A Cluster-Based Data-Centric Model for Network-Aware Task Scheduling in Distributed Systems

Big Data processing architectures are now widely recognized as one of the most significant innovations in Computing in the last decade. Their enormous potential in collecting and processing huge volumes of data scattered throughout the Internet is opening the door to a new generation of fully distributed applications that, by leveraging the large amount of resources available on the network will be able to cope with very complex problems achieving performances never seen before. However, the Internet is known to have severe scalability limitations in moving very large quantities of data, and such limitations introduce the challenge of making efficient use of the computing and storage resources available on the network, in order to enable data-intensive applications to be executed effectively in such a complex distributed environment. This implies resource scheduling decisions which drive the execution of task towards the data by taking network load and capacity into consideration to maximize data access performance and reduce queueing and processing delays as possible. Accordingly, this work presents a data-centric meta-scheduling scheme for fully distributed Big Data processing architectures based on clustering techniques whose goal is aggregating tasks around storage repositories and driven by a new concept of “gravitational” attraction between the tasks and their data of interest. This scheme will benefit from heuristic criteria based on network awareness and advance resource reservation in order to suppress long delays in data transfer operations and result into an optimized use of data storage and runtime resources at the expense of a limited (polynomial) computational complexity.     

The use of analogues in comparability analysis

Comparability studies are an engineering procedure that uses similar equipment as a basis for predicting features of equipment still in the design stage. Interviews with engineers who have performed comparability studies, and analysis of the reports prepared, showed patterns of inference that could not fit any existing account of reasoning by analogy. The reasoning patterns seemed to be task-driven rather than comparison-driven. Expertise in the use of vague knowledge, such as rules of thumb, played a key role in applying the analogy. We concluded that naturalistic problems constituted a use source of data for studying inferential processes.     

Identifying Quality-Based Requirements

This article proposes a model for identifying quality requirements based on three different views of quality. By specifying quality requirements in this manner, the framework establishes quailty requirements that can be quanitfied and measured as part of an overall quality assurance program. This results in two levels of quality: one at the system level that ensures a quality software product and another at the process level that mitigates project risk.     

Thesaurus-based 3D Object Retrieval with Part-in-Whole Matching

Research in content-based 3D retrieval has already started, and several approaches have been proposed which use in different manner a similarity assessment to match the shape of the query against the shape of the objects in the database. However, the success of these solutions are far from the success obtained by their textual counterparts. A major drawback of most existing 3D retrieval solutions is their inability to support partial queries, that is, a query which does not need to be formulated by specifying a whole query shape, but just a part of it, for example a detail of its overall shape, just like documents are retrieved by specifying words and not whole texts. Recently, researchers have focused their investigation on 3D retrieval which is solved by partial shape matching. However, at the extent of our knowledge, there is still no 3D search engine that provides an indexing of the 3D models based on all the interesting subparts of the models. In this paper we present a novel approach to 3D shape retrieval that uses a collection-aware shape decomposition combined with a shape thesaurus and inverted indexes to describe and retrieve 3D models using part-in-whole matching. The proposed method clusters similar segments obtained trough a multilevel decomposition of models, constructing from such partition the shape thesaurus. Then, to retrieve a model containing a sub-part similar to a given query, instead of looking on a large set of subparts or executing partial matching between the query and all models in the collection, we just perform a fast global matching between the query and the few entries in the thesaurus. With this technique we overcame the time complexity problems associated with partial queries in large collections.     

A comparison of formulations and solution methods for the minimum-envy location problem

We consider a discrete facility location problem with a new form of equity criterion. The model discussed in the paper analyzes the case where demand points only have strict preference order on the sites where the plants can be located. The goal is to find the location of the facilities minimizing the total envy felt by the entire set of demand points. We define this new total envy criterion and provide several integer linear programming formulations that reflect and model this approach. The formulations are illustrated by examples. Extensive computational tests are reported, showing the potentials and limits of each formulation on several types of instances. Finally, some improvements for all the formulations previously presented are developed, obtaining in some cases much better resolution times.     

Characterization of mesoscale spatio‐temporal patterns and variability of remotely sensed Chl a and SST in the Interior Sea of Chiloe (41.4–43.5° S)

