Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a discourse-based model of English sentence intonation

English non-expressive, declarative sentence intonation is examined in a discourse context. A rule system, geared to a text-to-speech context is developed which assigns sentence prominences related to information focus. The realization of these prominences with respect to the phonetic parameter of pitch (F0) is also described with a pitch generating algorithm. The dynamics of information focus in English is shown to be accountable for by a hierarchy of grammatical functions interacting with a contextual notion of coreference. Projection of focal prominence is effected by a subroutine which assigns prominence either to the head or to the modifier of the constituent realizing a particular grammatical function, depending on the coreferential status of the associated lexical items. The model assumes three degrees of prominence associated with the three grammatical functions (Subject, Predicate, Predicate complement). These are realized by varying heights of F0 peaks described in relation to the baseline of a phonological 'grid' characterizing the speaker's F0 range and declination constants. A fourth degree of prominence is also shown to be required on prefocal lexical heads. The notion of contrastive prominence conditioned by syntactic parallelism and identity relations is examined and preliminary rules are formulated for assigning and synthesizing the associated F0 contours. The problem of noncontraction of the copula is also related to the issue of contrastive prominence. The status of the notion of 'phonological phrase' is examined in the light of a grammar which includes a focus assigning component and a number of phonological processes (e.g. 'rhythm rule') are reanalyzed. (Less)
Language and Computers

Language, writing, and the computer, as Wang (1986) states, must rank among the highest of our most important inventions since each has profoundly determined the development of the human species. There have been numerous interactions among these three types of human institutions. Writing, of course, is largely a reflection of language. Since its inception four decades ago, the computer has depended on various forms of language and writing to communicate with its human creators. Some of these forms were specially invented programming languages for a small breed of people.
Learning bayesian networks for solving real-world problems

LEARNING BAYESIAN NETWORKS FOR SOLVING REAL-WORLD PROBLEMS Moninder Singh Supervisor: Gregory M. Provan Bayesian networks, which provide a compact graphical way to express complex probabilistic relationships among several random variables, are rapidly becoming the tool of choice for dealing with uncertainty in knowledge based systems. However, approaches based on Bayesian networks have often been dismissed as un t for many real-world applications since probabilistic inference is intractable for most problems of realistic size, and algorithms for learning Bayesian networks impose the unrealistic requirement of datasets being complete. In this thesis, I present practical solutions to these two problems, and demonstrate their e ectiveness on several real-world problems. The solution proposed to the rst problem is to learn selective Bayesian networks, i.e., ones that use only a subset of the given attributes to model a domain. The aim is to learn networks that are smaller, and hence computationally simpler to evaluate, but retain the performance of networks induced using all attributes. I present two methods for inducing selective Bayesian networks from data and evaluate them on several di erent problems. Both methods are shown to induce selective networks that are not only signi cantly smaller and computationally simpler to evaluate, but also perform as well, or better, than networks using vi all attributes. To address the second problem, I propose a principled method, based on the EM algorithm, for learning both Bayesian network structure and probabilities from incomplete data, and evaluate its performance on several datasets with di erent amounts of missing data and di erent assumptions about the missing data mechanisms. The proposed algorithm is shown to induce Bayesian networks that are very close to the actual underlying model. Finally, I apply both methods to the task of diagnosing acute abdominal pain. Known to be a very di cult domain, this is a very high dimensional problem characterized by a large number of attributes and missing data. Several researchers have argued that the simplest Bayesian network, the naive Bayesian classi er, is optimal for this problem. My experiments on two datasets in this domain show that not only do selective Bayesian networks use only a small fraction of the attributes but they also signi cantly outperform other methods, including the naive Bayesian classi er. vii
Problem-solving with learning technology in the music studio

This article presents some of the findings from a mixed-methods case study that investigated studio recording for undergraduate students collaborating in pairs. The students were actively engaged in experiential learning (Dewey 1966) and the idea that students will develop within an environment with their peers (Pear and Crowne-Todd 2001). Using a stratified purposive sampling technique students were matched with a learner of similar ability via a pre-test, often referred to as a social-conflict approach (Schneider 2002). The groups of students were then allocated a support mechanism (either a learning technology interface or paper-based manual) to provide contingent on-demand assistance (Wood and Wood 1999) during the recording of a drum kit. Analysis of observational data revealed the types of studio-based problems the learners were encountering, and that the learning technology solution suggested a quicker and more reliable form of support.
On Large Cooperative Wireless Network Modeling through a Stochastic Geometry Approach.

