Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine Motion Planning for Dexterous Manipulation

This thesis investigates the problem of dexterous manipulation; how can robots affect the world around them by means of their end-effectors? Dexterous manipulation is fundamental to robots operating intelligently and independently in their environments and it is a special motion planning problem. Since the general motion planning problem with uncertainty is NEXP-hard, effort must be directed to defining classes of tasks that are tractable. We consider the reorientation problem: for a given robot hand, an arbitrary object, and a desired orientation with respect to the hand, find an algorithm to synthesize a robust plan for the fingers that accomplishes the desired reorientation. A reorientation algorithm devised for a robot hand should satisfy several properties. First, it must be able to accomplish arbitrarily large rotations. Second, since it must be implemented on a real device, it should involve simple finger motions that can be computed fast. Third, since the application domain is characterized by uncertainties that manifest themselves as imprecisions in calculations and inaccuracies in control, it must exhibit good stability properties. We propose algorithms for the reorientation problem that satisfy these properties. The basic idea is to use some of the robot fingers to constrain the motion of the object and others to generate motion. This results in the idea of finger tracking as a high-level primitive for manipulation. We also propose an algebraic framework for manipulation that is theoretically well-founded and in which it is possible to use systematically and effectively the differential equations describing the interaction between objects in contact. Finally, we describe a simulator for the reorientation of polyhedra by finger tracking.
Performance analysis for maximal-ratio combining in correlated generalized Rician fading

In this paper, we perform the analysis of average symbol error probability (SEP) for a diversity system over generalized Rician fading channels with correlated branches. This class of fading subsumes generalized Rayleigh fading, Nakagami-q (Hoyt) fading, generalized gamma fading, Nakagami fading, Rician fading, and Rayleigh fading as special cases, which are among the most popular fading models considered in the published literature. We derive a series expression of the average SEP for a general class of M-ary modulation schemes (including MPSK, MQAM, BFSK, and MSK) with maximal-ratio combining (MRC). The series expression is in canonical form as a weighted sum of elementary closed-form expressions, which are the closed-form expressions for the average SEP of a single-branch system in Nakagami fading environments.
Introduction to Programming Using Sml

From the Publisher: Introduction to Programming using SML provides a thorough introduction to the principles of programming and program design using the Standard ML programming language. The emphasis throughout is to p of programming into practice. The examples and exercises teach the student how to apply basic theoretical concepts to produce succinct and elegant programs and program designs. Coverage includes an introduction to fundamental data structures and their applications. The notions of binding, environment, store, closure and evaluation are introduced in order to explain the meaning of programs in an informal but precise way. Thus, the authors provide the reader with a set of durable programming concepts which will exist well into the next generation of programming languages. Features of the book include: Attractive and reader-friendly presentation Clear and careful explanations A rich collection of programming problems and a wide variety of examples Coverage of modelling and abstraction using data structures and the SML module system Overview and statement of objectives at the start of each chapter An introduction to producing technical documentation based on the SML module system Extensive material in the appendices covering the SML language and module system and selected parts of the SML basis library Accompanying Web Site supporting the book, containing all the program code, further teaching material and links to SML systems and other useful resources
Experiments in Expression Recognition

Abstract : Despite the significant effort devoted to methods for expression recognition, suitable training and test databases designed explicitly for expression research have been largely neglected. Additionally, possible techniques for expression recognition within an Man-Machine-Interface (MMI) domain are numerous, but it remains unclear what methods are most effective for expression recognition. In response, this thesis describes the means by which an appropriate expression database has been generated and then enumerates the results of five different recognition methods as applied to that database. An analysis of the results of these experiments is given, and conclusions for future research based upon these results is put forth.
Image analysis, classification and change detection in remote sensing: with algorithms for ENVI/IDL, by M.J. Canty

