Miracle-dz/UniFAR-FTUs huggingface dataset:
Boundary Detection of Color Images Using Neural Networks.

In boundary detection of color images, it is essential to form local edge elements that are detected by a local edge detection method into groups for finding straight or curved lines. A new boundary detection method based on the Hopfield neural network is proposed. First, an image is divided into blocks. In each block, two edge segments at the most are detected. Then, a unit of the network is assigned to each edge segment. Some properties of edge segments belonging to a boundary are embedded in an objective function of the network, and the boundary is detected by minimizing the function. Experimental results show that the method is applicable for partially disconnected and/or blurred boundaries.
Bilişim Teknolojileri Öğretmeni Adaylarının Programlama Sürecini Etkileyen Faktörler

The aim of this study is to determine the problems effecting programming process of preservice information technology teachers and the reasons of these problems by monitoring programming process. In this study, programming processes of pre-service teachers were investigated with cognitive task analysis. Six Information Technologies teacher candidates, taking Internet Based Programming course in Fall Semester of 2011-2012 academic year at the Department of Computer Education and Instructional Technologies (CEIT) at Hacettepe University, participated in the study. Participants were divided to three groups, namely advanced, intermediate and beginner according to their prior programming knowledge and experiences, Tasks of writing program and reading program were assigned to the participants. After the tasks of reading program and thinking and writing program simultaneously, interviews were made with retrospective thinking aloud procedure. The data was analyzed by the NVivo 8 package program. It was found that while participants in advanced level were more successful in the task of writing program, participants in intermediate level were more successful in the task of reading program. The findings indicated that these two processes, writing program and reading program, may require different skills and cognitive processes.
LabVIEW application for ultra-low power Wi-Fi sensor tags

G2 Microsystems of Campbell, California, USA, released in 2007 the first ever ultra-low power Wi-Fi System on a Chip (SoC) named G2C501. This SoC includes a 32-bit CPU, crypto accelerator, real-time clock and a versatile sensor interface that can serve as a standalone host subsystem. The G2C501 goes beyond today's basic radio frequency identification (RFID) technology to offer intelligent tracking and sensor capabilities that leverage IEEE 802.11 (Wi-Fi) networks. Due to its support for multiple location technologies, small form factor and ultra-low power consumption, the G2C502 SoC can be integrated into Wi-Fi sensor tags that lower cost of ownership and meet the needs of a variety of industries including consumer electronics, pharmaceuticals, chemical manufacturing, cold chain and more. A battery powered, small size ultra low-power Wi-Fi wireless measurement node name IP Sensor has been built using the G2C501 SoC. Sensors for measurement of temperature, humidity, light, and vibration or motion are currently mounted on the IP Sensor board. The node is able to read a sensor and send data to the network by using an IP-based application protocol such as UDP. This paper describes the new IP Sensor device and gives a programming methodology using Lab VIEW.
A Numerical Study of Circumferential Groove Effect on the Thermohydrodynamic Performance of a Journal Bearing

A circumferential feeding groove is frequently used in applications where the applied load has a variable direction such as crankcase bearings and automotive turbocharger bearings. In this work a journal bearing with a circumferential feeding groove in loaded zone has been simulated using Computational Fluid Dynamics (CFD). Navier–Stokes equation has been solved using CFD software FLUENT, heat transfer in bush has been considered and full cavitation model has been used. The agreement observed between theoretical predictions and classics published data is acceptable. The results show that: if viscosity is assumed to be a constant, the bearing carrying capacity will be overvalued. A central circumferential groove in load zone makes the maximum bush temperature be increased and bearing carrying capacity be decreased. The groove divided the oil film into two symmetry parts and decreased the maximum pressure at a given eccentricity ratio.
Distance Measurement Using the Kinect Sensor with Neuro-image Processing

This paper presents an approach to detect object distance with the use of the recently developed low-cost Kinect sensor. The technique is based on Kinect color depth-image processing and can be used to design various computer-vision applications, such as object recognition, video surveillance, and autonomous path finding. The proposed technique uses keypoint feature detection in the Kinect depth image and advantages of depth pixels to directly obtain the feature distance in the depth images. This highly reduces the computational overhead and obtains the pixel distance in the Kinect captured images.
Application of filtering strategies to Multiple Sclerosis tremor: analysis of results

