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Inaccurate cost estimates have substantial effects on the final cost of construction projects and erode profits. Cost estimation at conceptual phase is a challenge as inadequate information is available. For this purpose, approaches for cost estimation have been explored thoroughly, however they are not employed extensively in practice. The main goal of this paper is to comparing the performance of various models in predicting the cost of construction projects at early conceptual phase in the project development. In this study, on the basis of the actual project data, three modeling algorithms such as random forest, support vector machine and artificial neural networks are used to forecast the construction cost of Ethiopian highway projects. The three models were then compared based on the outcomes of prediction and root mean square error. The findings revealed that random forest outperforms neural network and support vector machine in realizing better prediction accuracy. Based on root mean square error, the random forest cost model provides 18.8% and 23.4% more accurate result than neural network and support vector machine models respectively. It is anticipated that a more reliable cost estimation model could be designed in the early project phases by using a random forest regression technique in the development of a highway construction cost estimation model. In conclusion, the practitioners in the highway construction industry can make sound financial decisions at the early phases of the project development in Ethiopia.

Transliteration is a key component of machine translation systems and software internationalization. This paper demonstrates that neural sequence-to-sequence models obtain state of the art or close to state of the art results on existing datasets. In an effort to make machine transliteration accessible, we open source a new Arabic to English transliteration dataset and our trained models.

Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the android studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.

Information Retrieval (IR) models need to deal with two difficult issues, vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to the difficulty of retrieving relevant documents that do not contain exact query terms but semantically related terms. Term dependencies refers to the need of considering the relationship between the words of the query when estimating the relevance of a document. A multitude of solutions has been proposed to solve each of these two problems, but no principled model solve both. In parallel, in the last few years, language models based on neural networks have been used to cope with complex natural language processing tasks like emotion and paraphrase detection. Although they present good abilities to cope with both term dependencies and vocabulary mismatch problems, thanks to the distributed representation of words they are based upon, such models could not be used readily in IR, where the estimation of one language model per document (or query) is required. This is both computationally unfeasible and prone to over-fitting. Based on a recent work that proposed to learn a generic language model that can be modified through a set of document-specific parameters, we explore use of new neural network models that are adapted to ad-hoc IR tasks. Within the language model IR framework, we propose and study the use of a generic language model as well as a document-specific language model. Both can be used as a smoothing component, but the latter is more adapted to the document at hand and has the potential of being used as a full document language model. We experiment with such models and analyze their results on TREC-1 to 8 datasets.

Information Retrieval (IR) models need to deal with two difficult issues, vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to the difficulty of retrieving relevant documents that do not contain exact query terms but semantically related terms. Term dependencies refers to the need of considering the relationship between the words of the query when estimating the relevance of a document. A multitude of solutions has been proposed to solve each of these two problems, but no principled model solve both. In parallel, in the last few years, language models based on neural networks have been used to cope with complex natural language processing tasks like emotion and paraphrase detection. Although they present good abilities to cope with both term dependencies and vocabulary mismatch problems, thanks to the distributed representation of words they are based upon, such models could not be used readily in IR, where the estimation of one language model per document (or query) is required. This is both computationally unfeasible and prone to over-fitting. Based on a recent work that proposed to learn a generic language model that can be modified through a set of document-specific parameters, we explore use of new neural network models that are adapted to ad-hoc IR tasks. Within the language model IR framework, we propose and study the use of a generic language model as well as a document-specific language model. Both can be used as a smoothing component, but the latter is more adapted to the document at hand and has the potential of being used as a full document language model. We experiment with such models and analyze their results on TREC-1 to 8 datasets.

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in $n$-gram models. Using this connection, we draw upon ideas from smoothing to develop effective noising schemes. We demonstrate performance gains when applying the proposed schemes to language modeling and machine translation. Finally, we provide empirical analysis validating the relationship between noising and smoothing.

Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Under various fine-grained label sets and a cross-linguistic setting, our feedforward models perform consistently better or at least just as well as systems that require hand-crafted surface features. Our models present the first neural Chinese discourse parser in the style of Chinese Discourse Treebank, showing that our results hold cross-linguistically.

Organizations of computing elements that follow the principles of physiological neurons, called neural network models, have been shown to have the capability of learning to recognize patterns and to retrieve complete patterns from partial representations. The implementation of neural network models as VLSI or USLI chips within a few years is certain. This report reviews a number of published papers on neural network models and their capabilities. Then, an outline of a speech recognition system that uses neural network modules for learning and recognition is proposed. It is based on the layered structure of existing speech recognition systems, and uses forced learning (feedback) for conditioning the neural modules at the various levels. (Author)

The article is devoted to the problem automatic decision-making in a mode of real time for difficult objects on basis of neural network emulators. The technology of modeling of base processes of automation of decision-making (recognition of conditions, allocation of the homogeneous data, forecasting of dynamics of behavior) for critical infrastructures in different subject domains is offered. At the heart of the approach there is the idea of creation of the additional, independent tool of decision-making in a mode of real time. The models and algorithms realized in the environment of standard emulators format Statistika Neural Network, providing well-founded indicators of reliability of accepted decisions are resulted. 

This research project is aimed at developing silicon based implementations of neural network models. The main advantages of our approach are its use of standard, present day technology and its highly memory due to the use of optics. Two different embodiments of the electronic part of the neural processor have been realized. A phototransistor based network using standard complementary metal oxide semiconductors technology has been built and tested. The CCD version of the optoelectronic architecture has been fabricated and tested, proving the viability of this architecture. All electronic loading has been explored and offers possibilities of rugged, compact systems. (KR)

The grant supported research efforts in two main research questions: (1) How do we segment and estimate the slant/tilt of natural scenes with highly irregular textures, using both biological and non-biological computing architectures; and (2) How do we design -machine perception and action systems and learning mechanisms that can improve their performance through repeated feedback and interaction with their environment? Section 2 describes the research and publications relating to the slant/tilt and segmentation problem. Section 3 describes research and publications using developmental psychological and machine learning frameworks for learning visuomotor tasks. Finally, Section 4 summarizes the two Ph.D dissertations in the Department of Computer and Information Science made possible with AFOSR support.

In order to determine the use, protection and economic life of water resources; it is important to make estimations about rainfall-runoff values. However, it is quite complicated to estimate rainfall-runoff. For this reason, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods, which are widely used today for complex hydrological problems, are preferred for the rainfall-runoff model. For model creation, the hydrological and seasonal data from the United States Muskegon basin are used. Estimation study was done with ANN and MLR methods using 1396 daily rainfall, temperature and rainfall data belonging to the region. According to the model results, it is seen that the ANN method has results with low error and high determination in the rainfall runoff model. ANN method can be used as an alternative way to classical methods in rainfall-runoff predictions.

This article is from Frontiers in Computational Neuroscience , volume 7 . Abstract The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

We hope to assess the completeness of reporting, using TRIPOD-AI, in studies leveraging artificial neural networks (ANNs) for cancer screening, detection, and characterisation in medical imaging applications. By evaluating 99 studies, this project aims to identify significant gaps in methodological transparency. Our findings on reporting completeness can inform efforts toe enhance reporting standards and promote model reproducibility. This would ultimately facilitate clinical translation and implementation.

