A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

A literary occupation: responses of German writers in service in occupied Europe

This thesis examines the literary output of German servicemen writers writing from the occupied territories of Europe in the period 1940-1944. Whereas literary-biographical studies and appraisals of the more significant individual writers have been written, and also a collective assessment of the Eastern front writers, this thesis addresses in addition the German literary responses in France and Greece, as being then theatres of particular cultural/ideological attention. Original papers of the writer Felix Hartlaub were consulted by the author at the Deutsches Literatur Archiv (DLA) at Marbach. Original imprints of the wartime works of the subject writers are referred to throughout, and citations are from these. As all the published works were written under conditions of wartime censorship and, even where unpublished, for fear of discovery written in oblique terms, the texts were here examined for subliminal authorial intention. The critical focus of the thesis is on literary quality: on aesthetic niveau, on applied literary form, and on integrity of authorial intention. The thesis sought to discover: (1) the extent of the literary output in book-length forms. (2) the auspices and conditions under which this literary output was produced. (3) the publication history and critical reception of the output. The thesis took into account, inter alia: (1) occupation policy as it pertained locally to the writers’ remit; (2) the ethical implications of this for the writers; (3) the writers’ literary stratagems for negotiating the constraints of censorship.

Women's help seeking behaviour and the associated influencing factors on self discovery of a breast symptom

To investigate women’s help seeking behavior (HSB) following self discovery of a breast symptom and determine the associated influencing factors. A descriptive correlation design was used to ascertain the help seeking behavior (HSB) and the associated influencing factors of a sample of women (n = 449) with self discovered breast symptoms. The study was guided by the ‘Help Seeking Behaviour and Influencing Factors” conceptual framework (Facione et al., 2002; Meechan et al., 2003, 2002; Leventhal, Brissette and Leventhal, 2003 and O’Mahony and Hegarty, 2009b). Data was collected using a researcher developed multi-scale questionnaire package to ascertain women’s help seeking behavior on self discovery of a breast symptom and determine the factors most associated with HSB. Factors examined include: socio-demographics, knowledge and beliefs (regarding breast symptom; breast changes associated with breast cancer; use of alternative help seeking behaviours and presence or absence of a family history of breast cancer),emotional responses, social factors, health seeking habits and health service system utilization and help seeking behavior. A convenience sample (n = 449 was obtained by the researcher from amongst women attending the breast clinics of two large urban hospitals within the Republic of Ireland. All participants had self-discovered breast symptoms and no previous history of breast cancer. The study identified that while the majority of women (69.9%; n=314) sought help within one month, 30.1% (n=135) delayed help seeking for more than one month following self discovery of their breast symptom. The factors most significantly associated with HSB were the presenting symptom of ‘nipple indrawn/changes’ (p = 0.005), ‘ignoring the symptom and hoping it would go away’ (p < 0.001), the emotional response of being ‘afraid@ on symptom discovery (p = 0.005) and the perception/belief in longer symptom duration (p = 0.023). It was found that women who presented with an indrawn/changed nipple were more likely to delay (OR = 4.81) as were women who ‘ignored the symptoms and hoped it would go away’ (OR = 10.717). Additionally, the longer women perceived that their symptom would last, they more likely they were to delay (OR = 1.18). Conversely, being afraid following symptom discovery was associated with less delay (OR = 0.37; p=0.005). This study provides further insight into the HSB of women who self discovered breast symptoms. It highlights the complexity of the help seeking process, indicating that is not a linear event but is influenced by multiple factors which can have a significant impact on the outcomes in terms of whether women delay or seek help promptly. The study further demonstrates that delayed HSB persists amongst women with self discovered breast symptoms. This has important implications for continued emphasis on the promotion of breast awareness, prompt help seeking for self discovered breast symptoms and early detection and treatment of breast cancer, amongst women of all ages.

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Putting meaning into NETMAR – the open service network for marine environmental data

The open service network for marine environmental data (NETMAR) project uses semantic web technologies in its pilot system which aims to allow users to search, download and integrate satellite, in situ and model data from open ocean and coastal areas. The semantic web is an extension of the fundamental ideas of the World Wide Web, building a web of data through annotation of metadata and data with hyperlinked resources. Within the framework of the NETMAR project, an interconnected semantic web resource was developed to aid in data and web service discovery and to validate Open Geospatial Consortium Web Processing Service orchestration. A second semantic resource was developed to support interoperability of coastal web atlases across jurisdictional boundaries. This paper outlines the approach taken to producing the resource registry used within the NETMAR project and demonstrates the use of these semantic resources to support user interactions with systems. Such interconnected semantic resources allow the increased ability to share and disseminate data through the facilitation of interoperability between data providers. The formal representation of geospatial knowledge to advance geospatial interoperability is a growing research area. Tools and methods such as those outlined in this paper have the potential to support these efforts.