This study characterizes the spatial and temporal distribution of chlorophyll a (Chl a) and sea surface temperature (SST) in the Interior Sea of Chiloe in Chile at a moderate spatial resolution using SeaWiFS and MODIS‐Aqua time series data from the Goddard Earth Science (GES) Data and Information Services Center (DISC) imported into the GES DISC Interactive Online Visualization and Analysis System (‘Giovanni’). The Interior Sea of Chiloe is home to Chile's salmon farming industry, the world's second largest salmonid producer. This study undertakes the characterization of the main patterns of spatial and temporal variability of Chl a and SST in the Interior Sea of Chiloe using a continuous set of time series ocean colour and SST data. Both Chl a and SST exhibit a marked spatial and temporal distribution, with values being significantly higher in the northern area (41.4–42.7° S; total area 41.4–43.5° S) and during the spring–summer period. Peak Chl a concentrations tend to occur in a temporal interval from October to April (austral spring–austral autumn), whereas monthly averaged peak SST values occur consistently in the month of February (austral summer). Chl a concentrations exhibit strong interannual variations, with monthly averaged peak Chl a concentrations experiencing a twofold increase between the year with the lowest and highest Chl a peak concentration in the time series. Results suggest that at the present scale of analysis two spatial domains can be distinguished, regarding the differential behaviour of SST and Chl a in the northern and southern areas of the Interior Sea of Chiloe. The concurrent analysis of Chl a time series data with accumulated rainfall time series data as a proxy of solar radiation, on the other hand, supports previous hypothesis suggesting solar radiation to be a limiting factor for phytoplankton development in the Interior Sea of Chiloe. The role of geomorphological factors on pattern formation and the results presented in this study in relation to results from seasonal oceanographic cruises in the area are briefly discussed.     

Nanostructured giant magneto-impedance multilayers deposited onto flexible substrates for low pressure sensing

Nanostructured FeNi-based multilayers are very suitable for use as magnetic sensors using the giant magneto-impedance effect. New fields of application can be opened with these materials deposited onto flexible substrates. In this work, we compare the performance of samples prepared onto a rigid glass substrate and onto a cyclo olefin copolymer flexible one. Although a significant reduction of the field sensitivity is found due to the increased effect of the stresses generated during preparation, the results are still satisfactory for use as magnetic field sensors in special applications. Moreover, we take advantage of the flexible nature of the substrate to evaluate the pressure dependence of the giant magneto-impedance effect. Sensitivities up to 1 Ω/Pa are found for pressures in the range of 0 to 1 Pa, demostrating the suitability of these nanostructured materials deposited onto flexible substrates to build sensitive pressure sensors.     

Centinela: A human activity recognition system based on acceleration and vital sign data

This paper presents Centinela, a system that combines acceleration data with vital signs to achieve highly accurate activity recognition. Centinela recognizes five activities: walking, running, sitting, ascending, and descending. The system includes a portable and unobtrusive real-time data collection platform, which only requires a single sensing device and a mobile phone. To extract features, both statistical and structural detectors are applied, and two new features are proposed to discriminate among activities during periods of vital sign stabilization. After evaluating eight different classifiers and three different time window sizes, our results show that Centinela achieves up to 95.7% overall accuracy, which is higher than current approaches under similar conditions. Our results also indicate that vital signs are useful to discriminate between certain activities. Indeed, Centinela achieves 100% accuracy for activities such as running and sitting, and slightly improves the classification accuracy for ascending compared to the cases that utilize acceleration data only.     

Thermal inactivation of <Emphasis Type=Italic>Alicyclobacillus acidoterrestris</Emphasis> spores under conditions simulating industrial heating processes of tangerine vesicles and its use in time temperature integrators

The aim of the present study was to characterize the thermal inactivation of Alicyclobacillus acidoterrestris spores under isothermal and non-isothermal conditions simulating industrial heating processes applied to tangerine vesicles. A microbiological time temperature integrator (TTI) suitable for estimating the severity of thermal processes applied to acid foods was also developed. The heat resistance of A. acidoterrestris was characterized by D _105 °C = 0.63 min and z = 10.8 °C in tangerine juice, showing linear survival curves, without shoulders and tails. Under non-isothermal conditions, the use of isothermal data allowed for an accurate prediction of the inactivation. The spores were included in alginate TTIs and they were used to estimate the severity of thermal treatments applied both in a thermoresistometer Mastia and in a food pilot plant scale system, which allows fast heating of the product to 93 °C and then a short holding time (2 min). In the thermoresistometer, tangerine juice was used as heating medium. In the food pilot plant scale system, thermal treatments were applied in batch to unpackaged tangerine vesicles. In both equipments, the TTIs accurately predicted the lethality of the thermal treatments applied. The percent coefficients of variation for survivor counting in TTIs showed that distribution of heat is homogeneous both in the thermoresistometer and in the reactor, being lower than 10% in all cases. The logistic and normal distributions were found to be the best for fitting the different survivor datasets.     

Non-Markovian control in the Situation Calculus

In reasoning about actions, it is commonly assumed that the dynamics of domains satisfies the Markov Property: the executability conditions and the effects of all actions are fully determined by the present state of the system. This is true in particular in Reiter's Basic Action Theories in the Situation Calculus. In this paper, we generalize Basic Action Theories by removing the Markov property restriction, making it possible to directly axiomatize actions whose effects and executability conditions may depend on past and even alternative, hypothetical situations. We then generalize Reiter's regression operator, which is the main computational mechanism used for reasoning with Basic Action Theories, so that it can be used with non-Markovian theories.