22,450 research outputs found

    Multi-source heterogeneous intelligence fusion

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    Digital Preservation, Archival Science and Methodological Foundations for Digital Libraries

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    Digital libraries, whether commercial, public or personal, lie at the heart of the information society. Yet, research into their long‐term viability and the meaningful accessibility of their contents remains in its infancy. In general, as we have pointed out elsewhere, ‘after more than twenty years of research in digital curation and preservation the actual theories, methods and technologies that can either foster or ensure digital longevity remain startlingly limited.’ Research led by DigitalPreservationEurope (DPE) and the Digital Preservation Cluster of DELOS has allowed us to refine the key research challenges – theoretical, methodological and technological – that need attention by researchers in digital libraries during the coming five to ten years, if we are to ensure that the materials held in our emerging digital libraries are to remain sustainable, authentic, accessible and understandable over time. Building on this work and taking the theoretical framework of archival science as bedrock, this paper investigates digital preservation and its foundational role if digital libraries are to have long‐term viability at the centre of the global information society.

    Users' trust in information resources in the Web environment: a status report

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    This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

    SIDEKICK: Genomic data driven analysis and decision-making framework

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    <p>Abstract</p> <p>Background</p> <p>Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.</p> <p>Results</p> <p>Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick.</p> <p>Conclusions</p> <p>Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.</p

    Fusing Automatically Extracted Annotations for the Semantic Web

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    This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories

    The Evolutionary Psychology of the Country-of-Origin Effect

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    Adopting the applicational protocols of the epistemological method of evolutionary psychology, this thesis examines the evolved cognitive biases that facilitate the country-of-origin effect, namely a consumer preference for home country or domestic products and brands as opposed to foreign, alternative equivalents. This thesis presents cumulative evidence, through the construction and presentation of a sequential analysis undertaken at both the proximate and ultimate levels of explanation, three distinct investigations exploring the effectiveness of common heuristic strategies adopted by manufacturers that seek to incite nationality biased behaviours of consumers within the field of consumer psychology, how such behaviours can be explained through the causal view offered by group-based behavioural dynamics within social constructivism, whilst ultimately concluding how evolved, adaptive group-based preferences facilitate a nationality bias within the field of evolutionary psychology. In doing so, differing yet complementary explanations of the country-of-origin effect are offered. Chapter Two investigates country-of-origin labelling frequency, design and consumer response across the Fast-Moving Consumer Goods industry within the United Kingdom during a time of an immense shift within the socio-political landscape in response to the United Kingdom’s withdrawal from the European Union, providing evidence of its widespread application and its importance as a marketing strategy by manufacturers whilst exploring consumer responses. Chapter Three investigates the distinct group-based cognitive biases that are activated within different consumer groupings when exposed to such labelling heuristics from the perspective of social constructivism. Conceptualising the effect within group-based behavioural dynamics allows for an exploration of the perceived reciprocal motivations that result in ingroup loyalty and outgroup avoidance behaviours. Chapter Four investigates such group-based motivations through the lens of evolutionary psychology, specifically acknowledging the evolved mechanisms that facilitate biased behaviours towards ingroups and outgroups whilst providing evidence of the adaptive, cognitive functions and conditioned emotions in operation, thereby offering an ultimate explanation of ingroup loyalty behaviours associated with the country-of-origin effect

    Evidence combination for incremental decision-making processes

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    The establishment of a medical diagnosis is an incremental process highly fraught with uncertainty. At each step of this painstaking process, it may be beneficial to be able to quantify the uncertainty linked to the diagnosis and steadily update the uncertainty estimation using available sources of information, for example user feedback, as they become available. Using the example of medical data in general and EEG data in particular, we show what types of evidence can affect discrete variables such as a medical diagnosis and build a simple and computationally efficient evidence combination model based on the Dempster-Shafer theory
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