Miracle-dz/UniFAR-FTUs huggingface dataset:
The Description Logic Handbook: OWL: a Description-Logic-Based Ontology Language for the Semantic Web

It has long been realized that the web could benefit from having its content understandable and available in a machine processable form. The Semantic Web aims to achieve this via annotations that use terms defined in ontologies to give well defined meaning to Web accessible information and services. OWL, the ontology language recommended by the W3C for this purpose, was heavily influenced by Description Logic research. In this chapter we review briefly some early efforts that combine Description Logics and the Web, including predecessors of OWL such as OIL and DAML+OIL. We then go on to describe OWL in some detail, including the various influences on its design, its relationship with RDFS, its syntax and semantics, and a range of tools and applications.1 14.1 Background and history The World Wide Web, while wildly successful in growth, may be viewed as being limited by its reliance on languages such as HTML that are focused on presentation (i.e., text formatting) rather than content. Languages such as XML do add some support for capturing the meaning of Web content (instead of simply how to render it in a browser), but more is needed in order to support intelligent applications that can better exploit the ever increasing range of information and services accessible via the Web. Such applications are urgently needed in order to avoid overwhelming users with 1 This chapter provides an update to the chapter in previous printings of this book entitled “Digital Libraries and Web-Based Information Systems” by Ian Horrocks, Deborah L. McGuinness and Christopher A. Welty. The previous chapter was written prior to the completion of OWL, and focused on earlier description logic based ontology languages for the Semantic Web. Some of the material in the current chapter has previously appeared in other forms in conference and journal publications, in particular in [Horrocks et al., 2003].
Pronunciation variation modeling for Dutch automatic speech recognition

This article describes how the performance of a Dutch continuous speech recognizer was improved by modeling pronunciation variation. We propose a general procedure for modeling pronunciation variation. In short, it consists of adding pronunciation variants to the lexicon, retraining phone models and using language models to which the pronunciation variants have been added. First, within-word pronunciation variants were generated by applying a set of ®ve optional phonological rules to the words in the baseline lexicon. Next, a limited number of cross-word processes were modeled, using two dierent methods. In the ®rst approach, cross-word processes were modeled by directly adding the cross-word variants to the lexicon, and in the second approach this was done by using multi-words. Finally, the combination of the within-word method with the two cross-word methods was tested. The word error rate (WER) measured for the baseline system was 12.75%. Compared to the baseline, a small but statistically signi®cant improvement of 0.68% in WER was measured for the within-word method, whereas both cross-word methods in isolation led to small, non-signi®cant improvements. The combination of the within-word method and cross-word method 2 led to the best result: an absolute improvement of 1.12% in WER was found compared to the baseline, which is a relative improvement of 8.8% in WER. O 1999 Elsevier Science B.V. All rights reserved.
Fast linear algorithms for machine learning

Nowadays linear methods like Regression, Principal Component Analysis and Canonical Correlation Analysis are well understood and widely used by the machine learning community for predictive modeling and feature generation. Generally speaking, all these methods aim at capturing interesting subspaces in the original high dimensional feature space. Due to the simple linear structures, these methods all have a closed form solution which makes computation and theoretical analysis very easy for small datasets. However, in modern machine learning problems it's very common for a dataset to have millions or billions of features and samples. In these cases, pursuing the closed form solution for these linear methods can be extremely slow since it requires multiplying two huge matrices and computing inverse, inverse square root, QR decomposition or Singular Value Decomposition (SVD) of huge matrices. In this thesis, we consider three fast algorithms for computing Regression and Canonical Correlation Analysis approximate for huge datasets. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Applied Mathematics First Advisor Dean P. Foster
An efficient feature descriptor and its real-time applications

An Efficient Feature Descriptor and Its Real-Time Applications Alok Hirenkumar Desai Department of Electrical and Computer Engineering, BYU Doctor of Philosophy Finding salient features in an image, and matching them to their corresponding features in another image is an important step for many vision-based applications. Feature description plays an important role in the feature matching process. A robust feature descriptor must works with a number of image deformations and should be computationally efficient. For resource-limited systems, floating point and complex operations such as multiplication and square root are not desirable. This research first introduces a robust and efficient feature descriptor called PRObability (PRO) descriptor that meets these requirements without sacrificing matching accuracy. The PRO descriptor is further improved by incorporating only affine features for matching. While performing well, PRO descriptor still requires larger descriptor size, higher offline computation time, and more memory space than other binary feature descriptors. SYnthetic BAsis (SYBA) descriptor is developed to overcome these drawbacks. SYBA is built on the basis of a new compressed sensing theory that uses synthetic basis functions to uniquely encode or reconstruct a signal. The SYBA descriptor is designed to provide accurate feature matching for real-time vision applications. To demonstrate its performance, we develop algorithms that utilize SYBA descriptor to localize the soccer ball in a broadcast soccer game video, track ground objects for unmanned aerial vehicle, and perform motion analysis, and improve visual odometry accuracy for advanced driver assistance systems. SYBA provides high feature matching accuracy with computational simplicity and requires minimal computational resources. It is a hardware-friendly feature description and matching algorithm suitable for embedded vision applications.
Performance Analysis of Cross Component Carrier Scheduling in LTE Small Cell Access Point System

