Search for direct chargino production in anomaly-mediated supersymmetry breaking models based on a disappearing-track signature in pp collisions at sqrts=7 TeV with the ATLAS detector

A search for direct chargino production in anomaly-mediated supersymmetry breaking scenarios is performed in p p collisions at root s = 7 TeV using 4.7 fb(-1) of data collected with the ATLAS experiment at the LHC. In these models, the lightest chargino is predicted to have a lifetime long enough to be detected in the tracking detectors of collider experiments. This analysis explores such models by searching for chargino decays that result in tracks with few associated hits in the outer region of the tracking system. The transverse-momentum spectrum of candidate tracks is found to be consistent with the expectation from the Standard Model background processes and constraints on chargino properties are obtained.

Search for charginos nearly mass-degenerate with the lightest neutralino based on a disappearing-track signature in pp collisions at sqrts = 8 TeV with the ATLAS detector

A search is presented for direct chargino production based on a disappearing-track signature using 20.3  fb−1 of proton-proton collisions at s√=8  TeV collected with the ATLAS experiment at the LHC. In anomaly-mediated supersymmetry breaking (AMSB) models, the lightest chargino is nearly mass degenerate with the lightest neutralino and its lifetime is long enough to be detected in the tracking detectors by identifying decays that result in tracks with no associated hits in the outer region of the tracking system. Some models with supersymmetry also predict charginos with a significant lifetime. This analysis attains sensitivity for charginos with a lifetime between 0.1 and 10 ns, and significantly surpasses the reach of the LEP experiments. No significant excess above the background expectation is observed for candidate tracks with large transverse momentum, and constraints on chargino properties are obtained. In the AMSB scenarios, a chargino mass below 270 GeV is excluded at 95% confidence level.

A sketch of the life and times of the Rev. Sydney Smith, based on family documents and the recollections of personal friends.

Mode of access: Internet.

Photo-sketch synthesis and recognition based on subspace learning

This paper aims to reducing difference between sketches and photos by synthesizing sketches from photos, and vice versa, and then performing sketch-sketch/photo-photo recognition with subspace learning based methods. Pseudo-sketch/pseudo-photo patches are synthesized with embedded hidden Markov model. Because these patches are assembled by averaging their overlapping area in most of the local strategy based methods, which leads to blurring effect to the resulted pseudo-sketch/pseudo-photo, we integrate the patches with image quilting. Experiments are carried out to demonstrate that the proposed method is effective to produce pseudo-sketch/pseudo-photo with high quality and achieve promising recognition results. © 2009.

Intelligent Search and Automatic Document Classification and Cataloging Based on Ontology Approach

This paper presents an approach to development of intelligent search system and automatic document classification and cataloging tools for CASE-system based on metadata. The described method uses advantages of ontology approach and traditional approach based on keywords. The method has powerful intelligent means and it can be integrated with existing document search systems.

Value creation and value capture fro open source software publisher : a new business model based on mutualisation

