The main objective of this paper is to talk about my work as a book and paper conservator in the light of the current rehabilitation project at the Notarial Archives in St Christopher Street, Valletta. With its six centuries of manuscript material spread over two kilometres of shelving, the state of preservation of the archives has presented numerous challenges over the last years. The EU funds granted in recent months are a crucial investment that will ensure the safeguarding of the collection, but putting one's house in order is not just about money. A number of other considerations such as careful planning, multidisciplinary collaboration, clever marketing, accessibility, team-building and creating a clear vision for the future have been some of the central factors that continue to contribute to the success of this project. A discussion on the general preservation and conservation strategies that are being undertaken for the project will also be given.
{"title":"The Notarial Archives, Valletta: Starting from Zero","authors":"T. Lupi","doi":"10.1145/3103010.3103025","DOIUrl":"https://doi.org/10.1145/3103010.3103025","url":null,"abstract":"The main objective of this paper is to talk about my work as a book and paper conservator in the light of the current rehabilitation project at the Notarial Archives in St Christopher Street, Valletta. With its six centuries of manuscript material spread over two kilometres of shelving, the state of preservation of the archives has presented numerous challenges over the last years. The EU funds granted in recent months are a crucial investment that will ensure the safeguarding of the collection, but putting one's house in order is not just about money. A number of other considerations such as careful planning, multidisciplinary collaboration, clever marketing, accessibility, team-building and creating a clear vision for the future have been some of the central factors that continue to contribute to the success of this project. A discussion on the general preservation and conservation strategies that are being undertaken for the project will also be given.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"64 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2017-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74480023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A ruling classification is presented in this paper. In contrast to state-of-the-art methods which focus on ruling line removal, ruling lines are analyzed for document clustering in the context of document snippet reassembling. First, a background patch is extracted from a snippet at a position which minimizes the inscribed content. A novel Fourier feature is then computed on the image patch. The classification into void, lined and checked is carried out using Support Vector Machines. Finally, an accurate line localization is performed by means of projection profiles and robust line fitting. The ruling classification achieves an F-score of 0.987 evaluated on a dataset comprising real world document snippets. In addition the line removal was evaluated on a synthetically generated dataset where an F-score of 0.931 is achieved. This dataset is made publicly available so as to allow for benchmarking.
{"title":"Ruling analysis and classification of torn documents","authors":"Markus Diem, Florian Kleber, Robert Sablatnig","doi":"10.1145/2644866.2644876","DOIUrl":"https://doi.org/10.1145/2644866.2644876","url":null,"abstract":"A ruling classification is presented in this paper. In contrast to state-of-the-art methods which focus on ruling line removal, ruling lines are analyzed for document clustering in the context of document snippet reassembling. First, a background patch is extracted from a snippet at a position which minimizes the inscribed content. A novel Fourier feature is then computed on the image patch. The classification into void, lined and checked is carried out using Support Vector Machines. Finally, an accurate line localization is performed by means of projection profiles and robust line fitting. The ruling classification achieves an F-score of 0.987 evaluated on a dataset comprising real world document snippets. In addition the line removal was evaluated on a synthetically generated dataset where an F-score of 0.931 is achieved. This dataset is made publicly available so as to allow for benchmarking.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"8 1","pages":"63-72"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89652172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Source retrieval for plagiarism detection involves using a search engine to retrieve candidate sources of plagiarism for a given suspicious document so that more accurate comparisons can be made. An important consideration is that only documents that are likely to be sources of plagiarism should be retrieved so as to minimize the number of unnecessary comparisons made. A supervised strategy for source retrieval is described whereby search results are classified and ranked as potential sources of plagiarism without retrieving the search result documents and using only the information available at search time. The performance of the supervised method is compared to a baseline method and shown to improve precision by up to 3.28%, recall by up to 2.6% and the F1 score by up to 3.37%. Furthermore, features are analyzed to determine which of them are most important for search result classification with features based on document and search result similarity appearing to be the most important.