Finite Element, Analytical, Artificial Neural Network Models For Carbon Fibre Reinforced Polymer Confined Concrete Filled Steel Columns With Elliptical Cross Sections

A complete evaluation of the tribological characteristics of a given material/mechanical system is a time-consuming operation since the friction and wear process is extremely systems sensitive. As a result, experimental designs (i.e., Latin Square, Taguchi) have been implemented in an attempt to not only reduce the total number of experimental combinations needed to fully characterize a material/mechanical system, but also to acquire life data for a system without having to perform an actual life test. Unfortunately, these experimental designs still require a great deal of experimental testing and the output does not always produce meaningful information. In order to further reduce the amount of experimental testing required, this study employs a computer neural network model to investigate different material/mechanical systems. The work focuses on the modeling of the wear behavior, while showing the feasibility of using neural networks to predict life data. The model is capable of defining which input variables will influence the tribological behavior of the particular material/mechanical system being studied based on the specifications of the overall system.

Hand-crafted features based on linguistic and domain-knowledge play crucial role in determining the performance of disease name recognition systems. Such methods are further limited by the scope of these features or in other words, their ability to cover the contexts or word dependencies within a sentence. In this work, we focus on reducing such dependencies and propose a domain-invariant framework for the disease name recognition task. In particular, we propose various end-to-end recurrent neural network (RNN) models for the tasks of disease name recognition and their classification into four pre-defined categories. We also utilize convolution neural network (CNN) in cascade of RNN to get character-based embedded features and employ it with word-embedded features in our model. We compare our models with the state-of-the-art results for the two tasks on NCBI disease dataset. Our results for the disease mention recognition task indicate that state-of-the-art performance can be obtained without relying on feature engineering. Further the proposed models obtained improved performance on the classification task of disease names.

Recent application of neural networks (NNs) to modeling interatomic interactions has shown the learning machines' encouragingly accurate performance for select elemental and multicomponent systems. In this study, we explore the possibility of building a library of NN-based models by introducing a hierarchical NN training. In such a stratified procedure NNs for multicomponent systems are obtained by sequential training from the bottom up: first unaries, then binaries, and so on. Advantages of constructing NN sets with shared parameters include acceleration of the training process and intact description of the constituent systems. We use an automated generation of diverse structure sets for NN training on density functional theory-level reference energies. In the test case of Cu, Pd, Ag, Cu-Pd, Cu-Ag, Pd-Ag, and Cu-Pd-Ag systems, NNs trained in the traditional and stratified fashions are found to have essentially identical accuracy for defect energies, phonon dispersions, formation energies, etc. The models' robustness is further illustrated via unconstrained evolutionary structure searches in which the NN is used for the local optimization of crystal unit cells.

Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-for-money (VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved (MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System (BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved (MW/hr) was Building Energy Index (BEI) (kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer Whale-Backpropagation Algorithm (KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come.

. Automatic object recognition from still imagery, insensitive to clutter and partial occlusion, is an unsolved computer vision problem with countless applications to military readiness. Ambiguity of segmentation of complex images into objects is the major stumbling block. Incorporation of certain structural features of the primate early visual system into computational models has been suggested as a potential solution. However, little is known about effects of these features on segmentation performance of either humans or computational models

A multilayer neural network has been developed that consists of slabs of single neuron models. Each slab is composed of a single type of neurons, which differs between the slabs. The network was trained using a biologically inspired, Hebbian-like, learning rule on EMG data and good training/testing classification performance was obtained. It was shown that the biologically inspired network, the novel architecture of which is derived from the functionally distinct hypercolumns of neurons in the brain, can be successfully applied on difficult classification tasks.

Parameter and architectural selection for Multiple Layered Perceptron (MLP) classifiers involve a number of heuristic design procedures, The mm in the design process of such classifiers is to achieve maximum generalization and avoid over-fitting of the training data. It has been the objective of this study to develop a symbolic prediction model to calculate the point at which training should cease for a given Neural Network (NN) based 12-lead ECG classifier to ensure maximum generalization. This prediction model has been obtained by means of Genetic Programming (GP), where a GP individual has been evolved to generate a symbolic model that predicts the optimal number of training epochs for three different ECG myocardial infarction classifiers: Anterior Myocardial Infarction (AMI), Inferior Myocardial Infarction (IMI), and Combined Myocardial Infarction (CMI). The GP model demonstrated to be a very accurate method showing no significant differences between the optimal number of epoch values and the predicted values for both: train and test data sets for the three aforementioned pathologies.

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods. 

The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.

Only a few parameters that affect the machining process in ECM are controllable. It is clearly established from reported works that results reported in literature cannot be extrapolated. So for any new material - electrolyte combination and machining conditions experiments need to be conducted to predict the effects of process parameters on machined geometry. For effective exploitation of ECM for machining SG Iron it is essential to develop models for predicting the nature of surface that will be generated. Two widely used methods for correlating controllable process parameters and surface roughness parameters are Design of Experiments and Artificial Neural Networks (ANN). The back–propagation algorithm is used widely as a learning algorithm in feed-forward multilayer neural network. A large data set is usually necessary to train the ANNs. However, generating a large number of data for training ANN is not only time consuming but very expensive. Hence to reduce the cost of developing NN models it is decided to train the network with only those thirteen data that are used for developing Box Behnken design based models. The three configurations of the ANN considered are 3-10-10-1, 3-20-20-20-1 and3-39-39-39-1. Because of the small data set the deviations noted between predictions obtained using Box-Behnken models and ANNs are high. Denoising is done in two stages using Multiscale Principal Component Analysis (PCA) and dropping the high frequency coefficients by filtering with Daubechies wavelet. The first stage denoising carried out using PCA has resulted in significant reduction in deviation (error). After second level denoising the errors have reduces further. Of the three network configurations studied lowest configuration gave the best results in majority of the cases and where as for the highest configuration, the denoising effect is small. Not only the error is reduced but the number of points with large errors has reduced substantially. The differences between the outputs from the neural network model and Box Behnken models are well within the confidence intervals calculated from ANOVA.

We observe that the standard log likelihood training objective for a Recurrent Neural Network (RNN) model of time series data is equivalent to a variational Bayesian training objective, given the proper choice of generative and inference models. This perspective may motivate extensions to both RNNs and variational Bayesian models. We propose one such extension, where multiple particles are used for the hidden state of an RNN, allowing a natural representation of uncertainty or multimodality.

Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components, we achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.

We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic models using such LSTM RNNs can be equaled by sequence trained phone models initialized with connectionist temporal classification (CTC). In this paper, we present techniques that further improve performance of LSTM RNN acoustic models for large vocabulary speech recognition. We show that frame stacking and reduced frame rate lead to more accurate models and faster decoding. CD phone modeling leads to further improvements. We also present initial results for LSTM RNN models outputting words directly.

This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-of-the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.

