Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Hybrid Hierarchy Creation: a Cost‐model Based Approach

While using hierarchical search structures has been proved as one of the most efficient acceleration techniques when rendering complex scenes, automatic creation of appropriate hierarchies is not solved yet. Well‐known algorithms for automatic creation of bounding volume hierarchies are not enough. Higher performance is achieved by introducing spatial uniform subdivision, although algorithms proposed up to now are not truly automatic, as they need some parameters to be adjusted. In this paper we present a full‐automatic hierarchy creation scheme that structures the scene in a hybrid way, combining bounding volumes and voxel grids in the same tree, selecting the search structure that best fits to each scene region. It uses no parameters at all. This efficient proposal relies on a new cost model that estimates the goodness of a hybrid hierarchy if used for rendering the scene.
Developing A Renewable Energy Awareness Scale For Pre-service Chemistry Teachers

In times when human beings used to live in a natural environment, their needs were also provided by natural resources. With the increases in population in time, human beings started to look for new resources willing to get “the more” and “the fastest”. Just like the invention of steam, firstly, they increased the density of the resources and produced “more” energy. However, instead of working on the density of water, which spreads with the solar energy, they chose an easier way, which was fuel that produced more energy when burnt. Unfortunately, the damages these fuel products create in the atmosphere and environment shaded their benefits. It did not take so long for the earth to run out of energy resources and to threaten environmental and human health. As a result of that, new energy resources were started to be sought and the studies enlightened the concepts of sustainable, renewable energy. Renewable energy is defined as “the energy source, which continues its existence for the following days within the evolution of nature”. Educators pointed out a need in students for gaining consciousness on renewable energy resources. In the light of the importance of renewable and sustainable energy, a “Renewable Energy Awareness Scale” that questioned to what extent the individuals were aware of renewable energy was developed. The Renewable Energy Awareness Scale, which consisted of 50 items, was administered as a pilot study. The factor analysis concluded with a scale of 39 items with a reliability coefficient of 0.944 was developed.
Spatialization of user-generated content to uncover the multirelational world city network

In this paper we tackle a fundamental and long-time challenge in urban geography, to uncover a functionally differentiated global city network. To this day, the empirical investigation of a global multifunctional city network remains a challenge given the scarcity of appropriate relational and multifunctional data sources. To overcome this research gap, we present an interdisciplinary network modelling approach that integrates methods from geographic information science with social network analysis, including automated semantic analyses.We apply our modelling framework to a globally available, user-generated database (ie, Wikipedia), still underutilized in urban geography and planning research. The proposed visual analytical investigation of the multifunctional world city network also includes a systematic evaluation to assess the robustness of the proposed approach, and the adequacy of crowd-sourced databases for scientific uses. By example, we discuss economical and political relations of a latent multifunctional global city network which we uncovered with our data-driven approach. Our results not only empirically replicate previously well-established world city network theory, but also generate new research questions about multiple functions of cities, as hypothesized in world city network research. Furthermore, we showcase the potential of coupling text-based user-generated data analysis with geovisual analytics for scientific investigations in urban studies.
Integrating learner corpora and natural language processing: A crucial step towards reconciling technological sophistication and pedagogical effectiveness1

Abstract Learner corpora, electronic collections of spoken or written data from foreign language learners, offer unparalleled access to many hitherto uncovered aspects of learner language, particularly in their error-tagged format. This article aims to demonstrate the role that the learner corpus can play in CALL, particularly when used in conjunction with web-based interfaces which provide flexible access to error-tagged corpora that have been enhanced with simple NLP techniques such as POS-tagging or lemmatization and linked to a wide range of learner and task variables such as mother tongue background or activity type. This new resource is of interest to three main types of users: teachers wishing to prepare pedagogical materials that target learners' attested difficulties; learners themselves for editing or language awareness purposes and NLP researchers, for whom it serves as a benchmark for testing automatic error detection systems.
Designing WANem : A Wide Area Network emulator tool

Software developers are never exposed to production networks which is mostly Wide Area Network (WAN) during the development and unit test cycle. This leads to poor performing applications in production. Often the reason for such poor performance is attributed to the network. Hence it is important for the developers to know the behavior of their applications on various types of networks users will access the application from. However, access to such networks is restricted to a developer who tests his application over a local area network. A local area network (LAN) often has more than the required bandwidth required by an application. Hence a device, which will emulate various network behaviors is required, something which will help developers to better understand the behavior of their application on various networks. In response to this need WANem is designed and developed which can emulate real WAN networks in a development or test environment inside a LAN. In this paper we will discuss the architecture of WANem and show how it affects application performance.
Friction compensation for drives with and without transmission compliance

