Identifying Critical Dependencies Using General Bayesian Network: An Application to Aviation Operation

Interval data are widely used in real applications to represent the values of quantities in uncertain situations. However, the implied probabilistic causal relationships among interval-valued variables with interval data cannot be represented and inferred by general Bayesian networks with point-based probability parameters. Thus, it is desired to extend the general Bayesian network with effective mechanisms of representation, learning and inference of probabilistic causal relationships implied in interval data. In this paper, we define the interval probabilities, the bound-limited weak conditional interval probabilities and the probabilistic description, as well as the multiplication rules. Furthermore, we propose the method for learning the Bayesian network structure from interval data and the algorithm for corresponding approximate inferences. Experimental results show that our methods are feasible, and we conclude that the Bayesian network with interval probability parameters is the expansion of the general Bayesian network.

General Bayesian Network Approach to Health Informatics Prediction: Emphasis on Performance Comparison

2U: an exact interval propagation algorithm for polytrees with binary variables

It is widely conjectured that the excellent ROC performance of biological vision systems is due in large part to the exploitation of context at each of many levels in a part/whole hierarchy. We propose a mathematical framework (a machine) for constructing probabilistic hierarchical image models, designed to accommodate arbitrary contextual relationships, and we build a demonstration system for reading Massachusetts license plates in an image set collected at Logan Airport. The demonstration system detects and correctly reads more than 98% of the plates, with a negligible rate of false detection. Unlike a formal grammar, the architecture of a composition machine does not exclude the sharing of sub-parts among multiple entities, and does not limit interpretations to single trees (e.g. a scene can have multiple license plates, or no plates at all). In this sense, the architecture is more like a general Bayesian network than a formal grammar. On the other hand, unlike a Bayesian network, the distribution is non-Markovian, and therefore more like a probabilistic context-sensitive grammar. The conceptualization and construction of a composition machine is facilitated by its formulation as the result of a series of non-Markovian perturbations of a Markov backbone.

The football match outcome prediction particularly has gained popularity in recent years. It attract lots type of fan from the analyst expert, managerial of football team and others to predict the football match result before the match start.There are three types of approaches had been proposed to predict win, lose or draw; and evaluate the attributes of the football team. The approaches are statistical approach, machine learningapproach and Bayesian approach. This paper propose the Bayesian approaches within machine learning approaches such as Naive Bayes (NB), Tree Augmented Naive Bayes (TAN) and General Bayesian Network (K2) to predict the football match outcome. The required of football data is the English Premier League match results for three seasons; 2016 – 2017, 2015 – 2016 and 2014 – 2015 downloaded from http://www.football-data.co.uk. The experimental results showed that TAN achieved the highest predictive accuracy of 90.0 % in average across three seasons among others Bayesian approach (K2 and NB). The result from this research is hope that it can be used in future research for predicting the football match outcome.

More Complex Cases: General Bayesian Networks

This paper deals with the problem of coordinating a team of mobile sensor platforms searching for a single mobile non-evading target. It follows the general Bayesian active sensor network approach introduced in [2] where each decision maker plans locally based on an equivalent representation of the target state probability density function (PDF). This paper focuses on the prediction stage of the decentralized Bayesian filter. It looks at how different types of realistic external constraints may affect the target motion and how they may be taken into account in the process model. Two general classes of constraints are identified soft and hard. A few constraint examples from each class are given to illustrate their impact on the evolution of the target state PDF. Multiple constraints of various types can be combined to increase the accuracy of the predicted PDF estimate, thus affecting the individual trajectories of the search platforms. The effectiveness of the framework is demonstrated for a team of airborne search vehicles looking for a drifting target lost in a storm at sea.

In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected robustness of the classifier, that is, the expected probability it maintains its decision after seeing more features. We propose the first algorithm to compute this expected same-decision probability for general Bayesian network classifiers, based on compiling the network into a tractable circuit representation. Moreover, we develop a search algorithm for optimal feature selection that utilizes efficient incremental circuit modifications. Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach.