Cellular neural circuit and networks consisting of interconnected neurons and glia are ulti- mately responsible for the information processing associated with information processing in the brain. While there are major efforts aimed at mapping the structural and (electro)physiological connectivity of brain networks, such as the White House BRAIN Initiative aimed at the devel- opment of neurotechnologies capable of high density neural recordings, theoretical and compu- tational methods for analyzing and making sense of all this data seem to be further behind. Here, we propose and provide a summary of an approach for calculating effective connectivity from experimental observations of neuronal network activity. The proposed method operates on network-level data, makes use of all relevant prior knowledge, such as dynamical models of individual cells in the network and the physical structural connectivity of the network, and is broadly applicable to large classes of biological and non-biological networks.

Distant speech recognition is a challenge, particularly due to the corruption of speech signals by reverberation caused by large distances between the speaker and microphone. In order to cope with a wide range of reverberations in real-world situations, we present novel approaches for acoustic modeling including an ensemble of deep neural networks (DNNs) and an ensemble of jointly trained DNNs. First, multiple DNNs are established, each of which corresponds to a different reverberation time 60 (RT60) in a setup step. Also, each model in the ensemble of DNN acoustic models is further jointly trained, including both feature mapping and acoustic modeling, where the feature mapping is designed for the dereverberation as a front-end. In a testing phase, the two most likely DNNs are chosen from the DNN ensemble using maximum a posteriori (MAP) probabilities, computed in an online fashion by using maximum likelihood (ML)-based blind RT60 estimation and then the posterior probability outputs from two DNNs are combined using the ML-based weights as a simple average. Extensive experiments demonstrate that the proposed approach leads to substantial improvements in speech recognition accuracy over the conventional DNN baseline systems under diverse reverberant conditions.

Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within a fuzzy environment. In this model, the uncertainty encountered in the forecasting process is taken as being produced by our incomplete understanding of nature. As a result, it is different from any stochastic methods. The major problem with this method is that the forecasted values depend to some degree on our interpretations of the outputs of the forecasting model, which makes the process quite subjective. Artificial neural networks represent an advanced technology applied in engineering. When applied in forecasting, uncertainty is ignored. Consequently, the model is a deterministic one. In spite of this, because of the ability to generalize once trained, the network model has robustness. Examples show how these methods are applied. Six figures and two tables illustrate these applications. (SLD)

Contemporary neural network models provide insights into some of the organizational principles that govern biological sensory-motor systems, and offer a level of computational precision that enables sharp comparisons and contrasts to be made between different sensory-motor systems. The capacity of these models to clarify, integrate, and predict behavioral and neural data is predicated upon the coordinated use of theoretical, mathematical, computational and empirical tools in a manner that reveals many more constraints on brain design than empirical tools alone. No single experimental paradigm in the behavioral and brain sciences provides sufficiently many data to uniquely characterize a neural system. Interdisciplinary theoretical and empirical approaches that can coordinate and discover both top-down and bottom-up constraints at multiple levels of behavioral and neural organization provide a much greater level of guidance towards characterizing brain designs. The present chapter takes as its point of departure one important design principle that has been clarified by such an interdisciplinary approach. This is the principle of vector encoding that has been described, for example, in both the control of saccadic eye movements by the superior colliculus and the control of arm movements by the motor cortex.

Unsaturated soil hydraulic properties determine the capacity of soils and rocks to retain and transmit water. Hydraulic properties may be needed in applications involving remediation and restoration of contaminated soils, trafficability of soils, flood control, and remotely sensed data. Current methods to measure hydraulic properties are perceived as inadequate to meet the data requirements for most (large scale) applications. Neural networks are used in our research to develop pedotransfer functions (PTFs) for the hierarchical estimation of hydraulic data from basic data such as soil texture and bulk density. Neural networks were calibrated on a database of more than 2000 soils. The predictions generally compared favorably with published PTFs. Especially noteworthy is the unsaturated hydraulic conductivity; we improved its prediction by almost half an order of magnitude compared to traditional methods. We have completed the computer program Rosetta to facilitate neural network based predictions of hydraulic parameters. The uncertainty of the estimates was shown to increase for lower water contents. We have also converted our database of soil hydraulic properties to Windows from DOS.

This thesis evaluates a possible use of artificial neural networks for military manpower and personnel analysis. Two neural network models were constructed to predict the reenlistment behavior of a select group of individuals in the Navy, from a sample of 680 individuals. The data were extracted from the 1985 DoD Survey of Officer and Enlisted Personnel. Explanatory variables were grouped into demographic/personal, military characteristics, perceived probability of civilian employment, educational level, and satisfaction with military life and military benefits. The first neural network model was compared to a more traditional method of statistical modeling (logistic regression analysis) to determine the strengths and weaknesses of the neural network model. Both models used the same set of 17 variables and were tested using a holdout sample of 100 observations. The neural network model was found to be comparable to the logistic regression model as a predictor, but deficient as a policy analysis model. The second neural network model was constructed using the same data set and architecture as the first neural network model, including the original 17 variables, plus an additional II variables that consisted of variables with and without theoretical foundation for predicting reenlistment. The two neural network models were then compared and found to be similar at predicting reenlistment. Both neural network models were considered to be deficient as tools for policy analysts.... Artificial neural networks, Neural networks, Reenlistment behavior.

This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that despite being more costly to train, RNNLMs obtain much lower perplexities on standard benchmarks than n-gram models. We train the largest known RNNs and present relative word error rates gains of 18% on an ASR task. We also present the new lowest perplexities on the recently released billion word language modelling benchmark, 1 BLEU point gain on machine translation and a 17% relative hit rate gain in word prediction.

It is today acknowledged that neural network language models outperform backoff language models in applications like speech recognition or statistical machine translation. However, training these models on large amounts of data can take several days. We present efficient techniques to adapt a neural network language model to new data. Instead of training a completely new model or relying on mixture approaches, we propose two new methods: continued training on resampled data or insertion of adaptation layers. We present experimental results in an CAT environment where the post-edits of professional translators are used to improve an SMT system. Both methods are very fast and achieve significant improvements without overfitting the small adaptation data.

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.

This thesis evaluates a possible use of artificial neural networks for military manpower and personnel analysis. Two neural network models were constructed to predict the reenlistment behavior of a select group of individuals in the Navy, from a sample of 680 individuals. The data were extracted from the 1985 DoD Survey of Officer and Enlisted Personnel. Explanatory variables were grouped into demographic/personal, military characteristics, perceived probability of civilian employment, educational level, and satisfaction with military life and military benefits. The first neural network model was compared to a more traditional method of statistical modeling (logistic regression analysis) to determine the strengths and weaknesses of the neural network model. Both models used the same set of 17 variables and were tested using a holdout sample of 100 observations. The neural network model was found to be comparable to the logistic regression model as a predictor, but deficient as a policy analysis model. The second neural network model was constructed using the same data set and architecture as the first neural network model, including the original 17 variables, plus an additional II variables that consisted of variables with and without theoretical foundation for predicting reenlistment. The two neural network models were then compared and found to be similar at predicting reenlistment. Both neural network models were considered to be deficient as tools for policy analysts...

