Miracle-dz/UniFAR-FTUs huggingface dataset:
Strength of religious faith, trusting beliefs and their role in technology acceptance

This paper examines the influence of religious faith on trusting beliefs in the context of the Technology Acceptance Model (TAM) and online behaviour. Using structural equation modelling, the research tests a model based on constructs from TAM, McKnight et al.'s (2002) trusting beliefs and the Santa Clara Strength of Religious Faith instrument. The model demonstrates strong validity and reliability and is supported by the data. The results suggest that religious faith increases benevolence, which in turn influences perceived ease of use, perceived usefulness and behavioural intention. The paper rounds off with some conclusions and implications for future research and practice.
Improved frame differencing based moving object detection using feet-step sound

Moving objects have been detected using various object detection techniques. Two categories for moving object detection techniques are frame differencing based and background subtraction based. These techniques are limited by camera scene complexity, light conditions, video type etc. Frame differencing based techniques process videos faster compared to background subtraction based techniques. Frame differencing based techniques detects only the boundary of the moving object and may fail for slow moving objects. These techniques for moving object detection can be improved by using sound data as most video recording cameras are equipped with a microphone. Sounds from human footsteps can be recorded with video and used with frame differencing techniques to improve moving object detection results. Camera microphones also record background noise with other background sound. This noisy data has been filtered out using the Fourier transform. When peak locations for each footstep sound are determined, and a Full Width at Half Maxima is computed for each peak, the number of frames within this width are counted, these frames are verify the presence of a moving object.
Assessment of an ICA-based noise reduction method for multi-channel auditory evoked potentials

In this work a new independent component analysis (ICA) based method for noise reduction in evoked potentials is evaluated on for auditory late responses (ALR) captured with a 63-channel electroencephalogram (EEG) from 10 normal-hearing subjects. The performance of the new method is compared with a single channel alternative in terms of signal to noise ratio (SNR), the number of channels with an SNR above an empirically derived statistical critical value and an estimate of hearing threshold. The results show that the multichannel signal processing method can significantly enhance the quality of the signal and also detected hearing thresholds significantly lower than with the single channel alternative.
Generating software from models

The key challenge in enterprise information technology is meeting the needs of changing business practices in a rapidly changing technological environment. The author describes an IT implementation approach based on the OMG's Meta Object Facility. The designer expresses the required system as a high-level model and then uses meta-model based tools to generate software for a variety of implementation technologies. We describe DSTC's progress to date and outline some challenges that need to be addressed.
Reconstructing the surface of unstructured 3D data

Building 3D models from unstructured data is a fundamental problem that arises increasingly in the medical field as new 3D scanning technology is able to produce large and complex databases of full 3D information. In addition, the huge efforts put into segmenting entire sets of 2D images demand robust tools that are then able to reconstruct any arbitrary 3D surface segmented from the images. In this paper we propose an algorithmic methodology that automatically produces a simplicial surface from a set of points in R3 about which we have no topological knowledge. Our method uses a spatial decomposition and a surface tracking algorithm to produce a rough approximation S' of the unknown manifold S. The produced surface S' serves as a robust initialization for a physically based modeling technique that incorporates the fine details of S and improves the quality of the reconstruction. The result of the reconstruction is a dense triangulation S' that undergoes a stage of mesh decimation to produce a compact representation of S.
A case of cyclogenesis over the western Mediterranean Sea with extraordinary convective activity

An interesting case of cyclogenesis over the western Mediterranean Sea, accompanied by extraordinary convective activity, is described in this paper. The event took place between 18 and 21 July 2001. Its synoptic features are examined on the basis of quasi‐geostrophic theory using numerical analyses from the global model of Deutscher Wetterdienst GME and from the ECMWF. Particularly remarkable is the large amount of latent heat released in the condensation process, which produces significant modifications of the flow. These effects are evident both in the fields of relative vorticity as well as potential vorticity in the upper levels. The infrared Meteosat satellite pictures are used to follow the evolution of the upper‐level cloudiness associated with the process and its connection with the values of vorticity and winds at 300 hPa. Radar pictures, reflectivity and radial velocity from Doppler surveillance radar in the Veneto region allowed us to outline the structure of the mesoscale convective system –a squall line that developed during the night of 19–20 July over Italy, in the Padana Valley. The features of this system were compared with a conceptual model proposed by Houze et al. (1989). The vertical wind shear, as inferred from the radial velocity pictures, was likely to play an important role in the development of the storm. Finally, an analysis of the vertical stability in the pre‐storm environment was performed using sounding data from Udine, 20 July 2001, 00 UTC. The analysis shows the importance in the storm development of the potential instability released by ascent during the transit of the baroclinic system, a process that was investigated using the vertical profile of equivalent potential temperature and an estimate of ascending motion given by the ECMWF model. Copyright © 2004 Royal Meteorological Society.
Password-free network security through joint use of audio and video

