Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Accurate estimation of capacities and cross-traffic of all links in a path using ICMP timestamps

The estimation of the link capacity and its available bandwidth in an end-to-end path is crucial for network management, admission control and flow control for adaptive applications. This paper introduces an estimation mechanism able to accurately estimate the available bandwidth of all links in an end-to-end path, through its capacity and cross-traffic estimation. The estimation procedures resort to the dispersions of packet pairs and trains using the concepts of ICMP Timestamp and Traceroute that, efficiently used together, enable the measurement of the dispersions in all links in the path. These mechanisms were evaluated through simulation experiments, where we analyzed the influence of the several network parameters on each estimation mechanism. The results show that these estimation methods are able to accurately estimate both the capacity and the cross-traffic of all links in a path with moderate length and with low overhead.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

智能工作站主机的MMPIS实验模型研究──一个全互连式结构MMPIS硬件环境的设计与实现

本文介绍中日国际合作开发研究的Ａ１型智能网（Ａ１型ＩＮ）智能工作站（ＩＷ）主机的并行推理硬件结构的实验模型设计与实现，给出了一种并行推理硬件支持环境实现方案和该方案在实现中若干问题的解决方法。     

一种产生式系统的并行推理模型

1.引言 在人工智能领域中,产生式系统是一种比较有效的知识表示方法,并得到了广泛应用。目前许多较为成功的专家系统都是用产生式系统实现的~[1]。随着应用领域的不断扩士其知识座的抓橄称渐增     

Fodor and Pylyshyn on connectionism

Fodor and Pylyshyn (1988) have argued that the cognitive architecture is not Connectionist. Their argument takes the following form: (1) the cognitive architecture is Classical; (2) Classicalism and Connectionism are incompatible; (3) therefore the cognitive architecture is not Connectionist. In this essay I argue that Fodor and Pylyshyn's defenses of (1) and (2) are inadequate. Their argument for (1), based on their claim that Classicalism best explains the systematicity of cognitive capacities, is an invalid instance of inference to the best explanation. And their argument for (2) turns out to be question-begging. The upshot is that, while Fodor and Pylyshyn have presented Connectionists with the important empirical challenge of explaining systematicity, they have failed to provide sufficient reason for inferring that the cognitive architecture is Classical and not Connectionist.     

Self-learning inductive inference machines

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions.
La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent à savoir si un algorithme d' apprentissage peut dans une certaine mesure posséder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive autodidactes sont ceux qui font l'apprentissage de programmes pour leurs propres algorithmes, en plus d' autres fonctions.     

Reasoning with Intensional Negative Adjectivals: Semantics, Pragmatics, and Context

Intensional negative adjectives alleged , artificial , fake , false , former , and toy are unusual adjectives that depending on context may or may not be restricting functions. A formal theory of their semantics, pragmatics, and context that uniformly accounts for their complex mathematical and computational characteristics and captures some peculiarities of individual adjectives is presented.
Such adjectives are formalized as new concept builders, negation‐like functions that operate on the values of intensional properties of the concepts denoted by their arguments and yield new concepts whose intensional properties have values consistent with the negation of the old values. Understanding these new concepts involves semantics, pragmatics and context‐dependency of natural language. It is argued that intensional negative adjectives can be viewed as a special‐purpose, weaker, conntext‐dependent negationin natural language. The theory explains and predicts many inferences licensed by expressions involving such adjectives. Implementation of sample examples demonstrates its computational feasibility. Computation of context‐dependent interpretation is discussed.
The theory allows one to enhance a knowledge representation system with similar concept building, negation‐like, context‐dependent functions, the availability of which appears to be a distinct characteristic of natural languages.     

Promise is Debt, Threat Another Matter: The Effect of Credibility on the Interpretation of Conditional Promises and Threats.

Evidence from reasoning tasks shows that promises and threats both tend to receive biconditional interpretations. They also both display high speaker control. On the face of it, the only difference seems to be the positive or negative signing of the consequent. In a promise, the speaker tries to persuade the hearer to do something by holding out the prospect of a particular reward; in a threat, the speaker tries to refrain the hearer from doing something by holding out the prospect of a particular punishment. This paper investigates the respects in which conditional promises and threats differ further by means of an inference task. The credibility of the consequent was manipulated in order to examine whether the acceptability ratings of inferences based on promises and on threats would be equally affected. The results of the inference task and an analysis of the reasons people give for their answers suggest that the credibility of promises is less affected by the use of excessive consequents than the credibility of threats. In other words, promise remains debt, whereas threat is another matter. (PsycINFO Database Record (c) 2010 APA, all rights reserved)