We investigated the effects of synaptic depression on the macroscopic behavior of stochastic neural networks. Dynamical mean field equations were derived for such networks by taking the average of two stochastic variables: a firing state variable and a synaptic variable. In these equations, their average product is decoupled as the product of averaged them because the two stochastic variables are independent. We proved the independence of these two stochastic variables assuming that the synaptic weight is of the order of 1/N with respect to the number of neurons N. Using these equations, we derived macroscopic steady state equations for a network with uniform connections and a ring attractor network with Mexican hat type connectivity and investigated the stability of the steady state solutions. An oscillatory uniform state was observed in the network with uniform connections due to a Hopf instability. With the ring network, high-frequency perturbations were shown not to affect system stability. Two mechanisms destabilize the inhomogeneous steady state, leading two oscillatory states. A Turing instability leads to a rotating bump state, while a Hopf instability leads to an oscillatory bump state, which was previous unreported. Various oscillatory states take place in a network with synaptic depression depending on the strength of the interneuron connections.

We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further improvements.

We present NN-grams, a novel, hybrid language model integrating n-grams and neural networks (NN) for speech recognition. The model takes as input both word histories as well as n-gram counts. Thus, it combines the memorization capacity and scalability of an n-gram model with the generalization ability of neural networks. We report experiments where the model is trained on 26B words. NN-grams are efficient at run-time since they do not include an output soft-max layer. The model is trained using noise contrastive estimation (NCE), an approach that transforms the estimation problem of neural networks into one of binary classification between data samples and noise samples. We present results with noise samples derived from either an n-gram distribution or from speech recognition lattices. NN-grams outperforms an n-gram model on an Italian speech recognition dictation task.

This article is from Frontiers in Computational Neuroscience , volume 7 . Abstract Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies.

The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied "in the wild", their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. The 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems.

We present a theoretical application of an optimal experiment design (OED) methodology to the development of mathematical models to describe the stimulus-response relationship of sensory neurons. Although there are a few related studies in the computational neuroscience literature on this topic, most of them are either involving non-linear static maps or simple linear filters cascaded to a static non-linearity. Although the linear filters might be appropriate to demonstrate some aspects of neural processes, the high level of non-linearity in the nature of the stimulus-response data may render them inadequate. In addition, modelling by a static non-linear input - output map may mask important dynamical (time-dependent) features in the response data. Due to all those facts a non-linear continuous time dynamic recurrent neural network that models the excitatory and inhibitory membrane potential dynamics is preferred. The main goal of this research is to estimate the parametric details of this model from the available stimulus-response data. In order to design an efficient estimator an optimal experiment design scheme is proposed which computes a pre-shaped stimulus to maximize a certain measure of Fisher Information Matrix. This measure depends on the estimated values of the parameters in the current step and the optimal stimuli are used in a maximum likelihood estimation procedure to find an estimate of the network parameters. This process works as a loop until a reasonable convergence occurs. The response data is discontinuous as it is composed of the neural spiking instants which is assumed to obey the Poisson statistical distribution. Thus the likelihood functions depend on the Poisson statistics. In order to validate the approach and evaluate its performance, a comparison with another approach on estimation based on randomly generated stimuli is also presented.

Includes bibliographical references (p. 36-37)

Compression Index (CI) is one of the frequently used soil parameters for the determination of possible settlement. In this study, the Compression Index of Marine clay is predicted using Artificial Neural network (ANN). Marine clay samples were collected from eight boreholes located at distance varying from 0.5 Km to 2.5 Km landward from the coastline of Pondicherry. The depth of boring was up to 12m. These samples were used for determining the Plastic Limit (PL), Liquid Limit (LL) and the Natural Moisture Content (NMC) and these were taken as input parameters for computing CI. These input parameters are taken as ‘data set 1’. Similar properties of soil from over 51 boreholes were considered for analysis designated as ‘Data set 2’where the depth of sampling was up to 52. These were located at a distance up to 5.0 Km from the shoreline of Puducherry distributed across the town covering a length of over 5.0 km. In Data set 2, the LL, PL, Plasticity index (PI) Specific Gravity (G), Swell Percentage, ‘N’value and the ratio of PL/LL of the soil samples were taken as input parameters for prediction of CI. The input variables were reduced in successive iterations to determine their influence in the prediction of CI. Multilinear Regression Models using the same set of inputs was compared with that of ANN. Both the analysis methods indicated that the LL and PL of soil are not only easy to determine but are competent to predict CI with a high degree of accuracy.

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.

Today's world is no longer that of yesterday, the pace with which we live and also the speed is enormous and rapid, that overnight we discover the appearance of new technologies and solutions in all the fields, in particular, that of scientific research. Artificial intelligence plays the main role. Predicting the behavior of new materials using artificial neural networks (ANNs) has become a frequently adopted solution by researchers today. The performance of neural networks depends mainly on the activation functions used. This work was designed to mainly study the impact of these functions on the response speed of an ANN in general, and particularly on the model we are working on to predict the thermomechanical behavior of innovative materials. By using tansig, purelin and logsig in a feed forward back propagation training by Levenberg-Marquardt algorithm, we were able to generate 9 models. For each of these models, we were interested in analyzing the speed’s response of the network and studying its regression. Thus, this work was able to show us that choosing the right neuron activation function from one layer to another can clearly influence the performance of the results. Depending on the problem studied, the desired objective and the chosen architecture, the activation function can radically change the result and provide us with the expected efficiency.

1Canterville Ghost

By Oscar Wilde

The American Minister and his family have bought the English stately home Canterville Chase, complete with the ghost of Sir Simon de Canterville - blood-stains, clanking chains and all. But these modern Americans will have no truck with ghostly goings-on, and set out to beat the spectre at his own game. (Summary by David Barnes)

2Importance of Being Earnest

By Oscar Wilde

The Importance of Being Earnest is a classic comedy of manners in which two flippant young men, in order to impress their respected beloveds, pretend that their names are “Ernest,” which both young ladies believe confers magical qualities on the possessor. It was first performed for the public on February 14, 1895 at the St. James’ Theatre in London, and is regarded by many critics and scholars as being the wittiest play in the English language. (Summary from Wikipedia.org)<br /><br /><b>Characters:</b><br /><em>John Worthing, J.P.</em> - Read by <a href="http://www.johngon.com" rel="nofollow">John Gonzalez</a><br /><em>Algernon Moncrieff</em> - Read by <a href="http://www.gordmackenzie.com/" rel="nofollow">Gord Mackenzie</a><br /><em>Rev. Canon Chasuble, D.D.</em> - Read by <a href="http://tis.goringe.net/" rel="nofollow">Chris Goringe</a><br /><em>Merriman, Butler</em> - Read by Peter Yearsley<br /><em>Lane, Manservant</em> - Read by <a href="http://dosemagazine.blogsome.com/" rel="nofollow">Hugh McGuire</a><br /><em>Lady Bracknell</em> - Read by <a href="http://www.mediatinker.com/" rel="nofollow">Kristen McQuillin</a><br /><em>Hon. Gwendolen Fairfax</em> - Read by Deana<br /><em>Cecily Cardew</em> - Read by <a href="http://kayray.org/" rel="nofollow">Kara Shallenberg</a><br /><em>Miss Prism, Governess</em> - Read by Sureka<br /><em>Narrator</em> - Read by <a href="http://betsie.info" rel="nofollow">Betsie Bush</a><br /><em>Project Editor</em> - <a href="http://www.writingshow.com/" rel="nofollow">Paula Berinstein</a>

