Miracle-dz/UniFAR-FTUs huggingface dataset:
RDFa in XHTML: Syntax and Processing

htmlabstractThe current Web is primarily made up of an enormous number of documents that have been created using HTML. These documents contain significant amounts of structured data, which is largely unavailable to tools and applications. When publishers can express this data more completely, and when tools can read it, a new world of user functionality becomes available, letting users transfer structured data between applications and web sites, and allowing browsing applications to improve the user experience: an event on a web page can be directly imported into a user's desktop calendar; a license on a document can be detected so that users can be informed of their rights automatically; a photo's creator, camera setting information, resolution, location and topic can be published as easily as the original photo itself, enabling structured search and sharing. RDFa is a specification for attributes to express structured data in any markup language. This document specifies how to use RDFa with XHTML. The rendered, hypertext data of XHTML is reused by the RDFa markup, so that publishers don't need to repeat significant data in the document content. The underlying abstract representation is RDF [RDF-PRIMER], which lets publishers build their own vocabulary, extend others, and evolve their vocabulary with maximal interoperability over time. The expressed structure is closely tied to the data, so that rendered data can be copied and pasted along with its relevant structure. The rules for interpreting the data are generic, so that there is no need for different rules for different formats; this allows authors and publishers of data to define their own formats without having to update software, register formats via a central authority, or worry that two formats may interfere with each other. RDFa shares some use cases with microformats [MICROFORMATS]. Whereas microformats specify both a syntax for embedding structured data into HTML documents and a vocabulary of specific terms for each microformat, RDFa specifies only a syntax and relies on independent specification of terms (often called vocabularies or taxonomies) by others. RDFa allows terms from multiple independently-developed vocabularies to be freely intermixed and is designed such that the language can be parsed without knowledge of the specific term vocabulary being used. This document is a detailed syntax specification for RDFa, aimed at: those looking to create an RDFa parser, and who therefore need a detailed description of the parsing rules; those looking to recommend the use of RDFa within their organisation, and who would like to create some guidelines for their users; anyone familiar with RDF, and who wants to understand more about what is happening 'under the hood', when an RDFa parser runs. For those looking for an introduction to the use of RDFa and some real-world examples, please consult the RDFa Primer.
Contextual Markovian Models

Modeling time series has practical applications in many domains : speech, gesture and handwriting recognition, synthesis of realistic character animations etc.The starting point of our modeling is that an important part of the variability between observation sequences may be the consequence of a few contextual variables that remain fixed all along a sequence or that vary slowly with time. For instance a sentence may be uttered quite differently according to the speaker emotion, a gesture may have more amplitude depending on the height of the performer etc. Such a variability cannot always be removed through preprocessing.We first propose the generative framework of Contextual Hidden Markov Models (CHMM) to model directly the influence of contextual information on observation sequences by parameterizing the probability distributions of HMMs with static or dynamic contextual variables. We test various instances of this framework on classification of handwritten characters, speech recognition and synthesis of eyebrow motion from speech for a virtual avatar.For each of these tasks, we investigate in what extent such modeling can translate into performance gains. We then introduce a natural and efficient way to exploit contextual information into Contextual Hidden Conditional Random Fields (CHCRF), the discriminative counter part of CHMMs.CHCRF may be viewed as an efficient way to learn a HCRF that exploit contextual information.Finally, we propose a Transfer Learning approach to learn Contextual HMMs from fewer examples. This method relies on sharing information between classes where in generative models classes are normally considered independent.
Knowledge clusters: Dealing with a multilevel phenomenon

Although there are a number of purported benefits of agile systems development methods over traditional forms, many in the Information Systems Development (ISD) community highlight the difficulties that the dynamic, turbulent nature of agile development environments may present. One prominent difference compared to traditional ISD projects is the emphasised presence of the customer in decision-making. Specifically, project management and decision-making can be significantly more challenging than in projects involving traditional development approaches. This paper describes a research-in-progress, that focuses on decision-making in agile projects, and aims to develop a better understanding of how agile teams present dynamic data to decision makers who may act “on the fly”, and in so doing lead to effective, high quality decision-making in agile environments. The goal of the paper with its three case studies is to develop a set of best practices for representing decision-making data in agile ISD projects.
Detection of white arrows in blue directional signs segmented from blue sky scenes using blue-yellow opponent color filters

