Spectrocolorimetric and microscopic techniques for the evaluation of plasticized PVC cleaning: a case study applicable to three-dimensional objects at museums

For the validation of a cleaning method in conservation, the examination of the object's surface after the cleaning and the quantification of the cleaning efficiency are significant steps. In this study, several cleaning solutions were tested on a plasticized poly(vinyl chloride) object whose surface was characterized before and after cleaning by optical microscopy and noncontact profilometry. In addition, different methods to quantify the cleaning efficiency based on spectrocolorimetric and microscopic techniques were provided. The results showed that noncontact profilometry supported by optical microscopy and spectrocolorimetry was very useful for the characterization of the plasticized poly(vinyl chloride) surface before and after the cleaning and also for the determination of the cleaners' efficiency.     

COACH: COllaborative certificate stAtus CHecking mechanism for VANETs

Vehicular Ad Hoc Networks (VANETs) require mechanisms to authenticate messages, identify valid vehicles, and remove misbehaving vehicles. A public key infrastructure (PKI) can be used to provide these functionalities using digital certificates. However, if a vehicle is no longer trusted, its certificates have to be revoked and this status information has to be made available to other vehicles as soon as possible. In this paper, we propose a collaborative certificate status checking mechanism called COACH to efficiently distribute certificate revocation information in VANETs. In COACH, we embed a hash tree in each standard Certificate Revocation List (CRL). This dual structure is called extended-CRL. A node possessing an extended-CRL can respond to certificate status requests without having to send the complete CRL. Instead, the node can send a short response (less than 1 kB) that fits in a single UDP message. Obviously, the substructures included in the short responses are authenticated. This means that any node possessing an extended-CRL can produce short responses that can be authenticated (including Road Side Units or intermediate vehicles). We also propose an extension to the COACH mechanism called EvCOACH that is more efficient than COACH in scenarios with relatively low revocation rates per CRL validity period. To build EvCOACH, we embed an additional hash chain in the extended-CRL. Finally, by conducting a detailed performance evaluation, COACH and EvCOACH are proved to be reliable, efficient, and scalable.     

Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.     

Robust Saturated PI Joint Velocity Control for Robot Manipulators

It is well known that many industrial manipulators use an embedded linear proportional‐integral (PI) joint velocity controller to guarantee motion control through proper velocity commands. However, although this control scheme has been very successful in practice, not much attention has been paid to designing new PI velocity control structures. The problem of analyzing a saturated PI velocity joint velocity controller is addressed in this paper. By using the theory of singularly perturbed systems, the closed‐loop system is studied. The robot dynamics assumed in this paper take into account bounded time–varying disturbances which may include the friction at the joints. An experimental study in a planar two degrees‐of‐freedom direct‐drive robot is also presented, confirming the advantage of the new saturated PI joint velocity controller.     

Regularized continuous estimation of distribution algorithms

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.     

WHAD: Wikipedia historical attributes data

This paper describes the generation of temporally anchored infobox attribute data from the Wikipedia history of revisions. By mining (attribute, value) pairs from the revision history of the English Wikipedia we are able to collect a comprehensive knowledge base that contains data on how attributes change over time. When dealing with the Wikipedia edit history, vandalic and erroneous edits are a concern for data quality. We present a study of vandalism identification in Wikipedia edits that uses only features from the infoboxes, and show that we can obtain, on this dataset, an accuracy comparable to a state-of-the-art vandalism identification method that is based on the whole article. Finally, we discuss different characteristics of the extracted dataset, which we make available for further study.     

Evaluation of messaging middleware for high-performance cloud computing

Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.     

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies.     

Dynamic load balancing with adaptive factoring methods in scientific applications

To improve the performance of scientific applications with parallel loops, dynamic loop scheduling methods have been proposed. Such methods address performance degradations due to load imbalance caused by predictable phenomena like nonuniform data distribution or algorithmic variance, and unpredictable phenomena such as data access latency or operating system interference. In particular, methods such as factoring, weighted factoring, adaptive weighted factoring, and adaptive factoring have been developed based on a probabilistic analysis of parallel loop iterates with variable running times. These methods have been successfully implemented in a number of applications such as: N-Body and Monte Carlo simulations, computational fluid dynamics, and radar signal processing. The focus of this paper is on adaptive weighted factoring (AWF), a method that was designed for scheduling parallel loops in time-stepping scientific applications. The main contribution of the paper is to relax the time-stepping requirement, a modification that allows the AWF to be used in any application with a parallel loop. The modification further allows the AWF to adapt to load imbalance that may occur during loop execution. Results of experiments to compare the performance of the modified AWF with the performance of the other loop scheduling methods in the context of three nontrivial applications reveal that the performance of the modified method is comparable to, and in some cases, superior to the performance of the most recently introduced adaptive factoring method.