Miracle-dz/UniFAR-FTUs huggingface dataset:
Designs and topology

About the book: This volume contains survey articles based on the invited lectures given at the Twenty-first British Combinatorial Conference, held in July 2007 at the University of Reading. This biennial conference is a well-established international event and the articles are of the high quality that befits the event. By its nature this volume provides an up-to-date overview of current research activity in several areas of combinatorics, ranging from graph theory to current applications of combinatorial mathematics, including efficient approximability of NP-hard optimization problems and cryptographic key management. The authors are some of the world's foremost researchers in their fields, and here they summarize existing results, and give a unique preview of work currently being written up. The book provides a valuable survey of the present state of knowledge in combinatorics. It will be useful to research workers and advanced graduate students, primarily in mathematics but also in computer science, statistics and engineering.
Survey Paper On Different Techniques Of Measuring Efficiency Of Clustering

Data Mining is a process that uses technology to bridge the gap between data and logical decision making. The terminology itself provides a promising view of systematic data manipulation for extracting useful information and knowledge from high volume of data. Clustering is the process of organizing similar objects into the same clusters and dissimilar objects into different clusters. Similarities between objects are evaluated by using the attribute value of object, a distance metric is used for evaluating
Multispectral satellite image analysis based on the method of blind separation and fusion of sources

The number of satellite images is in full evolution allowing us to improve the extraction of useful information related to the physical reality of natural scenes. In this paper, we propose a new hybrid approach of multi-spectral analysis of satellite images. This approach consists in a method of blind separation of sources. This method allows us to decompose a pixel into information coming from independent sources. Algorithms adapted in the context of our work, operating in the two dimensional space, have been used for the separation. These algorithms produces many sources, in order to choose among them the most significant having a maximum value of entropy representing a maximum information about one class of land use. In order to have a classified image with good classification, we proceed with the fusion of these sources using a technique of maximum likelihood classification. We validated our approach on optical images of the satellite SPOT 4 and radar images of the satellite ERS 2 representing a central Tunisian region. The results obtained consists in the production of five classes of land use.
Managing wire delay in chip multiprocessor caches

Increasing on-chip wire delay and growing off-chip miss latency, present two key challenges in designing large Level-2 (L2) CMP caches. Currently, some CMPs use a shared L2 cache to maximize cache capacity and minimize off-chip misses. Others use private L2 caches, replicating data to limit the delay from slow on-chip wires and minimize cache access time. Ideally, to improve performance for a wide variety of workloads, CMPs prefer both the capacity of a shared cache and the access latency of private caches. In this thesis, we propose three techniques that combine the benefits of shared and private caches. In particular, to reduce access latency in a shared cache, we investigate cache block migration and on-chip transmission lines. Migration reduces access latency by moving frequently used blocks towards the lower-latency banks. We show migration successfully reduces latency to blocks requested by only one processor, but doesn't reduce the latency to shared blocks. In contrast, transmission lines can reduce on-chip wire delay by an order of magnitude versus conventional wires and provide low latency to all shared cache banks. We demonstrate on-chip transmission lines consistently improve performance versus a baseline shared cache, but bandwidth contention can limit them from reaching their full potential. To improve the effective capacity of private caches, we propose Adaptive Selective Replication (ASR). ASR dynamically monitors workload behavior and replicates cache blocks only when it estimates the benefit of replication (lower L2 hit latency) exceeds the cost (more L2 misses). When ASR detects replication is less beneficial, processors coordinate writebacks with remote on-chip caches to conserve cache storage. ASR provides a robust CMP cache hierarchy: improving performance versus both shared and private caches. Additionally, ASR can leverage the fast remote cache access latency provided by transmission lines and reduce off-chip misses versus a design using conventional wires. We demonstrate the combination of transmission lines and ASR outperforms either isolated technique and preforms similarly to a shared cache using four times the transmission line bandwidth.
Multilingual Italian – Lithuanian Small Vocabulary Speech Recognition via Selection of Phonetic Transcriptions