Traffic accidents are a serious worldwide problem. Therefore, safety driving systems and driver assistant systems are becoming more popular research field to reduce accidents. Traffic sign recognition system is a major part of these researches. Traffic sign recognition is not a new field of research. It has been studied for more than three decades. In general, traffic signs can be grouped into several types such as danger warning signs, priority signs, prohibitory or restrictive signs and mandatory signs. This research is dedicated to a real-time recognition system of blue traffic signs indicating directions using an in-vehicle video camera. These signs are important because they guide drivers to follow the mandatory directions and to choose a correct lane on the road. Missing this information may lead to accidents. The recognition system presented in this thesis consists of three main processing stages. The first stage is to label the blue objects in each frame and segment them by cropping into rectangular areas circumscribing them. Next stage is to verify if the segmented blue object is a sign candidate or not. The third stage is classification. If the blue object is verified as a sign candidate, inner white objects, which are arrow candidates, are segmented and classified to one of the target patterns or rejected. Besides these three main stages the recognition system may be enhanced by applying a tracking algorithm which will facilitate detection of the same signs in consecutive video frames. There are difficult problems in every processing step such as determination of sign positions, variations in ambient light, signs’ color fading, the vehicle speed, and real-time constraint. Besides these challenges, a blue traffic sign recognition system has two special difficulties. The first is sign-sky discrimination in the labeling stage. The existence of the sky as a blue background of the blue objects makes popular color segmentation algorithms insufficient. The second difficulty is fragmentary segmentation of signs due to motion blur or some lighting conditions. One important aspect of the work presented in this thesis is to propose solutions for these two special problems. For sign-sky discrimination, two solutions have been presented. The first solution is to use a multi-threshold labeling technique according to the brightness of the image. The second solution is to use blue-yellow type opponent color filters trying to discriminate between color components of sky and signs. For fragmentary segmentation of signs, two solutions have been proposed also. The first solution is to apply a structured dilation
Vision Measurement Scheme Using Single Camera Rotation

We propose vision measurement scheme for estimating the distance or size of the object in static scene, which requires single camera with 3-axis accelerometer sensor rotating around a fixed axis. First, we formulate the rotation matrix and translation vector from one coordinate system of the camera to another in terms of the rotation angle, which can be figured out from the readouts of the sensor. Second, with the camera calibration data and through coordinate system transformation, we propose a method for calculating the orientation and position of the rotation axis relative to camera coordinate system. Finally, given the rotation angle and the images of the object in static scene at two different positions, one before and the other after camera rotation, the 3D coordinate of the point on the object can be determined. Experimental results show the validity of our method.
Infant Mortality in An Aboriginal Community: An Historical and Biocultural Analysis

There is little information on secular trends in the infant mortality rate (IMR) among aboriginal populations in Canada. Although the Canadian government began collecting vital statistics for Canadian Indians at the turn of the century, records prior to the 1960s have been deemed inaccurate (Romaniuk and Piche 1972; Latulippe-Sakamoto 1 97 1). This thesis presents an historical study of infant mortality in the Cree community of Fisher River, Manitoba during the period from 1907 to 1939. The two major purposes of this thesis are: 1) to investigate infant mortality and community health conditions at Fisher River during the early twentieth century using parish records from the Methodist mission; and 2) to evaluate microlevel parish record data (as opposed to aggregate statistics used in national studies) as a source of information on historical trends in the IMR among aboriginal Canadians. The infant mortality rate was found to be persistently high during this time period Q49 per 1000 live births). The IMR demonstrated a seasonal distribution pattern; high rates were strongly associated with winter, linked to undernutrition and airborne infectious disease. High fertility rates at young maternal ages during this period are thought to have contributed to high IMRs, although the underlying mechanisms are still elusive. There is some indication that a segment of families in the community had lower IMRs due to greater access to resources in the communify. However, it is concluded that high infant mortality reflected the overall health problems of the Fisher River reserve environment during this period. Findlng studies, both historical and modern, comparable to these results is a challenge. Parish records, when used judiciously, are adv_ocated as a good source of data for investigating aboriginal health conditions prior to W.W. II_. i It is suggested that future research on infant mortality in aboriginal populations be community-based.
EAACK-A Secure Intrusion Detection System forMANET

The migration to wireless network from wired network has been a global trend in the past few decades. The open medium and wide distribution of nodes make MANET vulnerable to malicious attackers. A new technique EAACK (Enhanced Adaptive Acknowledgement) method designed for MANET was proposed for intrusion detection. EAACK demonstrates higher malicious-behavior-detection rates in certain circumstances while does not greatly affect the network performances.
A Machine-Aided Approach to Generating Grammar Rules from Japanese Source Text for Use in Hybrid and Rule-based Machine Translation Systems

