Scalable,low complexity,and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems

For heterogeneous distributed computing systems, important design issues are scalability and system optimization. Given such systems, it is crucial to develop low computational complexity algorithms to schedule tasks in a manner that exploits the heterogeneity of the resources and applications. In this paper, we report and evaluate three scalable, and fast scheduling heuristics for highly heterogeneous distributed computing systems. We conduct a comprehensive performance evaluation study using simulation. The benchmarking outlines the performance of the schedulers, representing scalability, makespan, flowtime, computational complexity, and memory utilization. The set of experimental results shows that our heuristics perform as good as the traditional approaches, for makespan and flowtime, while featuring lower complexity, lower running time, and lower used memory. The experimental results also detail the various scenarios under which certain algorithms excel and fail.     

Background subtraction model based on color and depth cues

Background subtraction consists of segmenting objects in movement in a video captured by a static camera. This is typically performed using color information, but it leads to wrong estimations due to perspective and illumination issues. We show that multimodal approaches based on the integrated use of color and depth cues produce more accurate and robust results than using either data source independently. Depth is less affected by issues such as shadows or foreground objects similar to background. However, objects close to the background may not be detected when using only range information, being color information complementary in those cases. We propose an extension of a well-known background subtraction technique which fuses range and color information, as well as a post-processing mask fusion stage to get the best of each feature. We have evaluated the method proposed using a well-defined dataset and different disparity estimation algorithms, showing the benefits of our method for fusion color and depth cues.     

Agricultural use of three organic residues: effect on orange production and on properties of a soil of the ‘Comarca Costa de Huelva’ (SW Spain)

Disposal of urban, agricultural and industrial organic residues impliesan increasing problem because of all the economic and environmentalrepercussions involved. One of the most adequate ways of managing this problemis the agricultural use of these wastes as organic amendments. Three organicresidues (AC, olive mill waste water sludge compost; MWC, municipal solid wastecompost; and PS, paper mill sludge) were used in a 3-year field experimentinvolving orange production. The effect of their application on crop productionand on soil quality was investigated. Soil samples (0–20 cm depth)collected 11 months after the last soil amendment were analysed for: pH and EC,Kjeldahl-N, available-P, available-K, total organic carbon, humic substances,dehydrogenase, phosphatase, -glucosidase, urease andbenzoyl-argininamidehydrolysing protease (BAA-protease) activities. Generally, the application of the MWC and PSincreased orange yield when compared to control. Moreover, total organic carbonand humic substances significantly increased in soils treated with all theorganic amendments. Organic fertilisation increased the Kjeldahl-N andavailable-P contents of the soil. The application of the organic residues also causedsignificant increases in dehydrogenase, -glucosidase, urease andBAA-protease activities of the soil. Significant positive correlations (p <0.01) between these enzymatic activities and total organic carbon were foundforall treatments. Significant positive correlation between dehydrogenase, urease,-glucosidase, and BAA-protease and orange yield was also found. However,a clear inhibition of phosphatase activity was observed in soils treated withPS. The results indicate that the repeated application to the soil of moderateamounts of organic amendments has positive effects on the chemical andbiochemical properties of the soil, as well as on the orange yield.     

Effect of water on the oxygen barrier properties of poly(ethylene terephthalate) and polylactide films

The aim of this work was to study the variations in the oxygen diffusion, solubility, and permeability coefficients of polylactide (PLA) films at different temperatures (5, 23, and 40°C) and water activities (0–0.9). The results were compared with the oxygen diffusion, solubility, and permeability coefficients obtained for poly(ethylene terephthalate) (PET) films under the same experimental conditions. The water sorption isotherm for PLA films was also determined. Diffusion coefficients were determined with the half‐sorption time method. Also, a consistency test for continuous‐flow permeability experimental data was run to obtain the diffusion coefficient with the lowest experimental error and to confirm that oxygen underwent Fickian diffusion in the PLA films. The permeability coefficients were obtained from steady‐state permeability experiments. The results indicated that the PLA films absorbed very low amounts of water, and no significant variation of the absorbed water with the temperature was found. The oxygen permeability coefficients obtained for PLA films (2–12 × 10^?18 kg m/m² s Pa) were higher than those obtained for PET films (1–6 × 10^?19 kg m/m² s Pa) at different temperatures and water activities. Moreover, the permeability coefficients for PLA and PET films did not change significantly with changes in the water activity at temperatures lower than 23°C. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 1790–1803, 2004     

Polymerization by phase transfer catalysis

Poly(amide-carbonate)s and poly(amide-thiocarbonate)s derived from the diphenol-amides N-(2,6-dichloro-4-nitrophenyl)-2,2-bis(hydroxyphenyl)-propylamide (I), N-(2,6-dichloro-4-nitrophenyl)-3,3-bis(hydroxyphenyl)-butylamide (II), and N-(2,6-dichloro-4-nitrophenyl)-4,4-bis(hydroxyphenyl)-pentylamide (III), and phosgene or thiophosgene, have been synthesized under phase transfer conditions using several quaternary ammonium salts as phase transfer catalysts. Benzyltriethylammonium chloride (BTEAC) was effective in practically all cases due the hydrophilicity of this catalysts. Received: 21 February 1997/Revised: 28 April 1997/Accepted: 30 April 1997     

The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500

The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

String analysis by sliding positioning strategy

Discovering frequent factors from long strings is an important problem in many applications, such as biosequence mining. In classical approaches, the algorithms process a vast database of small strings. However, in this paper we analyze a small database of long strings. The main difference resides in the high number of patterns to analyze. To tackle the problem, we have developed a new algorithm for discovering frequent factors in long strings. We present an Apriori-like solution which exploits the fact that any super-pattern of a non-frequent pattern cannot be frequent. The SANSPOS algorithm does a multiple-pass, candidate generation and test approach. Multiple length patterns can be generated in a pass. This algorithm uses a new data structure to arrange nodes in a trie. A Positioning Matrix is defined as a new positioning strategy. By using Positioning Matrices, we can apply advanced prune heuristics in a trie with a minimal computational cost. The Positioning Matrices let us process strings including Short Tandem Repeats and calculate different interestingness measures efficiently. Furthermore, in our algorithm we apply parallelism to transverse different sections of the input strings concurrently, speeding up the resulting running time. The algorithm has been successfully used in natural language and biological sequence contexts.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.