Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of the accuracy in a reverse engineering process employing white light scanned data to develop constraint-based three dimensional computer models

The statistical accuracy of constraint-based three-dimensional (3D) models created using reverse engineering software to post process scan data collected by an Advanced Topometric Sensor (ATOS) system is currently unpublished information useful to the end-user. Throughout the process of scanning an object and converting the scanned data into a constraint-based 3D model, error can be introduced into the final model. The error introduced into the constraint-based 3D model is difficult to calculate due to a large number of variables and factors. The current study sought to characterize the accuracy of this process based on different measurement volumes and object sizes. Optical 3D metrology techniques have become an accepted method in the field of reverse engineering. The popularity of optical 3D metrology is due in large part to the non-contact approach, which can quickly produce a dense point cloud. Using post-processing software, these point clouds can be converted into a constraint-based 3D model and used in much the same manners as 3D models created using CAD software. To simulate a variety of measurement conditions, four measurement volumes and three object sizes were selected generating a total of 36-point clouds. The 36-point clouds were converted into constraint-based 3D models. Four measurements were collected from each 3D model. To analyze the data collected, hypothesis testing was conducted to compare the data and inferential statistics were applied. The statistical tests include one-sample t-tests, two-sample t-tests, a General Linear Model (GLM), and multiple 1-way ANOVA. The statistical test found that a difference existed between the measured values and the actual values for both the object size and measurement volume.
Towards a reference model for m-commerce applications

Not long ago m-commerce was proclaimed as the next technological revolution and opportunity for companies to provide a new distribution channel for customers. But, first examples of m-commerce applications show that only those applications are accepted by customers that provide clear added value compared to the on- and off-line channel. But how can the suitability for m-commerce be assessed by companies? Concepts, guidelines and methodologies for the development of strategies for m-commerce are not available yet. This paper tries to fill the gap and provides guidelines for the design and implementation of m-commerce applications based on the media reference model.
MAPbox: Using Parameterized Behavior Classes to Confine Applications

Designing a suitable mechanism to confine commonly used applications is challenging as such a mechanism needs to satisfy conflicting requirements. The trade-off is between configurability and ease of use. In this paper, we present the design, implementation and evaluation of MAPbox, a general-purpose confinement mechanism that retains the ease of use of specialized sandboxes such as Janus and SBOX while providing significantly more configurability. The key idea is to group application behaviors into classes based on the expected functionality and the resources required to achieve that functionality. Classification of behaviors provides a set of behavior labels (class names) that can be used to concisely communicate the expected functionality of programs between the provider and the users. This is similar to the MIME-types used to concisely describe the expected format of data files. Classification of application behaviors also allows class-specific sandboxes to be built and instantiated for each behavior class. We present a study of the behavior and resource requirements of a set of commonly used applications and use the results of this study to define a set of application behavior classes. We also evaluate how effective this technique is in confining a variety of commonly used applications and how much overhead it introduces.
A Comparative Evaluation of a Pursuit Moving-Airplane Steering Display

An experiment was conducted using a flight simulator to determine the relative ease with which pilots can learn to use four types of airborne fire-control system steering displays. The four displays compared were 1) a moving-horizon display with a space-stabilized error dot, 2) a moving-horizon display with an aircraft-stabilized error dot, 3) a compensatory type moving-airplane display, and 4) a pursuit type moving-airplane display. Of the four displays, the moving-airplane types were found to yield significantly smaller firing errors and require less learning as compared to either moving-horizon type display. Furthermore, the pursuit type of moving-airplane display yielded learning performance and terminal performance error levels superior to any of the other displays.
A New Single-Phase Multilevel Modulator - A Fast and Accurate Method to Compute the Modulator Harmonic Spectra

In the first part, a fast and accurate method to compute the harmonic spectra of PWM modulators is presented. This method allows to compute all measurements based on the harmonics values such as the THD or FWTHD. It is especially advantageous for multilevel modulators. In the second part a new modulation method that achieves very good performances for the single-phase multilevel inverters is presented. Finally, several modulation methods are compared with the proposed measurement tool.
Distributed consensus tracking for multi‐agent systems under two types of attacks