With respect to the carrier aggregation (CA) in a LTE small cell access point system, the performance of an independent and cross component carrier (CC) scheduling is simulated and analyzed in this paper. According to the results, we obtained a capacity increase of about 17~46% for the cross CC max rate (MR) algorithm and about 12~45% for the cross CC proportional fair (PF) algorithm. On the other hand, in the round robin (RR) scenario, we cannot notice the differences between the independent CC and cross CC scheduling policies on allocating resources. We also found that CA frequency combination 1.8GHz CC + 800MHz CC has a better performance when comparing to the other combinations with respect to total average UE throughput.
Bilateral control of teleoperators with time delay

When a robot is operated remotely by use of a teleoperator, it is desirable to communicate contact force information from the slave to the master, in order to kinesthetically couple the operator to the environment and increase the sense of telepresence. One problem, however, recognized as early as 1965, has remained unsolved until now: How to maintain stability in a force-reflecting bilateral teleoperator in the presence of substantial time delay? In this paper, we present a solution to this problem.
Technology and the Writing Skills of Students with Learning Disabilities

Abstract A year-long study was conducted involving 5 fifth-grade students with learning disabilities with written language deficits from a parochial elementary school in the midwestern United States using a specially designed computer program as a writing tool to assist them in a weekly based writing curriculum. The study showed positive effects on the participating students’ writing behaviors and their written products.
Declarative user interfaces for handheld devices

This paper proposes a new approach to user interfaces for a specific category of Web services. The approach requires developers to "declare" the user interface, using implementation-independent specifications that are downloaded and "interpreted" on the fly. While this allows different Web applications to have their tailored user interfaces, these interfaces are interpreted differently on different devices, taking into consideration the capabilities and limitations of the target devices. This does not only separate the presentation and logic components in a Web service, but more importantly, it requires the developer to specify the user interface only once.
Live Migration of Virtual Machines in Cloud Environment: A Survey

Virtualization is a useful tool for administrators of data centers and clusters. Migration process of VMs mitigates the overloaded condition of data centers and provides the uninterrupted services. The main objective of migration process is to achieve less down time and migration time. Various migration techniques like pre-copy, post-copy and suspend-copy are available to perform live migration of VMs. Pre-copy migration method has optimum performance compare to the other techniques. A great deal of research and study on pre-copy methods has been proposed by researchers. This paper gives a comprehensive survey investigation of different pre-copy live migration methods based on their working principles along with pros and cons.
PANGEA: A New Platform for Developing Virtual Organizations of Agents

This paper presents an agent platform called PANGEA (Platform for Automatic coNstruction of orGanizations of intElligent Agents). This platform enables to develop multiagent systems modelled as Virtual Organizations. The concepts of roles, organizations and norms are fully supported by the platform assuring flexibility and scalability. Moreover, a communication protocol based on IRC gives high performance and reliability to this kind of distributed systems.
MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies

midas is a user-written command for idiot-proof implementation of some of the contemporary statistical methods for meta-analysis of binary diagnostic test accuracy. Primary data synthesis is performed within the bivariate mixed-effects logistic regression modeling framework. Likelihood-based estimation is by adaptive gaussian quadrature using xtmelogit (Stata release 10) with post-estimation procedures for model diagnostics and empirical Bayes predictions. Average sensitivity and specificity (optionally depicted in SROC space with or without confidence and prediction regions), and their derivative likelihood and odds ratios are calculated from the maximum likelihood estimates. midas facilitates exploratory analysis of heterogeneity, threshold-related variability, methodological quality bias, publication and other precision-related biases. Bayes' nomograms, likelihood-ratio matrices, and probability modifying plots may be derived and used to guide patient-based diagnostic decision making. A dataset of studies evaluating axillary staging performance of positron emission tomography in breast cancer patients is provided for illustration of the omnibus capabilities of midas.
New simulated annealing algorithms

