104,420 research outputs found
A pragmatic approach to multi-class classification
We present a novel hierarchical approach to multi-class classification which
is generic in that it can be applied to different classification models (e.g.,
support vector machines, perceptrons), and makes no explicit assumptions about
the probabilistic structure of the problem as it is usually done in multi-class
classification. By adding a cascade of additional classifiers, each of which
receives the previous classifier's output in addition to regular input data,
the approach harnesses unused information that manifests itself in the form of,
e.g., correlations between predicted classes. Using multilayer perceptrons as a
classification model, we demonstrate the validity of this approach by testing
it on a complex ten-class 3D gesture recognition task.Comment: European Symposium on artificial neural networks (ESANN), Apr 2015,
Bruges, Belgium. 201
Context-aware Captions from Context-agnostic Supervision
We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight
Construction of a Pragmatic Base Line for Journal Classifications and Maps Based on Aggregated Journal-Journal Citation Relations
A number of journal classification systems have been developed in
bibliometrics since the launch of the Citation Indices by the Institute of
Scientific Information (ISI) in the 1960s. These systems are used to normalize
citation counts with respect to field-specific citation patterns. The best
known system is the so-called "Web-of-Science Subject Categories" (WCs). In
other systems papers are classified by algorithmic solutions. Using the Journal
Citation Reports 2014 of the Science Citation Index and the Social Science
Citation Index (n of journals = 11,149), we examine options for developing a
new system based on journal classifications into subject categories using
aggregated journal-journal citation data. Combining routines in VOSviewer and
Pajek, a tree-like classification is developed. At each level one can generate
a map of science for all the journals subsumed under a category. Nine major
fields are distinguished at the top level. Further decomposition of the social
sciences is pursued for the sake of example with a focus on journals in
information science (LIS) and science studies (STS). The new classification
system improves on alternative options by avoiding the problem of randomness in
each run that has made algorithmic solutions hitherto irreproducible.
Limitations of the new system are discussed (e.g. the classification of
multi-disciplinary journals). The system's usefulness for field-normalization
in bibliometrics should be explored in future studies.Comment: accepted for publication in the Journal of Informetrics, 20 July 201
Mechanical MNIST: A benchmark dataset for mechanical metamodels
Metamodels, or models of models, map defined model inputs to defined model outputs. Typically, metamodels are constructed by generating a dataset through sampling a direct model and training a machine learning algorithm to predict a limited number of model outputs from varying model inputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to multi-scale simulation. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community, we introduce a benchmark data set (Mechanical MNIST) for constructing metamodels of heterogeneous material undergoing large deformation. We then show examples of how our benchmark dataset can be used, and establish baseline metamodel performance. Because our dataset is readily available, it will enable the direct quantitative comparison between different metamodeling approaches in a pragmatic manner. We anticipate that it will enable the broader community of researchers to develop improved metamodeling techniques for mechanical data that will surpass the baseline performance that we show here.Accepted manuscrip
Contract Aware Components, 10 years after
The notion of contract aware components has been published roughly ten years
ago and is now becoming mainstream in several fields where the usage of
software components is seen as critical. The goal of this paper is to survey
domains such as Embedded Systems or Service Oriented Architecture where the
notion of contract aware components has been influential. For each of these
domains we briefly describe what has been done with this idea and we discuss
the remaining challenges.Comment: In Proceedings WCSI 2010, arXiv:1010.233
Learning dispositif and emotional attachment:a preliminary international investigation
This research investigated the significance of learning dispositif (LD) and emotional attachment (EA) on perceived learning success (LS) across a diaspora of Western, Russian, Asian, Middle Eastern and Chinese student cohorts. Foucault’s LD captures the disparate socio-cultural contexts, institutional milieus and more or less didactic teaching styles that moderate learning. EA is a multi-dimensional notion involving affective bonds that emerged in child psychology and spread to marketing and other fields. The sequential explanatory research reviewed the learning and EA literatures and generated an LD–EA framework to structure the quantitative phase of its mixed investigations. In 2017 and 2018, the research collected 150 responses and used a range of statistical techniques for quantitative analysis. It found that LS varied significantly across cohorts, intimating that dispositifs influence learning. Nonparametric analysis suggested that EA also influenced learning, but regressions were inconclusive. Exploratory techniques hint at a dynamic mix of emotional or cognitive motivations during the student learning journey, involving structural breaks in student/instructor relationships. Cluster analysis identified distinct student groupings, linked to years of learning. Separately, qualitative analysis of open-ended survey questions and expert interviews intimates that frequent teacher interactions can increase EA. The synthesis of quantitative with qualitative results and pedagogical reflection suggests that LD and EA both influence learning in a complex, dynamic system. The key constituents for EA are Affection, Connection, Social Presence (SP), Teaching Presence (TP) and Flow but student emotional engagement is conditioned by the socio-cultural milieu (LD) and associated factors like relationships and trust. Unlike in the Community of Learning framework, in the EA framework Cognitive Presence (CP) is an outcome of the interaction between these EA constituents, associated factors and the socio-cultural milieu. Finally, whilst awareness of culture and emotions is a useful pedagogical consideration, learning mainstays remain inclusive educational systems that identify student needs and support well-designed programmes. Within these, scaffolded modules should include a variety of engaging learning activities with non-threatening formative and trustworthy summative feedback. We acknowledge some statistical study limitations, but its tentative findings make a useful preliminary contribution
Adapting to climate change : Assessing the vulnerability of ecosystem services in Europe in the context of rural development
This document is the Accepted Manuscript version. The final publication is available at Springer via https://doi.org/10.1007/s11027-013-9507-6.Over the past decade, efforts to move towards a low carbon economy have been increasingly coupled with the acknowledgement that we also need to develop climate resilient economies, capable of adapting and responding to changes in climate. To shift society in these directions we need to quantify impacts in relation to these objectives and develop cost-effective interventions. Techniques for quantifying greenhouse gas emissions are relatively well established and enable identification of hotspots where there is emissions reduction potential. However, there are no established techniques to assess and quantify adaptation vulnerability issues and identify hotspots for intervention. This paper presents work undertaken at a European level with the objective of identifying potential hotspots where ecosystem services may be vulnerable to climate change and thus where intervention may be required under the European Rural Development Programme. A pragmatic and relatively simple approach is presented, based on data that is readily available across Europe. The vulnerability assessments cover: Water (quality: dilution and filtration, regulation: flooding and provision); soils (erosion and organic matter); and biodiversity (forest fires, migration and pollination). The framework and assessments presented are considered fit for purpose (at a basic level) and they are potentially valuable tools for targeting limited resources to achieve desirable outcomes. They also contribute towards providing a better understanding of the climate change challenges we face and support the formulation of solutions to optimally address those challenges. There is scope to further improvement and a number of options are discussed and explored within this paperPeer reviewe
The Ultimate Solution Approach to Intractable Problems
There is now strong belief that P ? NP. This means that some very common problems cannot be solved efficiently under current and so called Von Neumann type computer architectures including parallel configurations. And, this will remain the case even in relatively low dimensions. What one may hope to achieve is the best possible solution given the available facilities within the allowed time. This makes the current definition of the optimum redundant for practical purposes. Therefore, a new definition of the optimum is required as well as appropriate approaches to find it. This paper will put forward a definition for the practical or sensible optimum, the s-optimum, consider its consequences and suggest what can be the ultimate approach to finding it. Although this approach is generic and can be applied in any context, optimisation and search are the specific contexts we will be concerned with here
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