Miracle-dz/UniFAR-FTUs huggingface dataset:
The phonology of English : a prosodic optimality-theoretic approach

1. Some Basic Ideas The sounds of English Characterizing the sounds of English Phonological generalizations Treating phonological generalizations Optimality Theory Further reading 2. Syllables Intuitive evidence for the syllable Distributional evidence for a syllable A general theory of the syllable Further reading 3. English Syllables: margins and consonants Word onsets Word-final clusters Medial clusters Clusters vs. margins Linear restrictions Summary Further reading 4. English syllables: peaks and moras Vowels and diphthongs Co-occurrence restrictions Mora-based restrictions Syllabic consonants and [r] Summary Further reading 5. Stress, Accent, and Feet What is stress? What is a metrical foot? Intuitive evidence for the foot Distributional evidence for the foot General theory of the foot Further reading 6. Syllables and Stress Basic distributional regularities The analysis Summary Further reading 7. The Rightmost Stress Review of previous results The basic analysis The role of syllable weight Other nominal stress patterns Final syllables Verbs and adjectives Summary Further reading 8. Other Stresses Other stresses Long vowels Nonfinal primary stress Morphemes and feet Summary Further reading 9 Some remaining issues Statistics Russian Remaining issues Further reading References
Is a graph-based interface more efficient than a traditional one for channel discovery?

There are several applications which make it possible for users to search and find their favorite channels; these channels could be anything such as TV channels, radio channels, blogs, websites, etc. Is a graph-based interface more efficient than a traditional one for channel discovery? The aim of this thesis is to answer the question by developing a new browsing method in a graphical user-friendly interface and compare it with a traditional hierarchical method. A graphical application is a graph-based application, in which the nodes (or vertices) represent channels and the edges represent the links among channels. The graphical application is built with the newest Hypertext Markup Language Standard (HTML5) and Cascading Style Sheets (CSS3). A large part of the graphical application is built using the JavaScript library Sigma.js. The resulting graphical application is tested with users and compared with the traditional hierarchical user interface. A questionnaire is provided for end-users to gather feedback. After analyzing the participant‟s feedback, the results were surprising and interesting. A majority of participants enjoyed interacting with graphical application, and they were interested to use it in the future. The final result is merged into the thesis report. This thesis includes all the work of creating graphical and hierarchical user interfaces, testing the hierarchical and graphical user interfaces, and analyzing the user‟s feedback.
English Ditransitive Verbs

The present book offers fresh insights into the description of ditransitive verbs and their complementation in present-day English. In the theory-oriented first part, a pluralist framework is developed on the basis of previous research that integrates ditransitive verbs as lexical items with both the entirety of their complementation patterns and the cognitive and semantic aspects of ditransitivity. This approach is combined with modern corpus-linguistic methodology in the present study, which draws on an exhaustive semi-automatic analysis of all patterns of ditransitive verbs in the British component of the International Corpus of English (ICE-GB) and also takes into account selected data from the British National Corpus (BNC). In the second part of the study, the complementation of ditransitive verbs (e.g. give, send ) is analysed quantitatively and qualitatively. Special emphasis is placed here on the identification of significant principles of pattern selection, i.e. factors that lead language users to prefer specific patterns over other patterns in given contexts (e.g. weight, focus, pattern flow in text, lexical constraints). In the last part, some general aspects of a network-like, usage-based model of ditransitive verbs, their patterns and the relevant principles of pattern selection are sketched out, thus bridging the gap between the performance-related description of language use and a competence-related model of language cognition.
Data Mining and Mathematical Programming

Support vector machines and distance minimization by E. Carrizosa 0-1 semidefinite programming for graph-cut clustering: Modelling and approximation by H. Chen and J. Peng Artificial attributes in analyzing biomedical databases by Z. Csizmadia, P. L. Hammer, and B. Vizvari Recent advances in mathematical programming for classification and cluster analysis by Y. -J. Fan, C. Iyigun, and W. A. Chaovalitwongse Nonlinear skeletons of data sets and applications-Methods based on subspace clustering by P. G. Georgiev Current classification algorithms for biomedical applications by M. R. Guarracino, S. Cuciniello, D. Feminiano, G. Toraldo, and P. M. Pardalos Bilevel model selection for support vector machines by M. Kunapuli, K. P. Bennett, J. Hu, and J.-S. Pang Algorithms for detecting complete and partial horizontal gene transfers: Theory and practice by V. Makarenkov, A. Boc, A. Boubacar Diallo, and A. Banire Diallo Nonlinear knowledge in kernel machines by O. L. Mangasarian and E. W. Wild Ultrametric embedding: Application to data fingerprinting and to fast data clustering by F. Murtagh Selective linear and nonlinear classification by O. Seref, O. E. Kundakcioglu, and P.M. Pardalos.
Deep learning for multivariate financial time series

