Probabilistic Testing Semantics

2014 ◽  
pp. 71-102
Author(s):  
Yuxin Deng
Keyword(s):  
Testing Semantics

scholarly journals Testing semantics for unbounded nondeterminism

1997 ◽  
pp. 538-545 ◽  
Author(s):  
Luis Fdo. Llana Díaz ◽  
Manuel Núñez
Keyword(s):  
Testing Semantics

Testing semantics for a probabilistic-timed process algebra

1997 ◽  
pp. 353-367 ◽  
Author(s):  
Carlos Gregorio-Rodríguez ◽  
Luis Llana-Díaz ◽  
Manuel Núñez ◽  
Pedro Palao-Gostanza
Keyword(s):  
Process Algebra ◽  
Testing Semantics

scholarly journals Order algebras: a quantitative model of interaction

2017 ◽  
Vol 28 (7) ◽  
pp. 1126-1168
Author(s):  
EMMANUEL BEFFARA
Keyword(s):  
Partial Order ◽  
Algebraic Structure ◽  
Linear Logic ◽  
Quantitative Model ◽  
Process Algebras ◽  
Linear Combinations ◽  
Order Relations ◽  
Standard Notion ◽  
Testing Semantics

A quantitative model of concurrent interaction is introduced. The basic objects are linear combinations of partial order relations, acted upon by a group of permutations that represents potential non-determinism in synchronisation. This algebraic structure is shown to provide faithful interpretations of finitary process algebras, for an extension of the standard notion of testing semantics, leading to a model that is both denotational (in the sense that the internal workings of processes are ignored) and non-interleaving. Constructions on algebras and their subspaces enjoy a good structure that make them (nearly) a model of differential linear logic, showing that the underlying approach to the representation of non-determinism as linear combinations is the same.

scholarly journals A fully abstract may testing semantics for concurrent objects

2005 ◽  
Vol 338 (1-3) ◽  
pp. 17-63 ◽  
Author(s):  
Alan Jeffrey ◽  
Julian Rathke
Keyword(s):  
Concurrent Objects ◽  
Testing Semantics

scholarly journals SEMANTIC BORROWING FOR GENERALIZED ZERO-SHOT LEARNING

2021 ◽  
Author(s):  
Xiao-wei CHEN
Keyword(s):  
Metric Learning ◽  
Training Process ◽  
Training Set ◽  
Shot Classification ◽  
Testing Data ◽  
Benchmark Datasets ◽  
Novel Method ◽  
Testing Semantics ◽  
The Relationship ◽  
Generalized Zero

<p>Generalized zero-shot learning (GZSL) is one of the most realistic problems, but also one of the most challenging problems due to the partiality of the classifier to supervised classes. Instance-borrowing methods and synthesizing methods solve this problem to some extent with the help of testing semantics, but therefore neither can be used under the class-inductive instance-inductive (CIII) training setting where testing data are not available, and the latter require the training process of a classifier after generating examples. In contrast, a novel method called Semantic Borrowing for improving GZSL methods with compatibility metric learning under CIII is proposed in this paper. It borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unseen or unknown classes would not be available for training while this approach does NOT need any information of semantics of unseen or unknown classes. The experimental results on representative GZSL benchmark datasets show that it can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification.</p>

Sign in / Sign up

Export Citation Format

Share Document