bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brand Clans: Consumption and Rituals Among Low-income Young People in the City of Porto Alegre
Authors: Pinheiro-Machado Rosana , Scalco Lucia Mury 
Published: May 2015

Abstract:
This article aims to analyze the Brazilian phenomenon of ‘brand clans’ or ‘brand communities’, which means a group of young people who spend all their money buying branded clothes. The research is based on an ethnography carried out about the youngsters, mostly male, from Morro da Cruz – the largest lower class community in Porto Alegre, Brazil. On the one hand, we discuss the tension between poverty and brand consumption and on the other hand, we aim to make an inventory of subjectivities, emotions, logic and meaning of these young people coming from lower class who make great sacrifices in order to become ‘fashionable’ and thus feel socially included. The article is divided into five sections. In the first three, we discuss more theoretical topics, such as consumption, class, youth, totems and rituals. In the last two, we enter more thoroughly in the empirical universe, showing the results obtained through an ethnographic study. We argue that brands play a vital role in the life of the informants. Brands are not only a source of prestige, but also of vitality, power, and citizenship.
On Emotion and Memories: the Consumption of Mobile Phones as ‘Affective Technology’
Authors: Silva Sandra Rubia 
Published: May 2015

Abstract:
In this article, which is theoretically affiliated to studies of consumption as material culture as developed especially by Miller (1987; 2010), the goal is to analyze the consumption of mobile phones in its interaction with social dynamics related to gender and generation categories. How do the consumption practices related to mobile phones influence the emotions experienced in the relationships between parents and children and between men and women? Drawing from an ethnographic approach, I am investigating which emotional investments the social agents express through the consumption of mobile phones, and I am also considering how emotions change when mediated by the consumption of this mobile technology. Through the analysis, I expect both to highlight the richness and complexity of the material and symbolic dimensions involved in the daily processes of consumption, as well as enlighten the study of consumption practices from a segment that is still poorly studied: the popular groups.
Amazonian Exchanges: Txema's Lessons With Outboard Engines, Mosquito Nets and Images
Authors: Arisi Bárbara Maisonnave 
Published: May 2015

Abstract:
This article provides an ethnographic analysis of lessons given by an Amazonian Indian elder called Txema concerning transactions of objects and images. Txema has lived until 1978 in the Amazon as a hunter and farmer in very small communities spread in the forest mainly avoiding conflict with non-indigenous people. The Matis established contact with the Brazilians in 1978 and now Txema deals with British TV crews and researchers. This paper brings detailed information on how Txema transacts many objects like outboard engines and mosquito nets for images and care. Values are discussed in those transactions. Drawing an ethnographic description as the way to achieve reflection, this paper tries to show a close approach to what kind of transformation a lot of industrialized objects provoke in an indigenous community. Material culture exchanges are closely related to values and Txema teaches us what is valuable for this elder Amazonian man.
Least Support Orthogonal Matching Pursuit Algorithm With Prior Information
Authors: Tawfic Israa Sh. , Kayhan Sema Koc 
Published: May 2015

Abstract
This paper proposes a new fast matching pursuit technique named Partially Known Least Support Orthogonal Matching Pursuit (PKLS-OMP) which utilizes partially known support as a prior knowledge to reconstruct sparse signals from a limited number of its linear projections. The PKLS-OMP algorithm chooses optimum least part of the support at each iteration without need to test each candidate independently and incorporates prior signal information in the recovery process. We also derive sufficient condition for stable sparse signal recovery with the partially known support. Result shows that inclusion of prior information weakens the condition on the sensing matrices and needs fewer samples for successful reconstruction. Numerical experiments demonstrate that PKLS-OMP performs well compared to existing algorithms both in terms of reconstruction performance and execution time.
Applying LCS To Affective Image Classification In Spatial-Frequency Domain
Authors: Lee Po-Ming , Hsiao Tzu-Chien 
Published: May 2015

Abstract
Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.
Effect Of Robot Utterances Using Onomatopoeia On Collaborative Learning
Authors: Jimenez Felix , Kanoh Masayoshi , Yoshikawa Tomohiro , Furuhashi Takeshi , Nakamura Tsuyoshi 
Published: May 2015

Abstract
We investigated the effect of robot’s utterances using onomatopoeia in collaborative learning. The robot was designed to provide encouragement using onomatopoeia when students are given problems to be solved issued by a learning system. Eight college students used a mathematics learning system with a robot for three weeks and then took exams. The results indicated that the robot using utterances with onomatopoeia could comfort learners more than the robot without onomatopoeia. It suggests that the robot that praises or comforts using onomatopoeia helps learners maintain their motivation in collaborative learning.
Web–Based Framework For Breast Cancer Classification
Authors: Bruździński Tomasz , Krzyżak Adam , Fevens Thomas , Jeleń Łukasz 
Published: May 2015

Abstract
The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked.
The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods.
A Data Mining Approach To Improve Military Demand Forecasting1
Authors: Thiagarajan Rajesh , Rahman Mustafizur , Gossink Don , Calbert Greg 
Published: May 2015

Abstract
Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.
Advanced Supervision Of Oil Wells Based On Soft Computing Techniques
Authors: Camargo Edgar , Aguilar Jose 
Published: May 2015

