Contextualized Perturbation for Textual Adversarial Attack

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of generating such examples are typically driven by local heuristic rules that are agnostic to the context, often resulting in unnatural and ungrammatical outputs. This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure. CLARE builds on a pre-trained masked language model and modifies the inputs in a context-aware manner. We propose three contextualized perturbations, Replace, Insert and Merge, that allow for generating outputs of varied lengths. CLARE can flexibly combine these perturbations and apply them at any position in the inputs, and is thus able to attack the victim model more effectively with fewer edits. Extensive experiments and human evaluation demonstrate that CLARE outperforms the baselines in terms of attack success rate, textual similarity, fluency and grammaticality.

The Impact of Conditional Accounting Conservatism on the Equity Cost An Empirical Study in Kuwait Financial Market

The Results show that the studied companies applied conditional conservatism practices at a good level, and following accounting conservatism practices reduce the cost of equity.

Discount for pre-trial detention In the Syrian and Egyptian legislation

The discount for pre-trial detention of the sentence period is a fair idea because it prevents looting freedom sentenced for a longer period of time set by virtue of a conviction, but this idea becomes more fairer if a person commits more than one offense so he stopped up at the disposal of one of them and acquitted them, while spent his conviction for another crime. This research tried to study this issue through the comparison between the Syrian and Egyptian legislation by analyzing legal texts governing it.