This paper introduces a new class of D-dimensional density probability functions to be used in Simulated Annealing algorithms and derives an appropriate cooling schedule that is proved to be inversely proportional to a previously chosen power n of time. This generates a new algorithm, the nFast Simulated Annealing (nFSA), from which the Fast Simulated Annealing (FSA) is a particular case. As will be shown, this new algorithm achieves results with an accuracy that increases with n, at the expense of an initial convergence speed that decreases with n. This drawback is solved by the use of an adaptive algorithm, the Adaptive nFast Simulated Annealing (AnFSA), where the parameter n starts at small value, producing a fast initial convergence, and is raised as the algorithm runs, finding global minima points quickly and with great accuracy.
Electronic Resource Management Systems

Computer applications which deal with electronic resource management (ERM) are quite a recent development. They have grown out of the need to manage the burgeoning number of electronic resources particularly electronic journals. Typically, in the early years of e-journal acquisition, library staff provided an easy means of accessing these journals by providing an alphabetical list on a web page. Some went as far as categorising the e-journals by subject and then grouping the journals either on a single web page or by using multiple pages. It didn't take long before it was recognised that it would be more efficient to dynamically generate the pages from a database rather than to continually edit the pages manually. Of course, once the descriptive metadata for an electronic journal was held within a database the next logical step was to provide administrative forms whereby that metadata could be manipulated. This in turn led to demands for incorporating more information and more functionality into the developing application.
Asymmetric latches and nonideal performance of parallel ADCs

We study the contribution of both bipolar and CMOS latches to the input offset voltage of the latched comparators that are used in the front-end of high-speed analog to digital converters. We then relate the occurrence of missing codes and the nonlinearity errors in the converter to the comparators' offset voltage. This provides useful guidelines for the design of parallel converters. <<ETX>>
Compiler and run-time support for irregular computations

There are many important applications in computational fluid dynamics, circuit simulation and structural analysis that can be more accurately modeled using iterations on unstructured grids. In these problems, regular compiler analysis for Massively Parallel Processors (MPP) with distributed address space fails because the communication can only be determined at run-time. However, in many of these applications the communication pattern repeats for every iteration. Therefore, equivalent optimizations to the regular case can be achieved with a combination of run-time support (RTS) and compiler analysis. Moreover, many real applications have irregular access patterns with certain structure. This structure is needed to improve sequential performance, better exploiting the memory hierarchy. In many cases this structure is reflected in the array subscript expressions in the form of hybrid accesses, that is, affine combinations of array indirection and regular accesses. We will show that when we get advantage of this structure, we can dramatically reduce pre-processing overheads, both in terms of memory and time, and improve communication and single node performance. In order to achieve these goals we need a combination of run-time and compiler support. Unfortunately, existing irregular run-time support cannot get advantage of regularity and therefore, our first requirement was to develop a new run-time support for irregular computations, that we called PILAR (Parallel Irregular Library with Application of Regularity). Our second requirement was to automate the task of exploiting regularity in irregular applications through compiler support. For this task we extended the PARADIGM compiler, developed at the University of Illinois, to handle irregular problems. Experimental results will validate the efficacy of our approach.
Code Performance as a Function of Block Size

The recent development of turbo codes has stirred new interest in trying to un-derstand how closely practical codes can approach the theoretical limits on codeperformance. This article reformulates Shannon’s sphere-packing bound in a nor-malized form conducive to analyzing the relative performance of families of turbocodes. Even though this bound is achievable only by a (generally mythical)
Using Wiki to Promote Collaborative Learning in Statistics Education

This article attempts to make a strong case for the use of Wiki to support collaborative learning experiences for students in the statistics classroom. Wiki is an innovative Website that allows all users to add and edit content with relative simplicity. Wiki features empowered learners and bottom-up organization that enable easy authoring of Web content, open access and unrestricted collaboration. We first introduce statistics as a collaborative discipline and therefore compatible with Wiki as a collaborative learning space. We then show evidence that collaboration can improve the learning of individuals in the statistics classroom as well as the whole class. Finally we demonstrate how Wiki can facilitate collaborative learning and bring about instructional change to improve student learning of statistics. We present several types of Wiki-based activities: collaborative writing, glossaries, discussion and review, statistical projects, self-reflective journals, and assessment.
A Combined Phase and Amplitude Based Approach to Onset Detection for Audio Segmentation

In this paper, we present a new approach to solving the problem of sound onset detection for note based segmentation of musical audio. The more traditional solution of looking at dierences in energy, and the more recently proposed approach of using deviations in expected phase characteristics have been combined to produce a more robust scheme, leading to overall improvements in detection accuracy.
B.D.L.E.X. : A data and cognition base of spoken French

