Collection-Level Subject Access in Aggregations of Digital Collections: Metadata Application and Use

Problems in subject access to information organization systems have been under investigation for a long time. Focusing on item-level information discovery and access, researchers have identified a range of subject access problems, including quality and application of metadata, as well as the complexity of user knowledge required for successful subject exploration. While aggregations of digital collections built in the United States and abroad generate collection-level metadata of various levels of granularity and richness, no research has yet focused on the role of collection-level metadata in user interaction with these aggregations. This dissertation research sought to bridge this gap by answering the question “How does collection-level metadata mediate scholarly subject access to aggregated digital collections?” This goal was achieved using three research methods: • in-depth comparative content analysis of collection-level metadata in three large-scale aggregations of cultural heritage digital collections: Opening History, American Memory, and The European Library • transaction log analysis of user interactions, with Opening History, and • interview and observation data on academic historians interacting with two aggregations: Opening History and American Memory. It was found that subject-based resource discovery is significantly influenced by collection-level metadata richness. The richness includes such components as: 1) describing collection’s subject matter with mutually-complementary values in different metadata fields, and 2) a variety of collection properties/characteristics encoded in the free-text Description field, including types and genres of objects in a digital collection, as well as topical, geographic and temporal coverage are the most consistently represented collection characteristics in free-text Description fields. Analysis of user interactions with aggregations of digital collections yields a number of interesting findings. Item-level user interactions were found to occur more often than collection-level interactions. Collection browse is initiated more often than search, while subject browse (topical and geographic) is used most often. Majority of collection search queries fall within FRBR Group 3 categories: object, concept, and place. Significantly more object, concept, and corporate body searches and less individual person, event and class of persons searches were observed in collection searches than in item searches. While collection search is most often satisfied by Description and/or Subjects collection metadata fields, it would not retrieve a significant proportion of collection records without controlled-vocabulary subject metadata (Temporal Coverage, Geographic Coverage, Subjects, and Objects), and free-text metadata (the Description field). Observation data shows that collection metadata records in Opening History and American Memory aggregations are often viewed. Transaction log data show a high level of engagement with collection metadata records in Opening History, with the total page views for collections more than 4 times greater than item page views. Scholars observed viewing collection records valued descriptive information on provenance, collection size, types of objects, subjects, geographic coverage, and temporal coverage information. They also considered the structured display of collection metadata in Opening History more useful than the alternative approach taken by other aggregations, such as American Memory, which displays only the free-text Description field to the end-user. The results extend the understanding of the value of collection-level subject metadata, particularly free-text metadata, for the scholarly users of aggregations of digital collections. The analysis of the collection metadata created by three large-scale aggregations provides a better understanding of collection-level metadata application patterns and suggests best practices. This dissertation is also the first empirical research contribution to test the FRBR model as a conceptual and analytic framework for studying collection-level subject access.

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Redes de influencias y líderes de opinión en las redes sociales

Este Trabajo de Fin de Grado (TFG) se engloba en la línea general Social CRM. Concretamente, está vinculado a un trabajo de investigación llamado “Knowledge discovery in social networks by using a logic-based treatment of implications” desarrollado por P. Cordero, M. Enciso, A. Mora, M. Ojeda-Aciego y C. Rossi en la Universidad de Málaga, en el cual se ofrecen nuevas soluciones para la identificación de influencias de los usuarios en las redes sociales mediante herramientas como el Analisis de Conceptos Formales (FCA). El TFG tiene como objetivo el desarrollo de una aplicación que permita al usuario crear una configuración minimal de usuarios en Twitter a los que seguir para conocer información sobre un número determinado de temas. Para ello, obtendremos información sobre dichos temas mediante la API REST pública que proporciona Twitter y procesaremos los datos mediante algoritmos basados en el Análisis de Conceptos Formales (FCA). Posteriormente, la interpretación de los resultados de dicho análisis nos proporcionará información útil sobre lo expuesto al principio. Así, el trabajo se ha dividido en tres partes fundamentales: 1. Obtención de información (fuentes) 2. Procesamiento de los datos 3. Análisis de resultados El sistema se ha implementado como una aplicación web Java EE 7, utilizando JSF para las interfaces. Para el desarrollo web se han utilizado tecnologías y frameworks como Javascript, JQuery, CSS3, Bootstrap, Twitter4J, etc. Además, se ha seguido una metodología incremental para el desarrollo del proyecto y se ha usado UML como herramienta de modelado. Este proyecto se presenta como un trabajo inicial en el que se expondrán, además del sistema implementado, diversos problemas reales y ejemplos que prueben su funcionamiento y muestren la utilidad práctica del mismo

Conocimientos, actitudes y prácticas nutricionales en diabéticos tipo 2 que integran los clubes de Diabéticos Urbanos, Cuenca, 2015

