University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learning

Abstract: In an attempt to discover the facial action units for affective states that occur during complex learning, this study adopted an emote-aloud procedure in which participants were recorded as they verbalised their affective states while interacting with an intelligent tutoring system (AutoTutor). Participants’ facial expressions were coded by two expert raters using Ekman's Facial Action Coding System and analysed using association rule mining techniques. The two expert raters received an overall kappa that ranged between .76 and .84. The association rule mining analysis uncovered facial actions associated with confusion, frustration, and boredom. We discuss these rules and the prospects of enhancing AutoTutor with non-intrusive affect-sensitive capabilities.

Mechanisms linking realistic job previews with turnover: a meta‐analytic path analysis

Abstract: A meta-analytic path analysis with k = 52 studies and sample size of roughly 17,000 showed that enhanced perceptions of organizational honesty is the primary mechanism by which realistic job previews (RJPs) influence voluntary turnover. This suggests revisions to RJP theory to incorporate social exchange and the way RJPs lead individuals to feel about the organization. Results assessing several new potential moderators of relationships between RJPs and turnover found that the most effective RJP design may be an oral or written RJP delivered posthire and designed to signal organizational honesty. We discuss several key theoretical and practical implications of the results based on a novel signaling theory perspective on RJPs.

Eigen-CAM: Class Activation Map using Principal Components

Abstract: Deep neural networks are ubiquitous due to the ease of developing models and their influence on other domains. At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data. Making sense of such complex models (i.e., millions of parameters and hundreds of layers) remains challenging for developers as well as the end-users. This is partially due to the lack of tools or interfaces capable of providing interpretability and transparency. A growing body of literature, for example, class activation map (CAM), focuses on making sense of what a model learns from the data or why it behaves poorly in a given task. This paper builds on previous ideas to cope with the increasing demand for interpretable, robust, and transparent models. Our approach provides a simpler and intuitive (or familiar) way of generating CAM. The proposed Eigen-CAM computes and visualizes the principle components of the learned features/representations from the convolutional layers. Empirical studies were performed to compare the Eigen-CAM with the state-of-the-art methods (such as Grad-CAM, Grad-CAM++, CNN-fixations) by evaluating on benchmark datasets such as weakly-supervised localization and localizing objects in the presence of adversarial noise. Eigen-CAM was found to be robust against classification errors made by fully connected layers in CNNs, does not rely on the backpropagation of gradients, class relevance score, maximum activation locations, or any other form of weighting features. In addition, it works with all CNN models without the need to modify layers or retrain models. Empirical results show up to 12% improvement over the best method among the methods compared on weakly supervised object localization.

Biocomplexity: adaptive behavior in complex stochastic dynamical systems.

Abstract: Existing methods of complexity research are capable of describing certain specifics of bio systems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimes-abrupt state transitions. Chaos tools have the potential of reaching to the essential driving mechanisms that organize matter into living substances. Our basic thesis is that while established chaos tools are useful in describing complexity in physical systems, they lack the power of grasping the essence of the complexity of life. This thesis illustrates sensory perception of vertebrates and the operation of the vertebrate brain. The study of complexity, at the level of biological systems, cannot be completed by the analytical tools, which have been developed for non-living systems. We propose a new approach to chaos research that has the potential of characterizing biological complexity. Our study is biologically motivated and solidly based in the biodynamics of higher brain function. Our biocomplexity model has the following features, (1) it is high-dimensional, but the dimensionality is not rigid, rather it changes dynamically; (2) it is not autonomous and continuously interacts and communicates with individual environments that are selected by the model from the infinitely complex world; (3) as a result, it is adaptive and modifies its internal organization in response to environmental factors by changing them to meet its own goals; (4) it is a distributed object that evolves both in space and time towards goals that is continually re-shaping in the light of cumulative experience stored in memory; (5) it is driven and stabilized by noise of internal origin through self-organizing dynamics. The resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and stochastic differential equations. This paper outlines several possible avenues to analyze these systems. Of special interest are input-induced and noise-generated, or spontaneous state-transitions and related stability issues.