There are a wide range of textbooks that offer a detailed overview of the physics and technology underpinning remote sensing. However, given the general and high-level approach of many text books, it is often difficult to translate this theory into practice due to the complexities of different data sources, programming languages, and software platforms. This book addresses the gap between theory and implementation by providing exhaustive explanations of image manipulation and analysis techniques. This is aided by numerous implementation examples provided in the Exelis ENVI/IDL programming and image analysis environment. Most of the examples are also provided online by the author and can be easily incorporated into ENVI. The book is aimed at the fairly advanced remote-sensing scientist/student; readers without a reasonable grounding in physics and maths will find the book difficult to follow without further reading. Indeed the author suggests that the target audience is graduate students, and this shows in the heavily mathematical nature of much of the book. Early chapters of the book (chapters 1 and 2) cover basic numerical concepts that underpin remote sensing. Topics reviewed include images, arrays and matrices, image statistics, hypothesis testing, and Bayes theory. These subjects are introduced quickly and often with little explanation, and I suspect these early chapters will present the biggest challenge for less mathematically inclined readers. I feel that the brevity of these introductory chapters is somewhat of a missed opportunity as, for many students, one of the most difficult conceptual leaps is understanding that any satellite or airborne image is simply an array of numbers that can be readily manipulated. A more expansive explanation of these core subjects would help broaden the impact of this book beyond its mathematically literate target audience. Chapters 3 and 4 cover spatial transformations, including Fourier, wavelet, and principal component techniques, and convolution theorem at length. In these chapters, the combination of mathematics, code examples, explanation, and figures helps take the reader through this rather complicated area in a relatively straightforward fashion and provides tools that can be readily incorporated into your own analysis workflows. Chapter 5 provides a mixed bag of image pre-processing techniques that somewhat lacks focus, perhaps as many of the techniques introduced fill gaps in the existing ENVI toolkit. For example, while panchromatic sharpening is discussed at length, atmospheric correction is left to the very end of the book to be briefly mentioned in the context of change detection. This is a pity as this broad and often confusing subject would benefit from being explained in the thorough fashion of this book. Other techniques discussed, such as C-correction and image-to-image registration, are of clear significance and well worth inclusion.
A Maximum Entropy Approach to Named Entity Recognition

This thesis describes a novel statistical named-entity (i.e. “proper name”) recognition system known as “MENE” (Maximum Entropy Named Entity). Named entity (N.E.) recognition is a form of information extraction in which we seek to classify every word in a document as being a person-name, organization, location, date, time, monetary value, percentage, or “none of the above”. The task has particular significance for Internet search engines, machine translation, the automatic indexing of documents, and as a foundation for work on more complex information extraction tasks. Two of the most significant problems facing the constructor of a named entity system are the questions of portability and system performance. A practical N.E. system will need to be ported frequently to new bodies of text and even to new languages. The challenge is to build a system which can be ported with minimal expense (in particular minimal programming by a computational linguist) while maintaining a high degree of accuracy in the new domains or languages. MENE attempts to address these issues through the use of maximum entropy probabilistic modeling. It utilizes a very flexible object-based architecture which allows it to make use of a broad range of knowledge sources in making its tagging decisions. In the DARPA-sponsored MUC-7 named entity evaluation, the system displayed an accuracy rate which was well-above the median, demonstrating that it can achieve the performance goal. In addition, we demonstrate that the system can be used as a post-processing tool to enhance the output of a hand-coded named entity recognizer through experiments in which MENE improved on the performance of N.E. systems from three different sites. Furthermore, when all three external recognizers are combined under MENE, we are able to achieve very strong results which, in some cases, appear to be competitive with human performance. Finally, we demonstrate the trans-lingual portability of the system. We ported the system to two Japanese-language named entity tasks, one of which involved a new named entity category, “artifact”. Our results on these tasks were competitive with the best systems built by native Japanese speakers despite the fact that the author speaks no Japanese.
Neural networks for offline segmentation of teleoperation tasks

The authors present two artificial neural network architectures that perform offline segmentation of telerobotics tasks using force data. Two network architectures have been tested. The first one is based on turning temporal sequences into spatial patterns. The second architecture extends the first model by including the network's output in the input array. Experimental data are classified offline by a hidden Markov model providing the transition times and the corresponding segmentation for the training data. It was found that the first architecture needs a high number of iterations for learning the associations, whereas the latter has a high convergence speed. <<ETX>>
Experimental and efficient algorithms : second International Workshop, WEA 2003, Ascona, Switzerland, May 26-28, 2003 : proceedings

Improving Linear Programming Approaches for the Steiner Tree Problem. - Algorithms and Experiments on Colouring Squares of Planar Graphs. - Experimental Analysis of Online Algorithms for the Bicriteria Scheduling Problem. - Fast-Search: A New Efficient Variant of the Boyer-Moore String Matching Algorithm. - An On-Line Algorithm for the Rectangle Packing Problem with Rejection. - New Lower and Upper Bounds for Graph Treewidth. - Search Data Structures for Skewed Strings. - Evaluation of Basic Protocols for Optical Smart Dust Networks. - Linear Time Local Improvements for Weighted Matchings in Graphs. - Experimental Studies of Graph Traversal Algorithms. - A Nondifferentiable Optimization Approach to Ratio-Cut Partitioning. - Comparing Push- and Pull-Based Broadcasting. - Experimental Comparison of Heuristic and Approximation Algorithms for Uncapacitated Facility Location. - A Lazy Version of Eppstein's K Shortest Paths Algorithm. - Linear Algorithm for 3-Coloring of Locally Connected Graphs. - A Clustering Algorithm for Interval Graph Test on Noisy Data. - Core Instances for Testing: A Case Study. - The Reliable Algorithmic Software Challenge RASC. - A New Class of Greedy Heuristics for Job Shop Scheduling Problems. - Algorithmic Techniques for Memory Energy Reduction. - A Framework for Designing Approximation Algorithms for Scheduling Problems. - Analysis and Visualization of Social Networks.
CODATA recommended values of the fundamental constants

