17,099 research outputs found
Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
With the advent of large scale surveys the manual analysis and classification
of individual radio source morphologies is rendered impossible as existing
approaches do not scale. The analysis of complex morphological features in the
spatial domain is a particularly important task. Here we discuss the challenges
of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project
and introduce a proper transfer mechanism via quantile random forest
regression. By using parallelized rotation and flipping invariant Kohonen-maps,
image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio
continuum and WISE infrared all sky surveys are first projected down to a
two-dimensional embedding in an unsupervised way. This embedding can be seen as
a discretised space of shapes with the coordinates reflecting morphological
features as expressed by the automatically derived prototypes. We find that
these prototypes have reconstructed physically meaningful processes across two
channel images at radio and infrared wavelengths in an unsupervised manner. In
the second step, images are compared with those prototypes to create a
heat-map, which is the morphological fingerprint of each object and the basis
for transferring the user generated labels. These heat-maps have reduced the
feature space by a factor of 248 and are able to be used as the basis for
subsequent ML methods. Using an ensemble of decision trees we achieve upwards
of 85.7% and 80.7% accuracy when predicting the number of components and peaks
in an image, respectively, using these heat-maps. We also question the
currently used discrete classification schema and introduce a continuous scale
that better reflects the uncertainty in transition between two classes, caused
by sensitivity and resolution limits
Character and theory of mind: an integrative approach
Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system for action prediction, and serves to inform hypotheses about agents’ beliefs and desires, which are in turn used to predict and interpret behavior
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
Third Conference on Artificial Intelligence for Space Applications, part 2
Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed
On Practical machine Learning and Data Analysis
This thesis discusses and addresses some of the difficulties
associated with practical machine learning and data
analysis. Introducing data driven methods in e.g industrial and
business applications can lead to large gains in productivity and
efficiency, but the cost and complexity are often
overwhelming. Creating machine learning applications in practise often
involves a large amount of manual labour, which often needs to be
performed by an experienced analyst without significant experience
with the application area. We will here discuss some of the hurdles
faced in a typical analysis project and suggest measures and methods
to simplify the process.
One of the most important issues when applying machine learning
methods to complex data, such as e.g. industrial applications, is that
the processes generating the data are modelled in an appropriate
way. Relevant aspects have to be formalised and represented in a way
that allow us to perform our calculations in an efficient manner. We
present a statistical modelling framework, Hierarchical Graph
Mixtures, based on a combination of graphical models and mixture
models. It allows us to create consistent, expressive statistical
models that simplify the modelling of complex systems. Using a
Bayesian approach, we allow for encoding of prior knowledge and make
the models applicable in situations when relatively little data are
available.
Detecting structures in data, such as clusters and dependency
structure, is very important both for understanding an application
area and for specifying the structure of e.g. a hierarchical graph
mixture. We will discuss how this structure can be extracted for
sequential data. By using the inherent dependency structure of
sequential data we construct an information theoretical measure of
correlation that does not suffer from the problems most common
correlation measures have with this type of data.
In many diagnosis situations it is desirable to perform a
classification in an iterative and interactive manner. The matter is
often complicated by very limited amounts of knowledge and examples
when a new system to be diagnosed is initially brought into use. We
describe how to create an incremental classification system based on a
statistical model that is trained from empirical data, and show how
the limited available background information can still be used
initially for a functioning diagnosis system.
To minimise the effort with which results are achieved within data
analysis projects, we need to address not only the models used, but
also the methodology and applications that can help simplify the
process. We present a methodology for data preparation and a software
library intended for rapid analysis, prototyping, and deployment.
Finally, we will study a few example applications, presenting tasks
within classification, prediction and anomaly detection. The examples
include demand prediction for supply chain management, approximating
complex simulators for increased speed in parameter optimisation, and
fraud detection and classification within a media-on-demand system
May I help you? How stereotypes and innuendoes influence service encounters
You only get one chance to make a good first impression. The dissertation focuses on marketing agents; among the most visible is the service provider. Previous research establishes the important role of cognitive social schemata in determining the way consumers react to different types of marketing agents, including service providers. In the literature review, a classification schema is developed for service provider stereotypes derived from theory using social stereotypes. The development of the Service Provider Perception Framework (SPPF) creates a classification for the individual service provider along two main dimensions: competence and affect.
