Michael J. Henry

MultiFacet: A Faceted Interface for Browsing Large Multimedia Collections

Faceted browsing is a common technique for exploring collections where the data can be grouped into a number of pre-defined categories, most often generated from textual metadata. Historically, faceted browsing has been applied to a single data type such as text or image data. However, typical collections contain multiple data types, such as information from web pages that contain text, images, and video. Additionally, when browsing a collection of images and video, facets are often created based on the metadata which may be incomplete, inaccurate, or missing altogether instead of the actual visual content contained within those images and video. In this work we address these limitations by presenting MultiFacet, a faceted browsing interface that supports multiple data types. MultiFacet constructs facets for images and video in a collection from the visual content using computer vision techniques. These visual facets can then be browsed in conjunction with text facets within a single interface to reveal relationships and phenomena within multimedia collections. Additionally, we present a use case based on real-world data, demonstrating the utility of this approach towards browsing a large multimedia data collection.

Speech information retrieval: a review

Speech is an information-rich component of multimedia. Information can be extracted from a speech signal in a number of different ways, and thus there are several well-established speech signal analysis research fields. These fields include speech recognition, speaker recognition, event detection, and fingerprinting. The information that can be extracted from tools and methods developed in these fields can greatly enhance multimedia systems. In this paper, we present the current state of research in each of the major speech analysis fields. The goal is to introduce enough background for someone new in the field to quickly gain high-level understanding and to provide direction for further study.

Coherent image layout using an adaptive visual vocabulary

When querying a huge image database containing millions of images, the result of the query may still contain many thousands of images that need to be presented to the user. We consider the problem of arranging such a large set of images into a visually coherent layout, one that places similar images next to each other. Image similarity is determined using a bag-of-features model, and the layout is constructed from a hierarchical clustering of the image set by mapping an in-order traversal of the hierarchy tree into a space-filling curve. This layout method provides strong locality guarantees so we are able to quantitatively evaluate performance using standard image retrieval benchmarks. Performance of the bag-of-features method is best when the vocabulary is learned on the image set being clustered. Because learning a large, discriminative vocabulary is a computationally demanding task, we present a novel method for efficiently adapting a generic visual vocabulary to a particular dataset. We evaluate our clustering and vocabulary adaptation methods on a variety of image datasets and show that adapting a generic vocabulary to a particular set of images improves performance on both hierarchical clustering and image retrieval tasks.

Sensalert: A Real-time Group and Individual Health Tracking Application

Sensalert is a wearable health tracking application that enables monitoring of a target populations health status in real-time. The application integrates health monitoring parameters from wearable sensors, e.g., temperature and heart rate, with relevant environmental parameters, e.g., weather and topography, and calculates the corresponding physiological strain index for each point in time. The objective of this application is to provide early warning, support decision making, and increase situational awareness by displaying comprehensive information and providing alerts at the right time and place. This application is the first of its kind being developed for integration into the Defense Threat Reduction Agencies Bio Surveillance Ecosystem (BSVE). We present the user requirements and design principles for developing this type of application. We hope this work helps and inspires future research in the area of wearable technologies for surveillance.

Design Science Methodology Applied to a Chemical Surveillance Tool

Public health surveillance systems gain significant benefits from integrating existing early incident detection systems, supported by closed data sources, with public available data. However, identifying potential alerting incidents relies on finding accurate, reliable sources and presenting the high volume of data in a way that increases analysts work efficiency; a challenge for any system that leverages publicly available data. In this paper, we present the design concept and the applied design science research methodology of ChemVeillance, a chemical analyst surveillance system. Our work portrays a system design and approach that translates theoretical design methodology into practice creating a powerful surveillance system built for specific use cases. Researchers, designers, developers, and related professionals in the health surveillance community can build upon the principles and methodology described here to enhance and broaden current surveillance systems leading to improved situational awareness based on a robust integrated early warning system.

A global climate model agent for high spatial and temporal resolution data

Fine cell granularity in modern climate models can produce terabytes of data in each snapshot, causing significant I/O overhead. To address this issue, a method of reducing the I/O latency of high-resolution climate models by identifying and selectively outputting regions of interest is presented. Working with a global cloud-resolving model and running with up to 10,240 processors on a Cray XE6, this method provides significant I/O bandwidth reduction depending on the frequency of writes and the size of the region of interest. The implementation challenges of determining global parameters in a strictly core-localized model and properly formatting output files that only contain subsections of the global grid are addressed, as well as the overall bandwidth impact and benefits of the method. The gains in I/O throughput provided by this method allow dual output rates for high-resolution climate models: a low-frequency global snapshot as well as a high-frequency regional snapshot when events of particular interest occur.

LESSONS LEARNED Biosurveillance Mobile App Development Intern Competition (Summer 2013)

The purpose of the lessons learned document for the BEOWulf Biosurveillance Mobile App Development Intern Competition is to capture the project’s lessons learned in a formal document for use by other project managers on similar future projects. This document may be used as part of new project planning for similar projects in order to determine what problems occurred and how those problems were handled and may be avoided in the future. Additionally, this document details what went well with the project and why, so that other project managers may capitalize on these actions. Project managers may also use this document to determine who the project team members were in order to solicit feedback for planning their projects in the future. This document will be formally communicated with the organization and will become a part of the organizational assets and archives.

Lessons Learned from the Development of an Example Precision Information Environment for International Safeguards

The International Atomic Energy Agency (IAEA) is interested in increasing capabilities of IAEA safeguards inspectors to access information that would improve their situational awareness on the job. A mobile information platform could potentially provide access to information, analytics, and technical and logistical support to inspectors in the field, as well as providing regular updates to analysts at IAEA Headquarters in Vienna or at satellite offices. To demonstrate the potential capability of such a system, Pacific Northwest National Laboratory (PNNL) implemented a number of example capabilities within a PNNL-developed precision information environment (PIE), and using a tablet as a mobile information platform. PNNL’s safeguards proof-of-concept PIE intends to; demonstrate novel applications of mobile information platforms to international safeguards use cases; demonstrate proof-of-principle capability implementation; and provide “vision” for capabilities that could be implemented. This report documents the lessons learned from this two-year development activity for the Precision Information Environment for International Safeguards (PIE-IS), describing the developed capabilities, technical challenges, and considerations for future development, so that developers working to develop a similar system for the IAEA or other safeguards agencies might benefit from our work.

Precision Information Environment (PIE) for International Safeguards: Pre-Demonstration Development Use Cases

In FY2013, the PIE International Safeguards team demonstrated our development progress to U.S. Department of Energy (DOE) staff from the Office of Nonproliferation and International Security (NA-24, our client) and the Office of Defense Nuclear Nonproliferation Research and Development (NA-22). Following the demonstration, the team was asked by our client to complete additional development prior to a planned demonstration at the International Atomic Energy Agency (IAEA), scheduled tentatively for January or spring of 2014. The team discussed four potential areas for development (in priority order), and will develop them as time and funding permit prior to an IAEA demonstration. The four capability areas are: 1. Addition of equipment manuals to PIE-accessible files 2. Optical character recognition (OCR) of photographed text 3. Barcode reader with information look-up from a database 4. Add Facilities to Data Model 5. Geospatial capabilities with information integration Each area will be described below in a use case.