In this paper, nonlinear robust adaptive controller is proposed for friction compensation. The design has been performed for a six parameter dynamic model of friction. The advocated control design does not utilize any knowledge of these six parameters and requires just an upper bound on the static friction level, which can be obtained experimentally. The controller is robust to dynamic uncertainties and can compensate the effect of friction which is separated from the control input through drive compliance. Simulation and experimental results are also presented.
Clickers: enabling valuable diagnostic feedback to both students and instructors in real time

This paper is devoted to the investigation of the capability of classroom response systems (clickers) in enabling real-time diagnostic assessment during tutorial sessions. The study involved two hundred and seven students who were divided into five tutorial groups. Clickers were used to conduct the Task Evaluation and Reflection Instrument for Student Self-Assessment (TERISSA) procedure in order to anonymously reveal students' individual evaluations and reflections to the whole class. It was discovered that 62% of the surveyed participants were able to clearly identify the study areas that required their immediate attention and that 54% of the surveyed addressed these learning needs. It was also found that the use of clickers with TERISSA enabled tutors to obtain a real-time evaluation of students' prior knowledge and their misconceptions. Differences in prior knowledge and misconceptions between five tutorial groups were also established.
Recruitment of Sex Buyers

In this article we draw upon data from a large-scale mixed methods investigation of clients of commercial sex workers in Canada to illustrate the potential value that understanding and integrating computer and network technology has for enhancing access to, and participation from, marginalized and stigmatized populations. In particular, we present qualitative data from analysis of our research field notes as well as an analysis of quantitative data from response monitoring and feedback features built into the actual data collection process to help support our argument that, for some populations, network technology–based recruitment strategies should be recognized as the preferred recruitment option. In addition, we discuss the potential utility and application of viral solicitation, a newly emerging computer network-based nonprobability technique, for contacting and securing the participation of stigmatized and marginalized research participants. Our recruitment of sex buyers through web-based listserves was the most successful participant solicitation strategy, generating 63.18% (n = 544) of our survey respondents. Conventional recruitment (advertising in print-based media and in adult-oriented businesses) generated few participants (2.90%, n = 25). Viral solicitation acted as an important low-cost supplemental means of recruitment, generating a further 164 survey participants (19.05% of survey participants).
Wireless Sensor Networks: Realizing the Dream

This paper presents a thought experiment as to the feasibility of using large scale wireless sensor networks as a vehicle for high level scientific investigation. The discussion is framed by a demanding scenario, that of planetary exploration, and the assessment of feasibility is driven by the available micromachining technologies and recent advances in the areas of ad-hoc networking, communications and intelligent sensing. Following the development of the mission, the next question addressed is that of how MEMS and VLSI technologies may be integrated to provide a self-contained space probe, 40mm in length. A sensor payload is proposed, which includes seismic, chemical, temperature and visual exploration. Possible means are discussed for developing integrated sensors to address these sensing requirements. The energy and communications systems are also discussed, based on the needs of a mission profile, which provides no special ‘base station’ nodes on the surface, requiring each sensor package to be capable of communication with an orbiting satellite. It is concluded that the sensor node hardware would appear to be feasible, with current levels of MEMS and VLSI technologies. It is hoped that the study will enthuse and encourage researchers from a variety of domains, including micro-engineering and the computer sciences to explore in depth the potential of the wireless sensor networks and give them some high level awareness as to the constraints the design of such systems impose.
A New Framework for Managing and Analyzing Multiply Imputed Data in Stata

A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data imputed by any method as long as a few simple rules are followed in creating the imputed data. mim can validly fit most of the regression models available in Stata to multiply imputed datasets, giving parameter estimates and confidence intervals computed according to Rubin's results for multiple imputation inference. Particular attention is paid to limiting the available postestimation commands to those that are known to be valid within the multiple imputation context. However, the user has flexibility to override these defaults. Features of these new tools are illustrated using two previously published examples.
Improved Algorithm for Complex Distribution System Reliability and its Programming