The main goal of this work is to study cooperative aspects of large wireless networks from the perspective of stochastic geometry. This allows the consideration of important effects such as the random spatial distribution of nodes, as well as the effects of interference and interference correlation at receivers, which are not possible when a single link is considered in isolation.First, some aspects of the performance of the relay channel in the context of a large wireless network are considered. Mainly, the performance, in terms of outage probability (OP), of a single full-duplex relay channel utilizing decode-and-forward (DF) or compress-and-forward, when the interference is generated by uniform spatial deployment of nodes, modeled as a Poisson point process. The OP performance of these two protocols is compared with a point-to-point transmission and with a half-duplex DF protocol. Afterwards, the case in which more than one transmitter in the network may use a relay is considered. The effects of cooperation versus interference are studied, when the users use either full-duplex DF, or point-to-point transmissions. In a second phase, this work explores the advantages that could be obtained through out-of-band device-to-device (D2D) video file exchanges in cellular networks. These advantages are measured in terms of the fraction of requests that can be served in a time-block through D2D, thus avoiding a downlink file transfer from the base station. For this, a stochastic geometry framework is introduced, in which the user file-caching policy, user pairing strategy, and link quality and scheduling issues are considered.
Combined multilevel turbo-code with 8PSK modulation

Different concepts of iterative decoding algorithms for serial or parallel concatenated coding (called turbo-code) as well as for the special case of the generalized concatenated coding schemes have been introduced. These algorithms being very efficient, are based on soft output decoding, where, the soft decisions are used as feed-forward information for the next steps of iterations. In order to achieve the high performance expected by turbo-codes and to assume a great flexibility for the system, we combine a multilevel turbo-code with 8PSK modulation. This allow us to examine in detail the performance of this combination compared with different coding and decoding approaches: turbo-code with Gray mapping and iterative multistage decoding using convolutional codes or a combination of turbo codes, convolutional and single parity check codes. In all of our studies we consider a super-concatenated coding scheme, where the outer code is based on Reed-Solomon code. The results showed that the coding gain at a BER of 10/sup -4/ (since we employ a powerful outer RS decoder) for the iterative convolutional coding and for the combination of convolutional/turbo coding is quite the same compared to turbo coding with Gray mapping, which is 0.5 dB. However, compared to uncoded QPSK, the coding gain is 4 dB (at a BER of 10/sup -4/), which is quite significant.
Schematic road network map progressive generalization based on spatial cognition

In contrast to traditional cartography, schematic network map including road and river network needs new cartographical generalization methods. In this paper, the cartographic design criteria of schematic road network map are analyzed and summarized. Based on this, a set of quantitive constrains are set up, and a new road network generalization method including progressive selection and displacement is proposed. Furthermore, we research topological checking method for road network. Based on these constrains, the points in a road network are classified, and a satisfied and effective schematic map is designed in a concrete experiment, while keeping the topological consistency of the road network between original and schematic map.
Optimal curvature-compensated BiCMOS bandgap reference

A bandgap voltage reference with a circuit providing compensation of the thermal nonlinearity for the base emitter voltage is described. The temperature dependence of V/sub BE/(T) is linearized using, as the bias current, a current which is proportional with the n-th degree of absolute temperature. Theoretical calculations show that the thermal instability of the output reference voltage can be strongly reduced if the parameter n is choosen equal with a value fixed only by the technological process. The curvature-corrected bandgap voltage reference is implemented in BiCMOS technology and presents a very small temperature coefficient. SPICE simulation using MOSIS 0.8/spl mu/ is used to verify the analysis.
Influence of Servo Characteristics on Motion Accuracy of Parallel Kinematic Mechanism

Parallel kinematic mechanism (PKM) is applied to machine tools and robots since its flexibility and speed. In machine tools, additionally, motion accuracy is strongly desired. Although various research works aimed to calibrate static geometrical deviations of the PKM machines in order to improve motion accuracy, influence of dynamic motion error of servo system has not been investigated well up to now. In this study, the influence of servo characteristics on motion accuracy of a PKM machine tool driven by six servo motors is discussed, because it is expected that the contouring motion accuracy of a PKM machine tool is strongly depends on the servo characteristics. A servo motor which installed to the machine is modeled to simulate the motion characteristics of each servo motor. The motor models are coupled with a model of link mechanism, and the motion accuracy of a tool center point is simulated. Also, both of positional and angular errors are simulated successfully. As the results of the simulations, it is clarified that the motion accuracy is strongly depends on the servo characteristics and location of the tool center point. In addition, the motion errors are observed as six dimensional errors. It is also discussed that the influence of friction torques on the motion accuracy.
Rate control in H.264 wireless video communication system

