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"EX-LIBRIS" The Art of Book marks the Russian Pattern

»الاكس – ليبريس« LIBRIS-EX فن شارات الكتب »الأنموذج الروسي«

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 Publication date 2001
and research's language is العربية
 Created by Shamra Editor




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The axis of my research is the RUSSIAN EX-LIBRIS , which is spread ,and its subject has developed in Russia , and was used in the printing factories and in the general and special libraries , it has been divided into 4 divisions: 1- With motto 2- With the first letters of the name and the surname. 3- With a subject. 4- With letters ( The letter plays the principle role in the formation.)



References used
متحف (جيفكو للغرافيك ) برنيكو – ايطاليا ( مترجم عن الإنكليزية)
المركز الدولي ل الاكس – ليبريس –سانت – نيكلاي – بلجيكا (مترجم عن الانكليزية)
مالين.أ.و. – هواة الكتب القدماء – من مجموعات الهواة – لينغراد 1929 (مترجم عن الروسية)
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I admired the charm of "Signatures", their richness of vocabulary, construction, perfection and musical fusion .Each word is tightly connected to the other within an accurate literary form. And these might be a consequence of the characteristics o f this prose art and its being a product of persons who have many contributions in consolidating the pillars of the Islamic state, and its present and future . The Importance of this research comes from addressing one of these rhetorical arts (Antithesis) which are heavily present in "Signatures". It is clear that using (Antithesis) had special importance built on the personal experience, the aesthetic sense and the psychological effect, it is not only a mere articular decoration or playing on words .
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Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering (QA) and Fact Verification. Recently, pre-tr ained sequence to sequence (seq2seq) models have proven to be very effective in jointly making predictions, as well as generating NL explanations. However, these models have many shortcomings; they can fabricate explanations even for incorrect predictions, they are difficult to adapt to long input documents, and their training requires a large amount of labeled data. In this paper, we develop FiD-Ex, which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance. FiD-Ex significantly improves over prior work in terms of explanation metrics and task accuracy on five tasks from the ERASER explainability benchmark in both fully supervised and few-shot settings.
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