Deep learning is a framework for training and modelling neural networks which recently have surpassed all conventional methods in many learning tasks, prominently image and voice recognition. This thesis uses deep learning algorithms to forecast financial data. The deep learning framework is used to train a neural network. The deep neural network is a Deep Belief Network (DBN) coupled to a Multilayer Perceptron (MLP). It is used to choose stocks to form portfolios. The portfolios have better returns than the median of the stocks forming the list. The stocks forming the S&P 500 are included in the study. The results obtained from the deep neural network are compared to benchmarks from a logistic regression network, a multilayer perceptron and a naive benchmark. The results obtained from the deep neural network are better and more stable than the benchmarks. The findings support that deep learning methods will find their way in finance due to their reliability and good performance.
Forward-Secure Blind Signature Schemes Based on the Variants of EIGamal

Due to forward-secure-digital-signature’s capability of effectively reducing loss caused by exposure of secret keys and significant in-application benefits of blind signature aiming at protecting senders’ privacy,they have been hot spots for decades in the field of cryptography.Illuminated by the integration of forward secure digital signature and blind signature,based on the variants of ElGamal and assumption of difficulty in solving the discrete logarithm problem in galois field,a forward-secure weak blind signature scheme and a forward-secure strong blind signature scheme are proposed and their security is analyzed thoroughly in this paper.It turns out that forward security,blindness and aptitude of resisting forging attack demonstrated by these two schemes benefit a lot theoretically and practically.
Imitation learning for a robotic precision placement task

In industrial environments robots are used for various tasks. At this moment it is not feasible for companies to deploy robots for productions with a limited batch size or for products with large variations. The use of robots for such environments can become feasible through a new generation of robots and software which can adapt quickly to new situations and learn from their mistakes while being programmable without needing an expert. A concept that can enable the transition to flexible robotics is the combination of imitation learning and reinforcement learning. The purpose of imitation learning is to learn a task by generalizing from observations. The power of imitation learning is that the robot is programmed in an intuitive way while the insight of the teacher is incorporated in the execution of the task. This research studies the combination of imitation and reinforcement learning, the research is applied to an industrial use-case. The research question of this study is: "Can imitation learning be combined with reinforcement learning to achieve a successful application in an industrial robotic precision placement task? " To imitate the demonstrated trajectories, Dynamic Movement Primitives (DMPs) are used. The DMPs are used to encode the observed trajectories. DMPs can be seen as a spring-damper like system with a non-linear forcing term. The forcing term is a sum of Gaussian basis functions with each its corresponding weight. Reinforcement learning can be applied to these weights to alter the shape of the trajectory created by a DMP. Policy Gradients with Parameter based Exploration (PGPE) is used as reinforcement learning algorithm to optimize the recorded trajectories. Experiments done on a UR5 show that without the learning step, the DMPs are able to provide a trajectory that results in a successful execution of a robotic precision placement task. The experiments also show that the learning algorithm is not able to remove noise from a demonstrated trajectory or complete a partial demonstrated trajectory. Therefore it can be concluded that the PGPE algorithm is not suited for reinforcement learning in robotics in its current form. It is therefore recommended to apply a data-efficient version of the PGPE algorithm in order to achieve better learning results.
Model Based Parameter Estimation: Theory and Applications

This judicious selection of articles combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts. These include fields as diverse as biology, medicine, chemistry, environmental physics, image processing and computer vision. The material chosen was presented at a multidisciplinary workshop on parameter estimation held in 2009 in Heidelberg. The contributions show how indispensable efficient methods of applied mathematics and computer-based modeling can be to enhancing the quality of interdisciplinary research.The use of scientific computing to model, simulate, and optimize complex processes has become a standard methodology in many scientific fields, as well as in industry. Demonstrating that the use of state-of-the-art optimization techniques in a number of research areas has much potential for improvement, this book provides advanced numerical methods and the very latest results for the applications under consideration.
Introduction to DWDM technology : data in a rainbow

Preface Acknowledgments Introduction FUNDAMENTALS OF LIGHT The Nature of Light Interaction of Light with Matter OPTICAL COMPONENTS The Optical Waveguide: The Fiber Optical Spectral Filters and Gratings Optical Demultiplexers Light Sources Photodetectors Light Amplifiers Other Optical Components Optical Cross-Connects Optical Add-Drop Multiplexers CODING OPTICAL INFORMATION Digital Transmission and Coding Techniques Decoding Optical Information DENSE WAVELENGTH DIVISION MULTIPLEXING DWDM Systems Engineering DWDM Systems DWDM Topologies DWDM CURRENT ISSUES AND RESEARCH State of the Art Acronyms and Abbreviations Answers Index About the Author.
Verbal Complement Clauses: A minimalist study of direct perception constructions

