Miracle-dz/UniFAR-FTUs huggingface dataset:
Communicative organization in natural language

The book defines the concept of Semantic-Communicative Structure [= Sem-CommS]-a formal object that is imposed on the starting Semantic Structure [= SemS] of a sentence (under text synthesis) in order to turn the selected meaning into a linguistic message. The Sem-CommS is a system of eight logically independent oppositions: 1. Thematicity (Rheme vs. Theme), 2. Givenness (Given vs. Old), 3. Focalization (Focalized vs. Non-Focalized), 4. Perspective (Foregrounded vs. Backgrounded), 5. Emphasis (Emphasized vs. Non-Emphasized), 6. Presupposedness (Presupposed vs. Non-Presupposed), 7. Unitariness (Unitary vs. Articulated), 8. Locutionality (Communicated vs. Signaled). The values of these oppositions mark particular subnetworks of the starting SemS and thus allow for the distinction between sentences such as (a) A man killed a dog vs. The dog was killed by a man, (b) John washed the window vs. It was John who washed the window or (c) It hurts! vs. Ouch! The proposed Sem-Comm-oppositions are conceived as an attempt at sharpening the well-known notions of Topic ~ Comment, Focus, etc. Possible linguistic strategies for expressing the values of the Sem-Comm-oppositions in different languages are discussed at some length, with linguistic illustrations.
Fuzzy Phoneme Classification Using Multi-speaker Vocal Tract Length Normalization

ABSTRACT The overall success of automatic speech recognition (ASR) depends on efficient phoneme recognition performance and quality of speech signal received in ASR. However, dissimilar inputs of speakers affect the overall recognition performance. One of the main problems that affect recognition performance is inter-speaker variability. Vocal tract length normalization (VTLN) is introduced to compensate inter-speaker variation on the speaker signal by applying speaker-specific warping of the frequency scale of a filter bank. Instead of measuring the performance on word level with speaker-specific warping, this research focuses on direct tackling at the phoneme level and applying VTLN on all speakers’ speech signals to analyse the best setting for the highest recognition performance. This research seeks to compare each phoneme recognition results from warping factor between 0.74 and 1.54 with 0.02 increments on nine different ranges of frequency warping boundary. The warp factor and frequency warping range that provides the highest phoneme recognition performance is applied on word recognition. The results show an improved performance in phoneme recognition by 0.7% and spoken word recognition by 0.5% using warp factor of 1.40 on frequency range of 300–5000 Hz in comparison to baseline results.
Afro-Paradise: Blackness, Violence, and Performance in Brazil

and institutions. Whereas Peru adheres to a modernist and rationalistic focus on universal truths expressed through efficiency and legal security, Bolivia has been shaped by claims to basic livelihoods rights expressed through a discourse of usos y costumbres (indigenous customary law). Consequently, the Peru chapter focuses more on the production of knowledge and power, where the Bolivia chapter emphasises social participation. The case-based chapters ( and ) look at local water rights and how they have been affected by the implementation of water licenses in Peru and a water registry in Bolivia. Given the differences detailed in the previous chapters, the formalisation of water rights has quite different expressions in the two sites. However, Seemann shows how in both instances the formalisation threatens to erase local, informal practices of allocating water and increase the risk of local water conflicts. This may have severe consequences in terms of water security, especially for those already in vulnerable positions who are unable to meet the requirements of this new formalisation. The conclusion of the book brings together the two cases which have so far been treated independently of each other. Seemann concludes that ‘whatever type of property rights are considered within formalisation policies, the process fundamentally maintains or even increases authority and power within the formalizing agency’ (p. ). This is important, and I share her hope, detailed in the last part of the conclusion, that this insight can inspire a different way of thinking about water rights that maintains a focus on water security and water justice. The book is strongest in its attention to detail when it comes to the legal–historical background. Seemann provides a very useful overview of the different legislative frameworks and the contexts within which they have emerged. Through her analysis the reader gains a good understanding of the micro-politics of water rights, their historical trajectories, and how these translate into (in)injustices on the ground. While I would recommend the book to anyone interested in understanding the contexts shaping water allocation and its attendant rights and injustices, it may not quite meet the expectations of more ethnographically oriented readers. It is based on relatively short fieldtrips and a total of  interviews of which only a handful in each site are with water users. While the introduction does mention everyday struggles, we learn little about this in the empirical chapters. The book can therefore fruitfully be used in teaching along with other more ethnographic work.
Odia-Conjunct character recogntion using evolutionary algorithm

