31,786 research outputs found
Graphs in machine learning: an introduction
Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This includes both unsupervised and supervised methods. Unsupervised learning
algorithms usually aim at visualising graphs in latent spaces and/or clustering
the nodes. Both focus on extracting knowledge from graph topologies. While most
existing techniques are only applicable to static graphs, where edges do not
evolve through time, recent developments have shown that they could be extended
to deal with evolving networks. In a supervised context, one generally aims at
inferring labels or numerical values attached to nodes using both the graph
and, when they are available, node characteristics. Balancing the two sources
of information can be challenging, especially as they can disagree locally or
globally. In both contexts, supervised and un-supervised, data can be
relational (augmented with one or several global graphs) as described above, or
graph valued. In this latter case, each object of interest is given as a full
graph (possibly completed by other characteristics). In this context, natural
tasks include graph clustering (as in producing clusters of graphs rather than
clusters of nodes in a single graph), graph classification, etc. 1 Real
networks One of the first practical studies on graphs can be dated back to the
original work of Moreno [51] in the 30s. Since then, there has been a growing
interest in graph analysis associated with strong developments in the modelling
and the processing of these data. Graphs are now used in many scientific
fields. In Biology [54, 2, 7], for instance, metabolic networks can describe
pathways of biochemical reactions [41], while in social sciences networks are
used to represent relation ties between actors [66, 56, 36, 34]. Other examples
include powergrids [71] and the web [75]. Recently, networks have also been
considered in other areas such as geography [22] and history [59, 39]. In
machine learning, networks are seen as powerful tools to model problems in
order to extract information from data and for prediction purposes. This is the
object of this paper. For more complete surveys, we refer to [28, 62, 49, 45].
In this section, we introduce notations and highlight properties shared by most
real networks. In Section 2, we then consider methods aiming at extracting
information from a unique network. We will particularly focus on clustering
methods where the goal is to find clusters of vertices. Finally, in Section 3,
techniques that take a series of networks into account, where each network i
Exploring the academic invisible web
Purpose: To provide a critical review of Bergman's 2001 study on the Deep
Web. In addition, we bring a new concept into the discussion, the Academic
Invisible Web (AIW). We define the Academic Invisible Web as consisting of all
databases and collections relevant to academia but not searchable by the
general-purpose internet search engines. Indexing this part of the Invisible
Web is central to scientific search engines. We provide an overview of
approaches followed thus far. Design/methodology/approach: Discussion of
measures and calculations, estimation based on informetric laws. Literature
review on approaches for uncovering information from the Invisible Web.
Findings: Bergman's size estimate of the Invisible Web is highly questionable.
We demonstrate some major errors in the conceptual design of the Bergman paper.
A new (raw) size estimate is given. Research limitations/implications: The
precision of our estimate is limited due to a small sample size and lack of
reliable data. Practical implications: We can show that no single library alone
will be able to index the Academic Invisible Web. We suggest collaboration to
accomplish this task. Originality/value: Provides library managers and those
interested in developing academic search engines with data on the size and
attributes of the Academic Invisible Web.Comment: 13 pages, 3 figure
Link Prediction in Complex Networks: A Survey
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure
Artificial table testing dynamically adaptive systems
Dynamically Adaptive Systems (DAS) are systems that modify their behavior and
structure in response to changes in their surrounding environment. Critical
mission systems increasingly incorporate adaptation and response to the
environment; examples include disaster relief and space exploration systems.
These systems can be decomposed in two parts: the adaptation policy that
specifies how the system must react according to the environmental changes and
the set of possible variants to reconfigure the system. A major challenge for
testing these systems is the combinatorial explosions of variants and
envi-ronment conditions to which the system must react. In this paper we focus
on testing the adaption policy and propose a strategy for the selection of
envi-ronmental variations that can reveal faults in the policy. Artificial
Shaking Table Testing (ASTT) is a strategy inspired by shaking table testing
(STT), a technique widely used in civil engineering to evaluate building's
structural re-sistance to seismic events. ASTT makes use of artificial
earthquakes that simu-late violent changes in the environmental conditions and
stresses the system adaptation capability. We model the generation of
artificial earthquakes as a search problem in which the goal is to optimize
different types of envi-ronmental variations
Moving outside the box: Researching e-learning in disruptive times
Indexación: Scopus.The rise of technology’s influence in a cross-section of fields within formal education, not to mention in the broader social world, has given rise to new forms in the way we view learning, i.e. what constitutes valid knowledge and how we arrive at that knowledge. Some scholars have claimed that technology is but a tool to support the meaning-making that lies at the root of knowledge production while others argue that technology is increasingly and inextricably intertwined not just with knowledge construction but with changes to knowledge makers themselves. Regardless which side one stands in this growing debate, it is difficult to deny that the processes we use to research learning supported by technology in order to understand these growing intricacies, have profound implications. In this paper, my aim is to argue and defend a call in the research on ICT for a critical reflective approach to researching technology use. Using examples from qualitative research in e-learning I have conducted on three continents over 15 years, and in diverse educational contexts, I seek to unravel the means and justification for research approaches that can lead to closing the gap between research and practice. These studies combined with those from a cross-disciplinary array of fields support the promotion of a research paradigm that examines the socio-cultural contexts of learning with ICT, at a time that coincides with technology becoming a social networking facilitator. Beyond the examples and justification of the merits and power of qualitative research to uncover the stories that matter in these socially embodied e-learning contexts, I discuss the methodologically and ethically charged decisions using emerging affordances of technology for analyzing and representing results, including visual ethnography. The implications both for the consumers and producers of research of moving outside the box of established research practices are yet unfathomable but excitinghttp://www.ejel.org/volume15/issue1/p5
Mitigating Gender Bias in Machine Learning Data Sets
Artificial Intelligence has the capacity to amplify and perpetuate societal
biases and presents profound ethical implications for society. Gender bias has
been identified in the context of employment advertising and recruitment tools,
due to their reliance on underlying language processing and recommendation
algorithms. Attempts to address such issues have involved testing learned
associations, integrating concepts of fairness to machine learning and
performing more rigorous analysis of training data. Mitigating bias when
algorithms are trained on textual data is particularly challenging given the
complex way gender ideology is embedded in language. This paper proposes a
framework for the identification of gender bias in training data for machine
learning.The work draws upon gender theory and sociolinguistics to
systematically indicate levels of bias in textual training data and associated
neural word embedding models, thus highlighting pathways for both removing bias
from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as
part of the ECIR Conference) - http://bias.disim.univaq.i
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