A Topological Correctness Criterion for Multiplicative Non-Commutative Logic

This paper presents a methodology to bridge the gap between business process modeling and workflow specification. While the first is concerned with intuitive descriptions that are mainly used for communication, the second is concerned with configuring a process-aware information system, thus requiring a more rigorous language less suitable for communication. Unlike existing approaches the gap is not bridged by providing formal semantics for an informal language. Instead it is assumed that the desired behavior is just a subset of the full behavior obtained using a liberal interpretation of the informal business process modeling language. Using a new correctness criterion (relaxed soundness), it is verified whether a selection of suitable behavior is possible. The methodology consists of five steps and is illustrated using event-driven process chains as a business process modeling language and Petri nets as the workflow specification language.

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Estimate the Performance of Multi-Model Estimation Algorithms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1506 ◽

2013 ◽

Vol 427-429 ◽

pp. 1506-1509

Author(s):

Yong Yan Yu

Keyword(s):

Estimation Procedure ◽

Model Parameters ◽

Data Sets ◽

Model Estimation ◽

Multiple Model ◽

Estimation Algorithms ◽

Performance Metric ◽

Testing Dataset ◽

Correctness Criterion ◽

Line Fitting

A robust estimation procedure is necessary to estimate the true model parameters in computer vision. Evaluating the multiple-model in the presence of outliers-robust is a fundamentally different task than the single-model problem.Despite there are many diversity multi-model estimation algorithms, it is difficult to pick an effective and advisably approach.So we present a novel quantitative evaluation of multi-model estimation algorithms, efficiency may be evaluated by either examining the asymptotic efficiency of the algorithms or by running them for a series of data sets of increasing size.Thus we create a specifical testing dataset,and introduce a performance metric, Strongest-Intersection.and using the model-aware correctness criterion. Finally, well show the validity of estimation strategy by the Experimention of line-fitting.

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TOPOLOGY-PRESERVING WATERMARKING OF VECTOR GRAPHICS

International Journal of Computational Geometry & Applications ◽

10.1142/s0218195914500034 ◽

2014 ◽

Vol 24 (01) ◽

pp. 61-86 ◽

Cited By ~ 3

Author(s):

STEFAN HUBER ◽

MARTIN HELD ◽

PETER MEERWALD ◽

ROLAND KWITT

Keyword(s):

Input Data ◽

Two Dimensional ◽

Statistical Features ◽

Embedding Capacity ◽

Vector Graphics ◽

Line Segments ◽

Circular Arcs ◽

Straight Line ◽

Watermark Embedding ◽

Topological Correctness

Watermarking techniques for vector graphics dislocate vertices in order to embed imperceptible, yet detectable, statistical features into the input data. The embedding process may result in a change of the topology of the input data, e.g., by introducing self-intersections, which is undesirable or even disastrous for many applications. In this paper we present a watermarking framework for two-dimensional vector graphics that employs conventional watermarking techniques but still provides the guarantee that the topology of the input data is preserved. The geometric part of this framework computes so-called maximum perturbation regions (MPR) of vertices. We propose two efficient algorithms to compute MPRs based on Voronoi diagrams and constrained triangulations. Furthermore, we present two algorithms to conditionally correct the watermarked data in order to increase the watermark embedding capacity and still guarantee topological correctness. While we focus on the watermarking of input formed by straight-line segments, one of our approaches can also be extended to circular arcs. We conclude the paper by demonstrating and analyzing the applicability of our framework in conjunction with two well-known watermarking techniques.

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Linear Logic as a Tool for Qualitative and Quantitative Analysis of Workow Processes

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213016500081 ◽

2016 ◽

Vol 25 (03) ◽

pp. 1650008 ◽

Cited By ~ 1

Author(s):

Lígia Maria Soares Passos ◽

Stéphane Julia

Keyword(s):

Quantitative Analysis ◽

Linear Logic ◽

Resource Planning ◽

Qualitative And Quantitative Analysis ◽

Property A ◽

Task Execution ◽

Workflow Nets ◽

Qualitative And Quantitative ◽

Correctness Criterion ◽

Proof Tree

This article presents a method for qualitative and quantitative analysis of WorkFlow nets based on the proof trees of Linear Logic. The qualitative analysis is concerned with the proof of Soundness correctness criterion defined for WorkFlow nets. To prove the Soundness property, a proof tree of Linear Logic is built for each different scenario of the WorkFlow net. The quantitative analysis is concerned with the resource planning for each task of the workflow process and is based on the computation of symbolic date intervals for task execution. In particular, such symbolic date intervals are computed using the proof trees used to prove Soundness property.

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A SOFTWARE RELIABILITY MODEL FOR ARTIFICIAL INTELLIGENCE PROGRAMS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194093000057 ◽

1993 ◽

Vol 03 (01) ◽

pp. 99-114 ◽

Cited By ~ 5

Author(s):

FAROKH B. BASTANI ◽

ING-RAY CHEN ◽

TA-WEI TSAO

Keyword(s):

Artificial Intelligence ◽

Software Reliability ◽

Planning Problem ◽

Reliability Growth ◽

Reliability Model ◽

Reliability Models ◽

Failure Data ◽

Software Reliability Models ◽

Software Reliability Model ◽

Correctness Criterion

In this paper we develop a software reliability model for Artificial Intelligence (AI) programs. We show that conventional software reliability models must be modified to incorporate certain special characteristics of AI programs, such as (1) failures due to intrinsic faults, e.g., limitations due to heuristics and other basic AI techniques, (2) fuzzy correctness criterion, i.e., difficulty in accurately classifying the output of some AI programs as correct or incorrect, (3) planning-time versus execution-time tradeoffs, and (4) reliability growth due to an evolving knowledge base. We illustrate the approach by modifying the Musa-Okumoto software reliability growth model to incorporate failures due to intrinsic faults and to accept fuzzy failure data. The utility of the model is exemplified with a robot path-planning problem.

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General conditions for full abstraction

Mathematical Structures in Computer Science ◽

10.1017/s0960129514000280 ◽

2014 ◽

Vol 26 (4) ◽

pp. 655-657 ◽

Cited By ~ 11

Author(s):

JOACHIM PARROW

Keyword(s):

The Other ◽

Models Of Computation ◽

Full Abstraction ◽

Correctness Criterion ◽

General Conditions

Full abstraction, i.e. that a function preserves equivalence from a source to a target, has been used extensively as a correctness criterion for mappings between models of computation. I here show that with fixed equivalences, fully abstract functions almost always exist. Also, with the function and one of the equivalences fixed the other equivalence can almost always be found.

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