73,187 research outputs found
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This essay aims to provide a modal logic for rational intuition. Similarly to treatments of the property of knowledge in epistemic logic, I argue that rational intuition can be codified by a modal operator governed by the axioms of a dynamic provability logic, which embeds GL within the modal -calculus. Via correspondence results between modal logic and the bisimulation-invariant fragment of second-order logic, a precise translation can then be provided between the notion of 'intuition-of', i.e., the cognitive phenomenal properties of thoughts, and the modal operators regimenting the notion of 'intuition-that'. I argue that intuition-that can further be shown to entrain conceptual elucidation, by way of figuring as a dynamic-interpretational modality which induces the reinterpretation of both domains of quantification and the intensions and hyperintensions of mathematical concepts that are formalizable in monadic first- and second-order formal languages. Hyperintensionality is countenanced via four models, without a decision as to which model is to be preferred. The first model makes intuition sensitive to hyperintensional topics, i.e. subject matters. The second model is a hyperintensional truthmaker semantics, in particular a novel epistemic two-dimensional truthmaker semantics. The third model is a topic-sensitive non-truthmaker epistemic two-dimensional semantics. The fourth model is a topic-sensitive epistemic two-dimensional truthmaker semantics
Interests Diffusion in Social Networks
Understanding cultural phenomena on Social Networks (SNs) and exploiting the
implicit knowledge about their members is attracting the interest of different
research communities both from the academic and the business side. The
community of complexity science is devoting significant efforts to define laws,
models, and theories, which, based on acquired knowledge, are able to predict
future observations (e.g. success of a product). In the mean time, the semantic
web community aims at engineering a new generation of advanced services by
defining constructs, models and methods, adding a semantic layer to SNs. In
this context, a leapfrog is expected to come from a hybrid approach merging the
disciplines above. Along this line, this work focuses on the propagation of
individual interests in social networks. The proposed framework consists of the
following main components: a method to gather information about the members of
the social networks; methods to perform some semantic analysis of the Domain of
Interest; a procedure to infer members' interests; and an interests evolution
theory to predict how the interests propagate in the network. As a result, one
achieves an analytic tool to measure individual features, such as members'
susceptibilities and authorities. Although the approach applies to any type of
social network, here it is has been tested against the computer science
research community.
The DBLP (Digital Bibliography and Library Project) database has been elected
as test-case since it provides the most comprehensive list of scientific
production in this field.Comment: 30 pages 13 figs 4 table
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model named as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the topic discovery in the subsequent time steps. We account for
the temporal ordering of documents by explicitly modeling a joint distribution
of latent topical dependencies over time, using distributional estimators with
temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
research, we demonstrate that compared to state-of-the art topic models, RNNRSM
shows better generalization, topic interpretation, evolution and trends. We
also introduce a metric (named as SPAN) to quantify the capability of dynamic
topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies (NAACL-HLT 2018
Knowability Relative to Information
We present a formal semantics for epistemic logic, capturing the notion of knowability relative to information (KRI). Like Dretske, we move from the platitude that what an agent can know depends on her (empirical) information. We treat operators of the form K_AB (‘B is knowable on the basis of information A’) as variably strict quantifiers over worlds with a topic- or aboutness- preservation constraint. Variable strictness models the non-monotonicity of knowledge acquisition while allowing knowledge to be intrinsically stable. Aboutness-preservation models the topic-sensitivity of information, allowing us to invalidate controversial forms of epistemic closure while validating less controversial ones. Thus, unlike the standard modal framework for epistemic logic, KRI accommodates plausible approaches to the Kripke-Harman dogmatism paradox, which bear on non-monotonicity, or on topic-sensitivity. KRI also strikes a better balance between agent idealization and a non-trivial logic of knowledge ascriptions
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
The recent drive towards achieving greater autonomy and intelligence in
robotics has led to high levels of complexity. Autonomous robots increasingly
depend on third party off-the-shelf components and complex machine-learning
techniques. This trend makes it challenging to provide strong design-time
certification of correct operation.
To address these challenges, we present SOTER, a robotics programming
framework with two key components: (1) a programming language for implementing
and testing high-level reactive robotics software and (2) an integrated runtime
assurance (RTA) system that helps enable the use of uncertified components,
while still providing safety guarantees. SOTER provides language primitives to
declaratively construct a RTA module consisting of an advanced,
high-performance controller (uncertified), a safe, lower-performance controller
(certified), and the desired safety specification. The framework provides a
formal guarantee that a well-formed RTA module always satisfies the safety
specification, without completely sacrificing performance by using higher
performance uncertified components whenever safe. SOTER allows the complex
robotics software stack to be constructed as a composition of RTA modules,
where each uncertified component is protected using a RTA module.
To demonstrate the efficacy of our framework, we consider a real-world
case-study of building a safe drone surveillance system. Our experiments both
in simulation and on actual drones show that the SOTER-enabled RTA ensures the
safety of the system, including when untrusted third-party components have bugs
or deviate from the desired behavior
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