Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Confronting the clinical relevance of biocide induced antibiotic resistance (BIOHYPO)

Biocides have been in use for hundreds of years for antisepsis, disinfection and preservation. Despite this widespread and ever increasing use most bacterial and fungal species remain susceptible to biocides. The dramatic increase and spread of resistance to antibiotics linked to reports of co- and cross-resistance between antibiotics and biocides raised speculations on potential hazard of biocide use. The overarching question which BIOHYPO is aimed to address is: has the use of biocides contributed to the development and spread of clinically significant antibiotic resistance in human pathogens? Core of BIOHYPO are a high throughput screening approach on collections of thousands of well characterized microorganisms and an interactive web based data analysis platform. Phenotypic screening for reduced susceptibility to biocides, detection of novel resistance genes and mobile elements, and screening for their molecular epidemiology and metagenomics will be accompanied by methodological innovation for testing, risk evaluation and registration of biocides. Altogether BIOHYPO aims to provide solid data and analysis to direct future issuing of guidelines for safe environmental, medical and industrial use of biocides.EU, Funded under :FP7-KBBE-2008-2

ARTIFICIAL IMMUNE SYSTEMS FOR INFORMATION FILTERING: FOCUSING ON PROFILE ADAPTATION

The human immune system has characteristics such as self-organisation, robustness and adaptivity that may be useful in the development of adaptive systems. One suitable application area for adaptive systems is Information Filtering (IF). Within the context of IF, learning and adapting user profiles is an important research area. In an individual profile, an IF system has to rely on the ability of the user profile to maintain a satisfactory level of filtering accuracy for as long as it is being used. This thesis explores a possible way to enable Artificial Immune Systems (AIS) to filter information in the context of profile adaptation. Previous work has investigated this issue from the perspective of self-organisation based on Autopoetic Theory. In contrast, this current work approaches the problem from the perspective of diversity inspired by the concept of dynamic clonal selection and gene library to maintain sufficient diversity. An immune inspired IF for profile adaptation is proposed and developed. This algorithm is demonstrated to work in detecting relevant documents by using a single profile to recognize a user’s interests and to adapt to changes in them. We employed a virtual user tested on a web document corpus to test the profile on learning of an emerging new topic of interest and forgetting uninteresting topics. The results clearly indicate the profile’s ability to adapt to frequent variations and radical changes in user interest. This work has focused on textual information, but it may have the potential to be applied in other media such as audio and images in which adaptivity to dynamic environments is crucial. These are all interesting future directions in which this work might develop

Population gene introgression and high genome plasticity for the zoonotic pathogen Streptococcus agalactiae

The influence that bacterial adaptation (or niche partitioning) within species has on gene spillover and transmission among bacteria populations occupying different niches is not well understood. Streptococcus agalactiae is an important bacterial pathogen that has a taxonomically diverse host range making it an excellent model system to study these processes. Here we analyze a global set of 901 genome sequences from nine diverse host species to advance our understanding of these processes. Bayesian clustering analysis delineated twelve major populations that closely aligned with niches. Comparative genomics revealed extensive gene gain/loss among populations and a large pan-genome of 9,527 genes, which remained open and was strongly partitioned among niches. As a result, the biochemical characteristics of eleven populations were highly distinctive (significantly enriched). Positive selection was detected and biochemical characteristics of the dispensable genes under selection were enriched in ten populations. Despite the strong gene partitioning, phylogenomics detected gene spillover. In particular, tetracycline resistance (which likely evolved in the human-associated population) from humans to bovine, canines, seals, and fish, demonstrating how a gene selected in one host can ultimately be transmitted into another, and biased transmission from humans to bovines was confirmed with a Bayesian migration analysis. Our findings show high bacterial genome plasticity acting in balance with selection pressure from distinct functional requirements of niches that is associated with an extensive and highly partitioned dispensable genome, likely facilitating continued and expansive adaptation

Self-adaptive attribute weighting for Naive Bayes classification

©2014 Elsevier Ltd. All rights reserved. Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance