scholarly journals The Novel in the Spanish Silver Age

2021 ◽  
Author(s):  
José Calvo Tello
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Historical Novel ◽  
Genre Theory ◽  
Literary Genre ◽  
The Novel ◽  
Silver Age ◽  
Interdisciplinary Field ◽  
Theoretical Approaches ◽  
Graph Based Model

What distinguishes an adventure novel from a historical novel? Can the same text belong to several genres? More to one than to another? Have some existing genres been overlooked? To answer these and similar questions, José Calvo Tello combines methods from Linguistics (lexicography), Literary Studies (genre theory), and Computer Science (machine learning, natural language processing). Located in the interdisciplinary field of Digital Humanities, this study analyzes a newly developed corpus of 358 Spanish novels of the silver age (1880-1939), which includes authors like Baroja, Pardo Bazán, or Valle-Inclán. Calvo Tello's key result is a graph-based model of literary genre that reconciles recent theoretical approaches.

Machine Learning Techniques Application

2021 ◽  
pp. 1396-1417
Author(s):  
Karthikeyan P. ◽  
Karunakaran Velswamy ◽  
Pon Harshavardhanan ◽  
Rajagopal R. ◽  
JeyaKrishnan V. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Modern World ◽  
Interdisciplinary Field ◽  
Sound Image ◽  
Learning Techniques

Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.

scholarly journals HISTORY AS A CHALLENGE TO THE MODERN TIME IN THE RUSSIAN BOOKER NOVEL

2018 ◽  
pp. 13-40 ◽  
Author(s):  
E. Pogorelaya
Keyword(s):  
Historical Novel ◽  
The State ◽  
Russian History ◽  
The Novel ◽  
Modern Time ◽  
Recent Past ◽  
Silver Age ◽  
Contemporary Novel ◽  
Literary Prizes ◽  
The Way

Held in the Silver Age extension of the State Literary Museum, the Booker Conference 2017 was devoted to the contemporary historical novel. Why is it that novelists are increasingly more likely to look for solutions to present-day problems through retrospection, invoking the long bygone or a more recent past, travelling back to the origins of Russian history and the 20th century’s turbulent revolutionary events that paved the way for our present existence? Which challenges seem particularly poignant, and where do they come from? The speakers include novelists Petr Aleshkovsky and Olga Slavnikova, critic and essayist Vladimir Novikov, and literary critics Evgeny Vezhlyan, Anna Zhuchkova, and Elena Pogorelaya. Also present was writer and historian Sergey Belyakov (Ekaterinburg) to talk about the novel Nomakh. All participants concurred that, while overcoming historical inertia remained a key challenge in contemporary novel-writing, serious progress has been achieved in recent years, judging by the shortlists and the winners of the major literary prizes.As usual at this conference, the Russian Booker’s literary secretary Igor Shaytanov was its moderator. The hosting institution was represented by Mikhail Shaposhnikov, head of the Silver Age Literature Department.

Machine Learning Techniques Application

2020 ◽  
pp. 380-401
Author(s):  
Karthikeyan P. ◽  
Karunakaran Velswamy ◽  
Pon Harshavardhanan ◽  
Rajagopal R. ◽  
JeyaKrishnan V. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Modern World ◽  
Interdisciplinary Field ◽  
Sound Image ◽  
Learning Techniques

Machine learning is the part of artificial intelligence that makes machines learn without being expressly programmed. Machine learning application built the modern world. Machine learning techniques are mainly classified into three techniques: supervised, unsupervised, and semi-supervised. Machine learning is an interdisciplinary field, which can be joined in different areas including science, business, and research. Supervised techniques are applied in agriculture, email spam, malware filtering, online fraud detection, optical character recognition, natural language processing, and face detection. Unsupervised techniques are applied in market segmentation and sentiment analysis and anomaly detection. Deep learning is being utilized in sound, image, video, time series, and text. This chapter covers applications of various machine learning techniques, social media, agriculture, and task scheduling in a distributed system.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Drawing the Written Woman

2014 ◽  
Vol 7 (2) ◽  
pp. 31-63
Author(s):  
Sonia Lagerwall
Keyword(s):  
Historical Novel ◽  
Dynamic Adaptation ◽  
Adaptation Process ◽  
The Novel ◽  
Artistic Training

This article deals with Philippe Druillet's three-volume comic adaptation (1980–1985) of Salammbô, Gustave Flaubert's historical novel from 1862, set three centuries BC. Flaubert was famous for not wanting his texts illustrated: he argued that the preciseness of images would undo the poetic vagueness of his written words. The article examines how Druillet tackles the challenge of graphically representing Flaubert's canonical work without reducing the priestess Salammbô into a given type. The analysis shows a dynamic adaptation process in which Druillet gives a kaleidoscopic form to Flaubert's text. His variation on the Salammbô character foregrounds photography, a medium historically relevant to the novel but also to Druillet's own artistic training. Featuring his character Lone Sloane in the role of Mathô, the adaptation proves to be a highly personal appropriation of the novel, where Druillet enhances an autobiographical dimension of his work previously hinted at in La Nuit and Gaïl.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

The Castle of Otranto

2014 ◽  
Author(s):  
Horace Walpole
Keyword(s):  
Middle Ages ◽  
Literary Genre ◽  
The Novel ◽  
Gothic Fiction ◽  
English Novel ◽  
Game Changer ◽  
The Way ◽  
The Grotesque

‘Look, my lord! See heaven itself declares against your impious intentions’ The Castle of Otranto (1764) is the first supernatural English novel and one of the most influential works of Gothic fiction. It inaugurated a literary genre that will be forever associated with the effects that Walpole pioneered. Professing to be a translation of a mysterious Italian tale from the darkest Middle Ages, the novel tells of Manfred, prince of Otranto, whose fear of an ancient prophecy sets him on a course of destruction. After the grotesque death of his only son, Conrad, on his wedding day, Manfred determines to marry the bride–to–be. The virgin Isabella flees through a castle riddled with secret passages. Chilling coincidences, ghostly visitations, arcane revelations, and violent combat combine in a heady mix that terrified the novel's first readers. In this new edition Nick Groom examines the reasons for its extraordinary impact and the Gothic culture from which it sprang. The Castle of Otranto was a game-changer, and Walpole the writer who paved the way for modern horror exponents.

Kidnapped

2014 ◽  
Author(s):  
Robert Louis Stevenson ◽  
Ian Duncan
Keyword(s):  
West Coast ◽  
Historical Novel ◽  
The Novel ◽  
The West ◽  
Scottish Highlands ◽  
Cross Country ◽  
First Time ◽  
Adventure Stories

Your bed shall be the moorcock’s, and your life shall be like the hunted deer’s, and ye shall sleep with your hand upon your weapons.’ Tricked out of his inheritance, shanghaied, shipwrecked off the west coast of Scotland, David Balfour finds himself fleeing for his life in the dangerous company of Jacobite outlaw and suspected assassin Alan Breck Stewart. Their unlikely friendship is put to the test as they dodge government troops across the Scottish Highlands. Set in the aftermath of the 1745 rebellion, Kidnapped transforms the Romantic historical novel into the modern thriller. Its heart-stopping scenes of cross-country pursuit, distilled to a pure intensity in Stevenson’s prose, have become a staple of adventure stories from John Buchan to Alfred Hitchcock and Ian Fleming. Kidnapped remains as exhilarating today as when it was first published in 1886. This new edition is based on the 1895 text, incorporating Stevenson’s last thoughts about the novel before his death. It includes Stevenson’s ‘Note to Kidnapped’, reprinted for the first time since 1922.

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