This paper studies a distributed coordinated control problem for a class of linear multi‐agent systems subject to two types of attacks. The problem boils down to how to achieve secure consensus tracking for multi‐agent systems with connected and disconnected (paralyzed) directed switching topologies caused by two types of attacks. The attacks on the edges instead of nodes lead to the loss of security performance. Two cases are studied in this paper. First, under only a class of connectivity‐maintained attacks, sufficient conditions are derived to achieve secure consensus tracking in mean‐square. Second, when the multi‐agent systems are further subject to a class of connectivity‐broken attacks, novel sufficient conditions are further obtained to ensure secure consensus tracking with a specified convergence rate by virtue of the idea of average dwell time switching between some stable and unstable subsystems. Three numerical simulations are finally given to illustrate the theoretical analysis. Copyright © 2015 John Wiley & Sons, Ltd.
The Examination of Online Self-Regulated Learning Skills in Web-Based Learning Environments in Terms of Different Variables

The purpose of this research is to determine whether online self-regulated learning skills differentiate student attitudes towards the internet and web-based education in web-based learning environments. Following survey method of research, the results were presented in descriptive manner. 169 university students participated in the study group. The data of this research were collected by using the Online Self-Regulated Learning Scale ( α =0.94), Attitudes toward the Internet Scale ( α =0.77), Web-Based Learning Attitude Scale ( α =0.86) and Computer Attitude Scale ( α =0.97). Percentage, frequency, arithmetic average, t and the Pearson’s r correlation statistics were employed on the collected data. The results suggested three major findings (1) online self-regulated learning skill levels affect attitudes of students towards the internet, (2) online self-regulated learning skill levels differentiate student attitudes towards web-based education in terms of the factor of the “Effectiveness of Web-Based Instruction”, and (3) online self-regulated learning skill levels differentiate attitudes of students towards computer.
Modelling regional economic growth : the role of human capital and innovation

This thesis investigates the role of human capital and innovation activity in the process of economic growth within a system of regions. It starts by reviewing existing theories of economic growth paying particular attention to the literature on “endogenous growth”, the large body of empirical literature addressing economic growth and that has investigated the “convergence issue”. A methodology based on the direct analysis of cross-sectional distributions of per capita income is then developed and applied to per capita income data for 122 European Union (EU) functionally defined regions over the period 1979-1990. The results show a clear tendency for some of the richest European regions to grow away from the others. The comparison of these results with those derived from a similar analysis for the commonly used administrative regions of the EU reveals some significant distortions imposed by adopting an administrative definition. A formal theoretical explanation of these results is then offered. In particular, it is argued that regional disparities in per capita income owe their existence to the pattern of specialisation between ‘knowledge creating’ and ‘knowledge applying’ regions. Specialisation is explained in terms of differences in the availability of useful knowledge at different locations. In the perfect foresight, stable equilibrium of the two-region model developed here, therefore, the region that specialises in innovation related activities (knowledge creating) enjoys a permanently higher level of per capita income. Moreover, it is shown that, on reasonable assumptions, a process of integration that reduces the cost of physical distance leads to faster growth in the long-run for the system as a whole, but at the expense of an increase in regional disparities. Finally, some predictions are derived and tested empirically. Using cross-sectional regressions, the fundamental determinants of the growth rate of a region are investigated. The results are supportive of the model, confirming the role played by the concentration of innovative activities and spatial spillovers of knowledge.
Pervasive, lifestyle‐integrated mobile learning for distance learners: an analysis and unexpected results from a podcasting study