In this paper, a new kind of recursive algorithm has been raised. It optimized the distribution network system reliability calculations, improved some efficiency, and met the user's needs closely. This algorithm needs no network equivalence process,has no complex principle and brings no difficulty to programming.Through its failure analysis, can be more easily identify key equipment of distribution systems for customers to adjust. This algorithm can easily cooperate with fault reconfiguration.No adjacency matrix and special chain table will be formed during searching procedure; it takes advantage of basic table of node data and branch data to do searching work, and easily to get access with field data. The algorithm uses recursive search also reduces programming greatly.
Multi-sensor multi-OOSM distributed sequential fusion filtering

Multi-sensor fusion for OOSM system is still an open question in the field of wireless sensor network. In this paper, a new distributed sequential fusion algorithm for multi-sensor system with one-step-lag OOSMs is proposed in the sense of minimum trace of error covariance matrix. The main idea of this real time sequential fusion algorithm is that: firstly, an equivalent measurement “sampled” at the fusion time is obtained, once a measurement comes to the fusion center; then, a local estimation of the current state is obtained based on the equivalent measurement; furthermore, first round fusion is carried out for the second local estimate and the first local estimate to obtain the first local fusion when the second measurement comes; another round of fusion process is carried out for the third local estimate and the first local fusion estimation. Repeating this process until all the measurements are fused, sequentially. The final simulation illustrates its efficiency and validity of the proposed method.
The DSTO Ingara airbone X-Band SAR polarimetric upgrade: first results

The Ingara airborne multi-mode X-Band imaging radar system developed by DSTO has been upgraded to a fully polarimetric collection capability with a 600 MHz bandwidth. The initial trial of the upgraded system in December 2002 included the collection of synthetic aperture radar data sets of a calibration scene including interferometric repeat passes. The application of calibration techniques to the data yields a measured channel cross talk less than -30 dB.
Development of the decision support subsystem in the systems of neural network pattern recognition by statistical information

The decision support subsystem in the neural network pattern recognition system, allowing to reduce the subjectivity and increase the quality of expert decisions in the construction of training samples by the statistical data of observations over the state of the management facilities of the production or social environment is developed. A functional structure of the NNPR system of the management facilities of the production or social environment by statistical information is proposed. According to the proposed structure, the problem-oriented NNPR system consists of subsystems of the initial data preparation for the PR and storage of the aggregated data, neural network models, and knowledge about the management facility of the production or social environment, as well as the support of the decision-making about its state. The decision support subsystem based on the proposed method of support of the classification decision-making using the Kohonen self-organizing layer is developed. The developed method of support of the classification decision-making provides a consistent implementation of the stages of self-organizing of neurons of the Kohonen computing layer, the calibration of the elements of the output vector of the training sample and their final labeling. An example of implementation of the proposed subsystem of support of decision-making about the class of the initialized labor protection project based on the expert estimates of the current level of the organization and working conditions at the enterprise is given. Using the developed decision support subsystem in the systems of NNPR by statistical information allows to adjust the decisions of the DM in accordance with the decisions of the neural network output machine, and thereby increase a relative share of correct expert estimates by 20 % on average and reduce false estimates for a number of pattern recognition problems by 50 %.
Research on Topology Discovery for IPv6 Networks

With some new features introduced by IPv6 network, such as a wider address space, a different scope based on addressing scheme, a modified protocol field and so on, IPv6 topology discovery faces some challenging problems. And many of topology discovery algorithms developed to collect IPv4 network topology information can not be readily applied to IPv6 network. In this paper, a few problems generated by IPv6 network, some methods and algorithms for IPv6 network topology discovery are discussed in detail. Their advantages and disadvantages are fully summarized. Finally, the new problems and some suggestions for the research work are put forward.
Multi-tone transmission for asymmetric digital subscriber lines (ADSL)

Multi-tone transmission is a potentially viable technique for application on asymmetric digital subscriber lines (ADSLs). The results of a preliminary study on the performance of an ideal discrete multi-tone (DMT) signaling technique for a 1.6-Mb/s ASDL are presented. The performance margins using single-tone 16-point quadrature amplitude modulation (QAM) with ideal decision feedback equalization (DFE) are presented. The feedforward filter of the DFE is a quarter-baud spaced fractionally spaced equalizer (FSE). The interference is assumed to be the sum of far-end crosstalk (FEXT) and additive white Gaussian noise (AWGN). The subscriber loops are assumed to be in a plant environment where the maximum resistance does not exceed 1300 /spl Omega/. The projected performance of an ideal DMT with a sampling rate of 1.024 MHz and 256-subchannel segmentation offers potential margin enhancement up to 3 dB over a quarter baud-spaced FSE-based 16-QAM signaling. <<ETX>>
Sentiment Classification for Chinese Microblog

