We propose an approach to index raster images of dictionary pages which in turn would require very little manual effort to enable direct access to the appropriate pages of the dictionary for lookup. Accessibility is further improved by feedback and crowdsourcing that enables highlighting of the specific location on the page where the lookup word is found, annotation, digitization, and fielded searching. This approach is equally applicable on simple scripts as well as complex writing systems. Using our proposed approach, we have built a Web application called "Dictionary Explorer" which supports word indexes in various languages and every language can have multiple dictionaries associated with it. Word lookup gives direct access to appropriate pages of all the dictionaries of that language simultaneously. The application has exploration features like searching, pagination, and navigating the word index through a tree-like interface. The application also supports feedback, annotation, and digitization features. Apart from the scanned images, "Dictionary Explorer" aggregates results from various sources and user contributions in Unicode. We have evaluated the time required for indexing dictionaries of different sizes and complexities in the Urdu language and examined various trade-offs in our implementation. Using our approach, a single person can make a dictionary of 1,000 pages searchable in less than an hour.
HathiTrust @cite_48 is a collaborative repository of digital content from various research institutions and libraries to preserve cultural records and to ensure accessibility and availability of the preserved content in future.
@cite_48
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In this paper we present the results of a study into the persistence and availability of web resources referenced from papers in scholarly repositories. Two repositories with different characteristics, arXiv and the UNT digital library, are studied to determine if the nature of the repository, or of its content, has a bearing on the availability of the web resources cited by that content. Memento makes it possible to automate discovery of archived resources and to consider the time between the publication of the research and the archiving of the referenced URLs. This automation allows us to process more than 160000 URLs, the largest known such study, and the repository metadata allows consideration of the results by discipline. The results are startling: 45 (66096) of the URLs referenced from arXiv still exist, but are not preserved for future generations, and 28 of resources referenced by UNT papers have been lost. Moving forwards, we provide some initial recommendations, including that repositories should publish URL lists extracted from papers that could be used as seeds for web archiving systems. LOCKSS (Lots Of Copies Keep Stuff Safe) is a tool designed for libraries to use to ensure their community's continued access to web-published scientific journals. LOCKSS allows libraries to take custody of the material to which they subscribe, in the same way they do for paper, and to preserve it. By preserving it they ensure that, for their community, links and searches continue to resolve to the published material even if it is no longer available from the publisher. Think of it as the digital equivalent of stacks where an authoritative copy of material is always available rather than the digital equivalent of an archive. LOCKSS allows libraries to run web caches for specific journals. These caches collect content as it is published and are never flushed. They cooperate in a peer-to-peer network to detect and repair damaged or missing pages. The caches run on generic PC hardware using open-source software and require almost no skilled administration, making the cost of preserving a journal manageable. Recently the Library of Congress began developing a strategy for the preservation of digital content. Efforts have focused on the need to select, harvest, describe, access and preserve Web resources. This poster focuses on the Library's initial investigation and evaluation of Web harvesting software tools. A future with widespread access to large digital libraries of video is nearing reality. Anticipating this future, a great deal of research is focused on methods of browsing and retrieving digital video, developing algorithms for creating surrogates for video content, and creating interfaces that display result sets from multimedia queries. Research in these areas requires that each investigator acquire and digitize video for their studies since the multimedia information retrieval community does not yet have a standard collection of video to be used for research purposes. The primary goal of the Open Video Project is to create and maintain a shared digital video repository and test collection to meet these research needs. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Academic libraries are increasingly looking to provide services that allow their users to work with digital collections in innovative ways, for example, to analyze large volumes of digitized collections. The HathiTrust Research Center (HTRC) is a large collaborative that provides an innovative research infrastructure for dealing with massive amounts of digital texts. In this poster, we report on the technical progress of the HTRC as well as on the efforts to build a user community around our cyberinfrastructure. Cultural institutions and museums have realized that annotations contribute valuable metadata for search and retrieval, which in turn can increase the visibility of the digital items they expose via their digital library systems. By exploiting annotations created by others, visitors can discover content they would not have found otherwise, which implies that annotations must be accessible and processable for humans and machines. Currently, however, there exists no widely adopted annotation standard that goes beyond specific media types. Most institutions build their own in-house annotation solution and employ proprietary annotation models, which are not interoperable with those of other systems. As a result, annotation data are usually stored in closed data silos and visible and processable only within the scope of a certain annotation system. As the main contribution of this paper, we present the LEMO Annotation Framework. It (1) provides a uniform annotation model for multimedia contents and various types of annotations, (2) can address fragments of various content-types in a uniform, interoperable manner and (3) pulls annotations out of closed data silos and makes them available as interoperable, dereferencable Web resources. With the LEMO Annotation Framework annotations become part of the Web and can be processed, linked, and referenced by other services. This in turn leads to even higher visibility and increases the potential value of annotations. Mood is a popular access point in music digital libraries and online music repositories, and is often represented as numerical values in a small number of emotion-related dimensions (e.g., valence and arousal). As music mood is recognized as culturally dependent, this study investigates whether regression models built with music data in one culture can be applied to music in another culture. Results indicate that cross-cultural predictions of both valence and arousal values are feasible. The OAI Object Reuse and Exchange (OAI-ORE) framework recasts the repository-centric notion of digital object to a bounded aggregation of Web resources. In this manner, digital library content is more integrated with the Web architecture, and thereby more accessible to Web applications and clients. This generalized notion of an aggregation that is independent of repository containment conforms more closely with notions in eScience and eScholarship, where content is distributed across multiple services and databases. We provide a motivation for the OAI-ORE project, review previous interoperability efforts, describe draft ORE specifications and report on promising results from early experimentation that illustrate improved interoperability and reuse of digital objects. As science becomes