This article opens with a discussion of how and why mobile learning (m‐learning) is purported to be the next step in the evolution of distance education, before looking at various perspectives on what m‐learning constitutes. It critically examines the degree to which ‘true’ m‐learning has been achieved, by offering pedagogical value beyond the mere use of mobile devices to deliver e‐learning content. The authors argue that podcasting, in combination with a variety of portable MPEG Layer 3 (MP3)‐capable devices that are increasingly ubiquitous, can be used to deliver a form of m‐learning that offers a higher degree of lifestyle integration than many current ‘state of the art’ m‐learning applications, despite not being as technically complex. They present an example of a study in which podcasting was used to deliver supplementary listening material to distance learners undertaking an information technology subject. An end‐of‐semester survey yielded extremely positive feedback about uptake levels and the perceived effectiveness of the podcasts in aiding the students’ learning of the subject matter. However, it also produced interesting results on the ways the students made use of the podcasts, which deviated from the researchers’ original intentions for ‘anytime, anywhere, any device’ learning. The results are discussed in the light of possible influencing factors, supported by follow‐up interview data. The study may have broader implications for the still nascent field of m‐learning.
Machine learning in complex networks: modeling, analysis, and applications

Machine learning is evidenced as a research area with the main purpose of developing computational methods that are capable of learning with their previously acquired experiences. Although a large amount of machine learning techniques has been proposed and successfully applied in real systems, there are still many challenging issues, which need be addressed. In the last years, an increasing interest in techniques based on complex networks (large-scale graphs with nontrivial connection patterns) has been verified. This emergence is explained by the inherent advantages provided by the complex network representation, which is able to capture the spatial, topological and functional relations of the data. In this work, we investigate the new features and possible advantages offered by complex networks in the machine learning domain. In fact, we do show that the network-based approach really brings interesting features for supervised, semisupervised, and unsupervised learning. Specifically, we reformulate a previously proposed particle competition technique for both unsupervised and semisupervised learning using a stochastic nonlinear dynamical system. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition to that, data reliability issues are explored in semisupervised learning. Such matter has practical importance and is found to be of little investigation in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this work, we propose a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the semantic meaning of the data, but also is able to improve the performance of traditional classification techniques. Finally, it is expected that this study will contribute, in a relevant manner, to the machine learning area.
Face recognition using improved principal component analysis

At the general recognition process, the feature vectors that are obtained from some facial images are transformed into recognition space by Fisher's linear discriminate method (Fisher's method) and principal component analysis (PCA). But at Fisher's method we must recalculate all recognition space when adding a registrant or registrant's learning patterns. In contrast, though at PCA we only recalculate added registrant's pace when adding, the face recognition rate obtained from the conventional PCA is bad, because the aim of the conventional PCA is dimension curtailment for compression of data and isn't dimension curtailment for recognition. Therefore we proposed improved principal component analysis (IPCA) for pattern recognition.
Videopapers as a tool for reflection on practice in initial teacher education

This article will discuss issues concerning the potential of videopapers, drawing on a research project investigating the use of videopapers as a tool for reflecting on practice and as an assignment in initial teacher education. Student teachers engaged in initial teacher education programmes often find it difficult to ‘see’ what is going on in their classrooms. They can further experience difficulties in linking theory and research with observations of experienced teachers and their own practice. Although the authors already provide opportunities to reflect on practice underpinned by theory in current classroom‐based tasks and assignments, and encourage optional videoing of lessons and seminar presentations, they believe that introducing student teachers to videopapers as a learning tool can provide novice teachers and their tutors with unique, new learning opportunities and insights. However, writing a videopaper does throw up new challenges.
SNP: A Program for Nonparametric Time Series Analysis. Version 8.4. User's Guide

