Miracle-dz/UniFAR-FTUs huggingface dataset:
SVDPACKC (Version 1.0) User''s Guide

SVDPACKC comprises four numerical (iterative) methods for computing the singular value decomposition (SVD) of large sparse matrices using ANSI C. This software package implements Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left- and right-singular vectors) for large sparse matrices. The package has been ported to a variety of machines ranging from supercomputers to workstations: CRAY Y-MP, IBM RS/6000-550, DEC 5000-100, HP 9000-750, SPARCstation 2, and Macintosh II/fx. This document {\it (i)} explains each algorithm in some detail, {\it (ii)} explains the input parameters for each program, {\it (iii)} explains how to compile/execute each program, and {\it (iv)} illustrates the performance of each method when we compute lower rank approximations to sparse {\it term-document} matrices from information retrieval applications. A user-friendly software interface to the package for UNIX-based systems and the Macintosh II/fx is also described.
A hardware implementation of MD4-family hash algorithms

This article presents the design of an integrated circuit which is able to perform various different hash algorithms of the MD4 family. Hash algorithms are used for digital signature schemes, data integrity, message authentication, and other cryptographic purposes (password protection, electronic bidding, confirmation of knowledge). The MD4 family is a group of hash algorithms which have similar properties. These properties can be exploited to build a universal hardware module for an efficient calculation of these hash algorithms. Special attention was turned to a well-structured hardware design.
A semantic transport networks for preferential routing

Summary. The paper presents a semantic network formalism based on frame logic. Advanced techniques, including HiLog extensions, transaction logic and dynamic module creation are applied to the problem of preferential routing and rerouting. The deductive program is implemented in the FLORA-2 reasoning engine, which allows the scalable query execution. Finally, possible extensions to the system and guidelines for a future research are presented.
An HDTV‐Compatible 3DTV Broadcasting System

We introduce a high‐definition three‐dimensional television (3DTV) broadcasting system that is fully compatible with the existing transmission system of high‐definition television (HDTV). Specifically, we developed high‐definition 3DTV broadcasting subsystems including a 3DTV camera, 3DTV video multiplexer and demultiplexer, 3DTV receiver, and 3DTV outdoor broadcast van. To verify the developed subsystems, we performed experimental services of 3DTV broadcasting during the 2002 FIFA World Cup Korea/Japan. According to our subjective evaluation test, 88% of 273 viewers rated the perceived depth of 3DTV as “Good,” and 36% of the viewers preferred 3DTV to other digital broadcasting services.
A language to express reactivity in the Semantic Web

The Semantic Web is a Web to describe by means of metadata the WWW data. In The Semantic Web, knowledge is modeled by using ontology and the reactive behavior is encoded by means of active rules. Hence, active knowledge cannot be used to derive new properties of the data instances, neither new reactive behavior information can be inferred directly from the schema. In this paper, we describe an alternative approach aiming to the enrichment of the Semantic Web with active information. We propose the ontology formalism ACTlON. In ACTION, events are categorized as concepts of the ontology and in conjunction with classes, properties and instances are considered during the query answering and reasoning tasks. We have conducted an empirical experimental study and we have observed that active knowledge representation increases the known information in a third of the active knowledge of the modeled domain. But, the augment of the expressive power implies the augment of the computational complexity and efficient strategies to evaluate reactive query are necessary.
Seeing physics, or: physics is for prediction [computer vision]

We describe how knowledge of the physics of the scene itself is important to computer vision. High-level knowledge of scene physics can help programs see the world, and programs that see and understand this way are useful for planning plan scene interactions. We illustrate these points with two of our most recent knowledge-intensive vision systems. One uses knowledge of physics and function to understand noisy and ambiguous images of gear-train machines; i.e. to report what the machine does. The other uses physical knowledge to guide a robotic eye-hand system to pick up a mug of coffee by its handle
Efficient Detection for Top Part of Research Paper with Layout Index

The detection of elements in top part of research paper is the key problem in layout analysis, because these elements are often used as the search items by user. This paper provides a index-based method for auto detection of top part from research papers. Different from existing methods, an index is used for detecting items from top part of research paper. Experiments show the advantage of our method over existing methods, and future work is also described in the paper.
ON THE MECHANISM OF INTERPRETING ENGLISH TENSES IN INDIRECT SPEECH COMPLEMENT CLAUSES

