49,828 research outputs found
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason
in presence of uncertain probabilities represented as Beta-distributed random
variables. We achieve the same performance of state-of-the-art algorithms for
highly specified and engineered domains, while simultaneously we maintain the
flexibility offered by aProbLog in handling complex relational domains. Our
motivation is that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision making under
uncertainty: unfortunately, these probability distributions can be highly
uncertain due to sparse data. To understand and accurately manipulate such
probability distributions we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution of
probabilities representing all the possible values of a probability when the
exact value is unknown.Comment: Accepted for presentation at AAAI 201
Rigorously assessing software reliability and safety
This paper summarises the state of the art in the assessment of software reliability and safety ("dependability"), and describes some promising developments. A sound demonstration of very high dependability is still impossible before operation of the software; but research is finding ways to make rigorous assessment increasingly feasible. While refined mathematical techniques cannot take the place of factual knowledge, they can allow the decision-maker to draw more accurate conclusions from the knowledge that is available
Handling uncertainty in information extraction
This position paper proposes an interactive approach for developing information extractors based on the ontology definition process with knowledge about possible (in)correctness of annotations. We discuss the problem of managing and manipulating probabilistic dependencies
Probabilities in Statistical Mechanics: What are they?
This paper addresses the question of how we should regard the probability distributions introduced into statistical mechanics. It will be argued that it is problematic to take them either as purely ontic, or purely epistemic. I will propose a third alternative: they are almost objective probabilities, or epistemic chances. The definition of such probabilities involves an interweaving of epistemic and physical considerations, and thus they cannot be classified as either purely epistemic or purely ontic. This conception, it will be argued, resolves some of the puzzles associated with statistical mechanical probabilities: it explains how probabilistic posits introduced on the basis of incomplete knowledge can yield testable predictions, and it also bypasses the problem of disastrous retrodictions, that is, the fact the standard equilibrium measures yield high probability of the system being in equilibrium in the recent past, even when we know otherwise. As the problem does not arise on the conception of probabilities considered here, there is no need to invoke a Past Hypothesis as a special posit to avoid it
Time indeterminacy and spatio-temporal building transformations: an approach for architectural heritage understanding
Nowadays most digital reconstructions in architecture and archeology describe buildings heritage as awhole of static and unchangeable entities. However, historical sites can have a rich and complex history, sometimes full of evolutions, sometimes only partially known by means of documentary sources. Various aspects condition the analysis and the interpretation of cultural heritage. First of all, buildings are not inexorably constant in time: creation, destruction, union, division, annexation, partial demolition and change of function are the transformations that buildings can undergo over time. Moreover, other factors sometimes contradictory can condition the knowledge about an historical site, such as historical sources and uncertainty. On one hand, historical documentation concerning past states can be heterogeneous, dubious, incomplete and even contradictory. On the other hand, uncertainty is prevalent in cultural heritage in various forms: sometimes it is impossible to define the dating period, sometimes the building original shape or yet its spatial position. This paper proposes amodeling approach of the geometrical representation of buildings, taking into account the kind of transformations and the notion of temporal indetermination
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
Personal photos of individuals when shared online, apart from exhibiting a
myriad of memorable details, also reveals a wide range of private information
and potentially entails privacy risks (e.g., online harassment, tracking). To
mitigate such risks, it is crucial to study techniques that allow individuals
to limit the private information leaked in visual data. We tackle this problem
in a novel image obfuscation framework: to maximize entropy on inferences over
targeted privacy attributes, while retaining image fidelity. We approach the
problem based on an encoder-decoder style architecture, with two key novelties:
(a) introducing a discriminator to perform bi-directional translation
simultaneously from multiple unpaired domains; (b) predicting an image
interpolation which maximizes uncertainty over a target set of attributes. We
find our approach generates obfuscated images faithful to the original input
images, and additionally increase uncertainty by 6.2 (or up to 0.85
bits) over the non-obfuscated counterparts.Comment: 20 pages, 7 figure
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