Se realizó un estudio descriptivo, en una muestra probabilística calculada con un universo finito de 682 pacientes; el tamaño de la muestra fue de 245; se calculó en base al 95% de confianza, actitudes buenas del 50% y 5% de error diferencia. Los datos de conocimientos, actitudes y prácticas se obtuvieron por entrevista directa; para la tabulación y análisis de los datos se utilizó el software SPSS, versión 2015. Resultados La edad fluctuó entre 40-85 años, la mediana, 67 años. El 72,25 % fueron mujeres, el 56,32 %, casados, y el 65,31%, tenían instrucción básica. El nivel de conocimientos buenos en nutrición fue del 12,65%, regulares, el 61,23% y malos, el 26,12%. Actitudes buenas, el 10,20%, regulares, el 64,90% y malas, el 24,90%. El 15,51%, tuvo buenas prácticas, regulares, el 58,78%, y malas, el 25,71%. Conclusiones La frecuencia de conocimientos, actitudes y prácticas regulares fueron superiores al 50%.

Avaliação e controlo da qualidade da água de albufeiras com base em modelos dinâmicos

A problemática relacionada com a modelação da qualidade da água de albufeiras pode ser abordada de diversos pontos de vista. Neste trabalho recorre-se a metodologias de resolução de problemas que emanam da Área Cientifica da Inteligência Artificial, assim como a ferramentas utilizadas na procura de soluções como as Árvores de Decisão, as Redes Neuronais Artificiais e a Aproximação de Vizinhanças. Actualmente os métodos de avaliação da qualidade da água são muito restritivos já que não permitem aferir a qualidade da água em tempo real. O desenvolvimento de modelos de previsão baseados em técnicas de Descoberta de Conhecimento em Bases de Dados, mostrou ser uma alternativa tendo em vista um comportamento pró-activo que pode contribuir decisivamente para diagnosticar, preservar e requalificar as albufeiras. No decurso do trabalho, foi utilizada a aprendizagem não-supervisionada tendo em vista estudar a dinâmica das albufeiras sendo descritos dois comportamentos distintos, relacionados com a época do ano. ABSTRACT: The problems related to the modelling of water quality in reservoirs can be approached from different viewpoints. This work resorts to methods of resolving problems emanating from the Scientific Area of Artificial lntelligence as well as to tools used in the search for solutions such as Decision Trees, Artificial Neural Networks and Nearest-Neighbour Method. Currently, the methods for assessing water quality are very restrictive because they do not indicate the water quality in real time. The development of forecasting models, based on techniques of Knowledge Discovery in Databases, shows to be an alternative in view of a pro-active behavior that may contribute to diagnose, maintain and requalify the water bodies. ln this work. unsupervised learning was used to study the dynamics of reservoirs, being described two distinct behaviors, related to the time of year.

Project team’s internal and external social networks and their influence on project performance

The research on project learning has recognised the significance of knowledge transfer in project based organisations (PBOs). Effective knowledge transfer across projects avoids reinventions, enhances knowledge creation and saves lots of time that is crucial in project environment. In order to facilitate knowledge transfer, many PBOs have invested lots of financial and human resources to implement IT-based knowledge repository. However, some empirical studies found that employees would rather turn for knowledge to colleagues despite their ready access to IT-based knowledge repository. Therefore, it is apparent that social networks play a pivotal role in the knowledge transfer across projects. Some scholars attempt to explore the effect of network structure on knowledge transfer and performance, however, focused only on egocentric networks and the groups’ internal social networks. It has been found that the project’s external social network is also critical, in that the team members can not handle critical situations and accomplish the projects on time without the assistance and knowledge from external sources. To date, the influence of the structure of a project team’s internal and external social networks on project performance, and the interrelation between both networks are barely known. In order to obtain such knowledge, this paper explores the interrelation between the structure of a project team’s internal and external social networks, and their effect on the project team’s performance. Data is gathered through survey questionnaire distributed online to respondents. Collected data is analysed applying social network analysis (SNA) tools and SPSS. The theoretical contribution of this paper is the knowledge of the interrelation between the structure of a project team’s internal and external social networks and their influence on the project team’s performance. The practical contribution lies in the guideline to be proposed for constructing the structure of project team’s internal and external social networks.

