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Traditional Static Timing Analysis (STA) assumes only single input switches at a time with the side input held at non-controlling value. This introduces unnecessary pessimism or optimism which may cause degradation of performance or chip failure. Modeling Multi-Input Switching (MIS) requires a good amount of simulations hence we provide a dataset comprising of SPICE simulations done on 2 input NAND and NOR gate.
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The dataset is the result of exhaustive search of primary extended 1-perfect unitrades in the 10-cube, or, equivalently, triangle-free 5-regular connected induced subgraphs of the halved 10-cube. We list representatives of all the 38 equivalence classes.
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PURPOSE
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This data package is provided as supplementary material of the paper:
“A GPU Approach to Efficiently Reusing Constraint Proofs”, by M. Chen, G. Denaro and M. Pezzè,
submitted for evaluation to IEEE Transactions on software engineering, Nov. 2019.
STRUCTURE
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The data package consists of 24 files:
- a file named _README.txt: this file
- a file named _programs.txt
- 22 files named <program_name>.sexpr
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The dataset stores multi-carpark occupancy records in JSON format.
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This matlab code allows to reproduce some of the constrained and unconstrained dynamic identification techniques for open-chain robots. Below you can find an summary of the underlying research.
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This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.
If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
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This data set includes records of the student data, data descriptions, student demographic survey, class size from students to participated in the 10 program Personal Software Process course.
This version of the course was delivered between 1995 and 2006.
The data was collected by the Software Engineering Institute (SEI) from classes taught by SEI staff and class data submitted by authorized instructors.
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Wide varieties of scripts are used in writing languages throughout the world. In a multiscript and multi-language environment, it is necessary to know the different scripts used in every part of a document to apply the appropriate document analysis algorithm. Consequently, several approaches for automatic script identification have been proposed in the literature, and can be broadly classified under two categories of techniques: those that are structure and visual appearance-based and those that are deep learning-based.
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