Collaboration Enabling Internet Resource Collection-Building Software and Technologies

Over the last decade the Library of the University of California, Riverside and its collaborators have developed a number of systems, service designs, and projects that utilize innovative technologies to foster better Internet finding tools in libraries and more cooperative and efficient effort in Internet link and metadata collection building. The open-source software and projects discussed represent appropriate technologies and sustainable strategies that we believe will help Internet portals, digital libraries, virtual libraries, library catalogs-with-portal-like-capabilities (IPDVLCs), and related collection-building efforts in academia to better scale and more accurately anticipate and meet the needs of scholarly and educational users.published or submitted for publicatio

Design for the contact zone. Knowledge management software and the structures of indigenous knowledges

This article examines the design of digital indigenous knowledge archives. In a discussion of the distinction between indigenous knowledge and western science, a decentred perspective is developed, in which the relationship between different local knowledges is explored. The particular characteristics of indigenous knowledges raise questions about if and how these knowledges can be managed. The role of technology in managing indigenous knowledges is explored with examples from fieldwork in India and Kenya and from web-based databases and digital archives. The concept of contact zone is introduced to explore the space in which different knowledges meet and are performed, such as indigenous knowledge and the technoscientific knowledge of the database. Design for the contact zone, this article proposes, is an intra-active and adaptive process for in creating databases that are meaningful for indigenous knowers. The meta-design approach is introduced as a methodology, which may provide indigenous knowers tools for self-representation and self-organisation through design

End-User Needs of Fragmented Databases in Higher Education Data Analysis and Decision Making

Indiana University-Purdue University Indianapolis (IUPUI)In higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. However, data sources can be accessed in a variety of ways and often do not seem to represent the same data set, presenting users with the confounding notion that data sources are in conflict with one another. As users are identifying new ways of accessing and analyzing this data, they are modifying existing work practices and sometimes creating their own databases. To understand how users are navigating these databases, the researchers employed a mixed methods research design including a survey and interview to understand the needs to end users who are accessing these seemingly fragmented databases. The study resulted in a three overarching categories – access, understandability, and use – that affect work practices for end users. The researchers used these themes to develop a set of broadly applicable design recommendations as well as six sets of sketches for implementation – development of a data gateway, training, collaboration, tracking, definitions and roadblocks, and time management

An architecture of a user-centred digital library for the academic community

An architecture of a user-centred digital library, designed to lead users of an academic community to the required information resources based on their tasks, is proposed. Information resources include full-text articles, databases, theses and dissertations, e-journals, e-books, multimedia databases, and so on. Other information resources such as university course calendars, university statutes, course registration, thesis and dissertation guidelines, style guides, and so on, are also needed by users. A prototype has been designed and developed using the School of Computer Engineering at Nanyang Technological University (NTU) as an example of such an environment to provide access to these information resources which are spread across different servers and in different home pages This prototype provides links to various information resources according to users' needs, as well as a personal work space to record/store his/her publications, frequently used or favorite hyperlinks and references or notes. Various stages of the prototype design and development are described and future works on this line are highlighted

Social Media for Cities, Counties and Communities

Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other tools and services with user- generated content have made a staggering amount of information (and misinformation) available. Some government officials seek to leverage these resources to improve services and communication with citizens, especially during crises and emergencies. Yet, the sheer volume of social data streams generates substantial noise that must be filtered. Potential exists to rapidly identify issues of concern for emergency management by detecting meaningful patterns or trends in the stream of messages and information flow. Similarly, monitoring these patterns and themes over time could provide officials with insights into the perceptions and mood of the community that cannot be collected through traditional methods (e.g., phone or mail surveys) due to their substantive costs, especially in light of reduced and shrinking budgets of governments at all levels. We conducted a pilot study in 2010 with government officials in Arlington, Virginia (and to a lesser extent representatives of groups from Alexandria and Fairfax, Virginia) with a view to contributing to a general understanding of the use of social media by government officials as well as community organizations, businesses and the public. We were especially interested in gaining greater insight into social media use in crisis situations (whether severe or fairly routine crises, such as traffic or weather disruptions)

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there. To make this possible, it is important to break free from the need for manual annotators. Recent work has begun to investigate how to use the massive amount of images available on the Web in place of manual image annotations. We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy. By combining these two results, we obtain a method for learning powerful deep object models automatically from the Web. We confirm the effectiveness of our approach through object categorization experiments using our Web-derived version of ImageNet on a popular robot vision benchmark database, and on a lifelong object discovery task on a mobile robot.Comment: 8 pages, 7 figures, 3 table