This paper provides a principled explanation of the mechanism of interpreting English tenses in indirect speech complement clauses. To this end, the paper proposes an interpretive principle which is based on two theories: a compositional tense theory and Hirose's (1995, 1997a, 1997b) theory of reported speech. The former theory requires a distinction between the level of tense structure and the level of tense interpretation, on one hand, and a distinction between the absolute and the relative tense component, on the other; the latter theory introduces the notions of public self and private self. It will be shown that the proposed principle can not only solve problems with previous analyses, but also account for a variety of related temporal phenomena in English indirect speech. *
Content-Based Image Retrial Based on Hadoop

Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.
Neural approximations for multistage optimal control of nonlinear stochastic systems

This paper deals with the problem of designing a feedback control law that drives a dynamic system (in general, nonlinear) so as to minimize a given cost function (in general, nonquadratic). Random noises (in general, non-Gaussian) act on both the dynamic system and the state observation channel, which may be nonlinear, too. As is well known, so general non-LQG optimal control problems are very difficult to solve. The proposed solution is based on two main approximating assumptions: (1) the control law is assigned a given structure in which a finite number of parameters have to be determined in order to minimize the cost function (the chosen structure is that of a multilayer feedforward neural network), and (2) the control law is given a "limited memory", which prevents the amount of data to be stored from increasing over time. The first assumption; enables the authors to approximate the original functional optimization problem by a nonlinear programming one. The errors resulting from both assumptions are discussed. Simulation results show that the proposed method constitutes a simple and effective tool for solving, to a sufficient degree of accuracy, optimal control problems traditionally regarded as difficult ones.
Template construction and matching for identification of cells in differential interference contrast microscope images

Template matching, for identification of cells in differential interference contrast (DIC) microscope images by digital image analysis, is complex. This is due to the nature of DIC images, namely an apparent illumination effect across cells in the image. Different cell sizes and orientations cause further complications. Three methods of template construction are proposed for different types of cell, and a successful automatic method for cell counting and sizing is described. These methods compensate for the complexities in the images.
A Bayesian model for visual space perception

A model for visual space perception is proposed that contains desirable features in the theories of Gibson and Brunswik. This model is a Bayesian processor of proximal stimuli which contains three important elements: an internal model of the Markov process describing the knowledge of the distal world, the a priori distribution of the state of the Markov process, and an internal model relating state to proximal stimuli. The universality of the model is discussed and it is compared with signal detection theory models. Experimental results of Kinchla are used as a special case.
One Speaker, Two Languages: Cross-Disciplinary Perspectives on Code-Switching

1. Introduction Lesley Milroy and Pieter Muysken Part I. Code-Switching in Institutional and Community Settings: 2. Bilingual speech of migrant people Louise Dabene and Daniele Moore 3. Code-switching in the context of dialect/standard language relations Anna Giacolone-Ramat 4. Code-switching in community, regional and national repertoires Penelope Gardner-Chloros 5. Code-switching in the classroom Marilyn Martin-Jones Part II. Code-Switching and Social Life: 6. The pragmatics of code-switching Peter Auer 7. A social network approach to code-switching Lesley Milroy and Li Wei 8. Code-switching and the politics of language Monica Heller Part III. Grammatical Constraints on Code-Switching: 9. Code-switching and grammatical theory Pieter Muysken 10. Patterns of language mixture Shana Poplack and Marjory Meechan 11. A lexically based production model of code-switching Carol Myers-Scotton Part IV. Code-Switching in Bilingual Development and Processing: 12. A psycholinguistic approach to code-switching Francois Grosjean 13. Code-switching in bilingual first language acquisition Regina Koppe and Jurgen M. Meisel 14. The code-switching behaviour of adults with language disorders Kenneth Hyltenstam Conclusion 15. Code-switching research as a theoretical challenge Andree Tabouret-Keller.
Benchmarking the system dynamics community

In this section the System Dynamics Review presents problems having the potential to stimulate system dynamics research. Articles may address real-world dynamic problems that could be approached fruitfully from the system dynamics perspective, or methodological problems affecting the field. A paper submitted to the Research Problems section should concisely motivate and define a problem and start a process of conceptualization or formulation that can open the way for further studies. Manuscripts not exceeding 2,000 words should be sent to George P. Richardson, Milne 308, Rockefeller College, S.U.N.Y. , Albany, NY 12222, U.S.A.
An output feedback controller for trajectory tracking of RLED robots using an observed backstepping approach

