VBPMN: Automated Verification of BPMN Processes (Tool Paper)

2017 ◽  
pp. 323-331 ◽  
Author(s):  
Ajay Krishna ◽  
Pascal Poizat ◽  
Gwen Salaün
Keyword(s):  
Automated Verification

scholarly journals Preface of Automated Verification of Critical Systems 2010 (AVoCS 2010)

2014 ◽  
Vol 81 ◽  
pp. 1-2
Author(s):  
Jens Bendisposto ◽  
Michael Leuschel ◽  
Markus Roggenbach
Keyword(s):  
Automated Verification ◽  
Critical Systems

scholarly journals CoLoR: a Coq library on well-founded rewrite relations and its application to the automated verification of termination certificates

2011 ◽  
Vol 21 (4) ◽  
pp. 827-859 ◽  
Author(s):  
FRÉDÉRIC BLANQUI ◽  
ADAM KOPROWSKI
Keyword(s):  
Term Rewriting ◽  
Automated Verification ◽  
Proof Assistant ◽  
Proof Assistants ◽  
Active Subject ◽  
Turing Complete

Termination is an important property of programs, and is notably required for programs formulated in proof assistants. It is a very active subject of research in the Turing-complete formalism of term rewriting. Over the years, many methods and tools have been developed to address the problem of deciding termination for specific problems (since it is undecidable in general). Ensuring the reliability of those tools is therefore an important issue.In this paper we present a library formalising important results of the theory of well-founded (rewrite) relations in the proof assistant Coq. We also present its application to the automated verification of termination certificates, as produced by termination tools.The sources are freely available athttp://color.inria.fr/.

Automated Verification and Validation of IoRT Systems

2021 ◽  
pp. 55-89
Author(s):  
S.V. Gayetri Devi ◽  
C. Nalini
Keyword(s):  
Verification And Validation ◽  
Automated Verification

scholarly journals Special issue on Automated Verification of Critical Systems (AVoCS'12)

2014 ◽  
Vol 96 ◽  
pp. 277-278
Author(s):  
Gerald Lüttgen ◽  
Stephan Merz
Keyword(s):  
Automated Verification ◽  
Critical Systems ◽  
Special Issue

scholarly journals The Verification of Ecological Citizen Science Data: Current approaches and future possibilities

2021 ◽  
Vol 5 ◽  
Author(s):  
Emily Baker ◽  
Jonathan Drury ◽  
Johanna Judge ◽  
David Roy ◽  
Graham Smith ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Applied Research ◽  
Automated Verification ◽  
Ecological Data ◽  
Science Data ◽  
Spatial And Temporal Scales ◽  
The Past ◽  
Published Research ◽  
Critical Process

Citizen science schemes (projects) enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably among schemes. Here, we systematically review approaches to verification across ecological citizen science schemes, which feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used (Baker et al. 2021). We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes. Community consensus was the second most common verification approach, used by schemes such as Snapshot Serengeti (Swanson et al. 2016) and MammalWeb (Hsing et al. 2018). It was more common among schemes with a larger number of participants and where photos or video had to be submitted with each record. Automated verification was not widely used among the schemes reviewed. Schemes that used automation, such as eBird (Kelling et al. 2011) and Project FeederWatch (Bonter and Cooper 2012) did so in conjunction with other methods such as expert verification. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this hierachical system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.

scholarly journals An Architecture for Automated Verification of Academic Testimonials in E-Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 2377-2383
Keyword(s):  
Success Rate ◽  
Hard Copy ◽  
Automated Verification ◽  
Online Application ◽  
Alternative Approach ◽  
E Learning ◽  
Digital Copy ◽  
The University ◽  
A Company ◽  
Manual Verification

Universities offering e-learning courses often provide their students with a hard copy of the marksheet. When that same student wants to apply for a job through the online application portal of a company, he/she must scan the marksheet and upload the scanned copy. This is a nuisance because there can be many such marksheets and not everyone has access to a scanner at home. The candidate is also required to provide the name of the University which issued the degree as well as the marks obtained, because these information cannot be extracted from the scanned marksheet image using OCR with 100% success rate due to many factors including: varying marksheet formats, presence of background watermarks, differing fonts, loss in quality during scanning, etc. The company must now manually verify each such application by matching the entered marks against the marks printed in the marksheet, which is a tedious process. In this paper, we propose an alternative approach where the data printed on the marksheet is also embedded in a digital copy of the marksheet. This digital copy, in the form of an image, can then be downloaded by the students from the University portal thereby eliminating the need for scanning. Furthermore, when this image is uploaded, the company, i.e. job provider, can easily verify the information by invoking a standard API exposed by the University (or some nodal agency), which will then extract the embedded information. This eliminates the need for any manual verification and the entire process is automated, simple, fast and hassle-free. Security features are also inherent in our approach thereby reducing any chances of fraud

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