University of Trento

About: University of Trento is a education organization based out in Trento, Italy. It is known for research contribution in the topics: Population & Context (language use). The organization has 10527 authors who have published 30978 publications receiving 896614 citations. The organization is also known as: Universitá degli Studi di Trento & Universita degli Studi di Trento.

CRePE: context-related policy enforcement for android

Abstract: Most of the research work for enforcing security policies on smartphones considered coarse-grained policies, e.g. either to allow an application to run or not. In this paper we present CRePE, the first system that is able to enforce fine-grained policies, e.g. that vary while an application is running, that also depend on the context of the smartphone. A context can be defined by the status of some variables (e.g. location, time, temperature, noise, and light), the presence of other devices, a particular interaction between the user and the smartphone, or a combination of these. CRePE allows context-related policies to be defined either by the user or by trusted third parties. Depending on the authorization, third parties can set a policy on a smartphone at any moment or just when the phone is within a particular context, e.g. within a building, or a plane.

Location-aware computing: a neural network model for determining location in wireless LANs

Abstract: The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented.

Automatic Analysis of GPR Images: A Pattern-Recognition Approach

Abstract: In this paper, we propose a novel pattern-recognition system to identify and classify buried objects from ground-penetrating radar (GPR) imagery. The entire process is subdivided into four steps. After a preprocessing step, the GPR image is thresholded to put under light the regions containing potential objects. The third step of the system consists of automatically detecting the objects in the obtained binary image by means of a search of linear/hyperbolic patterns formulated within a genetic optimization framework. In the genetic optimizer, each chromosome models the apex position and the curvature associated with the candidate pattern, while the fitness function expresses the Hamming distance between that pattern and the binary image content. Finally, in the fourth step, the problem of the recognition of the material type of the identified objects is approached as a classification issue, which is solved by means of an opportune feature-extraction strategy and a support vector machine classifier. To illustrate the performances of the proposed system, we conducted a thorough experimental study based on GPR images generated by a GPR simulator based on the finite-difference time-domain method so as to construct different acquisition scenarios by varying the number of buried objects, their position, their size, their shape, and their material type. In general, the obtained experimental results show that the proposed system exhibits promising performances both in terms of object detection and material recognition.

Fortune teller: predicting your career path

Abstract: People go to fortune tellers in hopes of learning things about their future. A future career path is one of the topics most frequently discussed. But rather than rely on "black arts" to make predictions, in this work we scientifically and systematically study the feasibility of career path prediction from social network data. In particular, we seamlessly fuse information from multiple social networks to comprehensively describe a user and characterize progressive properties of his or her career path. This is accomplished via a multi-source learning framework with fused lasso penalty, which jointly regularizes the source and career-stage relatedness. Extensive experiments on real-world data confirm the accuracy of our model.

Observation of gauge invariance in a 71-site Bose–Hubbard quantum simulator

Abstract: The modern description of elementary particles, as formulated in the standard model of particle physics, is built on gauge theories1. Gauge theories implement fundamental laws of physics by local symmetry constraints. For example, in quantum electrodynamics Gauss’s law introduces an intrinsic local relation between charged matter and electromagnetic fields, which protects many salient physical properties, including massless photons and a long-ranged Coulomb law. Solving gauge theories using classical computers is an extremely arduous task2, which has stimulated an effort to simulate gauge-theory dynamics in microscopically engineered quantum devices3–6. Previous achievements implemented density-dependent Peierls phases without defining a local symmetry7,8, realized mappings onto effective models to integrate out either matter or electric fields9–12, or were limited to very small systems13–16. However, the essential gauge symmetry has not been observed experimentally. Here we report the quantum simulation of an extended U(1) lattice gauge theory, and experimentally quantify the gauge invariance in a many-body system comprising matter and gauge fields. These fields are realized in defect-free arrays of bosonic atoms in an optical superlattice of 71 sites. We demonstrate full tunability of the model parameters and benchmark the matter–gauge interactions by sweeping across a quantum phase transition. Using high-fidelity manipulation techniques, we measure the degree to which Gauss’s law is violated by extracting probabilities of locally gauge-invariant states from correlated atom occupations. Our work provides a way to explore gauge symmetry in the interplay of fundamental particles using controllable large-scale quantum simulators. Quantum simulation in a 71-site optical lattice certifies gauge invariance, showing how this essential property of lattice gauge theories can be maintained across a quantum phase transition.