This paper presents an output feedback controller for a rigid-link electrically driven (RLED) robot. The controller is designed in conjunction with a velocity observer and an electrical current observer using an observed integrator backstepping procedure. The overall observer-controller achieves a semiglobal exponential stability result for the link position-velocity tracking error as well as the velocity and current observation errors. Simulation study as well as experimental results of the observer-controller for a RLED robot driven by de motors are also included. <<ETX>>
A theory of learning classification rules

The main contributions of this thesis are a Bayesian theory of learning classi cation rules, the uni cation and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learning class probability trees and bounding error when learning logical rules. The thesis is motivated by considering some current research issues in machine learning such as bias, over tting and search, and considering the requirements placed on a learning system when it is used for knowledge acquisition. Basic Bayesian decision theory relevant to the problem of learning classi cation rules is reviewed, then a Bayesian framework for such learning is presented. The framework has three components: the hypothesis space, the learning protocol, and criteria for successful learning. Several learning protocols are analysed in detail: queries, logical, noisy, uncertain and positive-only examples. The analysis is done by interpreting a protocol as a likelihood function and by performing conditional likelihood analysis. The aim of this framework and theoretical treatment is to consider how three important questions should be addressed: what actions a learner should take, whether the learner's initial subjective knowledge is appropriate, and how con dent the learner can be in results obtained. The resultant learning framework is compared and uni ed with the corresponding learning frameworks of Gold, Valiant, minimum encoding approaches such as MDL and MML, and classical methods from statistics and pattern recognition. Finally, two extensive case studies are reported. The rst case study works through conjunctive and complete hypothesis spaces to illustrate problems such as the inherent worst-case and sampleindependent analysis of the Valiantmodel and how this can be overcome in the Bayesian framework. Experiments reported indicate considerable improvements can be achieved. The second case study presents a Bayesian approach to learning decision trees based on the theory presented for the uncertain examples protocol. Experiments reported indicate the approach compares favourably with existing decision tree methods. The Bayesian method presented should readily transfer to the learning of disjunctive rules or other more exible representations, and to more exible search strategies. The Bayesian method should also provide much stronger feedback for an interactive style of learning.
Modified deadbeat digital controller for UPS with 3-phase PWM inverter

In this paper, a modified deadbeat control scheme is proposed for 3-phase PWM inverter control such as uninterruptible power supply (UPS). The deadbeat controller, which is generally constructed with voltage and current controller in UPS, is used for fast transient response, but this might have an instability due to the same pole placement of the double controller. To solve this instability problem, separation of pole placement can be considered, and it is performed by the proposed deadbeat control scheme. In this paper, this control scheme is called the modified deadbeat, which produces a first-order exponential response during one input step. As the overall controller is constructed by deadbeat control, the response of the proposed control system is fast and the structure of the controller is simple. In addition, the disturbance observer with full-order state is constructed, which can cancel the disturbances by adding a feedforward compensating loop in the controller and hence it can compensate the disturbances caused by a sudden load change in the UPS system.
Shape understanding: knowledge generation and learning

Image analysis and recognition applied in medical engineering requires specific methods of shape analysis and representation that need to be learnt. In this paper the method of knowledge generation as a part of a shape understanding method is proposed. The knowledge generation method used in the system of shape understanding is related to hierarchically organised knowledge of the shape classes. The system of shape understanding that is able to perform different tasks of shape analysis and recognition, based on the ability of the system to understand the different concepts of shape at the different levels of cognition, is proposed. The system consists of different types of experts that perform different processing and reasoning tasks and is designed to perform the visual diagnosis in medical applications.
Short-Term Wind Speed Forecast Based on B-Spline Neural Network Optimized by PSO

Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm optimization is proposed to predict the short-term wind speed. The B-spline neural network can change the division of input space and the definition of basis function flexibly. For any input, only a few outputs of hidden layers are nonzero, the outputs are simple, and the convergence speed is fast, but it is easy to fall into local minimum. The traditional method to divide the input space is thoughtless and it will influence the final prediction accuracy. Particle swarm optimization is adopted to solve the problem by optimizing the nodes. Simulated results show that it has higher prediction accuracy than traditional B-spline neural network and BP neural network.
Detection of vehicle occlusion using a generalized deformable model