Key topics: Since the birth of the Open Source movement in the mid-80's, open source software has become more and more widespread. Amongst others, the Linux operating system, the Apache web server and the Firefox internet explorer have taken substantial market shares to their proprietary competitors. Open source software is governed by particular types of licenses. As proprietary licenses only allow the software's use in exchange for a fee, open source licenses grant users more rights like the free use, free copy, free modification and free distribution of the software, as well as free access to the source code. This new phenomenon has raised many managerial questions: organizational issues related to the system of governance that underlie such open source communities (Raymond, 1999a; Lerner and Tirole, 2002; Lee and Cole 2003; Mockus et al. 2000; Tuomi, 2000; Demil and Lecocq, 2006; O'Mahony and Ferraro, 2007;Fleming and Waguespack, 2007), collaborative innovation issues (Von Hippel, 2003; Von Krogh et al., 2003; Von Hippel and Von Krogh, 2003; Dahlander, 2005; Osterloh, 2007; David, 2008), issues related to the nature as well as the motivations of developers (Lerner and Tirole, 2002; Hertel, 2003; Dahlander and McKelvey, 2005; Jeppesen and Frederiksen, 2006), public policy and innovation issues (Jullien and Zimmermann, 2005; Lee, 2006), technological competitions issues related to standard battles between proprietary and open source software (Bonaccorsi and Rossi, 2003; Bonaccorsi et al. 2004, Economides and Katsamakas, 2005; Chen, 2007), intellectual property rights and licensing issues (Laat 2005; Lerner and Tirole, 2005; Gambardella, 2006; Determann et al., 2007). A major unresolved issue concerns open source business models and revenue capture, given that open source licenses imply no fee for users. On this topic, articles show that a commercial activity based on open source software is possible, as they describe different possible ways of doing business around open source (Raymond, 1999; Dahlander, 2004; Daffara, 2007; Bonaccorsi and Merito, 2007). These studies usually look at open source-based companies. Open source-based companies encompass a wide range of firms with different categories of activities: providers of packaged open source solutions, IT Services&Software Engineering firms and open source software publishers. However, business models implications are different for each of these categories: providers of packaged solutions and IT Services&Software Engineering firms' activities are based on software developed outside their boundaries, whereas commercial software publishers sponsor the development of the open source software. This paper focuses on open source software publishers' business models as this issue is even more crucial for this category of firms which take the risk of investing in the development of the software. Literature at last identifies and depicts only two generic types of business models for open source software publishers: the business models of ''bundling'' (Pal and Madanmohan, 2002; Dahlander 2004) and the dual licensing business models (Välimäki, 2003; Comino and Manenti, 2007). Nevertheless, these business models are not applicable in all circumstances. Methodology: The objectives of this paper are: (1) to explore in which contexts the two generic business models described in literature can be implemented successfully and (2) to depict an additional business model for open source software publishers which can be used in a different context. To do so, this paper draws upon an explorative case study of IdealX, a French open source security software publisher. This case study consists in a series of 3 interviews conducted between February 2005 and April 2006 with the co-founder and the business manager. It aims at depicting the process of IdealX's search for the appropriate business model between its creation in 2000 and 2006. This software publisher has tried both generic types of open source software publishers' business models before designing its own. Consequently, through IdealX's trials and errors, I investigate the conditions under which such generic business models can be effective. Moreover, this study describes the business model finally designed and adopted by IdealX: an additional open source software publisher's business model based on the principle of ''mutualisation'', which is applicable in a different context. Results and implications: Finally, this article contributes to ongoing empirical work within entrepreneurship and strategic management on open source software publishers' business models: it provides the characteristics of three generic business models (the business model of bundling, the dual licensing business model and the business model of mutualisation) as well as conditions under which they can be successfully implemented (regarding the type of product developed and the competencies of the firm). This paper also goes further into the traditional concept of business model used by scholars in the open source related literature. In this article, a business model is not only considered as a way of generating incomes (''revenue model'' (Amit and Zott, 2001)), but rather as the necessary conjunction of value creation and value capture, according to the recent literature about business models (Amit and Zott, 2001; Chresbrough and Rosenblum, 2002; Teece, 2007). Consequently, this paper analyses the business models from these two components' point of view.

How can we exploit mobile data to customise learning based on a student’s environmental surroundings?

User-Based intelligent systems are already commonplace in a student’s online digital life. Each time they browse, search, buy, join, comment, play, travel, upload, download, a system collects, analyses and processes data in an effort to customise content and further improve services. This panel session will explore how intelligent systems, particularly those that gather data from mobile devices, can offer new possibilities to assist in the delivery of customised, personal and engaging learning experiences. The value of intelligent systems for education lies in their ability to formulate authentic and complex learner profiles that bring together and systematically integrate a student’s personal world with a formal curriculum framework. As we well know, a mobile device can collect data relating to a student’s interests (gathered from search history, applications and communications), location, surroundings and proximity to others (GPS, Bluetooth). However, what has been less explored is the opportunity for a mobile device to map the movements and activities of a student from moment to moment and over time. This longitudinal data provides a holistic profile of a student, their state and surroundings. Analysing this data may allow us to identify patterns that reveal a student’s learning processes; when and where they work best and for how long. Through revealing a student’s state and surroundings outside of schools hour, this longitudinal data may also highlight opportunities to transform a student’s everyday world into an inventory for learning, punctuating their surroundings with learning recommendations. This would in turn lead to new ways to acknowledge and validate and foster informal learning, making it legitimate within a formal curriculum.

Web searching interaction model based on user cognitive styles

As more and more information is available on the Web finding quality and reliable information is becoming harder. To help solve this problem, Web search models need to incorporate users’ cognitive styles. This paper reports the preliminary results from a user study exploring the relationships between Web users’ searching behavior and their cognitive style. The data was collected using a questionnaire, Web search logs and think-aloud strategy. The preliminary findings reveal a number of cognitive factors, such as information searching processes, results evaluations and cognitive style, having an influence on users’ Web searching behavior. Among these factors, the cognitive style of the user was observed to have a greater impact. Based on the key findings, a conceptual model of Web searching and cognitive styles is presented.