Intention tremor severely compromises everyday life tasks in patients suffering from Multiple Sclerosis or Parkinson's disease. We report the result of our investigation on the properties of Multiple Sclerosis tremor. We analysed the case of handwriting and the effects of filtering tremor with proper numerical systems. An index for the evaluation of movements has been proposed, its properties have been verified with healthy users and used for evaluating efficiency of filtering on Multiple Sclerosis patients. The achieved results have been applied in the development of some technological aids for assisting therapist and patients in their work/life.
TECHNOLOGY A REVIEW ON QUESTION CLASSIFICATION USING MACHINE LEARNING BASED ON SEMANTIC FEATURES

One of the most important aspects of the learning process is the assessment of the knowledge acquired by the learners. In typical assessment like Exam, Assignment or Quiz, a grader provides students with feedback on their answers to questions related to the subject matter. In this way Question classification plays an important role in Question answering. Its main role is to assign a suitable semantic category to the question posted in natural language that represents the type of the required answers. It is a major challenge for the automated Question Classification function. In order to solve this kind of complexity, learners use lexical, syntactic and semantic features to analyze the questions. This paper presents a review on various approaches for Question classification using Machine learning approach based on Semantic Feature.
Paging strategies in an object oriented environment

Paging issues which arise in object-oriented programming systems are considered. Unlike conventional virtual memory systems, object-oriented system provide clues about the underlying structure of the executing programs. This fact is used to suggest improved memory management strategies for object-oriented systems. These strategies are modeled using a kind of simple game on graphs. This game allows estimation of the performance needed to ensure object-fault-free computation. <<ETX>>
Nonlinear Hierarchical Models for Longitudinal Experimental Infection Studies

OF DISSERTATION NONLINEAR HIERARCHICAL MODELS FOR LONGITUDINAL EXPERIMENTAL INFECTION STUDIES Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common preand post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RMANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models.
The SugarCubes tool box: a reactive Java framework

SugarCubes is a set of Java classes used to implement dynamic, reactive, event‐based, parallel systems. SugarCubes can be seen as a low‐level basis upon which more complex reactive formalisms can be implemented. It also provides a convenient framework for prototyping experimental extensions to various reactive formalisms. SugarCubes is freely available on the Web. © 1998 John Wiley & Sons, Ltd.
30 ans de démosaïçage 30 years of demosaicing

Cet article propose une revue de trente annees de developpement des algorithmes de demosaicage utilises dans les cameras numeriques pour la reconstruction des images couleurs. La plupart des cameras numeriques actuelles utilisent un seul capteur devant lequel est placee une matrice de filtres couleurs. Ce capteur echantillonne par consequent une seule couleur par position spatiale et un algorithme d’interpolation est necessaire pour la definition d’une image couleur avec trois composantes par position spatiale. Cet article montre que l’ensemble des techniques du traitement du signal et des images a ete utilise pour resoudre ce probleme. Aussi, une nouvelle methode proposee recemment par l’auteur et collaborateurs est decrite. Cette methode, basee sur un modele d’echantillonnage couleur par les cones de la retine, revele la nature de l’echantillonnage spatio-chromatique dans les cameras couleur a un seul capteur.
Research of Motivation Problems in Agent Construction System of China’s Non-Profit Government Investment Project Based on Self-Determination Theory

Agent Construction System can be helpful in solving problems in traditional construction patterns of China’s government investment projects. Yet it itself has certain weaknesses. Effective motivation mechanism may make up for those deficiencies. However, it has rarely been mentioned in researches of Agent Construction System of China’s non-profit government investment projects. This paper tries to put forward specific solutions through analyzing motivation problems in Agent Construction System based on theory, so that Agent Construction System can work better in China’s non-profit government investment projects.
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO GRANDE DO SUL FACULDADE DE INFORMÁTICA PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO MINERAÇÃO DE OPINIÕES APLICADA A MÍDIAS SOCIAIS