3Picture of Dorian Gray

By Oscar Wilde

Dorian Gray, a young man of wealth and stature in late 1800's London, meets Lord Henry Wotton while posing for a portrait by his friend Basil Hallward. Once the painting is complete, Dorian realizes that it will always be young and attractive, while he will be forced to age and wither with the years. Carelessly, he wishes the opposite were true. What happens is a treatise on morals, self-indulgence and how crucial personal responsibility is towards one's self. (Summary by John Gonzalez)

4Happy Prince and Other Tales

By Oscar Wilde

Collection of children’s stories written in 1888, dealing primarily with love and selfishness. These stories are generally sad, with a moralistic message. The collection includes: The Happy Prince, The Nightingale and the Rose, The Selfish Giant, The Devoted Friend, and The Remarkable Rocket. (Summary written by Joy Chan)

5Reviews

By Oscar Wilde

Wilde’s literary reputation has survived so much that I think it proof against any exhumation of articles which he or his admirers would have preferred to forget. As a matter of fact, I believe this volume will prove of unusual interest; some of the reviews are curiously prophetic; some are, of course, biassed by prejudice hostile or friendly; others are conceived in the author’s wittiest and happiest vein; only a few are colourless. And if, according to Lord Beaconsfield, the verdict of a continental nation may be regarded as that of posterity, Wilde is a much greater force in our literature than even friendly contemporaries ever supposed he would become. <p>It should be remembered, however, that at the time when most of these reviews were written Wilde had published scarcely any of the works by which his name has become famous in Europe, though the protagonist of the æsthetic movement was a well-known figure in Paris and London. (Summary from Introduction by Robert Ross)</p>

6Ballad of Reading Gaol

By Oscar Wilde

Wilde’s meditation on capital punishment, the Ballad of Reading Gaol comes after he was convicted and imprisoned under charges of gross indecency. The charges stemmed from his affair with Lord Alfred Douglas, the son of the Marquis of Queensberry. It relates the story of an execution of a man who murdered his wife which Wilde witnessed during his internment. Published in 1898, it was Wilde’s last published poem as he would die in 1900 from cerebral meningitis, caused by syphilis.(Summary by John Gonzalez)

7Ballad of Reading Gaol, (version 2)

By Oscar Wilde

In 1895, Oscar Wilde was sentenced to 2 years of hard labor for acts of ‘gross indecency’. During his time at Reading Gaol, he witnessed a rare hanging, and in the three years between his release and his untimely death in 1900, was inspired to write the following poem, a meditation on the death penalty and the importance of forgiveness, even for (and especially for) something as heinous as murdering one’s spouse; for even the murderer, Wilde argues, is human and suffers more so for being the cause of his own pain, for ‘having killed the thing he loved’; for everyone is the cause of someone else’s suffering and suffers at the hands of another. It is this that Jesus Christ could see; he could continue to see the beauty of our humanity, despite all that we may do to each other, and encouraged us to love each other just the same<br /><br /> “The Ballad of Reading Gaol” was published in 1898 and would gain Wilde greater recognition as a poet (in addition to being a great playwright); although his only other volume of poetry, one of his earliest works that he’d published, was also well-received. Sadly, ‘The Ballad’ would be his last.<br />(Summary by Linda Leu).

8Ideal Husband

By Oscar Wilde

<i>An Ideal Husband</i> is an 1895 comedy by Oscar Wilde which revolves around blackmail and political corruption, and touches on the themes of public and private honor. The action is set in London, in "the present", and takes place within a single day. "Sooner or later," Wilde notes, "we shall all have to pay for what we do." But he adds that, "No one should be entirely judged by their past." (Summary from Wikipedia)<br /><br /> <b>Characters:</b><br /><em>Narrator</em> &#8211; Read by <a href="http://alexfoster.me.uk/" rel="nofollow">Alex Foster</a><br /><em>The Earl Of Caversham, K.G.</em> &#8211; Read by Linton<br /><em>Viscount Goring, his son</em> &#8211; Read by John Gonzalez<br /><em>Sir Robert Chiltern, Bart., Under-Secretary for Foreign Affairs</em> &#8211; Read by <a href="http://www.earthcallingdavid.com/" rel="nofollow">David Barnes</a><br /><em>Vicomte De Nanjac, Attache at the French Embassy in London</em> &#8211; Read by Juho Fröjd<br /><em>Mr. Montford</em> &#8211; Read by <a href="http://matthew-walton.co.uk/" rel="nofollow">Matthew Walton</a><br /><em> Mason, Butler to Sir Robert Chiltern </em> &#8211; Read by <a href="http://dosemagazine.blogsome.com/" rel="nofollow">Hugh McGuire</a> <br /><em>Phipps, Lord Goring&#8217;s servant</em> &#8211; Read by Juho Fröjd<br /><em> James, footman</em> &#8211; Read by Read by <a href="http://tis.goringe.net/" rel="nofollow">Chris Goringe</a><br /><em>Harold, footman</em> &#8211; Read by Zachary Brewster-Geisz<br /><em>Lady Chiltern</em> &#8211; Read by <a href="http://librivox.org/newcatalog/people_public.php?peopleid=28" rel="nofollow">Kristin Hughes</a><br /><em>Lady Markby</em> &#8211; Read by Heather Barnett<br /><em>The Countess Of Basildon</em> &#8211; Read by JemmaBlythe<br /><em>Mrs. Marchmont</em> &#8211; Read by <a href="http://librivox.org/wiki/moin.cgi/ezwa" rel="nofollow"> Ezwa</a><br /><em>Miss Mabel Chiltern, Sir Robert Chiltern&#8217;s sister</em> &#8211; Read by Ophelia Darcy<br /><em>Mrs. Cheveley</em> &#8211; Read by <a href="http://betsie.info" rel="nofollow">Betsie Bush</a><br /><em>Project Editor</em> &#8211; <a href="http://www.writingshow.com/" rel="nofollow">Paula Berinstein</a>

