63,120 research outputs found
Does Conservation Status Matter if You’re Ugly? An Experimental Survey of Species Appeal and Public Support
Wildlife conservation is of the utmost importance to the preservation of a healthy planet, with the extinction of wild animals increasing at previously unseen rates. However, conservation is also becoming increasingly difficult without strong public support, and this often varies in extent and success when it comes to different species and taxa. There is considerable research investigating how the physical characteristics of species affect public support of their conservation. Results suggest species seen as more charismatic, or even more likeable, are more likely to gain support for their conservation, regardless of conservation status. This study aimed to identify whether conservation status, and concern for it, is as important of a consideration for endangered species that are not seen as simply likeable or appealing, or whether this tends to be more ignored for such species. We found that for the treatments/species we chose in our experiment, and in the context we distributed the survey in, their conservation status was a more significant factor than their perceived appeal when it came to public support for their conservation. These results have implications for wildlife conservation efforts, as it shows that appeal is not always the most important factor when attempting to garner support, and that influencing the perception of concern for certain species may be a more effective avenue than relying on appeal for successful wildlife conservation
Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web
Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions
Characterizing and Subsetting Big Data Workloads
Big data benchmark suites must include a diversity of data and workloads to
be useful in fairly evaluating big data systems and architectures. However,
using truly comprehensive benchmarks poses great challenges for the
architecture community. First, we need to thoroughly understand the behaviors
of a variety of workloads. Second, our usual simulation-based research methods
become prohibitively expensive for big data. As big data is an emerging field,
more and more software stacks are being proposed to facilitate the development
of big data applications, which aggravates hese challenges. In this paper, we
first use Principle Component Analysis (PCA) to identify the most important
characteristics from 45 metrics to characterize big data workloads from
BigDataBench, a comprehensive big data benchmark suite. Second, we apply a
clustering technique to the principle components obtained from the PCA to
investigate the similarity among big data workloads, and we verify the
importance of including different software stacks for big data benchmarking.
Third, we select seven representative big data workloads by removing redundant
ones and release the BigDataBench simulation version, which is publicly
available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.Comment: 11 pages, 6 figures, 2014 IEEE International Symposium on Workload
Characterizatio
A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space
We are developing a system for human-robot communication that enables people
to communicate with robots in a natural way and is focused on solving problems
in a shared space. Our strategy for developing this system is fundamentally
data-driven: we use data from multiple input sources and train key components
with various machine learning techniques. We developed a web application that
is collecting data on how two humans communicate to accomplish a task, as well
as a mobile laboratory that is instrumented to collect data on how two humans
communicate to accomplish a task in a physically shared space. The data from
these systems will be used to train and fine-tune the second stage of our
system, in which the robot will be simulated through software. A physical robot
will be used in the final stage of our project. We describe these instruments,
a test-suite and performance metrics designed to evaluate and automate the data
gathering process as well as evaluate an initial data set.Comment: 2017 AAAI Fall Symposium on Natural Communication for Human-Robot
Collaboratio
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