Purpose – The purpose of this paper is to introduce an efficient rate control algorithm for the H.264/AVC video coding in wireless communication. The adaptive rate control algorithm can reduce rate fluctuation, effectively prevent the skipping frame, and be suitable to the characteristics of wireless channel. The experimental result indicates that this plan can guarantee the quality of service of video transmission.Design/methodology/approach – A motion complexity measure is developed to enhance the existing mean of absolute difference (MAD)‐based algorithm and is applied to bit allocation for rate control. Time and space relativities in frames are also used in the proposed rate control algorithm for MAD prediction. A quantitative parameter (QP) control scheme is used to adjust the computed QP based on actual encoding results of previous coded macroblocks (MBs) in a frame.Findings – The proposed rate control algorithm always outperforms JM 8.6. The adaptive MAD model can reduce the prediction error. Both .
Tagging text with a probabilistic model

Experiments on the use of a probabilistic model to tag English text, that is, to assign to each word the correct tag (part of speech) in the context of the sentence, are presented. A simple triclass Markov model is used, and the best way to estimate the parameters of this model, depending on the kind and amount of training data that is provided, is found. Two approaches are compared: the use of text that has been tagged by hand and comparing relative frequency counts; and use text without tags and training the model as a hidden Markov process, according to a maximum likelihood principle. Experiments show that the best training is obtained by using as much tagged text as is available, a maximum likelihood training may improve the accuracy of the tagging. <<ETX>>
Multi-target car license plate detection from complex environment

Summary form only given. In recent years, there has been much research on license plate recognition (LPR), and LPR systems are used frequently. The three major parts in LPR are plate detection, character segmentation and character recognition. A smart and simple algorithm for LPR is presented. This algorithm can be applied for multiple license plate detection. There may be many cars in an image with a complex background. We study how to extract license plates effectively. We use edge detection, image dilation, block filtration and license proportion checking to extract plates accurately. Experimental results show that the algorithm is robust in extracting multiple plates in an image with a complex background. Results yield more than 86.67% correct location of license plates.
CIM/SVG Based Graphics-Module Integration Power Platform with Secondary Development of Visio

The information island problem of interacting between graphic and module data of various power dispatching automation application systems can be solved by using the CIM (Common Information Model) to demonstrate the data of the equipment and SVG (Scalable Vector Graphics) to show the equipment. A visual intelligent calculating and analyzing CIM/SVG graphics-module integration power platform is proposed based on Microsoft Office Visio 2007 and secondary development of Visio Studio C#. The presented platform combined the powerful drawing function of Visio with IEC (International Electro technical Commission) 61970 standard and case study shows its feasibility and effectiveness.
SSAP: Augmentation based Secure Storage Authentication and Privacy System in Cloud Environment

In a cloud storage system storing of data in a third party cloud system that causes serious concern about data confidentiality and in protecting the data via encryption schemes though it provides protection mechanism the functionality of storage system are limited and supports some malicious operations over encrypted data. Developing a cloud based business solution suite for an organization that well equipped with resourceful environment. This suite follows Trusted user verification server that checks for the user authentication to grab the data from the persistence server and some improved schemes such as ISID is facilitated. In this paper we enhance a SSAP (Secure storage authentication and privacy) system that proposes a re-encryption scheme that formulates the secure distributed storage system which is named as erasure code-based cloud storage system. The main contribution is the proxy re-encryption scheme which encodes the messages and forwarding operation over encrypted data. There are certain parameters for a number of copies of a message dispatched to storage servers queried by key servers. By using SUV (Straight unsigned verification) scheme the improved secrecy ID (ISID) scheme is addressed at the receiving end. These parameters allow more flexible adjustment between the storage servers and authentication between the two mediators which an ISID scheme that provides efficiency and provable secure system.
Optimal Nonlocal means algorithm for denoising ultrasound image

We propose a new measure for denoising image by calculating mean distance of all pixels in an image in non-local means (NL-means) algorithm. We compute and analyze the original NL-means algorithm which total all the distance of the patches but, our proposed algorithm calculates the mean value of all distance of all the patches and then than get the sum of all distance. Our proposed algorithm exhibit better result with comparison of the existing NL-means algorithm. Keywords: NL-means, Patches, Mean Value, Measurement Matrix.
MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP 2005) BLIND SOURCE SEPARATION AND SPARSE BUMP MODELLING OF TIME FREQUENCY REPRESENTATION OF EEG SIGNALS: NEW TOOLS FOR EARLY DETECTION OF ALZHEIMER'S DISEASE