Most efforts have been devoted to the recognition of isolated printed and handwritten Odia basic fonts; however recognition of complex conjunct characters is still an area of active research. This paper presents an effective text recognition scheme for Odia conjunct characters. The shape of conjunct characters is inherently complex and imposes great challenges to the researcher group of Odia Optical Character Recognition. The weightiness of this work lies on design and development of an optimized classification method by combining the back-propagation neural network with genetic algorithm to achieve both the training swiftness and accuracy for recognition. We compare the back-propagation neural network (BPNN) and genetic algorithm optimized in artificial neural network (GAONN) and found that GAONN produces more promising result then BPNN. Finally we show that the method yields high accuracy rate and depict the most promising research guidelines in this field.
Position Constrained Bilateral Control by Oblique Coordinate Control Considering Priority of Tasks

This paper proposes two position constrained bilateral controllers. The constraint is given by coordinate transformation and additional compensator or command modiﬁcation methods are not necessary. The constraint therefore does not destabilize the system and maintains linearity. The di ﬀ erence between the two proposed methods is the task priority. The ﬁrst ensures slave position limitation and force regulation between two robots, the other ensures position limitation and position regulation between the robots. We analyzed the stability of the proposed controller. The validity of the proposed methods was conﬁrmed experimentally.
RECLINK: Stata module to probabilistically match records

Record linkage involves attempting match records from two different data files that do not share a unique and reliable key field. It can be a tedious and challenging task when working with multiple administrative databases where one wants to match subjects using names, addresses and other identifiers that may have spelling and formatting variations. Formal record linkage methods often employ a combination of approximate string comparators and probabilistic matching algorithms to identify the best matches and assess their reliability. Some stand-alone software is available for this task. This presentation will introduce -reclink-, a rudimentary probabilistic record matching program for Stata. -reclink- employs a modified bigram string comparator and allows user-specified match and non-match weights. The algorithm also provides for blocking (both "or" and "and") to help improve speed for this otherwise slow procedure.
Clinical and Personality Correlates of MMO Gaming

Massively-multiplayer online games (MMOs) are increasingly popular worldwide. MMO gaming can result in problematic Internet use (PIU; or Internet addiction), which is characterized by dysfunction in areas such as work or relationships. Because PIU in online gaming is increasingly seen in clinical populations, we explored PIU in the context of MMO gaming. Using a cross-sectional design, we sought to identify clinical and personality factors, as well as motivations for gaming, that differentiated between people who scored high or low on a measure of problematic Internet use. Subjects completed all study procedures via an online survey. Participants were 163 MMO users recruited from the community, from gaming websites, and from online forums. Subjects completed a series of demographic, mood, anxiety, and personality questionnaires. The study found that individuals in the high PIU group (n = 79) were more likely to have higher levels of social phobia (p = .000), state (p = .000) and trait (p = .000) anxiety, introversion (p = .000), neuroticism (p = .000) and absorption (p = .019) than individuals in the low-PIU group (n = 84). Different reasons for gaming also characterized the group with more problematic Internet use. Our findings provide support for the idea that high anxiety and absorption may be risk factors for problematic Internet use within the MMO gaming environment and suggest that gamers who endorse problematic Internet use identify different motivations for online gaming than gamers who do not.
Phrasal Verbs: Their Teaching and Acquisition

This research study intends to explore the importance of teaching phrasal verbs in the English classroom, as well as to identify the effects that teaching phrasal verbs following an explicit approach has on their acquisition as evidenced by the students’ oral performance. The paper will also point out the importance of engaging students in different systematic classroom activities to be able to determine whether their phrasal verbs acquisition becomes significant and improves their oral performance. Finally, the research intends to understand if there is a correlation between the acquisition of phrasal verbs through explicit and systematic activities used in the classroom, and the students’ performance and proceduralization of phrasal verbs; and if they when presented and studied explicitly in class, can help the students to incorporate them in their oral performance.
Maximum power point tracking for low power photovoltaic solar panels

A maximum power point tracker unit is developed for the optimum coupling of photovoltaic panels (PVP) to the batteries and load through a controlled DC-DC power converter (chopper). The system consists of three main units: (i) the photovoltaic panels that convert solar power to electricity; (ii) a chopper which couples the power of PVP to the load or batteries at a constant voltage; and (iii) maximum power point (MPP) computing unit that determines the set point of the chopper to keep the panel voltage at a maximum power transfer (MPT) condition. The tracking of the MPP for low power PVP (50 W-1 kW) is feasible only when the power consumption of the tracking unit is lower than the increase of the output power that they provide. The developed and tested circuit consumes only 40 mW, and therefore is suitable even for low power applications down to 50 W. The tracking unit performs MPP computation periodically through analog computing stages. The computation mode requires 20 mA from the /spl plusmn/5 V source for a 50 ms period. In the control and sleep mode, the consumption falls down to 4 mA. The developed unit regulates the panel output voltage at its optimum value in the control mode. The modes are switched by a timing circuit. The sleep mode is initiated when maximum PVP output power of the existent illumination level drops to a preset value, which cannot balance the losses of the chopper and the consumption of the MPP tracker unit. <<ETX>>
Modified Doppler centroid tracking method for phase compensation in ISAR

