Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and generation. However, these operate in natural channel of code, i.e., primarily concerned with human code. They are not robust changes input thus, potentially susceptible adversarial attacks channel. We propose, C...

A comparison between two known strategies of modeling lap joint interfaces, namely, zero-thickness and thin layer interface theories and their associated updating procedures, is made. Finite element...

background: there are several conflicting conceptual models to explain social determinants of health (sdh) as responsible for most health inequalities. this study aimed to present these models in historical perspective and provide main component of sdh models as an ses indicators. methods: this was a narrative study using international databases to retrieve literature dealing with conceptual mo...

Abstract This chapter presents the main architecture types of attention-based language models, which describe distribution tokens in texts: Autoencoders similar to BERT receive an input text and produce a contextual embedding for each token. Autoregressive models GPT subsequence as input. They token predict next In this way, all can successively be generated. Transformer Encoder-Decoders have t...

A language model combining word-based and category-based ngrams within a backoff framework is presented. Word n-grams conveniently capture sequential relations between particular words, while the category-model, which is based on part-of-speech classifications and allows ambiguous category membership, is able to generalise to unseen word sequences and therefore appropriate in backoff situations...