A MACHINE-AIDED APPROACH TO GENERATING GRAMMAR RULES FROM JAPANESE SOURCE TEXT FOR USE IN HYBRID STATISTICAL RULE-BASED MACHINE TRANSLATION SYSTEMS by Sean Jones The University of Wisconsin-Milwaukee, 2015 Under the Supervision of Professor Susan McRoy Many automatic machine translation systems available today use a hybrid of pure statistical translation and rule-based grammatical translations. This is largely due to the shortcomings of each individual approach, requiring a large amount of time for linguistics experts to hand-code grammar rules for a rule-based system and requiring large amounts of source text to generate accurate statistical models. By automating a portion of the rule generation process, the creation of grammar rules could be made to be faster, more efficient and less costly. By doing statistical analysis on a bilingual corpus, common grammar rules can be inferred and exported to a hybrid system. The resulting rules then provide a base grammar for the system. This helps to reduce the time needed for experts to hand-code grammar rules and make a hybrid system more effective. ii ©Copyright by Sean M. Jones, 2015 All Rights Reserved
Two Software Packages for Mapping Geographically Aggregated Data on the Apple Power Macintosh

Abstract In the world of the Physical sciences geographers often specialized software packages for their scientific work that are directly applicable to their own domain and related domains of research. Philcarto and philexplo for the Apple Power Macintosh are two such examples of this kind of software package. They are designed specifically to map spatially aggregated data for censuses (population and flats, agriculture, etc.) or specialized surveys (education, health, etc.) in which the 'national space' is divided up into spatial units (regions, departments, municipalities, etc.). Philcarto is an 'interactive cartomatic' program. Using two or three mouses clicks the user redefines the characteristics of the map which he wishes to obtain (the mode of representation, type of 'discretization', colours scales, symbols sizes, final annotations of the map, etc.). The value of this software does not lie with its methodological novelty, but with the ease of its use and the immediacy of displaying a map often comprising several thousand spatial units. Philexplo, by contrast, is a more innovative 'exploratory cartographic' program, the origins of which are based upon difficulties experienced in analyzing spatially aggregated data using 'traditional' statistical methods. The program uses linked windows to display various plots of the data, including a map window. Using various tools, all the displays can be dynamically explored: the plots can be transformed according to various actions of user (selection of spatial units, 'colourization', etc.) and these transformations are displayed in all the other windows, in all the other plots.
Nanostructuration de surfaces de GaAs : oxydation et nitruration

Ce memoire porte sur deux aspects du traitement des surfaces de GaAs faisant appel a des plasmas, dans le but de deposer des couches controlees d’oxyde et de nitrure. Dans les deux cas divers moyens d’analyse ont ete utilises pour controler la composition chimique ainsi que l’epaisseur et la structure des couches creees. Des calculs theoriques des signaux XPS etablis sur une representation schematique des echantillons ont ete a la base de la comprehension des phenomenes mis en jeu. La premiere etude exposee dans ce memoire a permis de demontrer l’efficacite d’un plasma micro-ondes compose d’O2 et de SF6 pour le nettoyage de substrats de GaAs ayant subis un ou plusieurs processus technologiques. La variation de parametres comme la composition ou la puissance du plasma et le temps d'exposition a celui-ci nous a permis de mieux comprendre les mecanismes d’oxydation se deroulant sur la surface. Notamment, la presence de SF6 dans le plasma diminue la concentration d’arsenic sur la surface et ameliore ainsi la stabilite de l’oxyde. La seconde etude realisee dans ce travail, qui presente la nitruration de substrats de GaAs suivant differentes orientations, a mis en evidence les avantages que procure l’utilisation d’une source GDS a faible puissance pour la creation d’une couche surfacique de GaN de tres bonne qualite. Ce processus optimise amene a une couche de nitrure exempte de composes dus a l’inter-diffusion de l’arsenic et de l’azote, et montre une quasi-saturation de l’epaisseur de la couche de nitrure en fonction du temps d’exposition. De plus cette couche se cristallise par recuit en une maille cubique de parametre proche de celui du GaN.
Use of majority votes in statistical learning

Today, algorithms such as the gradient boosting machine and the random forest are among the most competitive tools in prediction contests. We review how these algorithms came about. The basic underlying idea is to aggregate predictions from a diverse collection of models. We also explore a few very diverse directions in which the basic idea has evolved, and clarify some common misconceptions that grew as the idea steadily gained its popularity. WIREs Comput Stat 2015, 7:357–371. doi: 10.1002/wics.1362
Using inductive logic programming to automate the construction of natural language parsers