SNP is a method of nonparametric time series analysis. The method employs a polynomial series expansion to approximate the conditional density of a multivariate process. An appealing feature of the expansion is that it directly nests familiar models such as a pure VAR, a pure ARCH, a nonlinear process with homogeneous innovations, etc. An SNP model is fitted using conventional maximuml likelihood together with a model selection strategy that determines the appropriate degree of the polynomial. A Fortran program implementing the SNP method is available via anonymous ftp at ftp.econ.duke.edu (152.3.10.64) in directory ~ftp/home/arg/snp or from Carnegie-Mellon University e-mail server by sending a one-line e-mail message "send snp from general" to statlib@lib.stat.cmu.edu. The cose is provided at no charge for research purposes without warranty. The program has switches that allow direct computation of functionals of the fitted density such as conditional means, conditional variances, and points for plotting the density. Other switches generate simulated sample paths which can be used to compute nonlinear functionals of the density by Monte Carlo integration, notably the nonlinear analogs of the impulse-response mean and volatility profiles used in traditional VAR and ARCH analysis. Simulated sample paths can also be used to set bootstrapped sup-norm confidence bands on these and other functionals. The purpose of this Guide is to provide an expositional review of the underlying methodology and to walk the user through an application. Our hope is that the Guide will be essentially self contained and that very little reference to the cited literature will be required to use the program and the SNP method.
Annibal Caro as Iconographer: Sources and Method

IT IS commonly assumed that throughout the Renaissance secular pictures, especially fresco cycles, were habitually painted according to the instructions of humanists who assembled and adapted subjects from classical mythology into complex and allusive schemes. A group of iconographic programmes devised during the I560s by Annibal Caro for the Villa Farnese at Caprarola and for the Castello Orsini at Bomarzo has been regarded as exemplary in this respect.1 Here we find the appearances and the attributes of a whole series of often quite recondite mythological figures described in elaborate detail, with precise instructions even for the way they were to be arranged. Such programmes are documented only from the mid-sixteenth century. That this is not simply the result of an accident of survival, and that it cannot therefore be assumed that iconographic schemes before this period shared the same characteristics is, however, suggested by an examination ofCaro's method as an iconographer and of the material on which he drew.
Communicative syllabus design : a sociolinguistic model for defining the content of purpose-specific language programmes

Introduction 1. Communicative competence and a theoretical framework 2. Designing the model: parameters and process 3. Communication needs: interaction and instrumentality 5. Communication needs: dialect and target level 6. Communication needs: communicative event and communicative key 7. Language skills selection 8. Sociosemantic processing and linguistic encoding 9. The operational instrument 10. The instrument applied Epilogue.
Wayfinding in complex multilevel buildings A case study of University Utrecht Langeveld building

This study is concerned with human wayfinding in complex public buildings. Wayfinding design primarily includes the two aspects of how to prevent problems from occurring and how to resolve problems after they occur. The former is directly related to architectural design, while the latter is related to signage. This study is mainly focused on the architectural design of the built environment. Spatial configuration as well as wayfinding behavior of users is investigated. An empirical study in a complex multi-level University building is performed, comparing wayfinding performance measures of first-time-visitors to a variety of locations. In addition, an architectural analysis of the building is performed, and possible causes for wayfinding problems are depicted. An attempt is made to link wayfinding performance of participants to the architectural analysis. A relation is found, and a floor plan analysis could therefore be used to predict human wayfinding behavior. From the converging results of both experiments, some design modifications to improve navigability in the Langeveld building are suggested.
Evidence-Centered Design of Epistemic Games: Measurement Principles for Complex Learning Environments.

We are currently at an exciting juncture in developing effective means for assessing so-called 21st-century skills in an innovative yet reliable fashion. One of these avenues leads through the world of epistemic games (Shaffer, 2006a), which are games designed to give learners the rich experience of professional practica within a discipline. They serve to develop domain-specific expertise based on principles of collaborative learning, distributed expertise, and complex problem-solving. In this paper, we describe a comprehensive research program for investigating the methodological challenges that await rigorous inquiry within the epistemic games context. We specifically demonstrate how the evidence-centered design framework (Mislevy, Almond, & Steinberg, 2003) as well as current conceptualizations of reliability and validity theory can be used to structure the development of epistemic games as well as empirical research into their functioning. Using the epistemic game Urban Science (Bagley & Shaffer, 2009), we illustrate the numerous decisions that need to be made during game development and their implications for amassing qualitative and quantitative evidence about learners’ developing expertise within epistemic games.