146 research outputs found
Supporting public decision making in policy deliberations: An ontological approach
This is the post-print version of the Paper. The official published version can be accessed from the link below - Copyright @ 2011 SpringerSupporting public decision making in policy deliberations has been a key objective of eParticipation which is an emerging area of eGovernment. EParticipation aims to enhance citizen involvement in public governance activities through the use of information and communication technologies. An innovative approach towards this objective is exploiting the potentials of semantic web technologies centred on conceptual knowledge models in the form of ontologies. Ontologies are generally defined as explicit human and computer shared views on the world of particular domains. In this paper, the potentials and benefits of using ontologies for policy deliberation processes are discussed. Previous work is then extended and synthesised to develop a deliberation ontology. The ontology aims to define the necessary semantics in order to structure and interrelate the stages and various activities of deliberation processes with legal information, participant stakeholders and their associated arguments. The practical implications of the proposed framework are illustrated.This work is funded by the European Commission under the 2006/1 eParticipation call
Variational Deep Semantic Hashing for Text Documents
As the amount of textual data has been rapidly increasing over the past
decade, efficient similarity search methods have become a crucial component of
large-scale information retrieval systems. A popular strategy is to represent
original data samples by compact binary codes through hashing. A spectrum of
machine learning methods have been utilized, but they often lack expressiveness
and flexibility in modeling to learn effective representations. The recent
advances of deep learning in a wide range of applications has demonstrated its
capability to learn robust and powerful feature representations for complex
data. Especially, deep generative models naturally combine the expressiveness
of probabilistic generative models with the high capacity of deep neural
networks, which is very suitable for text modeling. However, little work has
leveraged the recent progress in deep learning for text hashing.
In this paper, we propose a series of novel deep document generative models
for text hashing. The first proposed model is unsupervised while the second one
is supervised by utilizing document labels/tags for hashing. The third model
further considers document-specific factors that affect the generation of
words. The probabilistic generative formulation of the proposed models provides
a principled framework for model extension, uncertainty estimation, simulation,
and interpretability. Based on variational inference and reparameterization,
the proposed models can be interpreted as encoder-decoder deep neural networks
and thus they are capable of learning complex nonlinear distributed
representations of the original documents. We conduct a comprehensive set of
experiments on four public testbeds. The experimental results have demonstrated
the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure
Zero-Shot Hashing via Transferring Supervised Knowledge
Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.Comment: 11 page
Combining multiple classifications of chemical structures using consensus clustering
Consensus clustering involves combining multiple clusterings of the same set of objects to achieve a single clustering that will, hopefully, provide a better picture of the groupings that are present in a dataset. This Letter reports the use of consensus clustering methods on sets of chemical compounds represented by 2D fingerprints. Experiments with DUD, IDAlert, MDDR and MUV data suggests that consensus methods are unlikely to result in significant improvements in clustering effectiveness as compared to the use of a single clustering method. (C) 2012 Elsevier Ltd. All rights reserved
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Recent progress in quantum algorithms and hardware indicates the potential
importance of quantum computing in the near future. However, finding suitable
application areas remains an active area of research. Quantum machine learning
is touted as a potential approach to demonstrate quantum advantage within both
the gate-model and the adiabatic schemes. For instance, the Quantum-assisted
Variational Autoencoder has been proposed as a quantum enhancement to the
discrete VAE. We extend on previous work and study the real-world applicability
of a QVAE by presenting a proof-of-concept for similarity search in large-scale
high-dimensional datasets. While exact and fast similarity search algorithms
are available for low dimensional datasets, scaling to high-dimensional data is
non-trivial. We show how to construct a space-efficient search index based on
the latent space representation of a QVAE. Our experiments show a correlation
between the Hamming distance in the embedded space and the Euclidean distance
in the original space on the Moderate Resolution Imaging Spectroradiometer
(MODIS) dataset. Further, we find real-world speedups compared to linear search
and demonstrate memory-efficient scaling to half a billion data points
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
A common task for recommender systems is to build a pro le of the interests
of a user from items in their browsing history and later to recommend items to
the user from the same catalog. The users' behavior consists of two parts: the
sequence of items that they viewed without intervention (the organic part) and
the sequences of items recommended to them and their outcome (the bandit part).
In this paper, we propose Bayesian Latent Organic Bandit model (BLOB), a
probabilistic approach to combine the 'or-ganic' and 'bandit' signals in order
to improve the estimation of recommendation quality. The bandit signal is
valuable as it gives direct feedback of recommendation performance, but the
signal quality is very uneven, as it is highly concentrated on the
recommendations deemed optimal by the past version of the recom-mender system.
In contrast, the organic signal is typically strong and covers most items, but
is not always relevant to the recommendation task. In order to leverage the
organic signal to e ciently learn the bandit signal in a Bayesian model we
identify three fundamental types of distances, namely action-history,
action-action and history-history distances. We implement a scalable
approximation of the full model using variational auto-encoders and the local
re-paramerization trick. We show using extensive simulation studies that our
method out-performs or matches the value of both state-of-the-art organic-based
recommendation algorithms, and of bandit-based methods (both value and
policy-based) both in organic and bandit-rich environments.Comment: 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,
Aug 2020, San Diego, United State
(So) Big Data and the transformation of the city
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality
Trioctahedral entities in palygorskite: Near-infrared evidence for sepiolite-palygorskite polysomatism
The mixed dioctahedral-trioctahedral character of Mg-rich palygorskite has been previously described by the formula
yMg5 Si8 O20(OH)2(OH2)4(1–y)[xMg2Fe2(1–x)Mg2 Al2] Si8 O20(OH)2(OH2)4, where y is the trioctahedral fraction of this two-chain
ribbon mineral with an experimentally determined upper limit of y 0.5 and x is the FeIII content in the M2 sites of the dioctahedral
component. Ideal trioctahedral (y ¼ 1) palygorskite is elusive, although sepiolite Mg8Si12O30(OH)4(OH2)4 with a similar composition,
three-chain ribbon structure and distinct XRD pattern is common. A set of 22 samples identified by XRD as palygorskite and
with variable composition (0 , x , 0.7, 0 , y , 0.5) were studied to extrapolate the structure of an ideal trioctahedral (y ¼ 1)
palygorskite and to compare this structure to sepiolite. Near-infrared spectroscopy was used to study the influence of octahedral
composition on the structure of the TOT ribbons, H2O in the tunnels and surface silanols of palygorskite, as well as their response to
loss of zeolitic H2O. All spectroscopic evidence suggests that palygorskite consists of discrete dioctahedral and trioctahedral entities.
The dioctahedral entities have variable structure determined solely by x=FeIII/(Al+FeIII) and their content is proportional to (1–y). In
contrast, the trioctahedral entities have fixed octahedral composition or ribbon structure and are spectroscopically identical to
sepiolite. The value of d200 in palygorskite follows the regression d200 (A°)= 6.362 + 0.129 x(1–y) + 0.305y, R2 = 0.96, σ = 0.013A°.
When extrapolated to y = 1,d200 is identical to sepiolite. Based on this analysis, we propose that palygorskite samples with non-zero
trioctahedral character should be considered as members of a polysomatic series of sepiolite and (dioctahedral) palygorskite described
by the new formula y'Mg8 Si12 O30(OH)4(OH2)4.(1–y')[x'Mg2Fe2(1–x')Mg2Al2]Si8O20(OH)2(OH2)4, with 0 < x'= x < 0.7 and 0 < y'
= y/(2–y) < 0.33
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