WAIS seminar: Application Performance Monitoring and Architecture Discovery with Kieker

Wednesday 23rd April 2014 Speaker(s): Willi Hasselbring Organiser: Leslie Carr Time: 23/04/2014 14:00-15:00 Location: B32/3077 File size: 802Mb Abstract The internal behavior of large-scale software systems cannot be determined on the basis of static (e.g., source code) analysis alone. Kieker provides complementary dynamic analysis capabilities, i.e., monitoring/profiling and analyzing a software system's runtime behavior. Application Performance Monitoring is concerned with continuously observing a software system's performance-specific runtime behavior, including analyses like assessing service level compliance or detecting and diagnosing performance problems. Architecture Discovery is concerned with extracting architectural information from an existing software system, including both structural and behavioral aspects like identifying architectural entities (e.g., components and classes) and their interactions (e.g., local or remote procedure calls). In addition to the Architecture Discovery of Java systems, Kieker supports Architecture Discovery for other platforms, including legacy systems, for instance, inplemented in C#, C++, Visual Basic 6, COBOL or Perl. Thanks to Kieker's extensible architecture it is easy to implement and use custom extensions and plugins. Kieker was designed for continuous monitoring in production systems inducing only a very low overhead, which has been evaluated in extensive benchmark experiments. Please, refer to http://kieker-monitoring.net/ for more information.

Collaborative personal profiling for web service ranking and recommendation

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Performance Analysis of a Semantics Enabled Service Registry

Service discovery is a critical task in service-oriented architectures such as the Grid and Web Services. In this paper, we study a semantics enabled service registry, GRIMOIRES, from a performance perspective. GRIMOIRES is designed to be the registry for myGrid and the OMII software distribution. We study the scalability of GRIMOIRES against the amount of information that has been published into it. The methodology we use and the data we present are helpful for researchers to understand the performance characteristics of the registry and, more generally, of semantics enabled service discovery. Based on this experimentation, we claim that GRIMOIRES is an efficient semantics-aware service discovery engine.

Performance Analysis of a Semantics Enabled Service Registry

Service discovery is a critical task in service-oriented architectures such as the Grid and Web Services. In this paper, we study a semantics enabled service registry, GRIMOIRES, from a performance perspective. GRIMOIRES is designed to be the registry for myGrid and the OMII software distribution. We study the scalability of GRIMOIRES against the amount of information that has been published into it. The methodology we use and the data we present are helpful for researchers to understand the performance characteristics of the registry and, more generally, of semantics enabled service discovery. Based on this experimentation, we claim that GRIMOIRES is an efficient semantics-aware service discovery engine.

Recycling workflows and services through discovery and reuse

Scientific workflows are becoming a valuable tool for scientists to capture and automate e-Science procedures. Their success brings the opportunity to publish, share, reuse and repurpose this explicitly captured knowledge. Within the myGrid project, we have identified key resources that can be shared including complete workflows, fragments of workflows and constituent services. We have examined the alternative ways these can be described by their authors (and subsequent users), and developed a unified descriptive model to support their later discovery. By basing this model on existing standards, we have been able to extend existing Web Service and Semantic Web Service infrastructure whilst still supporting the specific needs of the e-Scientist. myGrid components enable a workflow life-cycle that extends beyond execution, to include discovery of previous relevant designs, reuse of those designs, and subsequent publication. Experience with example groups of scientists indicates that this cycle is valuable. The growing number of workflows and services mean more work is needed to support the user in effective ranking of search results, and to support the repurposing process.

An autonomic management system for service oriented enterprise grid computing

Grid computing and service oriented architectures improve the way computational tasks are performed. Through this research a management system, utilising the autonomic characteristics of self discovery and negotiation, self configuration and self healing, was designed and implemented, ultimately removing the need for users to know the intricacies of these systems.

A technology to expose a cluster as a service in a cloud

Clouds refer to computational resources (in particular, clusters) that are accessible as scalable, on demand, pay-as- you-go services provided in the Internet. However, clouds are in their infancy and lack a high level abstraction. Specifically, there is no effective discovery and selection service for clusters and offer little to no ease of use for clients. Here we show a technology that exposes clusters as Web services in the form of a Cluster as a Service for publishing via WSDL, discovering, selecting and using clusters.

Dean's great discovery : multiplication, division and fractions

Multiplication, division and fractions are 'hotspots' for students in the middle years with many students experiencing difficulty with these concepts (Siemon, Virgona & Cornielle, 2001). Arrays effectively model multiplication and help children develop multiplicative thinking and learn multiplication facts (Young-Loveridge, 2005). In this article we show how an open-ended array problem enabled a Grade 5/6 student to think about the relationship between multiplication, division and fractions. In the article we describe the project and 'hot spot' mathematical tasks that we used and provide some background on multiplicative thinking before presenting the case and a commentary (Western Melbourne Roundtable, 1997) of one student's exploration. This case was documented whilst we were working on a collaborative project with a team of upper primary teachers and a group of pre-service teachers at a local primary school.

A survey of cloud-based service computing solutions for mammalian genomics

Cloud-based service computing has started to change the way how research in science, in particular biology, medicine, and engineering, is being carried out. Researchers in the area of mammalian genomics have taken advantage of cloud computing technology to cost-effectively process large amounts of data and speed up discovery. Mammalian genomics is limited by the cost and complexity of analysis, which require large amounts of computational resources to analyse huge amount of data and biology specialists to interpret results. On the other hand the application of this technology requires computing knowledge, in particular programming and operations management skills to develop high performance computing (HPC) applications and deploy them on HPC clouds. We carried out a survey of cloud-based service computing solutions, as the most recent and promising instantiations of distributed computing systems, in the context their use in research of mammalian genomic analysis. We describe our most recent research and development effort which focuses on building Software as a Service (SaaS) clouds to simplify the use of HPC clouds for carrying out mammalian genomic analysis.