{"title":"Classifying and ranking search engine results as potential sources of plagiarism","authors":"Kyle Williams, Hung-Hsuan Chen, C. Lee Giles","doi":"10.1145/2644866.2644879","DOIUrl":"https://doi.org/10.1145/2644866.2644879","url":null,"abstract":"Source retrieval for plagiarism detection involves using a search engine to retrieve candidate sources of plagiarism for a given suspicious document so that more accurate comparisons can be made. An important consideration is that only documents that are likely to be sources of plagiarism should be retrieved so as to minimize the number of unnecessary comparisons made. A supervised strategy for source retrieval is described whereby search results are classified and ranked as potential sources of plagiarism without retrieving the search result documents and using only the information available at search time. The performance of the supervised method is compared to a baseline method and shown to improve precision by up to 3.28%, recall by up to 2.6% and the F1 score by up to 3.37%. Furthermore, features are analyzed to determine which of them are most important for search result classification with features based on document and search result similarity appearing to be the most important.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"2 1","pages":"97-106"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73105632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The need to find similar documents occurs in many settings, such as in plagiarism detection or research paper recommendation. Manually constructing queries to find similar documents may be overly complex, thus motivating the use of whole documents as queries. This paper introduces SimSeerX, a search engine for similar document retrieval that receives whole documents as queries and returns a ranked list of similar documents. Key to the design of SimSeerX is that is able to work with multiple similarity functions and document collections. We present the architecture and interface of SimSeerX, show its applicability with 3 different similarity functions and demonstrate its scalability on a collection of 3.5 million academic documents.
{"title":"SimSeerX: a similar document search engine","authors":"Kyle Williams, Jian Wu, C. Lee Giles","doi":"10.1145/2644866.2644895","DOIUrl":"https://doi.org/10.1145/2644866.2644895","url":null,"abstract":"The need to find similar documents occurs in many settings, such as in plagiarism detection or research paper recommendation. Manually constructing queries to find similar documents may be overly complex, thus motivating the use of whole documents as queries. This paper introduces SimSeerX, a search engine for similar document retrieval that receives whole documents as queries and returns a ranked list of similar documents. Key to the design of SimSeerX is that is able to work with multiple similarity functions and document collections. We present the architecture and interface of SimSeerX, show its applicability with 3 different similarity functions and demonstrate its scalability on a collection of 3.5 million academic documents.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"96 1","pages":"143-146"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82794105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Kolberg, L. G. Fernandes, Mateus Raeder, Carolina Fonseca
Digital printers have consistently improved their speed in the past years. Meanwhile, the need for document personalization and customization has increased. As a consequence of these two facts, the traditional rasterization process has become a highly demanding computational step in the printing workflow. Moreover, Print Service Providers are now using multiple RIP engines to speed up the whole document rasterization process, and depending on the input document characteristics the rasterization process may not achieve the print-engine speed creating a unwanted bottleneck. In this scenario, we developed a tool called Job Adaptive Router (JAR) aiming at improving the throughput of the rasterization process through a clever load balance among RIP engines which is based on information obtained by the analysis of input documents content. Furthermore, along with this tool we propose some strategies that consider relevant characteristics of documents, such as transparency and reusability of images, to split the job in a more intelligent way. The obtained results confirm that the use of the proposed tool improved the rasterization process performance.
{"title":"JAR tool: using document analysis for improving the throughput of high performance printing environments","authors":"M. Kolberg, L. G. Fernandes, Mateus Raeder, Carolina Fonseca","doi":"10.1145/2644866.2644887","DOIUrl":"https://doi.org/10.1145/2644866.2644887","url":null,"abstract":"Digital printers have consistently improved their speed in the past years. Meanwhile, the need for document personalization and customization has increased. As a consequence of these two facts, the traditional rasterization process has become a highly demanding computational step in the printing workflow. Moreover, Print Service Providers are now using multiple RIP engines to speed up the whole document rasterization process, and depending on the input document characteristics the rasterization process may not achieve the print-engine speed creating a unwanted bottleneck. In this scenario, we developed a tool called Job Adaptive Router (JAR) aiming at improving the throughput of the rasterization process through a clever load balance among RIP engines which is based on information obtained by the analysis of input documents content. Furthermore, along with this tool we propose some strategies that consider relevant characteristics of documents, such as transparency and reusability of images, to split the job in a more intelligent way. The obtained results confirm that the use of the proposed tool improved the rasterization process performance.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"25 1","pages":"175-178"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77664085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers constantly read and annotate academic documents. While almost all documents are provided digitally, many are still printed and read on paper. We surveyed 162 academics in order to better understand their reading habits and preferences. We were particularly interested in understanding the barriers to digital reading and the features desired by academics for digital reading applications.
