7,615 research outputs found
Initial Experiments with TPTP-style Automated Theorem Provers on ACL2 Problems
This paper reports our initial experiments with using external ATP on some
corpora built with the ACL2 system. This is intended to provide the first
estimate about the usefulness of such external reasoning and AI systems for
solving ACL2 problems.Comment: In Proceedings ACL2 2014, arXiv:1406.123
Graph Based Reduction of Program Verification Conditions
Increasing the automaticity of proofs in deductive verification of C programs
is a challenging task. When applied to industrial C programs known heuristics
to generate simpler verification conditions are not efficient enough. This is
mainly due to their size and a high number of irrelevant hypotheses. This work
presents a strategy to reduce program verification conditions by selecting
their relevant hypotheses. The relevance of a hypothesis is determined by the
combination of a syntactic analysis and two graph traversals. The first graph
is labeled by constants and the second one by the predicates in the axioms. The
approach is applied on a benchmark arising in industrial program verification
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
Stereo and ToF Data Fusion by Learning from Synthetic Data
Time-of-Flight (ToF) sensors and stereo vision systems are both capable of acquiring depth information but they have complementary characteristics and issues. A more accurate representation of the scene geometry can be obtained by fusing the two depth sources. In this paper we present a novel framework for data fusion where the contribution of the two depth sources is controlled by confidence measures that are jointly estimated using a Convolutional Neural Network. The two depth sources are fused enforcing the local consistency of depth data, taking into account the estimated confidence information. The deep network is trained using a synthetic dataset and we show how the classifier is able to generalize to different data, obtaining reliable estimations not only on synthetic data but also on real world scenes. Experimental results show that the proposed approach increases the accuracy of the depth estimation on both synthetic and real data and that it is able to outperform state-of-the-art methods
More SPASS with Isabelle: superposition with hard sorts and configurable simplification
Sledgehammer for Isabelle/HOL integrates automatic theorem provers to discharge interactive proof obligations. This paper considers a tighter integration of the superposition prover SPASS to increase Sledgehammerâs success rate. The main enhancements are native support for hard sorts (simple types) in SPASS, simplification that honors the orientation of Isabelle simp rules, and a pair of clause-selection strategies targeted at large lemma libraries. The usefulness of this integration is confirmed by an evaluation on a vast benchmark suite and by a
case study featuring a formalization of language-based security
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