Hiding biometric data

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.     

Methodological Challenges in Union Commitment Studies.

Methodological problems in studies of union commitment were identified and illustrated with data from 4,641 members and 479 stewards in 297 local teachers' unions. Using a 20-item union commitment scale, results confirmed the existence of 3 substantive factors and 1 method factor at the individual level of analysis: loyalty to the union, responsibility to the union, willingness to work for the union, and a factor of negatively worded items. Tests of measurement invariance showed that the scale captured commitment for rank-and-file members but not for union stewards. The authors also found partial measurement invariance between long-time and newer members and full measurement invariance between men and women. Finally, the authors found that violation of the statistical assumption of independence reduced model fit when individual commitment scores were analyzed without attention to the hierarchical nature of the data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

Adapting application execution in CMPs using helper threads

In parallel to the changes in both the architecture domain–the move toward chip multiprocessors (CMPs)–and the application domain–the move toward increasingly data-intensive workloads–issues such as performance, energy efficiency and CPU availability are becoming increasingly critical. The CPU availability can change dynamically due to several reasons such as thermal overload, increase in transient errors, or operating system scheduling. An important question in this context is how to adapt, in a CMP, the execution of a given application to CPU availability change at runtime. Our paper studies this problem, targeting the energy-delay product (EDP) as the main metric to optimize. We first discuss that, in adapting the application execution to the varying CPU availability, one needs to consider the number of CPUs to use, the number of application threads to accommodate and the voltage/frequency levels to employ (if the CMP has this capability). We then propose to use helper threads to adapt the application execution to CPU availability change in general with the goal of minimizing the EDP. The helper thread runs parallel to the application execution threads and tries to determine the ideal number of CPUs, threads and voltage/frequency levels to employ at any given point in execution. We illustrate this idea using four applications (Fast Fourier Transform, MultiGrid, LU decomposition and Conjugate Gradient) under different execution scenarios. The results collected through our experiments are very promising and indicate that significant EDP reductions are possible using helper threads. For example, we achieved up to 66.3%, 83.3%, 91.2%, and 94.2% savings in EDP when adjusting all the parameters properly in applications FFT, MG, LU, and CG, respectively. We also discuss how our approach can be extended to address multi-programmed workloads.     

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

Thread vulnerability in parallel applications

Continuously reducing transistor sizes and aggressive low power operating modes employed by modern architectures tend to increase transient error rates. Concurrently, multicore machines are dominating the architectural spectrum today in various application domains. These two trends require a fresh look at resiliency of multithreaded applications against transient errors from a software perspective. In this paper, we propose and evaluate a new metric called the Thread Vulnerability Factor (TVF$TVF$). A distinguishing characteristic of TVF$TVF$ is that its calculation for a given thread (which is typically one of the threads of a multithreaded application) does not depend on its code alone, but also on the codes of the threads that share resources and data with that thread. As a result, we decompose TVF$TVF$ of a thread into two complementary parts: local and remote. While the former captures the TVF$TVF$ induced by the code of the target thread, the latter represents the vulnerability impact of the threads that interact with the target thread. We quantify the local and remote TVF$TVF$ values for three architectural components (register file, ALUs, and caches) using a set of ten multithreaded applications from the Parsec and Splash-2 benchmark suites. Our experimental evaluation shows that TVF$TVF$ values tend to increase as the number of cores increases, which means the system becomes more vulnerable as the core count rises. We further discuss how TVF$TVF$ metric can be employed to explore performance–reliability tradeoffs in multicores. Reliability-based analysis of compiler optimizations and redundancy-based fault tolerance are also mentioned as potential usages of our TVF$TVF$ metric.     

Analysis of cross-country variations in the depth of European mortgage markets

To date, quantitative analysis on the depth of mortgage markets across a broad set of countries has been very limited. This paper uses a rich and balanced data set on 31 European countries to investigate the cross-country variations in the depth of mortgage markets during the period 2005–2012. The present paper might be accepted as the updated and enriched version of existing research for the European region in terms of the coverage of countries, historical data and the empirical analyses. Our empirical findings reveal that developed countries have sizeable residential mortgage markets and this is mainly associated with higher urbanization rates and stronger legal rights. One of the most notable results of this study is that, statistical significance of explanatory variables in modeling the depth of the mortgage markets depends heavily on both the variables, countries (regions) included in the models and the regression methodology used. Hence, we argue that strong conclusions based on such analyses should be avoided and the findings of previous research on the variations of the depth of mortgage markets should be carefully evaluated.     

Performance evaluation of a submerged membrane bioreactor for the treatment of brackish oil and natural gas field produced water

Produced water, which is co-produced during oil and gas manufacturing, represents one of the largest sources of oily wastewaters. Therefore, treatment of this produced water may improve the economic viability and lead to a new source of water for beneficial use. In this study a submerged hollow fiber membrane bioreactor (MBR) has been studied experimentally for the treatment of brackish oil and natural gas field produced water. This type of wastewater is also characterized with relatively moderate to high amount of salt, oil and total petroleum hydrocarbons (TPH). However, the bacteria which are growing in conventional activated sludge and MBR cannot survive at these strict conditions, therefore acclimation of the bacteria is of vital importance. The performance of the biological system, membrane permeability, the rate and extent of TPH biodegradability have been investigated under different sludge age and F/M ratios. The results obtained by gas chromatography analyses showed that the MBR system could be very effective in the removal of TPH from produced water and a significant improvement in the effluent quality was achieved.     

Strategic effort allocation in online innovation tournaments

Online innovation tournaments, such as those hosted by crowdsourcing platforms (e.g., Kaggle), have been widely adopted by firms to evolve creative solutions to various problems. Solvers compete in these tournaments to earn rewards. In such competitive environments, it is imperative that solvers provide creative solutions with minimum effort. This article explores the factors that influence the solvers’ effort allocation decisions in a dynamic tournament setting. Specifically, comprehensive time variant data of teams that participated in crowdsourcing competitions on Kaggle were analyzed to gain insight into how solvers continually formulate strategies in light of performance feedback obtained through interim ranking. The results suggest that solvers strategically allocate their efforts throughout the contest to dynamically optimize their payoffs through balancing the probability of winning and the cost of expending effort. In particular, solvers tend to increase their efforts toward the end of tournaments or when they get closer to winning positions. Furthermore, our findings indicate that a last-minute surge in effort is more prevalent among high-skill solvers than in those with lower skill levels. In addition to providing insights that may help solvers develop strategies to improve their performance, the study has implications for the design of online crowdsourcing platforms, particularly in terms of incentivizing solvers to put forth their best effort.