9,903 research outputs found
Dark Matter investigation by DAMA at Gran Sasso
Experimental observations and theoretical arguments at Galaxy and larger
scales have suggested that a large fraction of the Universe is composed by Dark
Matter particles. This has motivated the DAMA experimental efforts to
investigate the presence of such particles in the galactic halo by exploiting a
model independent signature and very highly radiopure set-ups deep underground.
Few introductory arguments are summarized before presenting a review of the
present model independent positive results obtained by the DAMA/NaI and
DAMA/LIBRA set-ups at the Gran Sasso National Laboratory of the INFN.
Implications and model dependent comparisons with other different kinds of
results will be shortly addressed. Some arguments put forward in literature
will be confuted.Comment: review article, 71 pages, 25 figures, 8 tables; v2: minor
modifications. In publication on the International Journal of Modern Physics
Drawing, Handwriting Processing Analysis: New Advances and Challenges
International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline
Defending against Sybil Devices in Crowdsourced Mapping Services
Real-time crowdsourced maps such as Waze provide timely updates on traffic,
congestion, accidents and points of interest. In this paper, we demonstrate how
lack of strong location authentication allows creation of software-based {\em
Sybil devices} that expose crowdsourced map systems to a variety of security
and privacy attacks. Our experiments show that a single Sybil device with
limited resources can cause havoc on Waze, reporting false congestion and
accidents and automatically rerouting user traffic. More importantly, we
describe techniques to generate Sybil devices at scale, creating armies of
virtual vehicles capable of remotely tracking precise movements for large user
populations while avoiding detection. We propose a new approach to defend
against Sybil devices based on {\em co-location edges}, authenticated records
that attest to the one-time physical co-location of a pair of devices. Over
time, co-location edges combine to form large {\em proximity graphs} that
attest to physical interactions between devices, allowing scalable detection of
virtual vehicles. We demonstrate the efficacy of this approach using
large-scale simulations, and discuss how they can be used to dramatically
reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio
Coalitions in Cooperative Wireless Networks
Cooperation between rational users in wireless networks is studied using
coalitional game theory. Using the rate achieved by a user as its utility, it
is shown that the stable coalition structure, i.e., set of coalitions from
which users have no incentives to defect, depends on the manner in which the
rate gains are apportioned among the cooperating users. Specifically, the
stability of the grand coalition (GC), i.e., the coalition of all users, is
studied. Transmitter and receiver cooperation in an interference channel (IC)
are studied as illustrative cooperative models to determine the stable
coalitions for both flexible (transferable) and fixed (non-transferable)
apportioning schemes. It is shown that the stable sum-rate optimal coalition
when only receivers cooperate by jointly decoding (transferable) is the GC. The
stability of the GC depends on the detector when receivers cooperate using
linear multiuser detectors (non-transferable). Transmitter cooperation is
studied assuming that all receivers cooperate perfectly and that users outside
a coalition act as jammers. The stability of the GC is studied for both the
case of perfectly cooperating transmitters (transferrable) and under a partial
decode-and-forward strategy (non-transferable). In both cases, the stability is
shown to depend on the channel gains and the transmitter jamming strengths.Comment: To appear in the IEEE Journal on Selected Areas in Communication,
Special Issue on Game Theory in Communication Systems, 200
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201
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