REmote authentication is vital for many network based applications. As the number of such applications increases, user friendliness of the authentication process, particularly as it relates to password management, becomes as important as its reliability. The multimedia capabilities of the modern terminal equipment can provide the basis for a dependable and easy to use authentication system which does not require the user to memorize passwords. This paper outlines our implementation of an authentication system based on the joint use of the speech and facial video of a user. Our implementation shows that the voice and the video of the associated lip movements, when used together, can be very effective for password free authentication.
A 1:1 cocrystal of (1R,3S,4S,6R)‐1,2,3,4,5,6‐hexa­bromo‐1,2,3,4,5,6‐hexa­hydro­penta­lene and (1R,2R,4S,5S)‐1,2,3,4,5,6‐hexa­bromo‐1,2,4,5‐tetra­hydro­penta­lene

The components of the title compound, C8H6Br6 and C8H4Br6, are hexa- and tetra­bromo­penta­lene derivatives, which were obtained by the treatment of hexa­hydro­penta­lene with a tenfold excess of bromine. The asymmetric unit consists of two half-mol­ecules. Crystallographic centres of symmetry are located at the mid-points of the central C=C and C—C bonds. The repulsive inter­actions between the Br atoms may affect the mol­ecular geometry.
Techniques on Multi-Marker for the Implementation of Augmented Reality

This study is intended to propose a technique that can trace objects by utilizing the multimarker method in the Marker Recognition which is a bridge way connecting virtuality and reality in complex environments or in a condition hands become obstacles. In the existing marker method, the object becomes blinking when the maker is blocked since this method relies only on single marker. However, the researcher was able to confirm the appearance of augmented object when the marker was blocked for the multiple markers replaced the blocked one. In order to implement such technique, multi-marker estimation was utilized with perspective matrix. The advantage is the fast estimation process for there is no need of other calculation. The implemented technique can detect markers and locate their positions, represent 3-D object in various circumstances.
Facial animation in a nutshell: past, present and future

During its almost forty years of existence, facial animation has seen a host of technologies being invented, then fading into obsolescence. The modelling and animation methods have mostly been dictated by the available hardware, which greatly evolved through the years. Many animation desiderata that have been considered dreams at the time are now reality while ever new frontiers are encountered and explored. This paper provides an overview of the historically used technologies and attempts to predict the future trends in the field of facial animation as it permeates the entertainment industry with its feature films and computer games; the broad field of visualisation in science, including medical science and forensic analysis; and the fast growing field of information and communication technology and human-computer interaction.
Algorithm of Text Categorization Based on Cloud Computing

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing.
Tone classification of syllable-segmented Thai speech based on multilayer perceptron

Thai is a monosyllabic, tonal language that makes use of tone to convey lexical information about the meaning of a syllable. Thai has five distinctive tones, and each tone is well represented by a single F0 contour pattern. In general, a Thai syllable with a different tone has a different lexical meaning. Thus, to completely recognize a spoken Thai syllable, a speech recognition system has to not only recognize a base syllable but also to correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system. In this study, a tone classification of syllable-segmented Thai speech, which incorporates the effects of tonal coarticulation, stress and intonation, was developed Automatic syllable segmentation, which performs segmentation on the training and test utterances into syllable units, was also developed. The acoustical features, including fundamental frequency (F0), duration, and energy extracted from the processing syllable and neighboring syllables, were used as the main discriminating features. A multilayer perceptron (MLP) trained by a backpropagation method was employed to classify these features. The proposed system was evaluated on 920 test utterances spoken by five male and three female native Thai speakers who also uttered the training speech. The proposed system achieved an average accuracy rate of 91.36%.
Characterizing Farmer Users and Nonusers of Mass Media as Channels of Agricultural Information in Benue State, Nigeria