Abstract
In this work is presented a hybrid intelligent model of supervision based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells based on the gas lift (GL) method. The model is composed by two parts: a Multilayer Fuzzy System to identify the operational scenarios in an oil well and a genetic algorithm to maximize the production of oil and minimize the flow of gas injection, based on the restrictions of the process and the operational cost of production.
Additionally, the first layers of the Multilayer Fuzzy System have specific tasks: the detection of operational failures, and the identification of the rate of gas that the well requires for production. In this way, our hybrid intelligent model implements supervision and control tasks.
A Priori Approach Of Real-Time Ridesharing Problem With Intermediate Meeting Locations
Authors: Aissat Kamel , Oulamara Ammar 
Published: May 2015

Abstract
Ridesharing is a mobility concept in which a trip is shared by a vehicle’s driver and one or more passengers called riders. Ridesharing is considered as a more environmentally friendly alternative to single driver commutes in pollution-creating vehicles on overcrowded streets. In this paper, we present the core of a new strategy of the ridesharing system, making it more flexible and competitive than the recurring system. More precisely, we allow the driver and the rider to meet each other at an intermediate starting location and to separate at another intermediate ending location not necessarily their origins and destinations, respectively. This allows to reduce both the driver’s detour and the total travel cost. The term “A priori approach” means that the driver sets the sharing cost rate on the common path with rider in advance. An exact and heuristic approaches to identify meeting locations, while minimizing the total travel cost of both driver and rider are proposed. Finally, we analyze their empirical performance on a set of real road networks consisting of up to 3,5 million nodes and 8,7 million edges. Our experimental results show that our heuristics provide efficient performances within short CPU times and improves the recurring ridesharing approach in terms of cost-savings.
Concrete-Design
Authors: Leczovics Péter , Domonyi Erzsébet 
Published: May 2015

Abstract
Present paper introduces a new interpretation of concrete, demonstrating some extreme possibilities of this rigid material such as a design element. In the first part a brief overview of the previous achievements are shown. The second part of this paper focuses on the relationship between concrete and fashion.
The „typCon” studio was founded in the Institute of Construction Management of the Szent István University, Ybl Miklós Faculty of Architecture and Civil Engineering. The new developments, researches and formulas related to concrete design elements are also introduced.
Tuneable Current Mode RMS Detector
Authors: Petrović Predrag B. 
Published: May 2015

Abstract
A new realization of RMS detector, employing two CCCIIs (controlled current conveyors), metal-oxide-semiconductor transistors and single grounded capacitor is present in this paper, without any external resistors and components matching requirements. The proposed circuit can be applied in measuring the RMS value of periodic, band-limited signals. The proposed circuit is very appropriate to further develop into integrated circuits. The errors related to the signal processing and errors bound were investigated and provided. To verify the theoretical analysis, the circuit PSpice simulations have also been included, showing good agreement with the theory.
Multi–Frequency Analysis For Interstitial Microwave Hyperthermia Using Multi–Slot Coaxial Antenna
Authors: Gas Piotr 
Published: May 2015

Abstract
The presented paper shows a new concept of multi-slot coaxial antenna working at different frequencies to predict the best solution for interstitial microwave hyperthermia treatment. The described method concerns a microwave heating of unhealthy cells using a thin microwave antenna located in the human tissue. Therefore, the coupled wave equation in a sinusoidal steady-state and the transient bioheat equation under an axial symmetrical model are considered. The 4-Cole-Cole approximation has been used to compute the complex relative permittivity of the human tissues at different antenna operating frequencies. At the stage of numerical simulation the finite element method (FEM) is used. Special attention has been paid to estimate the optimal antenna parameters for thermal therapy for three microwave frequencies mainly used in medical practice and make comparison of the obtained results in the case of single-, double- and triple-slot antennas.
Versailles Redux? Eurozone Competitiveness in a Dynamic Balassa-Samuelson-Penn Framework
Authors: Stahler Kevin , Subramanian Arvind 
Published: May 2015

AbstractPrima facie, competitiveness adjustments in the eurozone, based on unit labor cost developments, appear sensible and in line with what the economic analyst might have predicted and the economic doctor might have ordered. But a broader and arguably better – Balassa-Samuelson-Penn (BSP) – framework for analyzing these adjustments paints a very different picture. Taking advantage of the newly released PPP-based estimates of the International Comparison Program (2011), we identify a causal BSP relationship. We apply this framework to computing more appropriate measures of real competitiveness changes in Europe and other advanced economies in the aftermath of the recent global crises. There has been a deterioration, not improvement, in competitiveness in the periphery countries between 2007 and 2013. Second, the pattern of adjustment within the eurozone has been dramatically perverse, with Germany having improved competitiveness by 9% and with Greece’s having deteriorated by 9%. Third, real competitiveness changes are strongly correlated with nominal exchange rate changes, which suggests the importance of having a flexible (and preferably independent) currency for effecting external adjustments. Fourth, internal devaluation – defined as real competitiveness improvements in excess of nominal exchange rate changes – is possible but seems limited in scope and magnitude. Our results are robust to adjusting the BSP framework to take account of the special circumstances of countries experiencing unemployment. Even if we ignore the BSP effect, the broad pattern of limited and lopsided adjustment in the eurozone remains.
Neuro-Fuzzy Modeling for Multi-Objective Test Suite Optimization
Authors: Anwar Zeeshan , Ahsan Ali , Catal Cagatay 
Published: May 2015

