22,479 research outputs found
Data centric trust evaluation and prediction framework for IOT
© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas
Historical collaborative geocoding
The latest developments in digital have provided large data sets that can
increasingly easily be accessed and used. These data sets often contain
indirect localisation information, such as historical addresses. Historical
geocoding is the process of transforming the indirect localisation information
to direct localisation that can be placed on a map, which enables spatial
analysis and cross-referencing. Many efficient geocoders exist for current
addresses, but they do not deal with the temporal aspect and are based on a
strict hierarchy (..., city, street, house number) that is hard or impossible
to use with historical data. Indeed historical data are full of uncertainties
(temporal aspect, semantic aspect, spatial precision, confidence in historical
source, ...) that can not be resolved, as there is no way to go back in time to
check. We propose an open source, open data, extensible solution for geocoding
that is based on the building of gazetteers composed of geohistorical objects
extracted from historical topographical maps. Once the gazetteers are
available, geocoding an historical address is a matter of finding the
geohistorical object in the gazetteers that is the best match to the historical
address. The matching criteriae are customisable and include several dimensions
(fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is
to facilitate historical work, we also propose web-based user interfaces that
help geocode (one address or batch mode) and display over current or historical
topographical maps, so that they can be checked and collaboratively edited. The
system is tested on Paris city for the 19-20th centuries, shows high returns
rate and is fast enough to be used interactively.Comment: WORKING PAPE
Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework
Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has
been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen
science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed
data on weather and habitats reflecting an increase in engagement with a diverse range of observational science.
Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community
groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen
science provides an indispensable means of combining environmental research with environmental education and
wildlife recording.
Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively
assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers.
2. Semi-systematic review of environmental citizen science projects in order to understand the variety of
extant citizen science projects.
3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review.
4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in
order to more fully understand how citizen science can fit into policy needs.
5. Review of technology in citizen science and an exploration of future opportunities
The ImageCLEF 2012 Plant Identification Task
International audienceThe ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of plant identification, more precisely on the 126 tree species identification based on leaf images. Three types of image content are considered: Scan, Scan-like (leaf photographs with a white uniform background), and Photograph (unconstrained leaf with natural background). The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of eleven groups from eight countries and with a total of 30 runs submitted, involving distinct and original methods, this second year pilot task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification
Semantic discovery and reuse of business process patterns
Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse
Galaxy Zoo: Morphological Classification and Citizen Science
We provide a brief overview of the Galaxy Zoo and Zooniverse projects,
including a short discussion of the history of, and motivation for, these
projects as well as reviewing the science these innovative internet-based
citizen science projects have produced so far. We briefly describe the method
of applying en-masse human pattern recognition capabilities to complex data in
data-intensive research. We also provide a discussion of the lessons learned
from developing and running these community--based projects including thoughts
on future applications of this methodology. This review is intended to give the
reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning
and Data Mining for Astronom
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