This study characterized farmer users and nonusers of mass media as channels of agricultural information. A structured interview was used to obtain data from 316 randomly selected farmers in Benue State, Nigeria. The data were subjected to discriminant analysis and the following socioeconomic characteristics significantly differed at a 5% level of probability between users and nonusers of mass media (and their F-ratio values): education (6.30), income (5.12), gender (4.84), and socioeconomic status (4.83). Farmer users of mass media are therefore those who have good level of education, belong to a relatively high income bracket, and are typically male and of a relatively high socioeconomic status. Farmers’ socioeconomic characteristics should be considered in planning mass media usage in agricultural information dissemination.
Traffic Network Models Correlated

The procedure and main result of a comparative study based on using an autoregressive model and an artificial intelligence technique applied to a Wimax traffic data series forecasting task are presented in this document. The time series forecasting methods being compared are: ANFIS model (Adaptive Network-based Fuzzy Inference Sys-tem) and ARIMA model (Auto-Regressive Integrated Moving Average). This article aims to present significant data showing each technique performance under the criteria of mean square error sum and the required processing time. As a result, in this study ARIMA models developed under RATS platforms are compared to the ANFIS models developed through MATLAB.
‘Children of the HMM’: Modeling Longitudinal Customer Behavior at Hulu.Com

Stand-alone marketing models are well-suited to deal with different behavioral features such as variation in transaction frequency (customer heterogeneity with latent classes), recency and attrition (“buy ‘till you die” models), and more general changes in customer transaction rates (hidden Markov models, HMMs). We unite these modeling approaches in a integrative framework as special cases or “children” of the HMM. We then selectively constrain the general model to assess the impact of each component on model performance. Instead of selecting latent-state models primarily using likelihood-based criteria, we favor a multi-faceted empirical evaluation using summaries of posterior predictive distributions; thus focusing model checking on managerially relevant features of the data, such as reach, frequency, and “streakiness. ”We apply our methods to daily viewing incidence data from Hulu.com, a leading U.S. online streaming video provider. We find that increasing model complexity can improve some aspects of model performance (as expected) but worsen others in non-obvious ways. For instance, only models allowing back-and-forth movements among latent states can capture streakiness (a pattern of growing importance given the increasing availability of digital media data); but as a trade-off, these models still perform worse than their simpler counterparts in both forecasting and capturing other audience measurement criteria. Finally, using machine-learning classification techniques, customers are grouped based on similar model fit and features of their past consumption patterns. This allows researchers and managers to portend the “winning model” prior to having to fit the models for all customers. We discuss the generality of the methods and findings for different mixes of patterns of customer behavior.
Expert Systems And The Map Label Placement Problem

Computer programs that place feature labels on maps are often cited as the one successful application of expert systems to automatic map compilation. This paper argues that the map label placement programs which are claimed to be expert systems stretch the defining boundaries of expert systems and that the most significant use of expert systems technology in existing label placement programs is an inappropriate use of that technology. The results which have been achieved using expert systems technology in this one cartographic application are not sufficient justification for assuming that similar technology will solve other map design problems. On decrit souvent le positionnement du nom d'un element sur une carte au moyen d'un programme informatique comme l'une des applications les mieux reussies des systemes experts en compilation cartographique automatique. Cet article soutient que les programmes de placement des etiquettes cartographiques, que l'on pretend etre des systemes experts, outrepassent les li.
On the relationship between multi-view data capturing and quality of rendered virtual view

Abstract According to the Nyquist sampling theorem, a large number of sampled images and small intervals between capturing cameras should be met for rendering high quality virtual views without aliasing, which is hard to realize in practice. Therefore, achieving a balance between multi-view data capturing and quality of the rendered view remains as open problems. To solve this problem, we analysed the spectral bounds of the scene and designed a reconstruction filter. A proper number for rendering and a three-dimensional surface describing the relation between multi-view data capturing and quality of the rendered view were derived. Experimental results for both the modelled scene and the real scene show that only about 20% of sample images are needed compared with Nyquist sampling, while the quality of the rendered view remains higher than that of a Nyquist sampled comparison.
A model-following adaptive controller using radial basis function networks

