الخزف في طب الاسنان التجميلي

توفرت ترميمات الخزف على معدن منذ أكثر من ثلاثة عقود وقد لاقى هذه النوع رواجا بفضل أداءه المتوقع وجماليته المعقولة

Recovering Lexically and Semantically Reused Texts

Writers often repurpose material from existing texts when composing new documents. Because most documents have more than one source, we cannot trace these connections using only models of document-level similarity. Instead, this paper considers methods for local text reuse detection (LTRD), detecting localized regions of lexically or semantically similar text embedded in otherwise unrelated material. In extensive experiments, we study the relative performance of four classes of neural and bag-of-words models on three LTRD tasks -- detecting plagiarism, modeling journalists' use of press releases, and identifying scientists' citation of earlier papers. We conduct evaluations on three existing datasets and a new, publicly-available citation localization dataset. Our findings shed light on a number of previously-unexplored questions in the study of LTRD, including the importance of incorporating document-level context for predictions, the applicability of of-the-shelf neural models pretrained on general'' semantic textual similarity tasks such as paraphrase detection, and the trade-offs between more efficient bag-of-words and feature-based neural models and slower pairwise neural models.

Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI

Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area. However, LMs have faced much criticism of whether they are truly capable of reasoning in NLU. In this work, we propose a diagnostic method for first-order logic (FOL) reasoning with a new proposed benchmark, LogicNLI. LogicNLI is an NLI-style dataset that effectively disentangles the target FOL reasoning from commonsense inference and can be used to diagnose LMs from four perspectives: accuracy, robustness, generalization, and interpretability. Experiments on BERT, RoBERTa, and XLNet, have uncovered the weaknesses of these LMs on FOL reasoning, which motivates future exploration to enhance the reasoning ability.

Building a simulator for Superscalar processors and Vector processors and comparing their performance in processing parallelism at data level

This paper presents parallel computers architectures especially Superscalar processors and Vector processors, building a simulator depending on the basic characteristics for each architecture, the simulator simulates their mechanism of work programmatically at the aim of comparing the performance of the two architectures in executing Data Level Parallelism (DLP) and Instruction Level Parallelism ILP. The results shows that the effectiveness of executing instructions in parallel depends significantly on choosing the appropriate architecture for execution, according to the type of parallelism that can be applied to instructions, and the vector features in the vector architecture achieve remarkable improvement in performance that cannot be ignored in execution of DLP, simplify the code and reduce the number of instruction. The provided simulator is a good core that can be developed and modified especially in the field of education for the students of Computer Science and Engineering and the research field.