{"title":"What academics want when reading digitally","authors":"Juliane Franze, K. Marriott, Michael Wybrow","doi":"10.1145/2644866.2644894","DOIUrl":"https://doi.org/10.1145/2644866.2644894","url":null,"abstract":"Researchers constantly read and annotate academic documents. While almost all documents are provided digitally, many are still printed and read on paper. We surveyed 162 academics in order to better understand their reading habits and preferences. We were particularly interested in understanding the barriers to digital reading and the features desired by academics for digital reading applications.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"9 1","pages":"199-202"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79912551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rodrigo Chamun, Daniele Pinheiro, Diego Jornada, J. B. Oliveira, I. Manssour
Printing web pages is usually a thankless task as the result is often a document with many badly-used pages and poor layout. Besides the actual content, superfluous web elements like menus and links are often present and in a printed version they are commonly perceived as an annoyance. Therefore, a solution for obtaining cleaner versions for printing is to detect parts of the page that the reader wants to consume, eliminating unnecessary elements and filtering the "true" content of the web page. In addition, the same solution may be used online to present cleaner versions of web pages, discarding any elements that the user wishes to avoid. In this paper we present a novel approach to implement such filtering. The method is interactive at first: The user samples items that are to be preserved on the page and thereafter everything that is not similar to the samples is removed from the page. This is achieved by comparing the path of all elements on the DOM representation of the page with the path of the elements sampled by the user and preserving only elements that have a path "similar" to the sample. The introduction of a similarity measure adds an important degree of adaptability to the needs of different users and applications. This approach is quite general and may be applied to any XML tree that has labeled nodes. We use HTML as a case study and present a Google Chrome extension that implements the approach as well as a user study comparing our results with commercial results.
{"title":"Extracting web content for personalized presentation","authors":"Rodrigo Chamun, Daniele Pinheiro, Diego Jornada, J. B. Oliveira, I. Manssour","doi":"10.1145/2644866.2644871","DOIUrl":"https://doi.org/10.1145/2644866.2644871","url":null,"abstract":"Printing web pages is usually a thankless task as the result is often a document with many badly-used pages and poor layout. Besides the actual content, superfluous web elements like menus and links are often present and in a printed version they are commonly perceived as an annoyance. Therefore, a solution for obtaining cleaner versions for printing is to detect parts of the page that the reader wants to consume, eliminating unnecessary elements and filtering the \"true\" content of the web page. In addition, the same solution may be used online to present cleaner versions of web pages, discarding any elements that the user wishes to avoid.\u0000 In this paper we present a novel approach to implement such filtering. The method is interactive at first: The user samples items that are to be preserved on the page and thereafter everything that is not similar to the samples is removed from the page. This is achieved by comparing the path of all elements on the DOM representation of the page with the path of the elements sampled by the user and preserving only elements that have a path \"similar\" to the sample. The introduction of a similarity measure adds an important degree of adaptability to the needs of different users and applications.\u0000 This approach is quite general and may be applied to any XML tree that has labeled nodes. We use HTML as a case study and present a Google Chrome extension that implements the approach as well as a user study comparing our results with commercial results.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"72 1","pages":"157-164"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86225398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Cabral, R. Lins, R. Mello, F. Freitas, B. T. Ávila, S. Simske, M. Riss
The text data available on the Internet is not only huge in volume, but also in diversity of subject, quality and idiom. Such factors make it infeasible to efficiently scavenge useful information from it. Automatic text summarization is a possible solution for efficiently addressing such a problem, because it aims to sieve the relevant information in documents by creating shorter versions of the text. However, most of the techniques and tools available for automatic text summarization are designed only for the English language, which is a severe restriction. There are multilingual platforms that support, at most, 2 languages. This paper proposes a language independent summarization platform that provides corpus acquisition, language classification, translation and text summarization for 25 different languages.
{"title":"A platform for language independent summarization","authors":"L. Cabral, R. Lins, R. Mello, F. Freitas, B. T. Ávila, S. Simske, M. Riss","doi":"10.1145/2644866.2644890","DOIUrl":"https://doi.org/10.1145/2644866.2644890","url":null,"abstract":"The text data available on the Internet is not only huge in volume, but also in diversity of subject, quality and idiom. Such factors make it infeasible to efficiently scavenge useful information from it. Automatic text summarization is a possible solution for efficiently addressing such a problem, because it aims to sieve the relevant information in documents by creating shorter versions of the text. However, most of the techniques and tools available for automatic text summarization are designed only for the English language, which is a severe restriction. There are multilingual platforms that support, at most, 2 languages. This paper proposes a language independent summarization platform that provides corpus acquisition, language classification, translation and text summarization for 25 different languages.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"43 1","pages":"203-206"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73953974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cascading Style Sheets (CSS) is a standard language for stylizing and formatting web documents. Its role in web user experience becomes increasingly important. However, CSS files tend to be designed from a result-driven point of view, without much attention devoted to the CSS file structure as long as it produces the desired results. Furthermore, the rendering intended in the browser is often checked and debugged with a document instance. Style sheets normally apply to a set of documents, therefore modifications added while focusing on a particular instance might affect other documents of the set. We present a first prototype of static CSS semantical analyzer and optimizer that is capable of automatically detecting and removing redundant property declarations and rules. We build on earlier work on tree logics to locate redundancies due to the semantics of selectors and properties. Existing purely syntactic CSS optimizers might be used in conjunction with our tool, for performing complementary (and orthogonal) size reduction, toward the common goal of providing smaller and cleaner CSS files.
