognition on GDBT was 71.1%. 22 noteworthy correlation features were chosen after Spearman and emotion state were used for correlation analysis. GDBT has a recognition rate of 74.61%, while XGBoost has a recognition rate of 75.63%. The experimental results prove the validity of our data set and provide data support for the next research.

No abstract

The ability of a CFD engineer to study, capture, and visualise multi-field, 3D flow simulation data is a challenge. Stream surfaces are a useful tool for visualising 3D flow because of their ability to convey many field attributes from their structure. It is important that the CFD engineer can interact with, and examine specific characteristics of the CFD data. We introduce an interactive, cluster based stream surface placement strategy for structured and unstructured CFD data. A two-phase hybrid clustering algorithm is used to visualise interesting subsets of the flow. An interactive tree map interface provides a visual overview and enables interactive selection of cluster details corresponding to interesting features of the data at which to place stream surfaces. We demonstrate the performance and effectiveness of our interactive framework on a range of flow simulations and provide domain expert evaluation of the results, providing valuable insight for the CFD engineers.