The amount of resources required to incorporate a new language into an ASR system is extensive and it is usually hard to provide more than one language per system. Naturally some research groups started developing the multilingual speech recognition principles. Opposite to most of the other researchers, in this work the original recognizer was not modified as we chose to use the phonetic “language” of the recognizer – the “transcriptions”, allowing the adaption of the original ASR engine to any language if enough acoustic resources are available. The results of our experiments proved that it is quite reasonable to use these methods to provide the multilingual speech interface for the user. It was established that the recognition accuracy can be acceptable for small vocabulary grammars (~93% for Lithuanian, and ~98% for Italian), satisfying the requirements of an IVR type of application where different small vocabulary grammars with a limited number of multilingual transcriptions can be loaded for each of the different stage of the HCI voice dialog, allowing the modeling of multilingual regional self-services. DOI: http://dx.doi. org/10.5755/j01.eee. 121.5. 1145
Mobile-Assisted Learning as a Condition for Effective Development of Engineering Students’ Foreign Language Competence

In this article we focus on the conditions for effective development of foreign language competence that is technologically oriented methods of teaching a foreign language. The use of computers provides ample opportunities for implementation of activity and student-centered approaches, reorganization of the usual lesson structure, as well as providing the students with more autonomy. Currently, due to the extensive use of wireless devices, there is a new form of e-learning i.e. mobile learning. The experimental results showed that the use of mobile electronic multimedia courses increase the effectiveness of foreign language teaching, as in the absence of a real language environment, creates opportunity for immersion in an authentic foreign language environment, improve language competence in all kinds of speech activity, all of which leads to the development of students’ foreign language competence. These tools allow achieving results with less expenditure of time and effort, which is also an indicator for the efficiency of mobile learning means application in the development of foreign language competence.
Modeling the role of social structures in population genetics : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Manawatu, New Zealand

Building on a theoretical framework, population genetics has been widely applied to diverse organisms, from bacteria to animals. On humans, this has led to the reconstruction of history, the timing of settlements, and migration between populations. Mostly based on the coalescent theory, modern population genetic studies are challenged by human social structures, which are difficult to incorporate into analytically models. The implications of social structure on population genetics are mostly unknown. This work presents new modeling and inference methods to model the role of social structure in poulation genetics. The applications of these new techniques permit to gain better understanding of the history and practices of a number of Indonesian island communities. This thesis comprises three published, organized as sequential chapters. The Introduction describes population genetic models and the statistical tools that are used to make inferences. The second chapter presents the first paper, which measures the change of population size through time on four Indonesian islands structured by history and geography. The third chapter presents SMARTPOP, a new simulation tool to study social structure, including mating systems and genetic diversity. The fourth chapter focuses on Asymmetric Prescriptive Alliance, a famous kinship system linking the migration of women between communities with cousin alliance. The fifth chapter presents a conclusion and future directions. In combination, this body of work shows the importance of including social structure in population genetics and proposes new ways to reconstruct aspects of social history.
Handwritten Digit Recognition Based on A Neural – SVM Combination

Abstract In this paper, we present a new multi-class method for handwritten digit recognition that is based on cascade combination of neural networks and support vector machines (SVMs). Binary SVMs are used to provide high linear separation between classes while the neural network generates automatic multi-class decision. In addition, we introduce Jaccard distance into negative distance (ND) SVM kernel. The performance evaluation of the proposed method is conducted on the well-known US Postal Service database. Experimental results indicate that it can significantly reduce the runtime while giving at least the same performance as conventional multi-class SVM methods. Besides, the use of Jaccard distance improves significantly the performance of the standard ND kernel.
Structured systems analysis and design method: application and context

Part 1 Introduction to SSADM: what is SSADM? SSADMs place in organizations using IT views of SADM conclusion. Part 2 Framework: introduction feasibility study module, FS requirements analysis module, RA requirements specification module, RS logical system specification module, LS physical design module, PD route maps conclusion. Part 3 Techniques: introduction case study requirements definition dialogue design data flow modelling logical data modelling business system options function description relational data analysis specification prototyping entity event modelling technical system options logical database process design physical data design physical process specification review of SSADM. Part 4 Application of SSADM: introducing SSADM project procedures project management quality risk assessment and management conclusion. Part 5 Context: introduction strategic planning of IS (SPIS) problem determination representing user's interests matching methods with projects politics and ISD supporting IS practitioners underpinning ISD conclusion.
Special Functions Of Mathematics For Engineers