This thesis aims to contribute for studies in Psycholinguistics, focusing on the development of reading comprehension through reading prediction activities of lexical cohesive elements in fables in English. The subjects of the study are three Psycholinguistic experts and three English teachers who are evaluators of the pedagogical materials created: Guide of the appropriateness of the activities – Experts in Psycholinguistic and Guide of the appropriateness of the activities – English Teachers. The instruments are intended to: regarding the teachers, evaluate whether the activities show correct use of the English language and check the suitability for their future use in classrooms with students from intermediate to post advanced levels of proficiency in English; regarding the experts, evaluate the relevance of the Reading prediction activities and the correct Psycholinguistic approach, respectively. The pedagogical materials are the Pre activities of reading prediction and the Main activities of reading prediction. The obtained results with the study are: a bank of 15 main activities and 9 pre reading activities which are adequate to be used in schools, laboratories/centers of language and courses/institution, the verification of the contribution on the work with the reading prediction in the development of the reading comprehension; as well as the relevance of the work as a reading facilitator through the analyses of the participant’s evaluations. Key-words: Reading comprehension. Reading prediction. Lexical cohesion. Fables in English
Intelligent Interpretation of World Wide Web Queries

The problem of finding information on the Internet or on intranets is becoming increasingly more difficult, because of its explosive growth and lack of structure. This paper describes IWWWQ, a system that improves the ranking of a search engine's results by incorporating other features of a page, including the page type, content of a page, page formatting, and the relationship between pages. An empirical evaluation shows that incorporating these features improves the ranking of the results returned by a popular search engine. Furthermore, the evaluation showed that a combination of features is necessary to obtain this improvement. Finally, it shows that the algorithms used are relative insensitive to specic parameter settings. 1 CITR, Tamaki Campus, University Of Auckland, Auckland, New Zealand Intelligent Interpretation of World Wide Web Queries Jacky Baltes Bryce Ewing Computer Science Department Tamaki Campus University of Auckland Postal Bag 92019 Phone: +64 (9) 373-7599 Ext. 8744 Fax: +64 (9) 308-2377 Email: j.baltes@auckland.ac. nz bryce@123.co.nz
Facilitating Decision Support through Decision Tree

A decision tree is a simple representation for classifying examples. Decision tree learning is the most successful technique for classification learning. Each element of the domain of the classification is called a class. In the context of classification problems, decision tree applies decision tests based on a combination of attributes. In this paper we presented the overview of decision tree and how the classification is performed in the tree with the help of example. The important technique for the extraction of information from a table is splitting. So further we study the binary and multiway splitting. When we got our target node we have to stop the growth of decision tree, for which well specified criteria for stopping the tree should be present. The reasons for popularity of decision tree are also explained. The big issue for representation of a decision tree is to determine which attribute is going to be the first chosen. The representation and enhancement of decision tree induction is also described in the next section. Index Terms—Attribute, Classification, Decision Tree, Induction. I. INTRODUCTION The use of data for data mining techniques is very important for data mining practitioners. The data which is used for particular applications responsible for taking the decision. Data mining is used to distinguish or classify the decisions based on available information like who can get a bank loan, who can get a special offer, or who is the most eligible candidates for the certain designations. The point is that data mining is just a tool in the whole decision making process which is used by people for obtaining the results as well as applying the further knowledge and decide what action to be taken. The data is only the facts and it becomes the information which can be the set of patterns after applying certain operations. Knowledge can be the aggregation of set of patterns or expectations on the basis of which the decisions are made. There are different styles of learning are used in data mining applications(1). The first step in learning is the classification in which the previously classified examples are presented to draw the conclusions. And it is expected to classify the unseen examples based on the drawn conclusions. The whole process of classification, drawing conclusions and predicting the decision for the new situations can be implemented in the form of a system which can be named as a decision support system. The outcome of classification is getting a class on which decisions are based. The success of the outcome of the classification is to be judged by the test data and then to be applied on real world situation. The success rate of outcome of the classification process determines the accuracy of the rules which are used in classification process.
Multi-view Rendering with Different Resolutions for Multiscreen Services

We present a novel multiscreen-based gaming service system which supports multiple-viewpoint rendering with different resolutions for visualizing a 3D game scene dataset at the same time. Our approach is based on multi-view rendering and reduces the computation to generating video streams for cloud-based gaming services. In addition, the performance speedup of our rendering system is achieved by utilizing both multicore CPUs and a GPU simultaneously without additional requirement for any special hardware. The experimental results demonstrate our multi-view rendering method can be successfully applied to the multiscreen services for the multiplayer games.
Rethinking E-Commerce Service Quality: Does Website Quality Still Suffice?

