Image Analysis for Archival Discovery (Aida)

Images created in the digitization of primary materials contain a wealth of machine-processable information for data mining and large-scale analysis, and this information should be leveraged both to connect researchers with the resources they need and to augment interpretation of human culture, as a complement to and extension of text-based approaches. The proposed project, "Image Analysis for Archival Discovery" (Aida), applies image processing and machine learning techniques from computer science to digitized materials to facilitate and promote archival discovery. Beginning with the automatic detection of poetic content in historic newspapers, this project will develop image processing as a methodology for humanities research and analysis. In doing so, it will advance work on two fronts: 1) it will contribute to the reevaluation of newspaper verse in American literary history; 2) it will assess the application of image analysis as a method for discovery in archival collections

ARKTOS: An Intelligent System for Satellite Sea Ice Image Analysis

We present an intelligent system for satellite sea ice image analysis named ARKTOS (Advanced Reasoning using Knowledge for Typing Of Sea ice). The underlying methodology of ARKTOS is to perform fully automated analysis of sea ice images by mimicking the reasoning process of sea ice experts and photo-interpreters. Hence, our approach is feature-based, rule-based classification supported by multisource data fusion and knowledge bases. A feature can be an ice floe, for example. ARKTOS computes a host of descriptors for that feature and then applies expert rules to classify the floe into one of several ice classes. ARKTOS also incorporates information derived from other sources, fusing different data towards more accurate classification. This modular, flexible, and extensible approach allows ARKTOS be refined and evaluated by expert users. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and GUI-based knowledge engineering and modification. As a research project over the past 10 years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation and deployment, and finally operationalization at the National Ice Center (NIC). In this paper, we will focus on the methodology of ARKTOS

Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent’s confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach—conducting multiple multi-issue negotiations—with its peers. Otherwise, it uses a pipelined approach—conducting multiple single-issue negotiations—with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent’s confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness

Using Chronicling America’s Images to Explore Digitized Historic Newspapers & Imagine Alternative Futures

This presentation situates the work of the Aida team broadly as well as hinges this work on some very specific challenges for digital libraries. In doing so demonstrate the many types of questions and domains to be explored in digitized newspapers

Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent’s confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach—conducting multiple multi-issue negotiations—with its peers. Otherwise, it uses a pipelined approach—conducting multiple single-issue negotiations—with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent’s confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness

An Online Survey Framework Using the Life Events Calendar

We describe an online survey framework programmed as a Java applet with a MySQL back-end. Our framework is built specifically as a Event History Calendar for the study of tobacco users and their behavior over a six month period. We introduce the notion of a Life Events Calendar and the relevance of an intelligent survey system in this context. We describe our methods and our component application approach and expand on the opportunities for artificial intelligence research with the system

White Paper, HD-51897-14, Image Analysis for Archival Discovery (Aida), October 2016

With its Office of Digital Humanities Start-up Grant, the Image Analysis for Archival Discovery (Aida) team set out to further develop image analysis as a methodology for the identification and retrieval of items of relevance within digitized collections of historic materials.1 Specifically, we sought to identify poetic content within historic newspapers, using Chronicling America\u27s newspapers (http://chroniclingamerica.loc.gov/) as our test case. The project activities we undertook—both those completed and those in process—support this goal and align well with the activities proposed in our original funding application and as approved by NEH. To achieve our goal of creating an image processing-based system to identify poetic content in historic newspaper collections, however, we also made strategic decisions along the way that shifted some of our efforts from those we initially planned when we drafted our funding proposal three years ago. During the grant period, the Aida team developed, trained, and tested a machine learning classifier that can identify poetic content in pages of digitized historic newspapers based only on visual signals. We published early results of this work in D-Lib Magazine in summer 2015. We have since undertaken a detailed case study that tests the application of our classifier and methodology to a test set of more than 22,000 newspaper page images from the period 1836-1840. Significantly, we shifted our emphasis from processing all pages from Chronicling America to conducting this thorough, critical analysis and case study. This shift in plans corresponds with our desire to explore image analysis as a methodology for connecting users of digital archives with materials of relevance

Final Report, HD-51897-14, Image Analysis for Archival Discovery (Aida), October 2016

With its Office of Digital Humanities Start-up Grant, the Image Analysis for Archival Discovery (Aida) team set out to further develop image analysis as a methodology for the identification and retrieval of items of relevance within digitized collections of historic materials. Specifically, we sought to identify poetic content within historic newspapers, using Chronicling America\u27s newspapers (http://chroniclingamerica.loc.gov/) as our test case. The project activities we undertook—both those completed and those in process—support this goal and align well with the activities proposed in our original funding application and as approved by NEH. To achieve our goal of creating an image processing-based system to identify poetic content in historic newspaper collections, however, we also made strategic decisions along the way that shifted some of our efforts from those we initially planned when we drafted our funding proposal three years ago. During the grant period, the Aida team developed, trained, and tested a machine learning classifier that can identify poetic content in pages of digitized historic newspapers based only on visual signals. We published early results of this work in D-Lib Magazine in summer 2015. We have since undertaken a detailed case study that tests the application of our classifier and methodology to a test set of more than 22,000 newspaper page images from the period 1836-1840. Significantly, we shifted our emphasis from processing all pages from Chronicling America to conducting this thorough, critical analysis and case study. This shift in plans corresponds with our desire to explore image analysis as a methodology for connecting users of digital archives with materials of relevance

Increasing Our Vision for 21st-Century Digital Libraries

This presentation Reads digital library interfaces—or their main door interfaces—as glimpses into what we have thus far valued in the development of digital libraries Frames a visual way of thinking about textual materials Introduces the work of our research team—where we are now, and where we\u27re headed Draws some connections between the parts This presentation is very much a look into thinking in process and work in progress and proposes the following ideas: As a community, we can do much more with the digital images we\u27re creating of textual materials than we\u27ve heretofore done. We aspire to have additional layers or levels of image analysis become part of the default processing work in the creation of digital libraries, not only as something that happens external or parallel to digital libraries, and not only toward the purpose of generating text. We aspire to more processing up front and iterative processing of materials—so that digital libraries\u27 materials are not once and done —and that this more processing is presented to users as additional options for how they can explore digital libraries, find materials of relevance, and imagine new possibilities Even as the digital libraries community focuses on supporting computational use of digital libraries—and our research team recognizes that our project very much depends on that computational use being supported—we should not leave behind, in 1998, those users of digital libraries for whom computational use is not their point of entry. (More on that date in a moment.