9Lady Windermere's Fan

By Oscar Wilde

<i>Lady Windermere's Fan: A Play About a Good Woman</i> is a four act comedy by Oscar Wilde, published in 1893. As in some of his other comedies, Wilde satirizes the morals of Victorian society, and attitudes between the sexes.<br /><br />The action centres around a fan given to Lady Windermere as a present by her husband, and the ball held that evening to celebrate her 21st birthday. (Summary by Ruth Golding)<br /><br /> <strong>Cast:</strong><br /> Lady Windermere - <a href="http://librivox.org/reader/28">Kristin Hughes</a><br /> Lord Windermere - <a href="http://librivox.org/reader/2195">Joe Earley</a><br /> Mrs. Erlynne - <a href="http://librivox.org/reader/1259">Elizabeth Klett</a><br /> Lord Darlington - <a href="http://librivox.org/reader/827">Simon Taylor</a><br /> The Duchess of Berwick - <a href="http://librivox.org/reader/2607">Ruth Golding</a><br /> Mr. Cecil Graham - <a href="http://librivox.org/reader/2358">Chugosh</a><br /> Lord Augustus Lorton - <a href="http://librivox.org/reader/2538">Chrystian J. Gonzalez</a><br /> Mr. Dumby - <a href="http://librivox.org/reader/1913">Geoff Cowgill</a><br /> Parker - <a href="http://librivox.org/reader/2427">Ed Meade</a><br /> Lady Agatha Carlisle - <a href="http://librivox.org/reader/123">Ezwa</a><br /> Lady Plymdale - <a href="http://librivox.org/reader/84">Christie Nowak</a><br /> Mr. Hopper - <a href="http://librivox.org/reader/1094">tubeyes</a><br /> Lady Jedburgh - <a href="http://librivox.org/reader/1649">Kalynda</a><br /> Rosalie - <a href="http://librivox.org/reader/1328">Duan</a><br /> Lady Stutfield - <a href="http://librivox.org/reader/1975">Sara Korsmo</a><br /> Mrs. Cowper-Cowper - <a href="http://librivox.org/reader/2592">Anna Roberts</a><br /> Narrator - <a href="http://librivox.org/reader/2748">Annoying Twit</a> <br /><br /> Audio edited by Rosalind Wills and Ruth Golding.

10De Profundis

By Oscar Wilde

This short work of Wilde's was written during his two year incarceration for "gross indecency". This work is a letter which sorts out his life, and his love toward Lord Alfred Douglas. Wilde wrote this as a farewell letter to Douglas. (summary by Aaron Elliott)<br /><br /> NOTE: "Transcribed from the 1913 Methuen & Co. edition. Note that later editions of De Profundis contained more material. The most complete editions are still in copyright in the U.S.A."

In 1894, Oscar Wilde (1854 - 1900) published two collections of aphorisms: <em>A Few Maxims For The Instruction Of The Over-Educated</em>, in the Saturday Review newspaper, and <em>Phrases and Philosophies for the Use of the Young</em>, in the Oxford student magazine The Chameleon. By turns witty, intellectual, counter-intuitive and obtruse, the collections came to be seen by many as emblematic of Wilde's style, and countless collections of Wildean aphorisms have since been published. (Summary by Carl Manchester)

LibriVox volunteers bring you 12 different recordings of <em>Serenade</em> by Oscar Wilde. This was the weekly poetry project for the week of April 20th, 2008.

13Importance of Being Earnest (version 2)

By Oscar Wilde

Oscar Fingal O’Flahertie Wills Wilde (1854 – 1900) was an Irish playwright, novelist, poet, and author of short stories. Known for his barbed wit, he was one of the most successful playwrights of late Victorian London, and one of the greatest celebrities of his day. <br /><br />In Wilde’s classic play The Importance of Being Earnest, Jack Worthing and Algernon Moncrieff discover the perils of love, assumed identities, and telling the truth. (Summary by Toby Paradis and Wikipedia)<br /><br /> <b>LibriVox cast:</b><br /> John Worthing, J.P.: <a href="https://librivox.org/reader/2700">Hans-Stefan Ducharme</a><br>Algernon Moncrieff: <a href="https://librivox.org/reader/2697">Simon Ferland</a><br>Rev. Canon Chasuble, D.D.: <a href="https://librivox.org/reader/2280">Toby Paradis</a><br>Merriman, Butler: <a href="https://librivox.org/reader/2280">Toby Paradis</a><br>Lane, Manservant: <a href="https://librivox.org/reader/2280">Toby Paradis</a><br>Lady Bracknell: <a href="https://librivox.org/reader/2698">Sarah A. Farnham</a><br>Hon. Gwendolen Fairfax: <a href="https://librivox.org/reader/2701">Nyssa Gatcombe</a><br>Cecily Cardew: <a href="https://librivox.org/reader/2699">Rebecca Bailey</a><br>Miss Prism, Governess: <a href="https://librivox.org/reader/2702">Eileen Nadeau</a><br />Sound Tech - Keagan Rae

14Picture of Dorian Gray (Lippincott)

By Oscar Wilde

The Picture of Dorian Gray is the only published novel written by Oscar Wilde, and first came out as the lead story in Lippincott's Monthly Magazine on June 20, 1890. Wilde later revised this edition, making several alterations, and adding new chapters; the amended version was published by Ward, Lock, and Company in April 1891. The novel tells of a young man named Dorian Gray, the subject of a painting by artist Basil Hallward. Basil is greatly impressed by Dorian's physical beauty and becomes strongly infatuated with him, believing that his beauty is responsible for a new mode in his art. Talking in Basil's garden, Dorian meets Lord Henry Wotton, a friend of Basil's, and becomes enthralled by Lord Henry's world view. Espousing a new kind of hedonism, Lord Henry suggests that the only thing worth pursuing in life is beauty, and the fulfilment of the senses. Realising that one day his beauty will fade, Dorian cries out, wishing that the portrait Basil has painted of him would age rather than himself. Dorian's wish is fulfilled, subsequently plunging him into a series of debauched acts. The portrait serves as a reminder of the effect each act has upon his soul, with each sin being displayed as a disfigurement of his form, or through a sign of aging. The Picture of Dorian Gray is considered one of the last works of classic gothic horror fiction with a strong Faustian theme. It deals with the artistic movement of the decadents, and homosexuality, both of which caused some controversy when the book was first published. However, in modern times, the book has been referred to as "one of the modern classics of Western literature." (Summary by Wikipedia)

15Florentine Tragedy and La Sainte Courtisane

By Robert Baldwin Ross and Oscar Wilde

Two short fragments: an unfinished and a lost play. A Florentine Tragedy, left in a taxi (not a handbag), is Wilde’s most successful attempt at tragedy – intense and domestic, with surprising depth of characterisation. It was adapted into an opera by the Austrian composer Alexander Zemlinsky in 1917. La Sainte Courtisane, or The Woman Covered in Jewels explores one of Wilde’s great idées fixes: the paradox of religious hedonism, pagan piety. Both plays, Wildean to their core, revel in the profound sadness that is the fruit of the conflict between fidelity and forbidden love. Written towards the end of his tragic life, these fragments give us a glimpse of a genius at his best: visceral, passionate, personal, poetic. (Summary by Simon Larois)<br><br> A Florentine Tragedy - cast:<BR> Narrator: <a href="https://catalog.librivox.org/people_public.php?peopleid=3157">TriciaG</a><br>GUIDO BARDI, A Florentine prince: <a href="https://catalog.librivox.org/people_public.php?peopleid=1492">mb</a><br>SIMONE, a merchant: <a href="https://catalog.librivox.org/people_public.php?peopleid=2986">Simon Larois</a><br>BIANNA, his wife: <a href="https://catalog.librivox.org/people_public.php?peopleid=2607">Ruth Golding</a><br> <BR> La Sainte Courtisane - cast:<BR> Narrator: <a href="https://catalog.librivox.org/people_public.php?peopleid=2607">Ruth Golding</a><br>First man: <a href="https://catalog.librivox.org/people_public.php?peopleid=1492">mb</a><br>Myrrhina: <a href="https://catalog.librivox.org/people_public.php?peopleid=2297">Philippa</a><br>Second man: <a href="https://catalog.librivox.org/people_public.php?peopleid=3332">L.French</a><br>Honorius: <a href="https://catalog.librivox.org/people_public.php?peopleid=3098">woggy298</a><br> <BR> Edited by <a href="https://catalog.librivox.org/people_public.php?peopleid=2607">Ruth Golding</a>

