Yang Feng – författare

A girl friend and a boyfriend together, An Xiaoxiao first secretly sent a video of revenge was the city wanted to escape.However, as a coincidence, she had entered the wolf's den and was imprisoned by Qin Zian who had misunderstood that she had stolen something important from him.When they first met, he said, "e;Woman, if you don't hand over my things, I'll kill you."e;She casually threw a broken can onto the bed and said, "e;I don't want anything. I want a life."e;He gritted his teeth in anger.When he understood, he said, "e;I want you to keep your eyes on me alone."e;

A girl friend and a boyfriend together, An Xiaoxiao first secretly sent a video of revenge was the city wanted to escape.However, as a coincidence, she had entered the wolf''s den and was imprisoned by Qin Zian who had misunderstood that she had stolen something important from him.When they first met, he said, "Woman, if you don''t hand over my things, I''ll kill you."She casually threw a broken can onto the bed and said, "I don''t want anything. I want a life."He gritted his teeth in anger.When he understood, he said, "I want you to keep your eyes on me alone."

This book constitutes the proceedings of the 21st China National Conference on Computational Linguistics, CCL 2022, held in Nanchang, China, in October 2022.The 22 full English-language papers in this volume were carefully reviewed and selected from 293 Chinese and English submissions.The conference papers are categorized into the following topical sub-headings: Linguistics and Cognitive Science; Fundamental Theory and Methods of Computational Linguistics; Information Retrieval, Dialogue and Question Answering; Text Generation and Summarization; Knowledge Graph and Information Extraction; Machine Translation and Multilingual Information Processing; Minority Language Information Processing; Language Resource and Evaluation; NLP Applications.

This book constitutes the proceedings of the 21st China National Conference on Computational Linguistics, CCL 2022, held in Nanchang, China, in October 2022.The 22 full English-language papers in this volume were carefully reviewed and selected from 293 Chinese and English submissions.

The conference papers are categorized into the following topical sub-headings: Linguistics and Cognitive Science; Fundamental Theory and Methods of Computational Linguistics; Information Retrieval, Dialogue and Question Answering; Text Generation and Summarization; Knowledge Graph and Information Extraction; Machine Translation and Multilingual Information Processing; Minority Language Information Processing; Language Resource and Evaluation; NLP Applications.

In an article for Wired Magazine in 2006, Jeff Howe defined crowdsourcing as an idea for outsourcing a task that is traditionally performed by a single employee to a large group of people in the form of an open call. Since then, by modifying crowdsourcing into different forms, some of the most successful new companies on the market have used this idea to make people’s lives easier and better. On the other hand, software testing has long been recognized as a time-consuming and expensive activity. Mobile application testing is especially difficult, largely due to compatibility issues: a mobile application must work on devices with different operating systems (e.g. iOS, Android), manufacturers (e.g. Huawei, Samsung) and keypad types (e.g. virtual keypad, hard keypad). One cannot be 100% sure that, just because a tested application works well on one device, it will run smoothly on all others.Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of softwaretesting and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft. This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.

In an article for Wired Magazine in 2006, Jeff Howe defined crowdsourcing as an idea for outsourcing a task that is traditionally performed by a single employee to a large group of people in the form of an open call. Since then, by modifying crowdsourcing into different forms, some of the most successful new companies on the market have used this idea to make people’s lives easier and better. On the other hand, software testing has long been recognized as a time-consuming and expensive activity. Mobile application testing is especially difficult, largely due to compatibility issues: a mobile application must work on devices with different operating systems (e.g. iOS, Android), manufacturers (e.g. Huawei, Samsung) and keypad types (e.g. virtual keypad, hard keypad). One cannot be 100% sure that, just because a tested application works well on one device, it will run smoothly on all others.

Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of softwaretesting and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft.

This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.

In an article for Wired Magazine in 2006, Jeff Howe defined crowdsourcing as an idea for outsourcing a task that is traditionally performed by a single employee to a large group of people in the form of an open call. Since then, by modifying crowdsourcing into different forms, some of the most successful new companies on the market have used this idea to make people’s lives easier and better. On the other hand, software testing has long been recognized as a time-consuming and expensive activity. Mobile application testing is especially difficult, largely due to compatibility issues: a mobile application must work on devices with different operating systems (e.g. iOS, Android), manufacturers (e.g. Huawei, Samsung) and keypad types (e.g. virtual keypad, hard keypad). One cannot be 100% sure that, just because a tested application works well on one device, it will run smoothly on all others.Crowdsourced testing is an emerging paradigm that can improve the cost-effectiveness of softwaretesting and accelerate the process, especially for mobile applications. It entrusts testing tasks to online crowdworkers whose diverse testing devices/contexts, experience, and skill sets can significantly contribute to more reliable, cost-effective and efficient testing results. It has already been adopted by many software organizations, including Google, Facebook, Amazon and Microsoft. This book provides an intelligent overview of crowdsourced testing research and practice. It employs machine learning, data mining, and deep learning techniques to process the data generated during the crowdsourced testing process, to facilitate the management of crowdsourced testing, and to improve the quality of crowdsourced testing.

This book constitutes the refereed proceedings of the 19th China Conference on Machine Translation, CCMT 2023, held in Jinan, China, during October 19–21, 2023. The 8 full papers and 3 short papers included in this book were carefully reviewed and selected from 71 submissions. They focus on machine translation; improvement of translation models and systems; translation quality estimation; document-level machine translation; low-resource machine translation.