In this paper, we propose a method to design a model-following adaptive controller using radial basis function networks (RBF-NNs). The method is very simple to implement by exploiting the properties of RBF-NNs. The proposed method identifies linear or nonlinear plants and implements a stable model-following adaptive controller by utilizing identification results. Simulation results show the effectiveness of the proposed control schemes.
Analysis of Room Responses, Motivated by Auditory Perception

An analysis and visualization method for room responses is presented. The method utilizes time and frequency resolution similar to the human hearing. With the proposed method it is possible to study the decaying sound field in a room with a resolution that mimics the human hearing better than simpler methods such as spectrograms or one-third octave band filtering. This method is applicable as well in the analysis of artificial reverberation and related audio effects. The analysis method includes the use of directional microphones, which yields information cues about the diffuseness and the directional characteristics of sound fields in the time-frequency domain. This approach is particularly interesting and promising in the visualization of concert hall acoustics. As case studies two example responses, one from a small and another one from a large concert hall, are analyzed.
A comparison of clustering algorithms in article recommendation system

Recommendation system is considered a tool that can be used to recommend researchers about resources that are suitable for their research of interest by using content-based filtering. In this paper, clustering algorithm as an unsupervised learning is introduced for grouping objects based on their feature selection and similarities. The information of publication in Science Cited Index is used to be dataset for clustering as a feature extraction in terms of dimensionality reduction of these articles by comparing Latent Dirichlet Allocation (LDA), Principal Component Analysis (PCA), and K-Mean to determine the best algorithm. In my experiment, the selected database consists of 2625 documents extraction extracted from SCI corpus from 2001 to 2009. Clustering into ranks as 50,100,200,250 is used to consider and using F-Measure evaluate among them in three algorithms. The result of this paper showed that LDA technique given the accuracy up to 95.5% which is the highest effective than any other clustering technique.
Research on Parallel Yen Algorithms on GPUs Using CUDA

With the arrival of the intermodality era, to design fast and efficient K shortest paths (KSP) algorithms becomes gradually one of the core technologies in traveler information systems. Yen is a classical algorithm for KSP. However, Yen is time-consuming. In view of powerful general-purpose computing capabilities, GPU(Graphics Processing Units) has received increasing attention in various fields. Based on CUDA software development environment, combined with the structure of the Yen algorithm itself, this paper proposed two parallel algorithms for Yen. The first parallel algorithm computes candidate shortest paths for very possible deviation nodes in parallel. The second one computes candidate shortest paths in serial, but computes very candidate path in parallel. Finally, the efficiency of the two parallel algorithms was tested through experiments.
Feature Selection Algorithm for Hyperlipidemia Classification

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ2 statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averagingF1 measure is used. DF is suitable for the task of large text classification.
Going beyond textbooks: a study on seamless science inquiry in an upper primary class

This paper reports an initial study on investigating inquiry-based learning in science in an upper primary class guided by an inquiry-based learning model in a seamless learning environment. Two questions are addressed: (1) how students advanced their domain knowledge? ; and (2) how students developed their inquiry skills? One teacher and 27 Grade-four students from a local primary school were involved in the study. Six inquiry-based learning lessons focusing on a scientific “rustproofing” learning unit were conducted in a seamless learning environment, initiated in a digital classroom and extended to online discussions on a social network platform. Qualitative data were collected and analysed over two weeks. The results show ways that the students advanced their domain knowledge and developed their inquiry skills.
A self-designing receding horizon optimal flight controller

A self-designing controller (SDC) for flight control could provide a cost-effective means for developing controllers for new aircraft by eliminating analyst-intensive off-line design of numerous individual controllers, each optimized for a single flight condition. Additionally, the SDC could improve the capabilities of existing aircraft by enhancing control performance in new flight regimes and by reconfiguring appropriately for sudden changes such as may result from airframe and/or effector impairment(s). In this paper, the authors present a self-designing controller based on a receding horizon linear-quadratic control law that uses a robust least squares-based online system identification technique. This approach provides: 1) robust real-time tracking of time-varying parameters, 2) efficient optimal control-law computations, and 3) ability to incorporate transient response shaping (flying qualities) directly into the control law. The authors demonstrate the response and reconfiguration capabilities of the SDC using linear and nonlinear F-16 aircraft simulations.