AbstractRegression testing is a type of testing activity, which ensures that source code changes do not affect the unmodified portions of the software adversely. This testing activity may be very expensive in, some cases, due to the required time to execute the test suite. In order to execute the regression tests in a cost-effective manner, the optimization of regression test suite is crucial. This optimization can be achieved by applying test suite reduction (TSR), regression test selection (RTS), or test case prioritization (TCP) techniques. In this paper, we designed and implemented an expert system for TSR problem by using neuro-fuzzy modeling-based approaches known as “adaptive neuro-fuzzy inference system with grid partitioning” (ANFIS-GP) and “adaptive neuro-fuzzy inference system with subtractive clustering” (ANFIS-SC). Two case studies were performed to validate the model and fuzzy logic, multi-objective genetic algorithms (MOGAs), non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms were used for benchmarking. The performance of the models were evaluated in terms of reduction of test suite size, reduction in fault detection rate, reduction in test suite execution time, and reduction in requirement coverage. The experimental results showed that our ANFIS-based optimization system is very effective to optimize the regression test suite and provides better performance than the other approaches evaluated in this study. Size and execution time of the test suite is reduced up to 50%, whereas loss in fault detection rate is between 0% and 25%.
Observed secular gravity trend at Onsala station
with the FG5 gravimeter from Hannover
Authors: Timmen L. , Engfeldt A. , Scherneck H.-G. 
Published: May 2015

Abstract
Annual absolute gravity measurements with a
FG5 instrument were performed in Onsala Space Observatory
by the Institute of Geodesy of the Leibniz Universität
Hannover from 2003 to 2011 and have been continued with
the upgraded meter FG5X in 2014. Lantmäteriet, Gävle,
with their FG5 absolute gravimeter have visited Onsala
since 2007. Because small systematic errors may be inherent
in each absolute gravimeter, their measuring level and
a resulting bias (offset) between the instruments must be
controlled over time by means of inter-comparison. From
2007 to 2014, 8 direct comparisons took place well distributed
over the time span. A complete re-processing of
the absolute gravity observations with the Hannover instrument
has been conducted to improve the reduction of
unwanted gravity effects. A new tidal model is based on
continuous time series recorded with the GWR superconducting
gravimeter at Onsala since 2009. The loading effect
of the Kattegat is described with a varying sea bottom
pressure (water and air mass load) and has been validated
with the continuous gravity measurements. For the
land uplift,which is a result of the still ongoing glacial isostatic
adjustment in Fennoscandia, a secular gravity trend
of −0.22 μGal/yr was obtained with a standard deviation
of 0.17 μGal/yr. That indicates a slight uplift but is still not
significantly different from zero.
A comparative study of friction factor correlations for high concentrate slurry flow in smooth pipes
Authors: Assefa K. M. , Kaushal D. R. 
Published: May 2015

Abstract
A number of correlations for friction factor determinations in smooth pipes have been proposed in the past decades. The accuracy and applicability of these friction factor formulas should be examined. Based on this notion the paper is designed to provide a comparative study of friction factor correlations in smooth pipes for all flow regimes of Bingham fluids. Nine models were chosen. The comparisons of the selected equations with the existing experimental results, which are available in the literature, were expressed through MARE, MRE+, MRE-, RMSE, Ѳ, and S. The statistical comparisons were also carried out using MSC and AIC. The analyses show that the Wilson-Thomas (1985) and Morrison (2013) models are best fit models to the experimental data for the Reynolds number up to 40000. Within this range, both models can be used alternately. But beyond this Re value the discrepancy of the Wilson-Thomas model is higher than the Morrison model. In view of the fact that the Morrison model requires fewer calculations and parameters as well as a single equation is used to compute the friction factor for all flow regimes, it is the authors’ advice to use this model for friction factor estimation for the flow of Bingham fluids in smooth pipes as an alternative to the Moody chart and other implicit formulae.
Stochastic structure of annual discharges of large European rivers
Authors: Stojković Milan , Prohaska Stevan , Plavšić Jasna 
Published: May 2015

Abstract
Water resource has become a guarantee for sustainable development on both local and global scales. Exploiting water resources involves development of hydrological models for water management planning. In this paper we present a new stochastic model for generation of mean annul flows. The model is based on historical characteristics of time series of annual flows and consists of the trend component, long-term periodic component and stochastic component. The rest of specified components are model errors which are represented as a random time series. The random time series is generated by the single bootstrap model (SBM). Stochastic ensemble of error terms at the single hydrological station is formed using the SBM method. The ultimate stochastic model gives solutions of annual flows and presents a useful tool for integrated river basin planning and water management studies. The model is applied for ten large European rivers with long observed period. Validation of model results suggests that the stochastic flows simulated by the model can be used for hydrological simulations in river basins.