Development and validation of novel methods for microbial source tracking based on Escherichia coli as an indicator of water quality

Microbial pollution in water periodically affects human health in Australia, particularly in times of drought and flood. There is an increasing need for the control of waterborn microbial pathogens. Methods, allowing the determination of the origin of faecal contamination in water, are generally referred to as Microbial Source Tracking (MST). Various approaches have been evaluated as indicatorsof microbial pathogens in water samples, including detection of different microorganisms and various host-specific markers. However, until today there have been no universal MST methods that could reliably determine the source (human or animal) of faecal contamination. Therefore, the use of multiple approaches is frequently advised. MST is currently recognised as a research tool, rather than something to be included in routine practices. The main focus of this research was to develop novel and universally applicable methods to meet the demands for MST methods in routine testing of water samples. Escherichia coli was chosen initially as the object organism for our studies as, historically and globally, it is the standard indicator of microbial contamination in water. In this thesis, three approaches are described: single nucleotide polymorphism (SNP) genotyping, clustered regularly interspaced short palindromic repeats (CRISPR) screening using high resolution melt analysis (HRMA) methods and phage detection development based on CRISPR types. The advantage of the combination SNP genotyping and CRISPR genes has been discussed in this study. For the first time, a highly discriminatory single nucleotide polymorphism interrogation of E. coli population was applied to identify the host-specific cluster. Six human and one animal-specific SNP profile were revealed. SNP genotyping was successfully applied in the field investigations of the Coomera watershed, South-East Queensland, Australia. Four human profiles [11], [29], [32] and [45] and animal specific SNP profile [7] were detected in water. Two human-specific profiles [29] and [11] were found to be prevalent in the samples over a time period of years. The rainfall (24 and 72 hours), tide height and time, general land use (rural, suburban), seasons, distance from the river mouth and salinity show a lack of relashionship with the diversity of SNP profiles present in the Coomera watershed (p values > 0.05). Nevertheless, SNP genotyping method is able to identify and distinquish between human- and non-human specific E. coli isolates in water sources within one day. In some samples, only mixed profiles were detected. To further investigate host-specificity in these mixed profiles CRISPR screening protocol was developed, to be used on the set of E. coli, previously analysed for SNP profiles. CRISPR loci, which are the pattern of previous DNA coliphages attacks, were considered to be a promising tool for detecting host-specific markers in E. coli. Spacers in CRISPR loci could also reveal the dynamics of virulence in E. coli as well in other pathogens in water. Despite the fact that host-specificity was not observed in the set of E. coli analysed, CRISPR alleles were shown to be useful in detection of the geographical site of sources. HRMA allows determination of ‘different’ and ‘same’ CRISPR alleles and can be introduced in water monitoring as a cost-effective and rapid method. Overall, we show that the identified human specific SNP profiles [11], [29], [32] and [45] can be useful as marker genotypes globally for identification of human faecal contamination in water. Developed in the current study, the SNP typing approach can be used in water monitoring laboratories as an inexpensive, high-throughput and easy adapted protocol. The unique approach based on E. coli spacers for the search for unknown phage was developed to examine the host-specifity in phage sequences. Preliminary experiments on the recombinant plasmids showed the possibility of using this method for recovering phage sequences. Future studies will determine the host-specificity of DNA phage genotyping as soon as first reliable sequences can be acquired. No doubt, only implication of multiple approaches in MST will allow identification of the character of microbial contamination with higher confidence and readability.

Constructing tag ontology from folksonomy based on WordNet

With the emergence of Web 2.0, Web users can classify Web items of their interest by using tags. Tags reflect users’ understanding to the items collected in each tag. Exploring user tagging behavior provides a promising way to understand users’ information needs. However, free and relatively uncontrolled vocabulary has its drawback in terms of lack of standardization and semantic ambiguity. Moreover, the relationships among tags have not been explored even there exist rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach to construct tag ontology based on the widely used general ontology WordNet to capture the semantics and the structural relationships of tags. Ambiguity of tags is a challenging problem to deal with in order to construct high quality tag ontology. We propose strategies to find the semantic meanings of tags and a strategy to disambiguate the semantics of tags based on the opinion of WordNet lexicographers. In order to evaluate the usefulness of the constructed tag ontology, in this paper we apply the extracted tag ontology in a tag recommendation experiment. We believe this is the first application of tag ontology for recommendation making. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Design optimisation of ironless motors based on magnet selection

This paper considers the design of a radial flux permanent magnet iron less core brushless DC motor for use in an electric wheel drive with an integrated epicyclic gear reduction. The motor has been designed for a continuous output torque of 30 Nm and peak rating of 60 Nm with a maximum operating speed of 7000 RPM. In the design of brushless DC motors with a toothed iron stator the peak air-gap magnetic flux density is typically chosen to be close to that of the remanence value of the magnets used. This paper demonstrates that for an ironless motor the optimal peak air-gap flux density is closer to the maximum energy product of the magnets used. The use of a radial flux topology allows for high frequency operation and can be shown to give high specific power output while maintaining a relatively low magnet mass. Two-dimensional finite element analysis is used to predict the air-gap flux density. The motor design is based around commonly available NdFeB bar magnet size

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

A novel algorithm based on honey bee mating optimization for distribution harmonic state estimation including distributed generators

This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).