A review is given of the latest Committee on Data for Science and Technology (CODATA) adjustment of the values of the fundamental constants. The new set of constants, referred to as the 1998 values, replaces the values recommended for international use by CODATA in 1986. The values of the constants, and particularly the Rydberg constant, are of relevance to the calculation of precise atomic spectra. The standard uncertainty (estimated standard deviation) of the new recommended value of the Rydberg constant, which is based on precision frequency metrology and a detailed analysis of the theory, is approximately 1/160 times the uncertainty of the 1986 value. The new set of recommended values as well as a searchable bibliographic database that gives citations to the relevant literature is available on the World Wide Web at physics.nist. gov/constants and physics.nist. gov/constantsbib, respectively.
Human Image Understanding

Abstract : This report summarizes the major research accomplishments performed under AFOSR Grant 99-0231, HUMAN IMAGE UNDERSTANDING. An extensive series of experiments assessing the visual priming of briefly presented images indicate that the visual representation that mediates real-time object recognition specifies neither the image edges or vertices nor an overall model of the object but an arrangement of simple volumes (or geons) corresponding to the object's parts. This representation can be activated with no loss in efficiency when the image is projected onto the retina at another position, size, or orientation in depth from when originally viewed. Consideration of these invariances suggests a computational basis for the evolution of two extrastriate visual systems, one for recognition and the other subserving motor interaction. The experiments suggest that it may be possible to assess the functioning of these systems behaviorally, that is, to split the cortex horizontally, through a comparison of performance on naming and episodic memory tasks. We have developed a neural network model (Hummel and Biederman, 1992) that captures the essential characteristics of human object recognition performance. The model takes a line drawing of an object as input and generates a structural description which is then used for object classification. The model's capacity for structural description derives from its solution to the dynamic binding problem of neural networks: Independent units representing an object's parts (in terms of their shape attributes and interrelations) are bound temporarily when those attributes occur in conjunction in the systems input.
EDUONTO: AN ONTOLOGY FOR EDUCATIONAL ASSESSMENT

The search for possible applications in the field of Education, by means of domain specific ontologies on the issues of assessment of learning (assessment) and system (evaluation), has been implemented through the construction, testing and evolution of an integrated learning environment called EduOntoWiki. It is supported by ontological structures related to science education based on active consent of specific communities of practice. It allows selecting and incorporating the ontologies in order to organize learning objects to improve learning process. It is created as a setting in which they had the relevant formal descriptions (coding ontology) and informal (and narrative contextualization of concepts); where the possible intra-and inter-community relations were made explicit and recognized by all participants through specific social software. The last point in particular, has led to our hypothesis: only the transition from a technology-driven model (formal, static) to a community-driven model (dynamic and integrated in an open learning environment) could make really effective and relevant the ontologies that we built in several communities of practice. Therefore, the final goal was the realization of an integrated learning environment attempting the difficult transition from an “ontological-formal” system to an “ontological-relational” one.
Experiences on model based disclosure limitation

National statistical institutes routinely apply imputation methods based on statistical models to survey nonresponses. This area of research is very important because it is at the basis of the production of economic data which are as accurate as possible. The idea is to take stock of the experiences gathered in the field of imputation methodology and to try to bridge the gap between this area of research and statistical disclosure limitation. In this paper we review our experiences on model based disclosure limitation techniques. In general, these techniques substitute the observed value of a certain variable with the estimated value via a statistical model. In particular, we discuss the problems encountered and the possible solutions found with two different models: a regression tree model [2] for a categorical variable [17] and a hierarchical model for a continuous variable [9].
Text Compression Algorithm Using Bits For Character Representation

Full text compression is among the most important principles found in computer science. This principle deals with a large given text to be stored in a smallest space and an efficient algorithm to be used for compressing the text. In this article, a new algorithm of full text compression is introduced. This solution divides the source data into suitable blocks; as well as, all characters in each block are assigned their positions. Afterwards, these positions are converted into the bit form. Empirically, the new algorithm is efficient for the source data which takes several bytes per one character. The experimental results showed that the source data could be saved 11.50% in minimum; meanwhile, the maximum is 76.56%.
A low-power differential CMOS bandgap reference