The modified Doppler centroid tracking (MDCT) method is developed which applies the phase gradient autofocus (PGA) algorithm developed by Wahl (1994) to improve the Doppler centroid tracking (DCT) method for the phase compensation in inverse synthetic aperture radar (ISAR). It can smartly eliminate the effect of the rotational phase component (RPC) on the estimation of the translational phase component (TPC) by circular shifting, windowing and iteration steps. After several iterations, the maximum likelihood estimation and compensation of the TPC of the target can be realized more effectively. The processing results of live data show that the proposed method can improve the imaging quality of ISAR significantly.
Reasoning about teleo-reactive programs under parallel composition

The teleo-reactive programming model is a high-level approach to implementing real-time controllers that react dynamically to changes in their environment. Teleo-reactive actions can be hierarchically nested, which facilitates abstraction from lower-level details. Furthermore, teleo-reactive programs can be composed using renaming, hiding, and parallelism to form new programs. In this paper, we present a framework for reasoning about safety, progress, and real-time properties of teleo-reactive programs under program composition. We use a logic that extends the duration calculus to formalise the semantics of teleo-reactive programs and to reason about their properties. We present rely/guarantee style specifications to allow compositional proofs and we consider an application of our theory by verifying a real-time controller for an industrial press.
Learning science by creating models

Inquiry learning environments increasingly incorporate modeling facilities. Such environments offer a platform where students learn about a phenomena through systematic experimentation with a simulation and express their newly acquired understanding in a runnable computer model. As the orchestration of inquiry and modeling activities is both new and demanding to students, support is needed. The series of studies described in this dissertation examine how students’ investigative efforts and model-building activities can best be supported. In the first study, domain novices’ need for support in such environments was assessed. In three subsequent studies the effect of support in the form of model progression (i.e., gradually increasing a tasks’ complexity) and worked out examples on students’ performance and learning were examined in real classroom settings in secondary education.
Object Duplicate Detection

With the technological evolution of digital acquisition and storage technologies, millions of images and video sequences are captured every day and shared in online services. One way of exploring this huge volume of images and videos is through searching a particular object depicted in images or videos by making use of object duplicate detection. Therefore, need of research on object duplicate detection is validated by several image and video retrieval applications, such as tag propagation, augmented reality, surveillance, mobile visual search, and television statistic measurement. Object duplicate detection is detecting visually same or very similar object to a query. Input is not restricted to an image, it can be several images from an object or even it can be a video. This dissertation describes the author's contribution to solve problems on object duplicate detection in computer vision. A novel graph-based approach is introduced for 2D and 3D object duplicate detection in still images. Graph model is used to represent the 3D spatial information of the object based on the local features extracted from training images so that an explicit and complex 3D object modeling is avoided. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Our method is shown to be robust in detecting the same objects even when images containing the objects are taken from very different viewpoints or distances. Furthermore, we apply our object duplicate detection method to video, where the training images are added iteratively to the video sequence in order to compensate for 3D view variations, illumination changes and partial occlusions. Finally, we show several mobile applications for object duplicate detection, such as object recognition based museum guide, money recognition or flower recognition. General object duplicate detection may fail to detection chess figures, however considering context, like chess board position and height of the chess figure, detection can be more accurate. We show that user interaction further improves image retrieval compared to pure content-based methods through a game, called Epitome.
Algebraic Characteristics of the CCITT#2 Communication Code

New characteristics of the CCITT#2 data communication code are deduced using a novel set theoretic presentation technique useful in the design and analysis of coded character sets(CCS) in computer system organization . The technique , called ‘code presentation’, is predicated on the classical internal arrangement of a word of a coded character set in terms of a ‘zoned portion’ and a ‘numeric portion’.
Extraction automatique de contour de lèvre à partir du modèle CLNF (Automatic lip contour extraction using CLNF model)[In French]