16Bildnis des Dorian Gray

By Oscar Wilde

Als der berückend gutaussehende Jüngling Dorian Gray sein Porträt betrachtet, kommt ihm der Gedanke, wie herrlich es sein müsste, wenn das Bild an seiner Stelle altern könnte, während er selbst für immer jung und schön bliebe. Als sich dieser fantastische Wunsch zu erfüllen beginnt, genießt Dorian seine ewige Jugend in vollen Zügen - während in seiner Seele ein grauenvolles Werk der Zerstörung seinen Lauf nimmt. (Zusammenfassung von Al-Kadi)

17Fisherman and his Soul

By Oscar Wilde

”The Fisherman and his Soul” is a fairy tale first published in November of 1891 in Wilde’s “A House of Pomegranates”. It tells of a fisherman who nets and falls in love with a mermaid. But to be with her he must shed his soul, which goes off to have adventures of its own. Will forbidden love endure? -- This reader has also recorded the three other tales in “A House of Pomegranates” which can be found in the LibriVox Short Story Collections, volumes 37, 38, and 39. (Summary by Gregg Margarite)

The original 1891 version of the play was in French. Three years later an English translation was published. The play tells in one act the Biblical story of Salome, stepdaughter of the tetrarch Herod Antipas, who, to her stepfather's dismay but to the delight of her mother Herodias, requests the head of Iokanaan (John the Baptist) on a silver platter as a reward for dancing the Dance of the Seven Veils. (Summary by wikipedia)<br /><br /> <strong>Cast:</strong><br /> Herod Antipas: <a href="http://librivox.org/reader/2149">Mark Penfold</a><br /> Iokanaan: <a href="http://librivox.org/reader/3699">Bruce Pirie</a><br /> The Young Syrian: <a href="http://librivox.org/reader/4358">Kim Stich</a><br /> Tigellinus: <a href="http://librivox.org/reader/5635">Matthew Reece</a><br /> A Cappadocian: <a href="http://librivox.org/reader/5386">Tim Ferreira</a><br /> A Nubian: <a href="http://librivox.org/reader/5303">D MoNeY</a><br /> First Soldier: <a href="http://librivox.org/reader/5346">Skythrock</a><br /> Second Soldier: <a href="http://librivox.org/reader/5404">Joshua Logan</a><br /> The Page of Herodias: <a href="http://librivox.org/reader/4572">om123</a><br /> Herodias: <a href="http://librivox.org/reader/5285">Sweetlilbirdy</a><br /> Salome: <a href="http://librivox.org/reader/3536">Arielle Lipshaw</a><br /> A Slave: <a href="http://librivox.org/reader/5103">Ana</a><br /> First Jew: <a href="http://librivox.org/reader/5506">Frank Booker</a><br /> Second Jew/Fourth Jew/Sadducee: <a href="http://librivox.org/reader/1259">Elizabeth Klett</a><br /> Third Jew/First Nazarene: <a href="http://librivox.org/reader/26">Denny Sayers</a><br /> Fifth Jew: <a href="http://librivox.org/reader/2911">David Lawrence</a><br /> Second Nazarene: <a href="http://librivox.org/reader/4705">Algy Pug</a><br /> A Pharisee: <a href="http://librivox.org/reader/1492">mb</a><br /> Narrator: <a href="http://librivox.org/reader/3157">Tricia G</a><br /><br /> <strong>Audio edited by:</strong> Arielle Lipshaw

19Picture of Dorian Gray (version 2 dramatic reading)

By Oscar Wilde

The novel tells of a young man named Dorian Gray, the subject of a painting by artist Basil Hallward. Basil is impressed by Dorian's beauty and becomes infatuated with him, believing his beauty is responsible for a new mode in his art. Dorian meets Lord Henry Wotton, a friend of Basil's, and becomes enthralled by Lord Henry's world view. Espousing a new hedonism, Lord Henry suggests the only things worth pursuing in life are beauty and fulfillment of the senses. Realizing that one day his beauty will fade, Dorian expresses his desire to sell his soul to ensure the portrait Basil has painted would age rather than himself. Dorian's wish is fulfilled, plunging him into debauched acts. The portrait serves as a reminder of the effect each act has upon his soul, with each sin displayed as a disfigurement of his form, or through a sign of aging. This reading uses the 20-chapter 1891 version of Wilde's novel. (Summary by Wikipedia)<br /><br/> <strong>Cast:</strong><br/><br/>NARRATOR - <a > Martin Geeson</a><br /> Lord Henry Wotton - <a > David Goldfarb</a><br /> Dorian Gray - <a > Volunteer </a><br /> Basil Hallward - <a > Anthony</a><br /> Sibyl Vane - <a > Miss Avarice</a><br /> James Vane - <a > David Lawrence</a><br /> Duchess of Monmouth - <a > Availle</a><br /> Victor - <a > Martin Geeson</a><br /> Francis - <a > Simon Pride</a><br /> Cab Driver - <a > Simon Pride</a><br /> Parker - <a > Elizabeth Klett</a><br /> Lord Fermor - <a > Anthony</a><br /> Lady Agatha - <a > Sarah</a><br /> Duchess of Harley - <a > Hannah Harris</a><br /> Sir Thomas Burdon - <a > Terence Taylor</a><br /> Mr. Erskine - <a > Frank Booker</a><br /> Mrs. Vandeleur - <a > Mary-Beth Blackburn</a><br /> Lady Henry - <a > Susanna</a><br /> Mrs. Vane - <a > Arielle Lipshaw</a><br /> Mrs. Leaf - <a > Rebeka Harris</a><br /> Mr. Hubbard - <a > Frank Booker</a><br /> Alan Campbell - <a > Ernst Pattynama</a><br /> Lady Narborough - <a > Elizabeth Klett</a><br /> Lady Ruxton - <a > Mary-Beth Blackburn</a><br /> Adrian Singleton - <a > Joseph Lawler</a><br /> Woman - <a > Lucy Perry</a><br /> Sir Geoffrey Clouston - <a > Mark F. Smith</a><br /> Gamekeeper - <a > Martin Geeson</a><br /> Gardener- <a > Joseph Lawler</a><br /> Young Man - <a > Elizabeth Klett</a><br /> Old Gentleman - <a > Mark F. Smith</a><br /> Constable - <a > Joseph Lawler</a><br /> <br />Audio edited by Arielle Lipshaw