The performance of many mixed analog/digital systems is limited by inaccuracies and power supply noise coupling errors in integrated voltage references. Reference circuitry also typically contributes a large fraction of the analog power consumption. The CMOS bandgap circuit described here provides a 2.0 V differential voltage reference for a 10.24 MHz oversampled data converter, and maintains high PSR and accuracy while consuming 2.2 mW. <<ETX>>
Automated-generating test case using UML statechart diagrams

Software testing plays a major role in software development because it accounts for a large part of the development cost. Moreover, manual testing technique always makes a problem. Consequently, this paper proposes the automatic testing technique to solve partially the testing process. This technique can automatically generate and select test cases from UML statechart diagrams. Firstly, we transform this diagram into intermediate diagram, called Testing Flow Graph (TFG), explicitly identify flows of UML statechart diagrams and enhance for testing. Secondly, from TFG we generate test case using the testing criteria that is the coverage of the state and transition of diagrams. Finally, the evaluation is performed using mutation analysis to assess the fault revealing power of our test cases.
Architektura CUDA w bezopóźnieniowych hurtowniach danych

Streszczenie. Rośnie znaczenie i potrzeba zapewnienia aktualności danych oraz efektywności ich przetwarzania w bezopoźnieniowych hurtowniach danych. Standardowe podejście, oparte na tradycyjnym procesie ekstrakcji danych (ETL), okazalo sie niewystarczające ze wzgledu na potrzebe podzialu czasu dostepu, na czas odświezania danych oraz czas ich analizy. W artykule przedstawiono system ETL dla bezopoźnieniowych hurtowniach danych. System ten realizuje algorytm WINE-HYBRIS, bazujący na architekturze CUDA oraz CPU. Przedstawiono testy wydajnościowe tego systemu opartego na dwoch calkowicie roznych architekturach, umozliwiając zobaczenie mozliwości, jakie niesie za sobą wykorzystanie architektury CUDA w systemach hurtowni danych. Slowa kluczowe: ETL, CUDA, WINE-HYBRIS, bezopoźnieniowe hurtownie danych
Fundamental studies of next generation SONET for photonic transport and interconnect

Fundamental frame structures for a photonic version of the Synchronous Optical Network (SONET) are investigated. Simple logical functions and low-power consumptions are required for photonic frame processing. Two types of frame structure are developed to meet these requirements, i.e., one that utilizes coding rule violations, and one that employs convolutional framing. The former and the latter are suitable for octet- and bit-wise multiplexing, respectively. Typical examples are studied. The application to photonic interconnect is discussed. <<ETX>>
OpenJIT—A Reflective Java JIT Compiler

The so-called ‘Open Compilers’ is a technique to incorporate various self-descriptive modules for language customization and optimization based on computational reflection. We apply the open compiler technique to a Java Just-In-Time compiler to develop the OpenJIT compiler, which allows class-specific customization and optimization, fostering research of new compilation techniques such as application-specific customization and dynamic optimizations. Benchmarks with the current prototype, which is almost entirely written in Java, have seen comparable benchmark results compared to traditional C-based JIT compilers.
Evolution of an Intelligent Information Fusion System

Consideration is given to the hardware and software needed to manage the enormous amount and complexity of data that the next generation of space-borne sensors will provide. An anthology is presented illustrating the evolution of artificial intelligence, science data processing, and management from the 1960s to the near future. Problems and limitations of technologies, data structures, data standards, and conceptual thinking are addressed. The development of an end-to-end Intelligent Information Fusion System that embodies knowledge of the user's domain-specific goals is proposed.
Improved multiple model target tracking algorithm based on particle filter

Due to the high computing complexity of interacting multiple model based on particle filter (IMM-PF), the improved multiple model particle filter tracking algorithm based on systematic resampling is proposed in combination with the requirement of air defense system for maneuvering target tracking model. Since the number of particles in this algorithm is not involved in model switch or interaction, this algorithm has reduced filter’s algorithm complexity and improved the real-time performance of target tracking while maintaining the same accuracy with IMM-PF. Therefore, this algorithm is of high practical application value in modern air defense system.
Hybrid BRAINNE: a method for developing symbolic disjunctive rules from a hybrid neural network

A method for learning disjunctive rules using a combination of two existing neural network schemes is proposed. The hybrid network consists of two layers; the first is an unsupervised network while the second is a supervised network. The first layer is used for ordering the inputs of training instances into clusters. Initial rules are extracted from this layer using an existing technique called Unsupervised BRAINNE. These rules are then fed into the second layer which is trained using the delta rule. The second layer is then examined to determine which clusters define the output nodes. This method is able to identify disjunctive rules directly rather than utilising a generate and test paradigm as was used in previous supervised versions of BRAINNE.