The early detection of Alzheimer’s disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using only electroencephalographic (EEG) recordings for patients with Mild Cognitive Impairment (MCI) without any clinical symptoms of the disease who later developed AD. In our method, first a blind source separation algorithm is applied to extract the most significant spatiotemporal uncorrelated components; afterward these components are wavelet transformed; subsequently the wavelets or more generally time frequency representation (TFR) is approximated with sparse bump modeling approach. Finally, reliable and discriminant features are selected and reduced with orthogonal forward regression and the random probe methods. The proposed features were finally fed to a simple neural network classifier. The presented method leads to a substantially improved performance (93% correctly classified - improved sensitivity and specificity) over classification results previously published on the same set of data. We hope that the new computational and machine learning tools provide some new insights in a wide range of clinical settings, both diagnostic and predictive.
Review on Different Localization Schemes in Wireless Sensor Networks

— Wireless sensors are used for sensing physical quantities from the environment. Usually sensors are scattered in the sensing field. These sensors collect data from the environment and send it to the data collecting device. In many applications like home, military, health sensor location is essential. There are different approaches to locate sensors in the field. Comparison of different localization schemes are studied in this paper. Fixed anchor, mobile anchor, range based, range free are some localization schemes being used.
Play and Digital Media

This article examines how play is affected by computers and digital toys. Research indicates that when computer software targeted at children is problem-solving oriented and open-ended, children tend to engage in creative play and interact with peers in a positive manner. On the other hand, drill-and-practice programs can be quite boring and limit children's initiative and decision-making. Digital toys with computer chips installed that make the toys talk or act in certain ways also appear to have a mixed impact on play. Digital toys can serve as catalysts for new forms of play and can have a positive influence on the content of more traditional forms of play. Others contend that children's development can be negatively influenced by playing with digital toys, because they do not encourage creative play. Technological advances in toy manufacture bring significant opportunities and challenges to early childhood education. Parents and teachers need to know how to use technology to promote learning and to enrich children's play and to protect children from possible negative influences.
A note on parallel executions of restrictive blind issuing protocols for secret-key certificates

The receiver in a restrictive blind certificate issuing protocol can blind the issued public key and the certificate but not a certain blinding-invariant predicate of the secret key. Recently a generally applicable technique was described for designing restrictive blind issuing protocols for a certain type of secret-key certificates, and it was shown that the resulting issuing protocols should not be run in parallel since that would enable an attack in which completely blinded triples can be retrieved. To allow the signer in highly demanding applications to run the restrictive blind certificate issuing protocol in parallel mode, a simple technique for modifying the issuing protocols was proposed. In this note it is shown that the proposed modification technique does not overcome the parallel attack problem.
An Intelligent Multipurpose Control Method Using Multifunctional Actuator

Multifunctional actuators are expected to be applied to humanoid robots, since these actuators are small, and have a lot of functions. However, they are difficult to use well in a multipurpose system. Since humanoid robots are a multipurpose system, this difficulty should be overcome. On the other hand, human can conduct multipurpose operation under various environments. In this paper, an intelligent multipurpose control method is proposed. This method is based on human’s operation scheme. And, an intelligent multipurpose control system using multifunctional actuators is constructed based on the proposed method. The effectiveness of the proposed method was confirmed by experiments.
Fault-Tolerant and Self-Stabilizing Protocols Using an Unreliable Failure Detector

We investigate possibility of fault-tolerant and self-stabilizing protocols (ftss protocols) using an unreliable failure detector. Our main contribution is (1) to newly introduce k-accuracy of an unreliable failure detector, (2) to show that kaccuracy of a failure detector is necessary for any ftss k-group consensus protocol, and (3) to present three ftss k-group consensus protocols using a k-accurate and weakly complete failure detector under the read/write daemon on complete networks and on (n−k+1)-connected networks, and under the central daemon on complete networks. key words: distributed algorithms, self-stabilization, fault-tolerance, failure detector, x-group consensus
Implementation of the Tools of Functions' Algebra: First Steps

Abstract A number of new Mathematica functions related to the mathematical approach, called the algebra of functions have been developed in order to expand the functionality of the symbolic software package NLControl that assist the solution of different modeling, analysis and synthesis problems for nonlinear control systems. These functions are integrated into the package NLControl and using the tools of webMathematica will be made available for researchers, not having access to Mathematica. Besides the smooth nonlinear control systems, the tools of functions' algebra are also applicable to non-smooth systems, unlike the differential geometric methods. Moreover, the approach provides the opportunity to consider the continuous- and discrete-time systems as well as discrete event systems from a unified viewpoint.