20Soul of Man

By Oscar Wilde

“(T)he past is what man should not have been. The present is what man ought not to be. The future is what artists are.”<br/><br/> Published originally as “The Soul of Man Under Socialism,” this is not so much a work of sober political analysis; rather it can be summed up as a rhapsodic manifesto on behalf of the Individual. Socialism having deployed technology to liberate the whole of humanity from soul-destroying labour, the State obligingly withers away to allow the free development of a joyful, anarchic hedonism...<br/><br/> “Is this Utopian? A map of the world that does not include Utopia is not worth even glancing at, for it leaves out the one country at which Humanity is always landing.”<br/><br/> Far from abandoning the epigram in favour of the slogan, Wilde wittily assails several of his favourite targets: the misguided purveyors of philanthropy; life-denying ascetics of various kinds; the army of the half-educated who constitute themselves the enemies of Art - and those venal popular journalists who cater to them...<br/><br/> “Behind the barricade there may be much that is noble and heroic. But what is there behind the leading-article but prejudice, stupidity, cant, and twaddle?” (Introduction by Martin Geeson)

21Happy Prince and Other Tales (version 2)

By Oscar Wilde

The Happy Prince and Other Tales (also sometimes called The Happy Prince and Other Stories) is an 1888 collection of stories for children by Oscar Wilde. It is most famous for The Happy Prince, the short tale of a metal statue who befriends a migratory bird. Together, they bring happiness to others, in life as well as in death.<br /><br /> The stories convey an appreciation for the exotic, the sensual and for masculine beauty. (summary from Wikipedia.)

22De Profundis (version 2)

By Oscar Wilde

This is a letter written from prison in 1897 by Oscar Wilde to Lord Alfred Douglas, in which he recounts how he came to be in prison and charts his spiritual development. (summary by AdamH)</p> NOTE: ."Transcribed from the 1913 Methuen & Co. edition Note that later editions of De Profundis contained more material. The most complete editions are still in copyright in the U.S.A."

23Woman of No Importance

By Oscar Wilde

A Woman of No Importance is a play by Irish playwright Oscar Wilde. The play premièred on 19 April 1893 at London's Haymarket Theatre. It is a testimony of Wilde's wit and his brand of dark comedy. It looks in particular at English upper class society and has been reproduced on stages in Europe and North America since his death in 1900. (Summary by Wikipedia) <br><br><strong>Cast:</strong><br>Lord Illingworth: <a href="https://catalog.librivox.org/people_public.php?peopleid=6037">Peter Bishop</a><br>Sir John Pontefract: <a href="https://catalog.librivox.org/people_public.php?peopleid=2825">Ernst Pattynama</a><br>Lord Alfred Rufford: <a href="https://catalog.librivox.org/people_public.php?peopleid=6446">Bob Gonzalez</a><br>Mr. Kelvil, M.P.: <a href="https://catalog.librivox.org/people_public.php?peopleid=6459">Nigel Boydell</a><br>The Ven. Archdeacon Daubeny, D.D. : <a href="https://catalog.librivox.org/people_public.php?peopleid=3595">Martin Geeson</a><br>Gerald Arbuthnot: <a href="https://catalog.librivox.org/people_public.php?peopleid=1492">mb</a><br>Farquhar, Butler: <a href="https://catalog.librivox.org/people_public.php?peopleid=6482">Delmar H Dolbier</a><br>Francis, Footman: <a href="https://catalog.librivox.org/people_public.php?peopleid=3664">Barry Eads</a><br>Lady Hunstanton: <a href="https://catalog.librivox.org/people_public.php?peopleid=5265">Susanna</a><br>Lady Caroline Pontefract: <a href="https://catalog.librivox.org/people_public.php?peopleid=4964">Rashada</a><br>Lady Stutfield: <a href="https://catalog.librivox.org/people_public.php?peopleid=6281">Beth Thomas</a><br>Mrs. Allonby: <a href="https://catalog.librivox.org/people_public.php?peopleid=1259">Elizabeth Klett</a><br>Miss Hester Worsley: <a href="https://catalog.librivox.org/people_public.php?peopleid=3157">TriciaG</a><br>Alice, Maid: <a href="https://catalog.librivox.org/people_public.php?peopleid=3816">Bev J. Stevens</a><br>Mrs. Arbuthnot: <a href="https://catalog.librivox.org/people_public.php?peopleid=3536">Arielle Lipshaw</a><br>Narrator: <a href="https://catalog.librivox.org/people_public.php?peopleid=2911">David Lawrence</a><br><br><strong>Audio edited by: </strong>Arielle Lipshaw<br>

24Picture Of Dorian Gray (1891 Version)

By Oscar Wilde

The Picture of Dorian Gray is the only published novel by Oscar Wilde, appearing as the lead story in Lippincott's Monthly Magazine on 20 June 1890, printed as the July 1890 issue of this magazine. Wilde later revised this edition, making several alterations, and adding new chapters; the amended version was published by Ward, Lock, and Company in April 1891. The novel tells of a young man named Dorian Gray, the subject of a painting by artist Basil Hallward. Basil is impressed by Dorian's beauty and becomes infatuated with him, believing his beauty is responsible for a new mode in his art. Dorian meets Lord Henry Wotton, a friend of Basil's, and becomes enthralled by Lord Henry's world view. Espousing a new hedonism, Lord Henry suggests the only things worth pursuing in life are beauty and fulfillment of the senses. Realizing that one day his beauty will fade, Dorian (whimsically) expresses a desire to sell his soul to ensure the portrait Basil has painted would age rather than himself. Dorian's wish is fulfilled, plunging him into debauched acts. The portrait serves as a reminder of the effect each act has upon his soul, with each sin displayed as a disfigurement of his form, or through a sign of aging. The Picture of Dorian Gray is considered a work of classic gothic fiction with a strong Faustian theme. (Summary by Wikipedia)

25Importance of Being Earnest (version 3)

By Oscar Wilde

Oscar Wilde's The Importance of Being Earnest is subtitled "A Trivial Comedy for Serious People," and has proved immensely popular since its first performance in 1895. The play certainly has its farcical and comic elements, such as the witty banter exchanged by the characters and the flippant attitude towards love and marriage that characterizes the action. However, the play also explores more serious themes through the central story of Jack Worthing's search for his identity. (Summary by Elizabeth Klett)<br/><br/> <strong>Cast</strong><br>John Worthing: <a href="http://librivox.org/reader/6553">Dublin Gothic</a><br>Algernon Moncrieff: <a href="http://librivox.org/reader/5719">John Fricker</a><br>Rev. Chasuble: <a href="http://librivox.org/reader/3595">Martin Geeson</a><br>Merriman: <a href="http://librivox.org/reader/1492">mb</a><br>Lane: <a href="http://librivox.org/reader/4705">Algy Pug</a><br>Lady Bracknell: <a href="http://librivox.org/reader/2607">Ruth Golding</a><br>Gwendolyn Fairfax: <a href="http://librivox.org/reader/1295">Elizabeth Klett</a><br>Cecily Cardew: <a href="http://librivox.org/reader/3536">Arielle Lipshaw</a><br>Miss Prism: <a href="http://librivox.org/reader/6173">Carol Box</a><br>Narrator: <a href="http://librivox.org/reader/6689">Tiffany Halla Colonna</a><br><br><strong>Audio edited by Elizabeth Klett</strong>