Designing computer systems to understand natural language input is a difficult task. In recent years there has been considerable interest in corpus-based methods for constructing natural language parsers. These empirical approaches replace hand-crafted grammars with linguistic models acquired through automated training over language corpora. A common thread among such methods to date is the use of propositional or probabilistic representations for the learned knowledge. This dissertation presents an alternative approach based on techniques from a subfield of machine learning known as inductive logic programming (ILP). ILP, which investigates the learning of relational (first-order) rules, provides an empirical method for acquiring knowledge within traditional symbolic parsing frameworks. This dissertation details the architecture, implementation and evaluation of C scHILL, a computer system for acquiring natural language parsers by training over corpora of parsed text. C scHILL treats language acquisition as the learning of search-control rules within a logic program that implements a shift-reduce parser. Control rules are induced using a novel ILP algorithm which handles difficult issues arising in the induction of search-control heuristics. Both the control-rule framework and the induction algorithm are crucial to C scHILL's success. The main advantage of C scHILL over propositional counterparts is its flexibility in handling varied representations. C scHILL has produced parsers for various analyses including case-role mapping, detailed syntactic parse trees, and a logical form suitable for expressing first-order database queries. All of these tasks are accomplished within the same framework, using a single, general learning method that can acquire new syntactic and semantic categories for resolving ambiguities. Experimental evidence from both artificial and real-world corpora demonstrates that C scHILL learns parsers as well or better than previous artificial neural network or probabilistic approaches on comparable tasks. In the database query domain, which goes beyond the scope of previous empirical approaches, the learned parser outperforms an existing hand-crafted system. These results support the claim that ILP techniques as implemented in C scHILL represent a viable alternative with significant potential advantages over neural-network, propositional, and probabilistic approaches to empirical parser construction.
Modeling flood discharge at ungauged sites across Turkey using neuro-fuzzy and neural networks

One of the most important problems in hydrology is the reliable forecasting of maximum discharge at an ungauged site of interest. Statistical techniques are commonly used for finding the maximum discharge and return period relationship. However, these techniques are generally considered to be inadequate because of the complexity of the problem. Hence, neural network techniques are preferred. In this study, two different neural network models developed based on the following techniques – a multi-layer perceptron neural network with Levenberg–Marquardt algorithm and a radial basis neural network behind an adaptive neuro-fuzzy inference system – are employed in order to capture the nonlinear relationship between discharge and five independent variables – drainage area (km 2 ), elevation (m), latitude, longitude, return period (year) and maximum discharge (m 3 /s). For a modeling study, watershed data from 543 catchments across Turkey were used. Statistical models with regression techniques were also applied to the same data, providing a wider comparison. The results of the models were then compared and assessed with respect to mean square errors, mean absolute error, mean absolute relative error and determination coefficient. Based on these results, it was found that the neural network techniques demonstrated better performance in predicting the maximum discharge based on five independent variables than the regression techniques, and were comparable to the adaptive neuro-fuzzy inference system.
Automatic shadow detection and radiometric restoration on digital aerial images

This paper develops automatic procedures for shadow detection and radiometric restoration of features under cast shadows in aerial images. Techniques including recursive quad-tree image partition, adaptive thresholding, region growing segmentation, mathematical morphology, and local histogram matching are applied to automatically extract the regions of building-cast shadows and to restore the brightness of those pixels within the shadow regions. Empirical results show that the detail information of features within the shadow regions is enhanced by applying the techniques developed in this paper.
Tendermint: Byzantine Fault Tolerance in the Age of Blockchains

TENDERMINT: BYZANTINE FAULT TOLERANCE IN THE AGE OF BLOCKCHAINS Ethan Buchman Advisor: University of Guelph, 2016 Professor Graham Taylor Tendermint is a new protocol for ordering events in a distributed network under adversarial conditions. More commonly known as consensus or atomic broadcast, the problem has attracted significant attention recently due to the widespread success of digital currencies, such as Bitcoin and Ethereum, which successfully solve the problem in public settings without a central authority. Tendermint modernizes classic academic work on the subject to provide a secure consensus protocol with accountability guarantees, as well as an interface for building arbitrary applications above the consensus. Tendermint is high performance, achieving thousands of transactions per second on dozens of nodes distributed around the globe, with latencies of about one second, and performance degrading moderately in the face of adversarial attacks.