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 pn→pΛpn \to p\Lambda 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 $NN \to N \Lambda$ reaction is identical to a previously developed weak strangeness-changing transition potential $\Lambda N \to NN$ that describes the nonmesonic decay of hypernuclei. The initial $NN$ and final $YN$ 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 $pn \to p\Lambda$. 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