Since the beginning of e-commerce, there has been a debate on whether customer service should be included as a component of website quality. This study reexamines the question in the context of e-commerce maturity. Based on the more general service quality framework, it proposes e-commerce service quality as a broader construct that covers technical service quality, content service quality, and customer service quality. The empirical results based on the survey observations collected from USA and China suggest that online shoppers emphasize different quality aspects at different stages of e-commerce development. When e-commerce gets mature, customer service becomes more salient.
Design and application of an automated system for camera photogrammetric calibration

This work presents the development of a novel Automatic Photogrammetric Camera Calibration System (APCCS) that is capable of calibrating cameras, regardless of their Field of View (FOV), resolution and sensitivity spectrum. Such calibrated cameras can, despite lens distortion, accurately determine vectors in a desired reference frame for any image coordinate, and map points in the reference frame to their corresponding image coordinates. The proposed system is based on a robotic arm which presents an interchangeable light source to the camera in a sequence of known discrete poses. A computer captures the camera’s image for each robot pose and locates the light source centre in the image for each point in the sequence. Careful selection of the robot poses allows cost functions dependant on the captured poses and light source centres to be formulated for each of the desired calibration parameters. These parameters are the Brown model parameters to convert from the distorted to the undistorted image (and vice versa), the focal length, and the camera’s pose. The pose is split into the camera pose relative to its mount and the mount’s pose relative to the reference frame to aid subsequent camera replacement. The parameters that minimise each cost function are determined via a combination of coarse global and fine local optimisation techniques: genetic algorithms and the Leapfrog algorithm, respectively. The real world applicability of the APCCS is assessed by photogrammetrically stitching cameras of differing resolutions, FOVs and spectra into a single multispectral panorama. The quality of these panoramas are deemed acceptable after both subjective and quantitative analyses. The quantitative analysis compares
Performance comparison of cascade and feed forward neural network for face recognition system

In this paper a neural network classifier is used for face recognition. The performance of a neural network to a large extent depends on its architecture. Two different architectures are investigated and presented in this paper. The cascade architecture (CASNN) and feed forward neural architecture (FFNN) are investigated. The feature extraction is performed using principal component analysis (PCA) as it reduces the computational burden. For a given database the features are extracted using PCA. The Olivetti Research Lab (ORL) database is used.The extracted features are divided into training set and testing set. The training data set is used to train both the neural network architectures. Both are tested extensively using testing data. A performance comparison is carried out and presented. (6 pages)
Online Social Network Internetworking Analysis

Online social networks (OSNs) contain data about users, their relations, interests and daily activities and the great value of this data results in ever growing popularity of OSNs. There are two types of OSNs data, semantic and topological. Both can be used to support decision making processes in many applications such as in information diffusion, viral marketing and epidemiology. Online Social network analysis (OSNA) research is used to maximize the benefits gained from OSNs’ data. This paper provides a comprehensive study of OSNs and OSNA to provide analysts with the knowledge needed to analyse OSNs. OSNs’ internetworking was found to increase the wealth of the analysed data by depending on more than one OSN as the source of the analysed data. Paper proposes a generic model of OSNs’ internetworking system that an analyst can rely on. Two different data sources in OSNs were identified in our efforts to provide a thorough study of OSNs, which are the OSN User data and the OSN platform data. Additionally, we propose a classification of the OSN User data according to its analysis models for different data types to shed some light into the current used OSNA methodologies. We also highlight the different metrics and parameters that analysts can use to evaluate semantic or topologic OSN user data. Further, we present a classification of the other data types and OSN platform data that can be used to compare the capabilities of different OSNs whether separate or in a OSNs’ internetworking system. To increase analysts’ awareness about the available tools they can use, we overview some of the currently publically available OSNs’ datasets and simulation tools and identify whether they are capable of being used in semantic, topological OSNA, or both. The overview identifies that only few datasets includes both data types (semantic and topological) and there are few analysis tools that can perform analysis on both data types. Finally paper present a scenario that shows that an integration of semantic and topologic data (hybrid data) in the OSNA is beneficial.