{"title":"Automated refactoring for size reduction of CSS style sheets","authors":"Martí Bosch, P. Genevès, Nabil Layaïda","doi":"10.1145/2644866.2644885","DOIUrl":"https://doi.org/10.1145/2644866.2644885","url":null,"abstract":"Cascading Style Sheets (CSS) is a standard language for stylizing and formatting web documents. Its role in web user experience becomes increasingly important. However, CSS files tend to be designed from a result-driven point of view, without much attention devoted to the CSS file structure as long as it produces the desired results. Furthermore, the rendering intended in the browser is often checked and debugged with a document instance. Style sheets normally apply to a set of documents, therefore modifications added while focusing on a particular instance might affect other documents of the set.\u0000 We present a first prototype of static CSS semantical analyzer and optimizer that is capable of automatically detecting and removing redundant property declarations and rules. We build on earlier work on tree logics to locate redundancies due to the semantics of selectors and properties. Existing purely syntactic CSS optimizers might be used in conjunction with our tool, for performing complementary (and orthogonal) size reduction, toward the common goal of providing smaller and cleaner CSS files.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"10 1","pages":"13-16"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87175522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Cochrane Schizophrenia Group's Register of studies details all aspects of the effects of treating people with schizophrenia. It has been gathered over the last 20 years and consists of around 20,000 documents, overwhelmingly in PDF. Document collections of this sort -- on a given theme but gathered from a wide range of sources -- will generally have huge variability in the quality of the PDF, particularly with respect to the key property of text searchability. Summarising the results from the best of these papers, to allow evidence-based health care decision making, has so far been done by manually creating a summary document, starting from a visual inspection of the relevant PDF file. This labour-intensive process has resulted, to date, in only 4,000 of the papers being summarised -- with enormous duplication of effort and with many issues around the validity and reliability of the data extraction. This paper describes a pilot project to provide a computer-assisted framework in which any of the PDF documents could be searched for the occurrence of some 8,000 keywords and key phrases. Once keyword tagging has been completed the framework assists in the generation of a standard summary document, thereby greatly speeding up the production of these summaries. Early examples of the framework are described and its capabilities illustrated.
{"title":"Generating summary documents for a variable-quality PDF document collection","authors":"Jacob Hughes, D. Brailsford, S. Bagley, C. Adams","doi":"10.1145/2644866.2644892","DOIUrl":"https://doi.org/10.1145/2644866.2644892","url":null,"abstract":"The Cochrane Schizophrenia Group's Register of studies details all aspects of the effects of treating people with schizophrenia. It has been gathered over the last 20 years and consists of around 20,000 documents, overwhelmingly in PDF. Document collections of this sort -- on a given theme but gathered from a wide range of sources -- will generally have huge variability in the quality of the PDF, particularly with respect to the key property of text searchability.\u0000 Summarising the results from the best of these papers, to allow evidence-based health care decision making, has so far been done by manually creating a summary document, starting from a visual inspection of the relevant PDF file. This labour-intensive process has resulted, to date, in only 4,000 of the papers being summarised -- with enormous duplication of effort and with many issues around the validity and reliability of the data extraction.\u0000 This paper describes a pilot project to provide a computer-assisted framework in which any of the PDF documents could be searched for the occurrence of some 8,000 keywords and key phrases. Once keyword tagging has been completed the framework assists in the generation of a standard summary document, thereby greatly speeding up the production of these summaries. Early examples of the framework are described and its capabilities illustrated.","PeriodicalId":91385,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering. ACM Symposium on Document Engineering","volume":"34 1","pages":"49-52"},"PeriodicalIF":0.0,"publicationDate":"2014-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74291369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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