Probabilistic multiple model neural network based leak detection system:experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.

Neural networks for time-varying data

This paper reviews some basic issues and methods involved in using neural networks to respond in a desired fashion to a temporally-varying environment. Some popular network models and training methods are introduced. A speech recognition example is then used to illustrate the central difficulty of temporal data processing: learning to notice and remember relevant contextual information. Feedforward network methods are applicable to cases where this problem is not severe. The application of these methods are explained and applications are discussed in the areas of pure mathematics, chemical and physical systems, and economic systems. A more powerful but less practical algorithm for temporal problems, the moving targets algorithm, is sketched and discussed. For completeness, a few remarks are made on reinforcement learning.

Training with noise is equivalent to Tikhonov regularization

It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. However, the regularization term, which involves second derivatives of the error function, is not bounded below, and so can lead to difficulties if used directly in a learning algorithm based on error minimization. In this paper we show that, for the purposes of network training, the regularization term can be reduced to a positive definite form which involves only first derivatives of the network mapping. For a sum-of-squares error function, the regularization term belongs to the class of generalized Tikhonov regularizers. Direct minimization of the regularized error function provides a practical alternative to training with noise.

Intergroup relations in action: questions asked about lesbian, gay and bisexual issues in diversity training

This paper focuses on the questions which heterosexual trainees ask about lesbian, gay and bisexual (LGB) experience within diversity training about LGB issues. Drawing on a data corpus of 162 questions asked by trainees in 13 tape-recorded training sessions, questions were coded into six categories: (1) general understanding questions; (2) questions about the trainer's life, experience and practices; (3) professional practice questions; (4) questions about lesbian and gay related legislation, policies and procedures; (5) questions about specific people and projects and (6) questions about the meanings, derivations and correct use of terms and symbols. Real questions are compared with the decontexualized questions (and answers to them) that are provided in training manuals and it is demonstrated that these questions differ markedly from how questions actually get asked and how they actually get answered. Recommendations are provided for improving training and the argument made for turning towards analyses of the real world in action, especially when considering intergroup relations. Copyright © 2008 John Wiley & Sons, Ltd.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Challenges for expatriate managers:an exploration of cross-cultural management, adjustment, and training issues faced by Anglo-American managers in Hong Kong

This field work study furthers understanding about expatriate management, in particular, the nature of cross-cultural management in Hong Kong involving Anglo-American expatriate and Chinese host national managers, the important features of adjustment for expatriates living and working there, and the type of training which will assist them to adjust and to work successfully in this Asian environment. Qualitative and quantitative data on each issue was gathered during in-depth interviews in Hong Kong, using structured interview schedules, with 39 expatriate and 31 host national managers drawn from a cross-section of functional areas and organizations. Despite the adoption of Western technology and the influence of Western business practices, micro-level management in Hong Kong retains a cultural specificity which is consistent with the norms and values of Chinese culture. There are differences in how expatriates and host nationals define their social roles, and Hong Kong's recent colonial history appears to influence cross-cultural interpersonal interactions. The inability of the spouse and/or family to adapt to Hong Kong is identified as a major reason for expatriate assignments to fail, though the causes have less to do with living away from family and friends, than with Hong Kong's highly urbanized environment and the heavy demands of work. Culture shock is not identified as a major problem, but in Hong Kong micro-level social factors require greater adjustment than macro-level societal factors. The adjustment of expatriate managers is facilitated by a strong orientation towards career development and hard work, possession of technical/professional expertise, and a willingness to engage in a process of continuous 'active learning' with respect to the host national society and culture. A four-part model of manager training suitable for Hong Kong is derived from the study data. It consists of a pre-departure briefing, post-arrival cross-cultural training, language training in basic Cantonese and in how to communicate more effectively in English with non-native speakers, and the assignment of a mentor to newly arrived expatriate managers.

Introduction to the theory and application of data envelopment analysis:a foundation text with integrated software

The book aims to introduce the reader to DEA in the most accessible manner possible. It is specifically aimed at those who have had no prior exposure to DEA and wish to learn its essentials, how it works, its key uses, and the mechanics of using it. The latter will include using DEA software. Students on degree or training courses will find the book especially helpful. The same is true of practitioners engaging in comparative efficiency assessments and performance management within their organisation. Examples are used throughout the book to help the reader consolidate the concepts covered. Table of content: List of Tables. List of Figures. Preface. Abbreviations. 1. Introduction to Performance Measurement. 2. Definitions of Efficiency and Related Measures. 3. Data Envelopment Analysis Under Constant Returns to Scale: Basic Principles. 4. Data Envelopment Analysis under Constant Returns to Scale: General Models. 5. Using Data Envelopment Analysis in Practice. 6. Data Envelopment Analysis under Variable Returns to Scale. 7. Assessing Policy Effectiveness and Productivity Change Using DEA. 8. Incorporating Value Judgements in DEA Assessments. 9. Extensions to Basic DEA Models. 10. A Limited User Guide for Warwick DEA Software. Author Index. Topic Index. References.

The de-gaying and re-gaying of AIDS:contested homophobias in lesbian and gay awareness training

From the first recognition of AIDS as a disease, it was publicly conceptualized as a 'gay plague'. In response, health education and diversity training sought to counter this association claiming that AIDS is an 'equal opportunity' virus - that it can affect anyone. In this article, we analyse talk about HIV/AIDS within a data corpus of 13 tape-recorded lesbian and gay awareness training sessions. Counter to the way in which interactions are described in the lesbian and gay awareness training literature, we found that it was trainees, rather than trainers, who pursued discussions about HIV/AIDS, and who did so in order to claim the 'de-gaying' of AIDS, which they treated as representing a 'non-prejudiced' position. By contrast, and in response to trainees' insistence on de-gaying AIDS, trainers were 're-gaying' AIDS. Our analysis highlights that in these sessions - designed explicitly to counter homophobic attitudes - apparently 'factual' claims and counter-claims about infection rates and risk groups are underpinned by essentially contested definitions of what constitutes a 'homophobic' attitude. We conclude by pointing to the value of detailed analysis of talk-in-interaction for understanding professional practices, and suggest strategies for improving the pedagogic value of training. Copyright © 2005 SAGE Publications.

Minimum description length, regularisation and multi-modal data

Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.

A study in Britain and France of the development of cross-cultural in-house sales training using distance learning.

The thesis is concerned with cross-cultural distance learning in two countries: Great Britain and France. Taking the example of in-house sales training, it argues that it is possible to develop courses for use in two or more countries of differing culture and language. Two courses were developed by the researcher. Both were essentially print-based distance-learning courses designed to help salespeople achieve a better understanding of their customers. One used a quantitative, the other qualitative approach. One considered the concept of the return on investment and the other, for which a video support was also developed, considered the analysis of a customer's needs. Part 1 of the thesis considers differences in the training context between France and Britain followed by a review of the learning process with reference to distance learning. Part 2 looks at the choice of training medium course design and evaluation and sets out the methodology adopted, including problems encountered in this type of fieldwork. Part 3 analyses the data and draws conclusions from the findings, before offering a series of guidelines for those concerned with the development of cross-cultural in-house training courses. The results of the field tests on the two courses were analysed in relation to the socio-cultural, educational and experiential background of the learners as well as their preferred learning styles. The thesis argues that it is possible to develop effective in-house sales training courses to be used in two cultures and identifies key considerations which need to be taken into account when carrying out this type of work.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.