11,058 research outputs found
Checking Computations of Formal Method Tools - A Secondary Toolchain for ProB
We present the implementation of pyB, a predicate - and expression - checker
for the B language. The tool is to be used for a secondary tool chain for data
validation and data generation, with ProB being used in the primary tool chain.
Indeed, pyB is an independent cleanroom-implementation which is used to
double-check solutions generated by ProB, an animator and model-checker for B
specifications. One of the major goals is to use ProB together with pyB to
generate reliable outputs for high-integrity safety critical applications.
Although pyB is still work in progress, the ProB/pyB toolchain has already been
successfully tested on various industrial B machines and data validation tasks.Comment: In Proceedings F-IDE 2014, arXiv:1404.578
Processor Verification Using Efficient Reductions of the Logic of Uninterpreted Functions to Propositional Logic
The logic of equality with uninterpreted functions (EUF) provides a means of
abstracting the manipulation of data by a processor when verifying the
correctness of its control logic. By reducing formulas in this logic to
propositional formulas, we can apply Boolean methods such as Ordered Binary
Decision Diagrams (BDDs) and Boolean satisfiability checkers to perform the
verification.
We can exploit characteristics of the formulas describing the verification
conditions to greatly simplify the propositional formulas generated. In
particular, we exploit the property that many equations appear only in positive
form. We can therefore reduce the set of interpretations of the function
symbols that must be considered to prove that a formula is universally valid to
those that are ``maximally diverse.''
We present experimental results demonstrating the efficiency of this approach
when verifying pipelined processors using the method proposed by Burch and
Dill.Comment: 46 page
Efficient Analysis of Complex Diagrams using Constraint-Based Parsing
This paper describes substantial advances in the analysis (parsing) of
diagrams using constraint grammars. The addition of set types to the grammar
and spatial indexing of the data make it possible to efficiently parse real
diagrams of substantial complexity. The system is probably the first to
demonstrate efficient diagram parsing using grammars that easily be retargeted
to other domains. The work assumes that the diagrams are available as a flat
collection of graphics primitives: lines, polygons, circles, Bezier curves and
text. This is appropriate for future electronic documents or for vectorized
diagrams converted from scanned images. The classes of diagrams that we have
analyzed include x,y data graphs and genetic diagrams drawn from the biological
literature, as well as finite state automata diagrams (states and arcs). As an
example, parsing a four-part data graph composed of 133 primitives required 35
sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in
MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at
ftp://ftp.ccs.neu.edu/pub/people/futrell
Symbolic Reachability Analysis of B through ProB and LTSmin
We present a symbolic reachability analysis approach for B that can provide a
significant speedup over traditional explicit state model checking. The
symbolic analysis is implemented by linking ProB to LTSmin, a high-performance
language independent model checker. The link is achieved via LTSmin's PINS
interface, allowing ProB to benefit from LTSmin's analysis algorithms, while
only writing a few hundred lines of glue-code, along with a bridge between ProB
and C using ZeroMQ. ProB supports model checking of several formal
specification languages such as B, Event-B, Z and TLA. Our experiments are
based on a wide variety of B-Method and Event-B models to demonstrate the
efficiency of the new link. Among the tested categories are state space
generation and deadlock detection; but action detection and invariant checking
are also feasible in principle. In many cases we observe speedups of several
orders of magnitude. We also compare the results with other approaches for
improving model checking, such as partial order reduction or symmetry
reduction. We thus provide a new scalable, symbolic analysis algorithm for the
B-Method and Event-B, along with a platform to integrate other model checking
improvements via LTSmin in the future
Exploiting multi-word units in history-based probabilistic generation
We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units,
showing a statistically significant improvement of generation accuracy. Tested on section 23 of the PennWall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18% to 99.96%
Contextual Media Retrieval Using Natural Language Queries
The widespread integration of cameras in hand-held and head-worn devices as
well as the ability to share content online enables a large and diverse visual
capture of the world that millions of users build up collectively every day. We
envision these images as well as associated meta information, such as GPS
coordinates and timestamps, to form a collective visual memory that can be
queried while automatically taking the ever-changing context of mobile users
into account. As a first step towards this vision, in this work we present
Xplore-M-Ego: a novel media retrieval system that allows users to query a
dynamic database of images and videos using spatio-temporal natural language
queries. We evaluate our system using a new dataset of real user queries as
well as through a usability study. One key finding is that there is a
considerable amount of inter-user variability, for example in the resolution of
spatial relations in natural language utterances. We show that our retrieval
system can cope with this variability using personalisation through an online
learning-based retrieval formulation.Comment: 8 pages, 9 figures, 1 tabl
Transfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine
interpretable meaning representation language (MRL). One of the constraints
that limits full exploration of deep learning technologies for semantic parsing
is the lack of sufficient annotation training data. In this paper, we propose
using sequence-to-sequence in a multi-task setup for semantic parsing with a
focus on transfer learning. We explore three multi-task architectures for
sequence-to-sequence modeling and compare their performance with an
independently trained model. Our experiments show that the multi-task setup
aids transfer learning from an auxiliary task with large labeled data to a
target task with smaller labeled data. We see absolute accuracy gains ranging
from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging
from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and
semantic auxiliary tasks.Comment: Accepted for ACL Repl4NLP 201
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