RI: Small: Print and Probability - A Statistical Approach to Analysis of Clandestine Publication

RI：小：印刷品和概率 - 秘密出版物分析的统计方法

• 批准号: 1816311
• 负责人: Taylor Berg-Kirkpatrick
• 金额: $ 49.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816311&HistoricalAwards=false
• 关键词: RI; Small; Print; Probability; Statistical

This Print and Probability project develops novel machine learning and computer vision techniques to infer thousands of book and pamphlet printers whose identities have eluded scholars for roughly 500 years. Before the modern era, the book trade was often dangerous and secretive. For fear of persecution and punishment, printers between 1473-1800 declined to attach their names to about a quarter of all known books and pamphlets. However, now that over 130,000 books have been digitized by the Early English Books Online (EEBO) project, defects and variations in the printing tools of this era may hold the key to identifying these printers. Once an individual piece of metal type is damaged, it creates unique stamps. Since typesets belonged to specific printers, impressions of damaged type can thus serve as the fingerprints to identify the printers of tens of thousands of clandestine publications. The Print and Probability project automatically detects and tracks these unique pieces of damaged type in order to uncover new information about the history of books. The methods developed in this project could be generalizable to other important tasks and domains - for example, digital forensics and authorship attribution. In addition, the Print and Probability project will train students in a multidisciplinary way, engaging them in collaboration across multiple fields.By developing new techniques for visual anomaly detection, the Print and Probability project detects damaged letterforms that create consistent aberrations. Based on these damaged type extractions, the project develops probabilistic models of both printer and damaged letter form identifications that allow direct inference of printers at scale. This framework also incorporates other sources of evidence into the identification model - most significantly, the spelling, punctuation, and spacing habits of individual press-house compositors, whose distinctive practices lend themselves to clustering and automatic attributions across all pages of text in the collection. Integrating a new method for automatic compositor attribution, this project develops a statistical model for printer identification that leverages the same sources of evidence compiled manually by scholars of rare books, but at a scale and speed never before possible.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个印刷品和概率项目开发了新颖的机器学习和计算机视觉技术，以推断成千上万的书籍和小册子打印机，其身份已经避开了大约500年的学者。 在现代时代之前，书籍交易通常是危险和秘密的。由于担心迫害和惩罚，1473 - 1800年之间的打印机拒绝将其名字附加到所有已知书籍和小册子的四分之一中。但是，既然已有13万本书已被早期英语在线（EEBO）项目数字化，该时代的印刷工具的缺陷和变化可能是识别这些打印机的关键。一旦单独的金属类型损坏，它就会产生独特的邮票。由于符号属于特定打印机，因此受损类型的印象可以用作指纹标识成千上万秘密出版物的打印机。印刷和概率项目自动检测并跟踪这些独特的损坏类型，以发现有关书籍历史的新信息。该项目中开发的方法可以推广到其他重要的任务和域，例如数字取证和作者归因。此外，印刷和概率项目将以多学科的方式培训学生，使他们在多个领域进行协作。通过开发新技术以进行视觉异常检测，打印和概率项目检测到创建一致畸变的受损字母形式。基于这些损坏的类型提取，该项目开发了打印机和损坏的字母形式标识的概率模型，从而可以直接推断打印机按大规模推断。该框架还将其他证据来源纳入了识别模型 - 最重要的是，单个新闻室合成器的拼写，标点符号和间距习惯，其独特的实践使自己在集合中所有文本的所有页面上都可以聚类和自动归因。 该项目整合了一种自动合成器归因的新方法，为打印机识别开发了一个统计模型，该模型利用了稀有书籍学者手动编制的相同证据来源，但从未有过以前的规模和速度。这一奖项反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的crietia crititia criteria criteria criteria criteria criteria criteria criteria crititia crititia crititia crititia crititia crititia crititia crititia crietia crititia crietia crietia crititia crititia cripitia cristeria均值得一提。