Ross King

A Service for Data-Intensive Computations on Virtual Clusters

Digital Preservation deals with the long-term storage, access, and maintenance of digital data objects. In order to prevent a loss of information, digital libraries and archives are increasingly faced with the need to electronically preserve vast amounts of data while having limited computational resources in-house. However, due to the potentially immense data sets and computationally intensive tasks involved, preservation systems have become a recognized challenge for e-science. We argue that grid and cloud technology can provide the crucial technology for building scalable preservation systems. In this paper, we present recent developments on a Job Submission Service that is based on standard grid mechanisms and capable of providing a large cluster of virtual machines. The service allows clients to specify and execute preservation tools on large data sets based on dynamically generated job descriptors. This approach allows us to utilize a cloud infrastructure that is based on platform virtualization as a scaling environment for the execution of preservation workflows. Finally, we present experimental results that have been conducted on the Amazon EC2 and S3 utility cloud infrastructure.

The Planets Interoperability Framework

We report on the implementation of a software infrastructure for preservation actions, carried out in the context of the European Integrated Project Planets – the Planets Interoperability Framework (IF). The design of the framework was driven by the requirements of logical preservation in the domain of libraries and archives. The IF is a Java-based software suite built on a number of open source components and Java standards. Specific features of interest are a web service architecture including specified preservation service interfaces for the integration of new and existing preservation tools and a workflow engine for the execution of service-based preservation plans.

The Planets IF: a framework for integrated access to preservation tools

The Planets project is driven by requirements for the long-term preservation faced by institutional libraries and archives. The project develops an integrated environment that allows archivists to seamlessly utilize and evaluate tools and strategies for the preservation of cultural heritage data. The Planets Interoperability Framework (IF) supports this vision by providing the technical backbone for integrating existing content repositories, preservation tools, and services into a service-oriented research infrastructure. It implements a number of common software components for user authentication, data access, or service orchestration. Moreover, it defines the interfaces and communication protocols for preservation services like identification, characterization, migration or rendering. It thereby assures the interoperability of the various heterogeneous preservation tools and applications in order to establish a coherent and extensible preservation system. In this paper, we present the service architecture as well as the runtime environment and its application.

A Framework for Distributed Preservation Workflows

The Planets Project is developing a service-oriented environment for the definition and evaluation of preservation strategies for human-centric data. It focuses on the question of logically preserving digital materials, as opposed to the physical preservation of content bit-streams. This includes the development of preservation tools for the automated characterisation, migration, and comparison of different types of Digital Objects as well as the emulation of their original runtime environment in order to ensure long-time access and interpretability. The Planets integrated environment provides a number of end-user applications that allow data curators to execute and scientifically evaluate preservation experiments based on composable preservation services. In this paper, we focus on the middleware and programming model and show how it can be utilised in order to create complex preservation workflows.

Cyber Threat Information Classification and Life Cycle Management using Smart Contracts.

Nowadays, cyber critical infrastructures (CIs) are increasingly targeted by highly sophisticated cyber attacks and should be protected. Advances in cyber situational awareness technology lead to the creation of increasingly complex tools. Human analysts face challenges finding relevant information in large, complex data sets, when exploring data to discover patterns and insights. To be effective in identifying and defeating future cyber-attacks, cyber analysts require novel tools for incident report classification and life cycle management that can automatically analyse and share result in secure way between CI stakeholders to achieve better situation comprehension. Our goal is to provide solutions in realtime that could replace human input for cyber incident classification and management tasks to eliminate irrelevant information and to focus on important information to promptly adopt suitable countermeasures in case of an attack. Another contribution relates to the provided support for document life cycle management that should reduce the number of manual operations and save storage space. In this paper we evaluate the application of so-called “smart contracts” to an incident classification system and assess its accuracy and performance. We demonstrate how the presented techniques can be applied to support incident handling tasks performed by security operation centers (SOCs).

Braille Vision Using Braille Display and Bio-inspired Camera

This paper presents a system for Braille learning support using real-time panoramic views generated from the novel smart panorama camera 360SCAN. The system makes use of the modern image processing libraries and state-of-the-art features extraction and clustering methods. We compare the real-time frames recorded by the bio-inspired camera to the reference images in order to determine particular figures. One contribution of the proposed method is that image edges can be transformed to the presentation on Braille display directly without any image processing. It is possible due to the bio-inspired construction of camera sensor. Another contribution is that our approach provides Braille users with images recorded from natural scenes. We conducted several experiments that verify the methods that demonstrate learning figures captured by the smart camera. Our goal is to process such images and present them on the Braille Display in a form appropriate for visually impaired people. All evaluations were performed in the natural environment with ambient illumination of 200 lux, which demonstrates high camera reliability in difficult light conditions. The system can be optimized by applying additional filters and features algorithms and by decreasing the rotational speed of the camera. The presented Braille learning support system is a building block for a rich and qualitative educational system for the efficient information transfer focused on visually impaired people.

Quality Assurance Tool Suite for Error Detection in Digital Repositories

Digitization workflows for automatic acquisition of image collections are susceptible to errors and require quality assurance. This paper presents the automated quality assurance tools aiming at detection of possible quality issues that supports decision making for document image collections. The main contribution of this research is the implementation of various image processing tools for different error detection scenarios and their combination in to a single tool suite. The tool suite includes: (1) The matchbox tool for accurate near-duplicate detection in document image collections, based on SIFT feature extraction. (2) The finger detection tool aims at automatic detection of fingers that mistakenly appear in scans from digitized image collections, which uses processing techniques for edge detection, local image information extraction and its analysis for reasoning on scan quality. (3) The cropping error detection tool supports the detection of common cropping problems such as text shifted to the edge of the image, unwanted page borders, or unwanted text from a previous page on the image. Another important contribution of this work is a definition of the quality assurance workflow and its automatic execution for error detection in digital document collections. The presented tool suite detects described errors and presents them for additional manual analysis and collection cleaning. A statistical overview of evaluated data and characteristics like performance and accuracy is delivered. The results of the analysis confirm our hypothesis that an automated approach is able to detect errors with reliable quality, thus making quality control for large digitisation projects a feasible and affordable process.

Quality control of real-time panoramic views from the smart camera 360SCAN

This paper presents a system for quality control of real-time panoramic views generated from the novel smart panorama camera 360SCAN. The system makes use of the modern image processing library OpenIMAJ and state-of-the-art features extraction and clustering methods. We compare a real-time frame collection recorded by the camera to a reference image collection in order to determine camera readiness. We conducted several experiments that verify the methods that demonstrate smart camera operational status and evaluate changes in the position or number of objects in the working location. All evaluations were performed in the natural environment with ambient illumination of 200 lux, which demonstrates high camera reliability in difficult light conditions. The system can be optimized for embedded applications by applying additional filters and features algorithms and by decreasing the rotational speed of the camera. The presented quality control system is a building block for a rich and qualitative expert system for the efficient control and support of the smart camera.