Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR.

Multiple Classification Ripple Down Rules (MCRDR) is a knowledge acquisition technique that produces representations, or knowledge maps, of a human expert's knowledge of a particular domain. However, work on gaining an understanding of the knowledge acquired at a deeper meta-level or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Weighted MCRDR (WM), which looks at deriving and learning information about the relationships between multiple classifications within MCRDR by calculating a meaningful rating for the task at hand. This is not intended to reduce the knowledge acquisition effort for the expert. Rather, it is attempting to use the knowledge received in the MCRDR knowledge map to derive additional information that can allow improvements in functionality of MCRDR in many problem domains. Preliminary testing shows that there exists a strong potential for WM to quickly and effectively learn meaningful weightings

"Stuff goes into the computer and doesn't come out": a cross-tool study of personal information management

This paper reports a study of Personal Information Management (PIM), which advances research in two ways: (1) rather than focusing on one tool, we collected cross-tool data relating to file, email and web bookmark usage for each participant, and (2) we collected longitudinal data for a subset of the participants. We found that individuals employ a rich variety of strategies both within and across PIM tools, and we present new strategy classifications that reflect this behaviour. We discuss synergies and differences between tools that may be useful in guiding the design of tool integration. Our longitudinal data provides insight into how PIM behaviour evolves over time, and suggests how the supporting nature of PIM discourages reflection by users on their strategies. We discuss how the promotion of some reflection by tools and organizations may benefit users

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Toward a Systematic Evidence-Base for Science in Out-of-School Time: The Role of Assessment

Analyzes the tools used in assessments of afterschool and summer science programs, explores the need for comprehensive tools for comparisons across programs, and discusses the most effective structure and format for such a tool. Includes recommendations

WebPicker: Knowledge Extraction from Web Resources

We show how information distributed in several web resources and represented in different restricted languages can be extracted from its original sources and transformed into a common knowledge model represented in XML using WebPicker. This information, which has been built to cover different needs and functionalities, can be later imported into WebODE, integrated, enriched and exported into different representation formats using WebODE specific modules. We show a case study in the e-commerce domain, using products and services standards from several organizations and/or joint initiatives of industrial and services companies, and a product catalogue from an e-commerce platform

Integrating e-commerce standards and initiatives in a multi-layered ontology

The proliferation of different standards and joint initiatives for the classification of products and services (UNSPSC, e-cl@ss, RosettaNet, NAICS, SCTG, etc.) reveals that B2B markets have not reached a consensus on the coding systems, on the level of detail of their descriptions, on their granularity, etc. This paper shows how these standards and initiatives, which are built to cover different needs and functionalities, can be integrated in an ontology using a common multi-layered knowledge architecture. This multi-layered ontology will provide a shared understanding of the domain for applications of e-commerce, allowing the information sharing between heterogeneous systems. We will present a method for designing ontologies from these information sources by automatically transforming, integrating and enriching the existing vocabularies with the WebODE platform. As an illustration, we show an example on the computer domain, presenting the relationships between UNSPSC, e-cl@ss, RosettaNet and an electronic catalogue from an e-commerce platform

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.Comment: 27 pages, 8 figures, 1 tabl