In services design (particularly situations involving a first impression or service encounter that has yet to develop into a committed relationship) consumers commonly possess and maintain stereotypes for service providers based on accumulated knowledge about people in a provider category. Prior to entering a service encounter, consumers use available information to form judgments based on descriptions of the selected service provider. Due to unfamiliarity with the specific provider, consumers are apt to focus on tangible cues (stereotypical attributes) of the service provider to evaluate the level of perceived quality and satisfaction associated with the service.
This research furthers our understanding of how consumers evaluate service providers and, subsequently, the service experience. Following the development of the SPPF, this research uses two empirical studies to examine stereotypes, the use of innuendos, and various service outcomes on service encounters.
The innuendo study confirms placement of four service provider types in the SPPF and examines how consumers\u27 perceptions of service providers change when subjects are provided incomplete information regarding only one dimension of the SPPF. The main study examines how consumers perceive service providers and the subsequent service encounter when the service provider is not what the consumer had expected to come into contact with.
This research integrates cognitive social psychology with services marketing to advance the marketing discipline. Key findings increase knowledge of service provider perceptions as viewed by consumers and recommends methods to create prosperous relationships and improve existing relationships between the provider and the consumer utilizing characteristics associated to the type of service provider
Knowledge-based design support and inductive learning
Designing and learning are closely related activities in that design as an ill-structure problem
involves identifying the problem of the design as well as finding its solutions. A
knowledge-based design support system should support learning by capturing and reusing
design knowledge. This thesis addresses two fundamental problems in computational
support to design activities: the development of an intelligent design support system
architecture and the integration of inductive learning techniques in this architecture.This research is motivated by the belief that (1) the early stage of the design process can
be modelled as an incremental learning process in which the structure of a design problem
or the product data model of an artefact is developed using inductive learning techniques,
and (2) the capability of a knowledge-based design support system can be enhanced by
accumulating and storing reusable design product and process information.In order to incorporate inductive learning techniques into a knowledge-based design
model and an integrated knowledge-based design support system architecture, the
computational techniques for developing a knowledge-based design support system
architecture and the role of inductive learning in Al-based design are investigated. This
investigation gives a background to the development of an incremental learning model for
design suitable for a class of design tasks whose structures are not well known initially.This incremental learning model for design is used as a basis to develop a knowledge-based
design support system architecture that can be used as a kernel for knowledge-based
design applications. This architecture integrates a number of computational techniques to
support the representation and reasoning of design knowledge. In particular, it integrates a
blackboard control system with an assumption-based truth maintenance system in an
object-oriented environment to support the exploration of multiple design solutions by
supporting the exploration and management of design contexts.As an integral part of this knowledge-based design support architecture, a design
concept learning system utilising a number of unsupervised inductive learning techniques is
developed. This design concept learning system combines concept formation techniques
with design heuristics as background knowledge to build a design concept tree from raw
data or past design examples. The design concept tree is used as a conceptual structure for
the exploration of new designs.The effectiveness of this knowledge-based design support architecture and the design
concept learning system is demonstrated through a realistic design domain, the design of
small-molecule drugs one of the key tasks of which is to identify a pharmacophore
description (the structure of a design problem) from known molecule examples.In this thesis, knowledge-based design and inductive learning techniques are first
reviewed. Based on this review, an incremental learning model and an integrated
architecture for intelligent design support are presented. The implementation of this
architecture and a design concept learning system is then described. The application of the
architecture and the design concept learning system in the domain of small-molecule drug
design is then discussed. The evaluation of the architecture and the design concept learning
system within and beyond this particular domain, and future research directions are finally
discussed
Modelling iteration in engineering design
This paper examines design iteration and its modelling in the simulation of New Product Development (NPD) processes. A framework comprising six perspectives of iteration is proposed and it is argued that the importance of each perspective depends upon domain-specific factors. Key challenges of modelling iteration in process simulation frameworks such as the Design Structure Matrix are discussed, and we argue that no single model or framework can fully capture the iterative dynamics of an NPD process. To conclude, we propose that consideration of iteration and its representation could help identify the most appropriate modelling framework for a given process and modelling objective, thereby improving the fidelity of design process simulation models and increasing their utility
- …