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Utilising individual interviews and focus group discussions, primarily from two Chinese cities, 'The Chinese Mafia' contributes to the understanding of organized crime and corruption in the Chinese context, filling a significant gap in criminological literature, by investigating how extra-legal protectors - corrupt public officials and street gangsters - emerge, evolve and operate in a rapidly changing society. China's economic reforms have been accompanied by a surge of social problems, such as ineffective legal institutions, booming black markets and rampant corruption. This has resulted in the rise of extra-legal means of protection and enforcement: such is the demand for protection that cannot be fulfilled by state-sponsored institutions.

Mafia --- Gangs --- Corruption --- Crime --- Corrupt practices --- Ethics --- Crews (Gangs) --- Crime syndicates --- Street gangs --- Teen gangs --- Teenage gangs --- Criminals --- Juvenile delinquents --- Hoodlums --- Maffia --- Organized crime --- Màfia --- Crim organitzat --- Corrupció --- Xina --- Delictes de corrupció --- Pràctiques corruptes --- Corrupció administrativa --- Corrupció en els esports --- Corrupció política --- Crim internacional --- Gansterisme --- Organitzacions criminals --- Sindicats del crim --- Blanqueig de diners --- Camorra --- Prostitució --- Thags (Organitzacions criminals) --- Tràfic de drogues --- Triades (Organitzacions criminals) --- Delictes --- Cathay --- China --- República de la Xina (1912-1949) --- República Popular de la Xina --- República Popular Xina --- Zhonghua Renmin Gongheguo --- Xina (República : 1912-1949) --- Àsia oriental --- Canton (Xina) --- Guangdong (Xina) --- Hainan (Xina : Sheng) --- Henan (Xina : Sheng) --- Hong Kong (Xina) --- Macau (Xina : Regió administrativa especial) --- Iang-Tsé (Xina : Curs d'aigua) --- Manxúria (Xina : Regió) --- Mongòlia Interior (Xina : Zizhiqu) --- Pequín (Xina) --- Sichuan (Xina : Sheng) --- Xinjiang (Xina : Regió) --- Xangai (Xina) --- Yunnan (Xina : Sheng) --- Brahmaputra (Àsia : Curs d'aigua) --- Tibet (Xina)

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Airplanes --- Electric equipment --- Safety measures. --- Aeroplanes --- Aircraft, Fixed wing --- Fixed wing aircraft --- Planes (Airplanes) --- Flying-machines --- Aircraft industry

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Mafia --- Gangs --- Corruption --- Crime

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"Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator's unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of Human-centered Machine Learning in Translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning. This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks"--

Translating and interpreting --- Machine learning --- Translation science --- Mathematical linguistics