Composite Deep Belief Network approach for enhanced Antepartum foetal electrocardiogram signal

Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.     

Order effects in belief updating with consistent and inconsistent evidence

The current study tests for the presence of differential order effects in evaluation tasks with consistent and inconsistent evidence as predicted by the Hogarth and Einhorn (1992) belief-adjustment model. The results, based on both between-subjects and within-subjects experiments, demonstrate that there were significant recency effects with inconsistent evidence as predicted, larger recency effects when the inconsistent evidence was farther apart in subjective value as predicted, and significant recency effects even when subjects were given training designed to both help them understand the task as completely as possible and to be better able to assess the pieces of evidence. By including a within-subjects design, we were able to demonstrate that the difference in subjective value between two pieces of evidence is the primary factor influencing the magnitude of the recency effect, regardless of whether the evidence is consistent or inconsistent. This latter finding is unique and contrary to previous research and theory.     

Belief and Survival

Our ability to survive in a world beset by looming global perils depends ultimately on our collective will to harness our intellects and change our behaviors. In order to respond appropriately, people must first believe that serious problems exist, that there are potential solutions, and that they have a role to play in finding and implementing them. Without such beliefs, individual change is unlikely. In order to promote belief change, it is important to understand how beliefs are learned, what their functions are, and why they are so often resistant to change. These issues are discussed in this article, along with the role that social dilemmas play in inhibiting individually prosocial behavior.     

Belief in a Just World and Employee Voice Behavior: The Mediating Roles of Perceived Efficacy and Risk

Abstract

In this research, we hypothesized that employees’ belief in a just world (BJW) would be positively related to their voice behavior, i.e., the expression of ideas or opinions with the intention of engendering organizational improvement or change, and that this relation would be mediated by perceived voice efficacy and perceived voice risk. To test these hypotheses, we collected self-reported data from employees in two different countries: China (N?=?313) and Germany (N?=?190). The results revealed a positive association between BJW and employee voice behavior in both samples. The two-mediator model was confirmed in the Chinese sample, while only perceived voice efficacy played a mediating role in the German sample. Possible reasons for these differences may be related to differences in cultural dimensions and education levels between the samples. The findings emphasize the importance of bolstering employees’ belief in justice and the organizational climate, which influence perceived voice efficacy and risk, as means to increase organizational voice behavior.     

On Revising Fuzzy Belief Bases

We look at the problem of revising fuzzy belief bases, i.e., belief base revision in which both formulas in the base as well as revision-input formulas can come attached with varying degrees. Working within a very general framework for fuzzy logic which is able to capture certain types of uncertainty calculi as well as truth-functional fuzzy logics, we show how the idea of rational change from “crisp” base revision, as embodied by the idea of partial meet (base) revision, can be faithfully extended to revising fuzzy belief bases. We present and axiomatise an operation of partial meet fuzzy base revision and illustrate how the operation works in several important special instances of the framework. We also axiomatise the related operation of partial meet fuzzy base contraction.This paper is an extended version of a paper presented at the Nineteenth Conference on Uncertainty in Arti.cial Intelligence (UAI’03).     

Finding MAPs using strongly equivalent high order recurrent symmetric connectionist networks

Belief revision is the problem of finding the most plausible explanation for an observed set of evidences. It has many applications in various scientific domains like natural language understanding, medical diagnosis and computational biology. Bayesian Networks (BN) is an important probabilistic graphical formalism widely used for belief revision tasks. In BN, belief revision can be achieved by finding the maximum a posteriori (MAP) assignment. Finding MAP is an NP-Hard problem. In previous work, we showed how to find the MAP assignment in BN using High Order Recurrent Neural Networks (HORN) through an intermediate representation of Cost-Based Abduction. This method eliminates the need to explicitly construct the energy function in two steps, objective and constraints. This paper builds on that previous work by providing the theoretical foundation and proving that the resultant HORN used to find MAP is strongly equivalent to the original BN it tries to solve.