Special Functions and Their ApplicationsAsymptotics and Special FunctionsSpecial FunctionsA Course of Modern AnalysisSpecial FunctionsOrthogonal Polynomials and Special FunctionsMathematical Methods in PhysicsSpecial FunctionsLectures on Orthogonal Polynomials and Special FunctionsSpecial Functions for Engineers and Applied MathematicsHandbook of Special FunctionsSpecial FunctionsSpecial Functions for Scientists and EngineersSpecial Functions and the Theory of Group RepresentationsSources in the Development of MathematicsSPECIAL FUNCTIONS AND COMPLEX VARIABLESSpecial Functions: Group Theoretical Aspects and ApplicationsSpecial Functions of Applied MathematicsElliptic and Modular Functions from Gauss to Dedekind to HeckeFormulas and Theorems for the Special Functions of Mathematical PhysicsHypergeometric Functions and Their ApplicationsSpecial Functions for Engineers and Applied MathematiciansThe Special Functions and Their ApproximationsSpecial Functions and Orthogonal PolynomialsSpecial Functions for Applied ScientistsSpecial Functions of Mathematical (Geo-)PhysicsSpecial Functions in Physics with MATLABOrthogonal Polynomials and Special FunctionsSpecial Functions of Mathematical PhysicsSpecial Functions of Mathematical PhysicsSpecial Functions, Partial Differential Equations, and Harmonic AnalysisRepresentation of Lie Groups and Special FunctionsSpecial Functions of Mathematics for EngineersA Handbook of Generalized Special Functions for Statistical and Physical SciencesSpecial FunctionsHandbook of Mathematical FunctionsSpecial FunctionsNumerical Methods for Special FunctionsSpecial Functions and Orthogonal PolynomialsSpecial Functions and Analysis of Differential Equations
A Syllabic Bottleneck in Prelexical Processing ? A Phoneme Monitoring Investigation

Previous research has found that phoneme detection latencies depend on the complexity of the syllable that bears the target phoneme. CV syllables give rise to faster latencies than CVC, that are faster than CCV (Treiman et al., 1982, Cutler et al., 1987). In Experiment 1, we replicate this result and extend it to a fourth structure: CCVC. In Experiment 2, we report a similar effect in first syllables of disyllabic items, showing that complexity effects cannot be reduced to stimulus duration effects. We argue that the complexity effect is inconsistent with the view that phonemes are the only units involved in speech perception, but supports models which stipulate larger sized units like syllables (Mehler, 1981; Segui, Dupoux & Mehler, 1990). In a series of post-hoc analyses, however, we show that the complexity effect is not uniform across subjects. Although both the complexity of onsets and codas of syllables influence phoneme detection latencies for slow subjects, fast subjects are only influenced by the nature of the onset. The interaction of speed of response with complexity effects is confirmed in Experiment 3, where it is found that when subjects are urged to respond as fast as possible, CVC items no longer show a complexity effect nor a lexical superiority effect. Implications for the existence of a syllabic bottleneck and the time course of prelexical processing are discussed.
Comparison of «draw-a-person» test elements in oppositional defiant disorder and normal children

Introduction: The present study evaluated the ability of possible components of Draw-A-Person (DAP) test in distinguishing oppositional defiant disorder (ODD). Method: 60 students (30 males 30 females) from 16 randomly selected schools in Tehran who had received scores above the cut off point on ODD subtest of child behavioral check list (CBCL) and teacher report form (TRF) were selected as the subjects of the study. They ranged from 7 to 10 years old. 60 other students from the same schools, compatible in age and sex, who scored below the cut off point on CBCL and TRF served as the control group. Both groups were administered DAP test. Data was analyzed by chi-square to compare groups across components of DAP. Results: ODD group rated significantly higher than the control group in 18 components of DAP (p Conclusion: Human figure drawings of individuals diagnosed with ODD, irrespective of their gender, are significantly different than those of the non-ODDs.
Connectionist Speech Recognition: A Hybrid Approach

From the Publisher: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.
The Computer Attitude Scale: Assessing Changes in Teachers' Attitudes toward Computers

Teachers' attitudes toward computers affect their instructional use of computers and likelihood of profiting from training. The reliability, factorial validity, and fit to a unidimensional model of the Computer Attitude Scale (CAS) was investigated. Pretest and posttest results were collected from 265 teachers who participated in training on classroom applications of computer hardware and software. Four factors were identified: computer anxiety, efficiency, liking, and instructional usefulness. Scale reliability was . 94 with post-test subscale reliabilities ranging from . 70 to . 90. Five items did not fit the multidimensional model. The scale is useful as a computer attitude measure but some item revisions might be considered.
STOCHASTIC METHODS IN MODELING THE IMMUNE RESPONSE