Dans cet article nous proposons une nouvelle solution pour extraire le contour interne des lèvres d’un locuteur sans utiliser d’artifices. La méthode s’appuie sur un algorithme récent d’extraction du contour de visage développé en vision par ordinateur, CLNF pour Constrained Local Neural Field. Cet algorithme fournit en particulier 8 points caractéristiques délimitant le contour interne des lèvres. Appliqué directement à nos données audio-visuelles du locuteur, le CLNF donne de très bons résultats dans environ 70% des cas. Des erreurs subsistent cependant pour le reste des cas. Nous proposons des solutions pour estimer un contour raisonnable des lèvres à partir des points fournis par CLNF utilisant l’interpolation par spline permettant de corriger ses erreurs et d’extraire correctement les paramètres labiaux classiques. Les évaluations sur une base de données de 179 images confirment les performances de notre algorithme.
Sparse Convex Optimization Methods for Machine Learning

Convex optimization is at the core of many of today’s analysis tools for large datasets, and in particular machine learning methods. In this thesis we will study the general setting of optimizing (minimizing) a convex function over a compact convex domain. In the first part of this thesis, we study a simple iterative approximation algorithm for that class of optimization problems, based on the classical method by Frank & Wolfe. The algorithm only relies on supporting hyperplanes to the function that we need to optimize. In each iteration, we move slightly towards a point which (approximately) minimizes the linear function given by the supporting hyperplane at the current point, where the minimum is taken over the original optimization domain. In contrast to gradient-descent-type methods, this algorithm does not need any projection steps in order to stay inside the optimization domain. Our framework generalizes the sparse greedy algorithm of Frank & Wolfe and its recent primal-dual analysis by Clarkson (and the low-rank SDP approach by Hazan) to arbitrary compact convex domains. Analogously, we give a convergence proof guaranteeing e-small error — which in our context is the duality gap — after O( 1e ) iterations. This method allows us to understand the sparsity of approximate solutions for any `1-regularized convex optimization problem (and for optimization over the simplex), expressed as a function of the approximation quality. Here we obtain matching upper and lower bounds of Θ ( 1 e ) for the sparsity. The same bounds apply to low-rank semidefinite optimization with bounded trace, showing that rank O ( 1 e ) is best possible here as well. For some classes of geometric optimization problems, our algorithm has a simple geometric interpretation, which is also known as the coreset concept. Here we will study linear classifiers such as support vector machines (SVM) and perceptrons, as well as general distance computations between convex hulls (or polytopes). Here the framework will allow us to understand the sparsity of SVM solutions, here being the number of support vectors, in terms of the required approximation quality.
A novel method for fault diagnosis of analog circuits based on WP and GPNN

A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet (WP) decomposition and probabilistic neural networks using genetic algorithm (GPNN) is proposed in this paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit being solved. Then by fusing various uncertain factors into probabilistic operations, GPNN methods to diagnose faults are proposed whose parameters and structure obtained form genetic optimisations resulting in best detection of faults. Finally, simulations indicated that GPNN classifiers are correct 7% more than BPNN of the test data associated with our sample circuits.
The Complexity of Parallel Computations

Recent advances in microelectronics have brought closer to feasibility the construction of computers containing thousands (or more) of processing elements. This thesis addresses the question of effective utilization of such processing power. We study the computational complexity of synchronous paarallel computations using a model of computation based on random access machines operating in parallel and sharing a common memory, the P-RAM. Two main areas within the field of parallel computational complexity are investigated. First, we explore the power of the P-RAM model viewed as an abstract computing device. Later, we study techniques for developing efficient algorithms for parallel computers. We are able to give concise characterizations of the power of deterministic and nondeterministic P-RAMS in terms of the more widely known space and time complexity classes for multi-tape Turing machines. Roughly speaking, time-bounded deterministic P-RAMS are equivalent in power to (can accept the same sets as) space-bounded Turing machines, where the time and space bounds differ by at most a polynomial. In the context of comparing models of computation, we consider such polynomial differences in resources to be insignificant. Adding the feature of nondeterminism to the time-bounded P-RAM changes its power to that of a nondeterministic Turing machine with an exponentially higher running time. The later sections of the thesis examine algorithm design techniques for parallel computers. We first develop efficient procedures for some common operations on linked lists and arrays. Given this background, we introduce three techniques that permit the design of parallel algorithms that are efficient in terms of both their time and processor requirements. We illustrate the use of these techniques by presenting time and processor efficient algorithms for three problems, in each case improving upon the best previously known parallel resource bounds. We show how to compute minimum string edit distances, using the technique of pairwise function composition. We describe an algorithm for the off-line MIN that organizes its computation in the form of a complete binary tree. Finally, we present an algorithm for undirected graph connectivity that relies on redundancy in its representation of the input graph.