This paper presents a vehicle occlusion detection algorithm based on a generalized deformable model. A 3D solid cuboid model with up to six vertices is employed to fit any vehicle images, by varying the vertices for a best fit. The advantage of using such a model is that the number of parameterized vertices is small which can be easily deformed. Occlusion is detected by recording the changes in the Area Ratio and the dimensions of the generalized deformable model. Our tests show that the new modeling algorithm is effective in detecting vehicle occlusion.
Metodología para la definición de sistemas de gestión de procesos estratégicos

Resumen. Los sistemas de gestión de procesos de negocio basados en las tecnologías de la información (TI) se han convertido en una de las principales soluciones para llevar a cabo los procesos de la organización y obtener beneficios que posicionen a una organización en una situación privilegiada frente a la competencia. Sin embargo, en los últimos tiempos se ha visto como muchas de estas propuestas se han alejado de los principios de las teorías de gestión de procesos que pretendían contemplar y de las necesidades y requerimientos sobre los que se fundamentaban las mismas, como el caso de la reingeniería de procesos y los sistemas ERP, lo que ha provocado que dichas soluciones no sólo no proveyesen a las organizaciones de los beneficios adecuados sino que han supuesto grandes pérdidas económicas. En este artículo se propone una metodología que permite, de forma sistemática, definir un sistema de gestión de procesos basado en las TI que asegure la alineación con las teorías de negocio que representan, los requerimientos del mercado y las necesidades del entorno donde serán implantados. A partir de la visión general, se propone una instancia de la metodología para abordar la gestión de procesos en las organizaciones manufactureras, contemplando la problemática actual en este ámbito y demostrando la validez de la metodología para obtener SGP estratégicos.
A flexible I/O framework for parallel and distributed systems

We propose a framework for I/O in parallel and distributed systems. The framework is highly customizable and extendible, and enables programmers to offer high level objects in their applications, without requiring them to struggle with the low level and sometimes complex details of high performance distributed I/O. Also, the framework exploits application specific information to improve I/O performance by allowing specialized programmers to customize the framework. Internally, we use indirection and granularity control to support migration, dynamic load balancing, fault tolerance, etc. for objects of the I/O system, including those representing application data. <<ETX>>
A 3.0 W 75SPECint92 85SPECfp92 superscalar RISC microprocessor

This superscalar microprocessor is a 32b implementation of the PowerPC Architecture. With an estimated performance/power ratio of 25SPECint92/W at 80 MHz, this RISC style chip offers workstation-level performance packed into a low-power consumption, low-cost design ideal for notebooks and desktop computers. <<ETX>>
The m4 macro processor

M4 is a macro processor available on UNIX† andGCOS. Its primary use has been as a front end for Ratfor for those cases where parameterless macros are not adequately powerful. It has also been used for languages as disparate as C and Cobol. M4 is particularly suited for functional languages like Fortran, PL/I and C since macros are specified in a functional notation. M4 provides features seldom found even in much larger macro processors, including •arguments •condition testing •arithmetic capabilities •string and substring functions •file manipulation This paper is a user’s manual for M4.
Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems

There has been much debate about instructional strategies for computerized learning environments. Many of the arguments designed to choose between the various philosophies have appealed, at least implicitly, to the behavior of effective human teachers. In this article, we compare the guidance and support offered by human tutors with that offered by intelligent tutoring systems. First, we review research on human tutoring strategies in various domains. Then we investigate the capabilities of a widely used technique for providing feedback, model tracing. Finally, we contrast the types of guidance and support provided by human tutors with those in intelligent tutoring systems, by examining the process of recovering from impasses encountered during problem solving. In general, the support offered by human tutors is more flexible and more subtle than that offered by model tracing tutors, but the two are more similar than sometimes argued.
Explaining Financial Results

CROSBY III automatically constructs explanations for financial results. The key elements of the program are 1 a body of raw financial data to be explained, 2 an extensible knowledge base of financial relations expressed as algebraic constraints, 3 a selection of possible explanatory variables and information about the relative likelihoods of individual hypotheses, 4 an algorithm for generating consistent, adequate, plausible and parsimonious interpretations of the data, 5 a facility for explaining how a particular interpretation supports the data and 6 a facility for explaining why one interpretation is preferred over another. This paper focuses on facilites 5 and 6. CROSBY III is an implemented but undeployed prototype that has been tested on historical data and financial models of a small high-technology company along with its closest competitors, on more than ten divisions of a large company and on a collection of large banks.