Bayesian network structures and inference techniques for automatic speech recognition

What is PSS design? – Explained with two industrial cases

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Radar data mining is the key module for signal analysis, where patterns hidden inside of signals are gradually available in the learning process and its superiority is significant for enhancing the security of the radar emitter classification (REC) system. Owing to the disadvantage that radio frequency fingerprinting (RFF) caused by the imperfection of emitter’s hardware is difficult to forge, current deep-learning REC methods based on deep-learning techniques, e.g., convolutional neural network (CNN) and long short term memory (LSTM) are difficult to capture the stable RFF features. In this paper, an online and non-cooperative multi-modal generic representation auxiliary learning REC model, namely muti-modal generic representation auxiliary learning networks (MGRALN), is put forward. Multi-modal means that multi-domain transformations are unified to a generic representation. After this, the representation is employed to facilitate mining the implicit information inside of the signals and to perform the better model robustness, which is achieved by using the available generic genenation to guide the network training and learning. Online means the learning process of REC is only once and the REC is end-to-end. Non-cooperative denotes no demodulation techniques are used before the REC task. Experimental results on the measured civil aviation radar data demonstrate that the proposed method enables one to achieve superior performance.

In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained once. Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way. Additionally, a plug-and-play model selection module is proposed to search for a spatial-spectral transform domain that is more suitable for AD task than the original data. To establish a unified benchmark to comprehensive evaluate our method and existing methods, we develop a large-scale hyperspectral AD dataset (HAD100) that includes 100 real test scenes with diverse anomaly targets. In comparison experiments, we combine our network with a parameter-free detector, and achieve the optimal balance between detection accuracy and inference speed among state-of-the-art AD methods. Experimental results also show that our method still achieves competitive performance when the training and test set are captured by different sensor devices. Our code is available at https://github.com/ZhaoxuLi123/AETNet.

The use of quorums is a well-known approach to achieving mutual exclusion in distributed environments. In this paper, we propose a new availability evaluation method for quorum-based mutual exclusion schemes in the presence of failures. Most of the previously proposed methods take neither the topology of systems nor link failures into consideration, and exhaustive state enumeration has been the only approach that can deal with them so far. By incorporating a notion called Minimal Quorum Spanning Trees, this method can efficiently evaluate the availability of quorum-based mutual exclusion schemes in general topology networks with unreliable nodes and links. Through experimental results, we show the superiority of the proposed method over exhaustive state enumeration.

Purpose– Literature review indicates lack of using the Kano model in organizational behavior domain and managers’ satisfaction. The purpose of this paper is to propose a Kano-based model for managers’ satisfaction.Design/methodology/approach– In order to examine one of the proposed behaviors in this model, the role of employees’ organizational citizenship behavior (OCB) as an attractive behavior, has been investigated. The statistical population includes managers of the Isfahan’s financial service industries. After distributing questionnaires, 224 accurate questionnaires have been used for data analysis. In designing the survey questionnaire, the OCB questionnaire developed by Podsakoffet al.(1990) and Bell and Menguc (2002) has been used. For each of the OCB dimensions, some questions have been initially designed; then after collecting data, by using Kano evaluation table, the behavior types have been determined.Findings– Findings imply that by using the Kano model, five types of behaviors, i.e. must-be, one-dimensional, attractive, indifferent, and reverse can be identified. The findings related to case study also indicate that out of five dimensions of OCB, three dimensions of altruism, sportsmanship, and civic virtue are located in Attractive category; the courtesy dimension is located in must-be category; the conscientiousness dimension is located in one-dimensional category; and in reverse category, no dimension is located. In general, OCB is located in attractive category.Research limitations/implications– By using the results of this survey, a new classification of employees’ behaviors types can be suggested. The results of this study can help employees in understanding what kind of their behaviors causes managers’ satisfaction.Originality/value– The results of this study have an important contribution in the literature of the Kano model and OCB.