A sentiment classification method for Chinese microblog is presented. For short sentence microblog, it is very challenging because the information of emotion is very limited. First, an emotion lexicon is built from training corpus. A simple measure - difference ratio is used to choose words from lexicon as features for classification. Support vector machine and voting on counts and accumulated difference ratio are jointly combined as classification method. The experimental results show that our recognition rate is better than the popular method using collocation strength. Our recognition improvement is about 2.06% in testing. Therefore, the difference ratio measure we used and the tactic in constructing the lexicon are proved very effective.
Phonetics and Phonology of Regressive Voicing Assimilation in Russian Native and Non-native Speech

In recent years, a great deal of research on second language (L2) phonetics and phonological acquisition has focused on non-target production patterns of L2 learners, addressing issues such as native language (L1) transfer into L2 and the nature and source of developmental errors. Previous studies of phonological processes in L2 acquisition have traditionally focused on the analysis of discrete L2 segments (Flege 1987, 1999; Major & Kim 1996), rather than on L2 production patterns. This study, however, examines the production of sequences of sounds in Russian L1 and L2 from both the phonetic and phonological perspectives. Two acoustic experiments in this dissertation analyze native and non-native subjects’ production of real and nonsense words containing obstruent clusters in which regressive voicing assimilation is required. For example, the form lodka ‘boat’ is rendered orthographically with a voiced stop /d/ which is, however, pronounced as a voiceless /t/ when followed by a voiceless obstruent. Both L1 and L2 speakers were found to exhibit a great deal of variability in their production of the devoiced obstruents in question, thus demonstrating gradiency in devoicing. Non-native production also suggests gradual acquisition of L2 phonological patterns. The findings of these experiments are analyzed within the stochastic Optimality Theory framework. Gradual L2 acquisition is accounted for by the reranking of L1 constraints; whereas, gradiency in production is viewed as a result of the re-ranking of constraints within phonetically detailed constraint families.
An advanced classification system based on the back-propagation of consensus

In this paper, we present an advanced classification system aimed at obtaining an accurate and reliable supervised classification of remote-sensing images. The proposed system is based on a closed-loop architecture composed of an ensemble of classifiers, which are combined and integrated within a framework based on Markov Random Fields (MRFs). The basic ideas considered in the definition of the system are: i) to jointly exploit the effectiveness of the aforementioned methodologies to increase the classification accuracy with respect to standard classification approaches; ii) to implement an iterative procedure based on the back- propagation of the consensus (obtained from the ensemble of classifiers integrated with the MRF approach) from the output to the input of the system, for increasing the accuracy in the estimation of classifier parameters and hence the effectiveness of the system. I. INTRODUCTION Automatic classification is one of the most important and studied topics in the field of the analysis of remote sensing images. In the literature several approaches have been proposed for the supervised classification of remote-sensing data. These approaches range from standard statistical methods to advanced neural-network techniques, from knowledge- based methodologies to fuzzy algorithms, and from multiple classifier systems to contextual techniques. Although many of these approaches revealed effective in different application domains, often they do not exhibit accuracies sufficient for meeting end-user requirements in complex classification problems. Consequently, it is necessary to develop novel and advanced classification approaches capable to further increase accuracies in automatic classification of remote-sensing images. In this paper, we present an advanced classification system aimed at obtaining an accurate and reliable supervised classification of remote-sensing data. The proposed system is based on a closed-loop architecture, composed of an ensemble of classifiers, which are combined and integrated with a Markov Random Field (MRF) approach and with a recursive procedure of back-propagation of the consensus obtained in output of the system. II. THE PROPOSED CLASSIFICATION SYSTEM
Classify the number of EEG current sources using support vector machines

The classifier based on support vector machines (SVMs) has had successful applications in many fields for its simple structure and excellent learning performance. In this paper we apply such classifiers to the EEG (electroencephalogram) data and use them to determine the number of EEG current sources according to the scalp potentials. Experimental results indicate that SVM classifiers are an effective and promising approach for this task.