This monograph examines the syntax of bare infinitival and participial complements of perception verbs in English and other European languages, and investigates the general conditions under which verbal complement clauses are licensed. The introductory chapter is followed by an overview of the major syntactic and semantic characteristics of non-finite complements of perception verbs in English. The third chapter presents an analysis within the framework of Chomsky's (1995) Minimalist Program according to which event-denoting complements are minimally realised as projections of an aspectual head. In the next chapter, it is argued that verbs capable of licensing aspectual complement clauses must be able to function as a special type of control predicate, an assumption which is shown to account for a number of seemingly unrelated properties of the constructions under consideration. The final chapter examines syntactically reduced clausal complements from a cross-linguistic perspective, showing that Southern Romance languages differ from Germanic ones with respect to the availability of 'bare' aspectual complement clauses, a difference that is attributed to morphological properties of verbs in these languages.
Improvement of Structure to Speech Conversion Using Iterative Optimization

This paper describes a new and improved method for the framework of structure to speech conversion we previously proposed. Most of the speech synthesizers take a phoneme sequence as input and generate speech by converting each of the phonemes into its corresponding sound. In other words, they simulate a human process of reading text out. However, infants usually acquire speech communication ability without text or phoneme sequences. Since their phonemic awareness is very immature, they can hardly decompose an utterance into a sequence of phones or phonemes. In this situation, as developmental psychology states, infants acquire the holistic sound pattern of words from the utterances of their parents, called word Gestalt, and they reproduce it with their vocal tubes. This behavior is called vocal imitation. In our previous studies, the word Gestalt was defined physically and a method of extracting it from an utterance was proposed. We already applied the word Gestalt to ASR, CALL, and also speech generation, which we call structure to speech conversion. Unlike a reading machine, our framework simulates infants’ vocal imitation. In this paper, a method for improving our speech generation framework using iterative optimization is proposed and evaluated.
Higher Order Numerical Discretization on Scattered Grids

Higher Order Numerical Discretization on Scattered Grids Karthik Jayaraman Raghuram Beginning with the suppression of Runge Phenomenon which arises in equispaced polynomial interpolation, we present the Minimum Sobolev Norm interpolation technique which we generalize to produce Finite Difference (MSNFD) type weights for differential operators. It is shown that these weights yield higher order approximations to these operators with increasing stencil sizes, and that the idea generalizes to non-uniform grids easily. Thus perhaps for the first time, a systematic means of producing higher order FD weights on irregular grids is discussed. After the basic theoretical discussions on the interpolation process and local convergence of weights, the idea of solving elliptic PDEs using these MSNFD weights is discussed. A set of extensive numerical experiments on standard second order problems as well as the Exterior Laplace problem and the biharmonic equations are discussed. In the absence of a theory for global convergence of the PDE solution, various numerical results that validate our claim of a higher order FD method on non-uniform grids are presented. The concluding section discusses the short-comings associated with solving ill-conditioned problems such as the bi-
Topics in High-Dimensional Unsupervised Learning.

Topics in High-Dimensional Unsupervised Learning by Jian Guo Co-Chairs: Elizaveta Levina and Ji Zhu The first part of the dissertation introduces several new methods for estimating the structure of graphical models. Firstly, we consider estimating graphical models with discrete variables, including nominal variables and ordinal variables. For the nominal variables, we prove the asymptotic properties of the joint neighborhood selection method proposed by Hoefling and Tibshirani (2009) and Wang et al. (2009), which is used to fit high-dimensional graphical models with binary random variables. We show that this method is consistent in terms of both parameter estimation and structure estimation and extend it to general nominal variables. For ordinal variables, we introduce a new graphical model, which assumes that the ordinal variables are generated by discretizing marginal distributions of a latent multivariate Gaussian distribution and the relationships of these ordinal variables are described by the underlying Gaussian graphical model. We develop an EM-like algorithm to estimate the underlying latent network and apply the mean field theory to improve computational efficiency. We also consider the problem of jointly estimating multiple graphical models which share the variables but come from different categories. Compared with separate estimation for each category, the proposed joint estimation method significantly improves xi performance when graphical models in different categories have some similarities. We develop joint estimation methods both for Gaussian graphical models and for graphical models for categorical variables. In the second part of the dissertation, we develop two methods to improve interpretability of high-dimensional unsupervised learning methods. First, we introduce a pairwise variable selection method for high-dimensional model-based clustering. Unlike existing variable selection methods for clustering problems, the proposed method not only selects the informative variables, but also identifies which pairs of clusters are separable by each informative variable. We also propose a new method to identify both sparse structures and “block” structures in factor loadings in principal component analysis. This is achieved by forcing highly correlated variables to have identical factor loadings via a regularization penalty.
Partial-update adaptive filters and adaptive signal processing : design, analysis and implementation