We discuss the application of deterministic and stochastic modeling techniques to problemsin immunology. First, we employ the example of a host response to influenza virusinfection to illustrate differences in the dynamical behavior of the deterministic and thestochastic models, and employ both versions in the analysis of the role of immune responsein controlling and suppressing the infection. Second, we develop a dynamical model of vocalfold inflammation and use random sampling techniques to calibrate the model againstavailable data.In the stochastic model, we analyze three solution techniques - Gillespie's stochastic simulationalgorithm, numerical solution of mean extinction time through the Laplace transformof the master equation, and approximate solution of a limiting case. Gillespie algorithm iscapable of dealing with large systems as the required memory depends linearly on number ofspecies, but is limited by computational time. Laplace's method is efficient and accurate butis limited by system size. We construct a novel combination of the two that takes advantageof both. We also derive an approximate Markov chain of the system and analytically computethe extinction times and probabilities in the limiting case when an infected cell generateslarge number of viruses. In the analysis of the human immune response, we find that innateresponse substantially reduces cell extinction probability, cellular response increases the virusextinction probability in limiting case, and adaptive response, combined with the other two,almost eliminates cell extinction and significantly increases virus extinction probability.In the model of vocal fold inflammation, a four-variable ordinary differential equation (ODE) model is presented. The ODE model characterizes cytokine interactions in phonotraumaand is calibrated with empirical data. Parameter values are estimated and theirprobability densities are sampled using Metropolis and parallel tempering algorithms. Sensitivityanalysis showed that 6 of 17 parameters suffice to retain the sensitivity of the modeltrajectories. The reduced parameter set is applied to individual data to be calibrated andpredict the individual outcome. The model is a part of larger study intended to find optimaltreatment strategy for phonotrauma through a personalized vocal exercise or rest program.
Computationally Efficient Symbol Detection Schemes in Multi-Device STBC-MIMO Systems

The multi input multi output (MIMO) communication system has significantly higher channel capacity than the Single-Input-Single-Output (SISO) system for the same total transmission power and bandwidth (Foschini et al., 1998 & Telatar, 1999). It is known that the use of Space Time Block Code (STBC) can realize the increased capacity of MIMO systems and thus improve data throughput and spectral efficiency (Tarokh et al., 1998). In this work, we focus on the system that comprises one receiving station and multiple transmitting devices (e.g., uplinks in cellular systems). The receiver’s front end has multiple antennas, and each transmitting device has multiple transmit antennas. The system discussed in this chapter is illustrated in Fig. 1. A Space Time Block Code (STBC) is used in each transmitting device, and joint signal detection is performed at the receiver. We refer to such a system as Multi-Device (MD) STBC-MIMO system. Generally in a MD-STBC-MIMO system, the number of receive antennas is typically smaller than the number of all transmit antennas used by all transmitting devices in the system. An example of MD-STBC-MIMO would be the uplink multiple access communication system, where the number of receive antennas at the base station or the access point is smaller than the total number of transmit antennas at the mobile devices. In this work, we address symbol detection in Multi-Device (MD) STBC-MIMO systems. As will be discussed in section 3, the maximum a posteriori (MAP) detection, which reduced to the Maximum Likelihood (ML) detection in the case of a priori equally likely symbol blocks, minimizes the probability of detection error, and thus is optimal. However, a computationally efficient algorithm for achieving MAP or ML detection is not known. Some studies with sphere decoding (SD) algorithms exhibit that their expected computational complexity grows polynomially with the problem size, say m, up to some value of m for the cases of small constellation sizes (Vikalo et al., 2005), but it grows exponentially for the cases of large constellation sizes. Also, for some sphere decoding algorithms, operation at a low SNR requires inordinately high computation, although operation at a high SNR is efficient. In any case, an algorithm with polynomial growth of expected complexity for all values of the problem size, m, has not yet been found. In fact, Jalden et al. (2005) shows that even the expected computational complexity of the sphere decoding grows exponentially with the problem size in MIMO communication systems. In this work, we present two evolutionary optimization methods, Biogeography-Based Optimization (BBO) and Estimation of Distribution Algorithm (EDA) to solve the problem