Signals,timers, and continuations for multithreaded user‐level protocols

Precise timing and asynchronous I/O are appealing features for many applications. Unix kernels provide such features on a per‐process basis, using signals to communicate asynchronous events to applications. Per‐process signals and timers are grossly inadequate for complex multithreaded applications that require per‐thread signals and timers that operate at finer granularity. To respond to this need, we present a scheme that integrates asynchronous (Unix) signals with user‐level threads, using the ARIADNE system as a platform. This is done with a view towards support for portable, multithreaded, and multiprotocol distributed applications, namely the CLAM (connectionless, lightweight, and multiway) communications library. In the same context, we propose the use of continuations as an efficient mechanism for reducing thread context‐switching and busy‐wait overheads in multithreaded protocols. Our proposal for integrating timers and signal‐handling mechanisms not only solves problems related to race conditions, but also offers an efficient and flexible interface for timing and signalling threads. Copyright © 2006 John Wiley & Sons, Ltd.     

Optimization techniques for curved path computing

Participating media with an inhomogeneous index of refraction make light follow curved paths. Simulating this in a global illumination environment has usually been neglected due to the complexity of the calculations involved, sacrificing accurate physical simulations for efficient visual results. This paper aims to simulate non-linear media in a more reasonable time than previous works without losing physical correctness. Accuracy is achieved by solving the Eikonal equation of geometrical optics, which describes the path followed by a light beam that traverses a non-linear medium. This equation is used in the context of a photon mapping extension.     

Effects of freezing and frozen storage on the physicochemical and sensory characteristics of four types of goat's cheese

Summary Physicochemical and organoleptic properties were studied in two batches of four different types of goat's milk cheese: fresh, washed curd, soft with surface flora, and Majorero, frozen for four months prior to ripening and/or chilled storage. Frozen storage did not produce significant changes in the rheological or sensory characteristics of the cheeses, except in the case of fresh cheese, the textural characteristics of which were adversely affected. The level of proteolysis, estimated from the amino-acid nitrogen, was higher in the pre-frozen washed curd, soft with surface flora, and Majorero cheeses. The level of lipolysis was comparable, except in the soft cheese with surface flora, which presented higher levels of free fatty acids when stored frozen before ripening.

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Einfluß des Gefrierens und der Gefrierlagerung auf die physikalisch-chemischen und organoleptischen Eigenschaften von vier Ziegenkäsen

Zusammenfassung Aus Ziegenmilch wurden jeweils zwei Chargen von vier spanischen Käsesorten hergestellt: Frischkäse, gewaschene Käsemasse, Weichkäse mit Oberflächenflora und Majorero -Käse. Von jeder Charge wurde die Hälfte gefroren gelagert und erst nach vier Monaten auf die übliche Weise gereift und/oder gekühlt aufbewahrt. Die physikalisch-chemischen und organoleptischen Eigenschaften wurden danach untersucht. Im Vergleich mit Käsen, die nicht gefroren gewesen waren, ergaben sich keine wesentlichen Unterschiede in den rheologischen und sensorischen Eigenschaften, mit Ausnahme bei den Frischkäsen, bei dem sich die Textur durch die Aufbewahrung in gefrorenem Zustand verschlechtert hatte. Der Gehalt an freien Aminosäuren, ein Maß für die Proteolyse, war höher in der gewaschenen Käsemasse, in dem Weichkäse mit Oberflächenflora und in dem Majorero-Käse, die vier Monate gefroren gewesen waren. Durch die Gefrierlagerung hatte sich der Fettabbau nicht verändert, mit Ausnahme bei dem Käse mit Oberflächenflora, bei dem der Gehalt an freien Fettsäuren erhöht war im Vergleich mit dem Käse, der nicht vier Monate gefriergelagert worden war.

     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images

The Journal of Supercomputing - The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation....     

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

Applied Intelligence - The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity...     

Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

Missing data imputation for fuzzy rule-based classification systems

Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.