Recovery and exercise interventions following breast cancer [Abstract]

Over 13,000 women are diagnosed with breast cancer each year in Australia and approximately 90% of these women will survive longer than 5-years. However, survival following treatment for breast cancer is often associated with adverse physical and psychosocial side effects, which persist beyond treatment cessation. As incidence and survival rates associated with breast cancer continue to rise, there is an imperative need to understand the extent of treatment-related concerns and ways in which these concerns can be minimized and/or overcome. A growing body of scientific evidence demonstrates that extensive quality of life benefits can be attained through exercise during and following breast cancer treatment. Such benefits observed include improvements in psychosocial and physical outcomes, as well as better compliance with treatment regimens and reduced impact of disease symptoms and treatment-related side effects. There is also evidence to suggest that post-diagnosis physical activity can improve survival. However, the majority of women newly diagnosed with breast cancer in Australia are not sufficiently active and the majority experience further declines in their physical activity levels during treatment. Throughout the course of this presentation, which draws on data from cohort studies and randomized trials of exercise interventions conducted in Queensland, the potential benefits of exercising during and following breast cancer treatment, the exercise prescription recommended for breast cancer survivors, the limits of our evidence-based knowledge and the issues faced by clinicians and patients with respect to exercise following a cancer diagnosis will be discussed. The question is no longer whether people with breast cancer should be active during and following their treatment, but is how do health care professionals best assist people to become and stay active in an endeavor to live healthy lives beyond their cancer experience.

Structural health monitoring of bridges using acoustic emission technology and signal processing techniques

Structural health monitoring (SHM) is the term applied to the procedure of monitoring a structure’s performance, assessing its condition and carrying out appropriate retrofitting so that it performs reliably, safely and efficiently. Bridges form an important part of a nation’s infrastructure. They deteriorate due to age and changing load patterns and hence early detection of damage helps in prolonging the lives and preventing catastrophic failures. Monitoring of bridges has been traditionally done by means of visual inspection. With recent developments in sensor technology and availability of advanced computing resources, newer techniques have emerged for SHM. Acoustic emission (AE) is one such technology that is attracting attention of engineers and researchers all around the world. This paper discusses the use of AE technology in health monitoring of bridge structures, with a special focus on analysis of recorded data. AE waves are stress waves generated by mechanical deformation of material and can be recorded by means of sensors attached to the surface of the structure. Analysis of the AE signals provides vital information regarding the nature of the source of emission. Signal processing of the AE waveform data can be carried out in several ways and is predominantly based on time and frequency domains. Short time Fourier transform and wavelet analysis have proved to be superior alternatives to traditional frequency based analysis in extracting information from recorded waveform. Some of the preliminary results of the application of these analysis tools in signal processing of recorded AE data will be presented in this paper.

Mining trading partners’ preferences for efficient multi-issue bargaining in E‑business

Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e‑marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e‑marketplaces.

Creating Active Futures : Edited Proceedings of the 26th ACHPER International Conference

Physical inactivity has become a major cause of the global increase in non-communicable disease (World Health Organisation, 2009}. In 2008, the World Economic Forum called for employers to be proactive in the prevention of non-communicable diseases in the workforce. A significant contributor to the development of a healthy workforce is a reliable pool of employees who are receptive to and aware of healthy lifestyle practices even before becoming employed. Health and Physical Education (HPE) is often stereotyped as 'doing sport'. However, if HPE is to play a part in the development of a healthy workforce, then the HPE learning environment must be about creating meaningful learning for all, which is clearly more than the creation of elite athletes. The ultimate aim of health and physical educators must be about 1) developing lifelong and habitual physical activity; 2) developing generic physical skills; 3) inspiring holistic and positive emotional attitudes and 4) instilling a focus on evidence based knowledge as a framework for inspiring active citizenship. As a response to the worldwide move to the development of healthier people, Australia currently has a strong momentum for an expanded and more unified role for HPE within a potential National curriculum. Other countries have engaged in such a process and much can be learned from their experiences of the process. The 2009 Australian Council for Health, Physical Education and Recreation (ACHPER) conference was a landmark conference that included an International group of experts from all continents and twenty three countries. Creating Active Futures: Edited Proceedings of the 26th ACHPER International Conference is an amalgamation of research and professional perspectives presented at the conference. The papers in this volume emerged from those presented for peer review, rather than through seeking specific articles. This volume is divided into sections based on the five conference themes: 1) Issues in Health and Physical Education (HPE) Pedagogy; 2) Practical Application of Science in HPE; 3) Lifestyle Enhancement; 4) Developing Sporting Excellence; 5) Contemporary Games Teaching. The 'Issues in HPE Pedagogy' section provides a diverse set of perspectives on teaching HPE with papers from a range of topics that include first aid, philosophy, access, cultural characteristics, methods and teaching styles, curriculum, qualifications and emotional development. The second section links science to teaching HPE and provides a range of valuable information on injury prevention, information technology, personality and skill development. Section 3 is a collection of writings and research about Lifestyle Enhancement. Topics include the important role of adventure, the natural world, curriculum, migrant viewpoints, beliefs and globally focused programs in the development of active citizens. The section on sporting excellence contains papers that undertake to explain an aspect of excellence in sport. The last section of this volume highlights some contemporary views on teaching games.

XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.