A fuzzy risk attitude classification based on prospect theory

Traditional methods do not actually measure peoples’ risk attitude naturally and precisely. Therefore, a fuzzy risk attitude classification method is developed. Since the prospect theory is usually considered as an effective model of decision making, the personalized parameters in prospect theory are firstly fuzzified to distinguish people with different risk attitudes, and then a fuzzy classification database schema is applied to calculate the exact value of risk value attitude and risk be- havior attitude. Finally, by applying a two-hierarchical clas- sification model, the precise value of synthetical risk attitude can be acquired.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Exploring occasion specificity in the assessment of DSM-5 maladaptive personality traits: A latent state-trait theory analysis.

The alternative classification system for personality disorders in DSM-5 features a hierarchical model of maladaptive personality traits. This trait model comprises five broad trait domains and 25 specific trait facets that can be reliably assessed using the Personality Inventory for DSM-5 (PID-5). Although there is a steadily growing literature on the validity of the PID-5, issues of temporal stability and situational influences on test scores are currently unexplored. We addressed these issues using a sample of 611 research participants who completed the PID-5 three times, with time intervals of two months. Latent state-trait (LST) analyses for each of the 25 PID-5 trait facets showed that, on average, 79.5% of the variance was due to stable traits (i.e., consistency), and 7.7% of the variance was due to situational factors (i.e., occasion specificity). Our findings suggest that the PID-5 trait facets predominantly capture individual differences that are stable across time.