more data-intensive and collaborative, researchers increasingly use larger and more complex data to answer research questions. The capacity of storage infrastructure, the increased sophistication and deployment of sensors, the ubiquitous availability of computer clusters, the development of new analysis techniques, and larger collaborations allow researchers to address grand societ al challenges in a way that is unprecedented. In parallel, research data repositories have been built to host research data in response to the requirements of sponsors that research data be publicly available. Libraries are re-inventing themselves to respond to a growing demand to manage, store, curate and preserve the data produced in the course of publicly funded research. As librarians and data managers are developing the tools and knowledge they need to meet these new expectations, they inevitably encounter conversations around Big Data. This paper explores definitions of Big Data that have coalesced in the last decade around four commonly mentioned characteristics: volume, variety, velocity, and veracity. We highlight the issues associated with each characteristic, particularly their impact on data management and curation. We use the methodological framework of the data life cycle model, assessing two models developed in the context of Big Data projects and find them lacking. We propose a Big Data life cycle model that includes activities focused on Big Data and more closely integrates curation with the research life cycle. These activities include planning, acquiring, preparing, analyzing, preserving, and discovering, with describing the data and assuring quality being an integral part of each activity. We discuss the relationship between institutional data curation repositories and new long-term data resources associated with high performance computing centers, and reproducibility in computational science. We apply this model by mapping the four characteristics of Big Data outlined above to each of the activities in the model. This mapping produces a set of questions that practitioners should be asking in a Big Data project The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based. Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102, 000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently These results can be applied to develop recommender systems for other types of digital libraries. In the age of algorithms, I focus on the question of how to ensure algorithms that will take over many of our familiar archival and library tasks, will behave according to human ethical norms that have evolved over many years. I start by characterizing physical archives in the context of related institutions such as libraries and museums. In this setting I analyze how ethical principles, in particular about access to information, have been formalized and communicated in the form of ethical codes, or: codes of conducts. After that I describe two main developments: digitalization, in which physical aspects of the world are turned into digital data, and algorithmization, in which intelligent computer programs turn this data into predictions and decisions. Both affect interactions that were once physical but now digital. In this new setting I survey and analyze the ethical aspects of algorithms and how they shape a vision on the future of archivists and librarians, in the form of algorithmic documentalists, or: codementalists. Finally I outline a general research strategy, called IntERMEeDIUM, to obtain algorithms that obey are human ethical values encoded in code of ethics. Computing metaphors have become an integral part of information systems design, yet they are deeply rooted in cultural practices. This paper presents an investigation of the cross-cultural use and usability of such metaphors by studying the library metaphor of digital libraries in the cultural context of the Maori, the indigenous population of New Zealand. The ethnographic study examines relevant features of the Maori culture, their form of knowledge transfer and their use of physical and digital libraries. On this basis, the paper points out why and when the library metaphor fails Maori and other indigenous users, and indicates how this knowledge can contribute to the improvement of future designs. Privacy Preserving Record Linkage is an emerging field of research which attempts to deal with the classical linkage problem from a privacy preserving point of view. In this paper we propose a novel approach for performing Privacy Preserving Blocking in order to minimize the computational cost of Privacy Preserving Record Linkage. We achieve this without compromising privacy by using Nearest Neighbors clustering, a well-known clustering algorithm and by using a reference table. A reference table is a publicly known table the contents of which are used as intermediate references. The combination of Nearest Neighbors and a reference table offers our approach k-anonymity characteristics. The Tapestry experimental mail system developed at the Xerox Palo Alto Research Center is predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering by recording their reactions to documents they read. The reactions are called annotations; they can be accessed by other people’s filters. Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader browser. Tapestry’s client server architecture, its various components, and the Tapestry query language are described. Hierarchical representations are common in digital repositories, yet are not always fully leveraged in their online search interfaces. This work describes ResultMaps, which use hierarchical treemap representations with query string-driven digital library search engines. We describe two lab experiments, which find that ResultsMap users yield significantly better results over a control condition on some subjective measures, and we find evidence that ResultMaps have ancillary benefits via increased understanding of some aspects of repository content. The ResultMap system and experiments contribute an understanding of the benefits-direct and indirect-of the ResultMap approach to repository search visualization. Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions. Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners. The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language. We study the distributed privacy preserving data collection problem: an untrusted data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multi-attributed record which contains both non-sensitive (e.g., quasi-identifiers) and sensitive information (e.g., a particular disease), and submits it to the data collector. Assuming T is the table formed by all the respondent data records, we say that the data collection process is privacy preserving if it allows the data collector to obtain a k-anonymized or l-diversified version of T without revealing the original records to the adversary. We propose a distributed data collection protocol that outputs an anonymized table by generalization of quasi-identifier attributes. The protocol employs cryptographic techniques such as homomorphic encryption, private information retrieval and secure multiparty computation to ensure the privacy goal in the process of data collection. Meanwhile, the protocol is designed to leak limited but noncritical information to achieve practicability and efficiency. Experiments show that the utility of the anonymized table derived by our protocol is in par with the utility achieved by traditional anonymization techniques. The Visual Understanding Environment (VUE) project at Tufts' Academic Technology department provides faculty and students with tools to successfully integrate digital resources into their teaching and learning. VUE provides a visual environment for structuring, presenting, and sharing digital information and an OKI-compliant software bridge for connecting to FEDORA-based digital repositories. Using VUE's concept mapping interface, faculty and students design customized semantic networks of digital resources drawing from digital libraries, local files and the Web. The resulting content maps can then be viewed and exchanged online.