BDLEX is a data-base project for French as it is written and spoken. This project is part of a C.N. R.S (National Center for Scientific Research, France) assignment to the spoken Communication G.R. E.C. O. (COncerted REsearch Group) assignment. An initial, simplified version, BDLEX-0, is right now in operation and a second version, BDLEX-1, is being set up. BDLEX-1 takes into account phonological and morphophonological specifications defined by the G.R. E.C. O work group. The purpose of this paper is to describe the BDLEX-0 version and discuss its development prospects.
Procedural Similarity and Its Effect on Transfer

Analogical problem solving involves transferring information from a particular domain (i.e., the analogue or source) to another (i.e., the target). The success of any transfer process depends on the modality of representation (verbal or pictorial) and the level of abstraction or similarity shared between the source and target problems. The Experiment predicted that procedural similarity will influence transfer more than the principle and strategy levels. One hundred and fifty-six female undergraduate Saudi students were randomly assigned to five experimental and one control condition that differed in the extent to which the pictorially depicted source shared a concrete procedure with the target. Contrary to prediction no significant difference in transfer performance among the different levels of similarity was found. Retrospective reports of participants indicated that the pictures lacked clarity and tended to process the information in the pictures in the R to L instead of L to R sequence which affected the interpretation of the process depicted. Index Terms - Analogy, Problem Solving, Transfer, procedural similarity
TSclust: An R Package for Time Series Clustering

Time series clustering is an active research area with applications in a wide range of fields. One key component in cluster analysis is determining a proper dissimilarity measure between two data objects, and many criteria have been proposed in the literature to assess dissimilarity between two time series. The R package TSclust is aimed to implement a large set of well-established peer-reviewed time series dissimilarity measures, including measures based on raw data, extracted features, underlying parametric models, complexity levels, and forecast behaviors. Computation of these measures allows the user to perform clustering by using conventional clustering algorithms. TSclust also includes a clustering procedure based on p values from checking the equality of generating models, and some utilities to evaluate cluster solutions. The implemented dissimilarity functions are accessible individually for an easier extension and possible use out of the clustering context. The main features of TSclust are described and examples of its use are presented.
Reconstruction algorithms for electrical impedance tomography

Electrical Impedance Tomography (EIT) is a new imaging technique which produces the conductivity distribution of material within an object from electrical measurement made at the object's boundary. EIT has applications in biomedical and geophysical engineering and various industrial applications are also under investigation. This thesis is a study of reconstruction algorithm of EIT and involves an examination of nonlinear reconstruction algorithms as well as the mathematical derivation of linear reconstruction techniques. A n iterative algorithm is derived using linear network theory and finite element modelling of an continuous object. Relationships and equivalences between a number of iterative algorithms are investigated. The algorithm is tested on both networks and continuous objects, using simulated and measured data. A modified algorithm is then developed to reduce the data errors caused by the finite element modelling and the electrode contact impedance. Meaningful results are obtained from the algorithm. A linear integral equation reconstruction method is developed from a linearized Poisson's equation. Properties of the integral equation are investigated and a numerical algorithm is derived. The relationship between the integral equation method, Radon's back-projection method [Herman and Natterer, 1981] and Barber-Brown's filtered back-projection method [Barber and Brown, 1986] is studied. Useful reconstructions are obtained from simulated and measured data, and problems associated with the integral equation method are discussed. A n error analysis of the linearized Poisson's equation is carried out with an emphasis on the applications to EIT. Three types of error are discussed and re-scaling error is found to be dominant. It is also found that the commonly used small perturbation assumption often fails. Case studies and numerical tests agree with the conclusions of the error study.
Simulation and Inference for Stochastic Differential Equations: With R Examples

where W (t) is the Wiener process. Here b(t,X) is the drift and σ2(t,X) is the variance of the process. Such equations have been successfully used to model an enormous variety of problems in fields as diverse as biology, physics, neuroscience and finance. Most of the examples treated in this volume are drawn from finance. Although only one-dimensional processes are considered, as the author notes, most of the simulation techniques that can be used with one-dimensional processes apply equally well to their multi-dimensional counterparts, and references are provided for multi-dimensional SDE simulations.
Maximum likelihood array SAR interferometry

Conventional SAR interferometers derive the surface height from an estimate of the phase difference between the SAR processed echoes received by two displaced phase centers. This paper introduces a maximum likelihood algorithm to process the SAR data from an array of K phase centers. The accuracy of the new technique is derived and compared to the conventional one. It is shown that this new approach to SAR interferometry provides better accuracy, together with adaptivity to the look angle and reduced phase ambiguity.