1,055 research outputs found
Location-related privacy in geo-social networks
Geo-social networks (GeoSNs) provide context-aware services that help associate location with users and content. The proliferation of GeoSNs indicates that they're rapidly attracting users. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins. " However, this ability to reveal users' locations causes new privacy threats, which in turn call for new privacy-protection methods. The authors study four privacy aspects central to these social networks - location, absence, co-location, and identity privacy - and describe possible means of protecting privacy in these circumstances
Bipolar querying of valid-time intervals subject to uncertainty
Databases model parts of reality by containing data representing properties of real-world objects or concepts. Often, some of these properties are time-related. Thus, databases often contain data representing time-related information. However, as they may be produced by humans, such data or information may contain imperfections like uncertainties. An important purpose of databases is to allow their data to be queried, to allow access to the information these data represent. Users may do this using queries, in which they describe their preferences concerning the data they are (not) interested in. Because users may have both positive and negative such preferences, they may want to query databases in a bipolar way. Such preferences may also have a temporal nature, but, traditionally, temporal query conditions are handled specifically. In this paper, a novel technique is presented to query a valid-time relation containing uncertain valid-time data in a bipolar way, which allows the query to have a single bipolar temporal query condition
Evolving Objects in Temporal Information Systems
This paper presents a semantic foundation of temporal conceptual models used to design temporal information systems. We consider a modelling language able to express both timestamping and evolution constraints. We conduct a deeper investigation of evolution constraints, eventually devising a model-theoretic semantics for a full-fledged model with both timestamping and evolution constraints. The proposed formalization is meant both to clarify the meaning of the various temporal constructors that appeared in the literature and to give a rigorous definition, in the context of temporal information systems, to notions like satisfiability, subsumption and logical implication. Furthermore, we show how to express temporal constraints using a subset of first-order temporal logic, i.e. DLRUS, the description logic DLR extended with the temporal operators Since and Until. We show how DLRUS is able to capture the various modelling constraints in a succinct way and to perform automated reasoning on temporal conceptual models
The weak strangeness production reaction in a one-boson-exchange model
The weak production of Lambdas in nucleon-nucleon scattering is studied in a
meson-exchange framework. The weak transition operator for the reaction is identical to a previously developed weak
strangeness-changing transition potential that describes the
nonmesonic decay of hypernuclei. The initial and final state
interaction has been included by using realistic baryon-baryon forces that
describe the available elastic scattering data. The total and differential
cross sections as well as the parity-violating asymmetry are studied for the
reaction . These observables are found to be sensitive to the
choice of the strong interaction potential and the structure of the weak
transition potential.Comment: 25 pages, 8 postscript figures. Submitted to Phys. Rev.
Supervised Domain Adaptation using Graph Embedding
Getting deep convolutional neural networks to perform well requires a large
amount of training data. When the available labelled data is small, it is often
beneficial to use transfer learning to leverage a related larger dataset
(source) in order to improve the performance on the small dataset (target).
Among the transfer learning approaches, domain adaptation methods assume that
distributions between the two domains are shifted and attempt to realign them.
In this paper, we consider the domain adaptation problem from the perspective
of dimensionality reduction and propose a generic framework based on graph
embedding. Instead of solving the generalised eigenvalue problem, we formulate
the graph-preserving criterion as a loss in the neural network and learn a
domain-invariant feature transformation in an end-to-end fashion. We show that
the proposed approach leads to a powerful Domain Adaptation framework; a simple
LDA-inspired instantiation of the framework leads to state-of-the-art
performance on two of the most widely used Domain Adaptation benchmarks,
Office31 and MNIST to USPS datasets.Comment: 7 pages, 3 figures, 3 table
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