Partial-update adaptive signal processing algorithms not only permit significant complexity reduction in adaptive filter implementations, but can also improve adaptive filter performance in telecommunications applications. This book gives state-of-the-art methods for the design and development of partial-update adaptive signal processing algorithms for use in systems development. Partial-Update Adaptive Signal Processing provides a comprehensive coverage of key partial updating schemes, giving detailed information on the theory and applications of acoustic and network echo cancellation, channel equalization and multiuser detection. It also examines convergence and stability issues for partial update algorithms, providing detailed complexity analysis and a unifying treatment of partial-update techniques. Features: . Advanced analysis and design tools . Application examples illustrating the use of partial-update adaptive signal processing . MATLAB codes for developed algorithms This unique reference will be of interest to signal processing and communications engineers, researchers, R&D engineers and graduate students. "This is a very systematic and methodical treatment of an adaptive signal processing topic, of particular significance in power limited applications such as in wireless communication systems and smart ad hoc sensor networks. I am very happy to have this book on my shelf, not to gather dust, but to be consulted and used in my own research and teaching activities" - Professor A. G. Constantinides, Imperial College, London About the author: Kutluyil Dogan ay is an associate professor of Electrical Engineering at the University of South Australia. His research interests span statistical and adaptive signal processing and he serves as a consultant to defence and private industry. He was the Signal Processing and Communications Program Chair of IDC Conference 2007, and is currently chair of the IEEE South Australia Communications and Signal Processing Chapter. * Advanced analysis and design tools * Algorithm summaries in tabular format * Case studies illustrate the application of partial update adaptive signal processing * MATLAB code listings on an accompanying website
Evolving Biologically Inspired Classifiers

This thesis argues that natural complex systems can provide an inspiring example for creating software which incorporates emergent, self-organizing and adaptive properties. The advantages of complex sys- tems are their natural resilience, redundancy and adaptivity. A generalization of neural networks and boolean networks called computational networks is presented as a model for complex systems. It is argued that this model satisfies the required properties for modeling complex systems. Furthermore, it is asserted that a computational network, being a network of mathematical functions, is appropriate for solving classification problems. For the design of computational networks an evolutionary design algorithm is constructed. Additionally, four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. An impor- tant component is a novel generative representation which can reuse substructures of computational networks. Experiments with this component have shown that it facilitates a higher level of complexity in the solution space, improving the computational network performance for more complex problems. Other components steer the evolutionary process towards a desired solution, either by introducing spe- cial stages during evolution, or by smoothing the fitness landscape. The experiments show that complex systems can be evolutionary designed to act as a classifier. The resulting computational network has a better performance on the Iris dataset compared to every classifier in the Weka classifier collection. Furthermore, an experiment was conducted using the TIMIT read speech dataset, the classifier was evo- lutionary designed using only 13 MFCC features, and a very small train set. Although the performance is not good enough to be of any practical use, the results are adequate given the limitations of the train data.
Business Analytics: Data Analysis & Decision Making

1. Introduction to Data Analysis and Decision Making. Part 1: EXPLORING DATA. 2. Describing the Distribution of a Single Variable. 3. Finding Relationships among Variables. Part 2: PROBABILITY AND DECISION MAKING UNDER UNCERTAINTY. 4. Probability and Probability Distributions. 5. Normal, Binomial, Poisson, and Exponential Distributions. 6. Decision Making under Uncertainty. Part 3: STATISTICAL INFERENCE. 7. Sampling and Sampling Distributions. 8. Confidence Interval Estimation. 9. Hypothesis Testing. Part 4: REGRESSION ANALYSIS AND TIME SERIES FORECASTING. 10. Regression Analysis: Estimating Relationships. 11. Regression Analysis: Statistical Inference. 12. Time Series Analysis and Forecasting. Part 5: OPTIMIZATION AND SIMULATION MODELING. 13. Introduction to Optimization Modeling. 14. Optimization Models. 15. Introduction to Simulation Modeling. 16. Simulation Models. Part 6: ADVANCED DATA ANALYSIS. 17. Data Mining. Part 7: BONUS ONLINE MATERIAL. 18. Importing Data into Excel. 19. Analysis of Variance and Experimental Design. 20. Statistical Process Control. Appendix A. Statistical Reporting.
Locus of Control: A Framework for Use in the Field of Information Systems

The information systems work environment challenges IS professionals with a seemingly continuous maelstrom of novelty, dynamism, uncertainty, and ambiguity. Social learning theory proposes that the locus of control (LOC) construct should operate most strongly under these conditions. However, with few exceptions, LOC remains largely underutilized in the IS literature. The authors review previous LOC research and propose a framework and specific recommendations for the use of LOC in IS research.