Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity

This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context

A user profiling component with the aid of user ontologies

Abstract: What follows is a contribution to the field of user modeling for adaptive teaching and learning programs especially in the medical field. The paper outlines existing approaches to the problem of extracting user information in a form that can be exploited by adaptive software. We focus initially on the so-called stereotyping method, which allocates users into classes adaptively, reflecting characteristics such as physical data, social background, and computer experience. The user classifications of the stereotyping method are however ad hoc and unprincipled, and they can be exploited by the adaptive system only after a large number of trials by various kinds of users. We argue that the remedy is to create a database of user ontologies from which readymade taxonomies can be derived in such a way as to enable associated software to support a variety of different types of users

Lightweight Ontologies

Ontologies are explicit specifications of conceptualizations. They are often thought of as directed graphs whose nodes represent concepts and whose edges represent relations between concepts. The notion of concept is understood as defined in Knowledge Representation, i.e., as a set of objects or individuals. This set is called the concept extension or the concept interpretation. Concepts are often lexically defined, i.e., they have natural language names which are used to describe the concept extensions (e.g., concept mother denotes the set of all female parents). Therefore, when ontologies are visualized, their nodes are often shown with corresponding natural language concept names. The backbone structure of the ontology graph is a taxonomy in which the relations are “is-a”, whereas the remaining structure of the graph supplies auxiliary information about the modeled domain and may include relations like “part-of”, “located-in”, “is-parent-of”, and many others

A reflective characterisation of occasional user

This work revisits established user classifications and aims to characterise a historically unspecified user category, the Occasional User (OU). Three user categories, novice, intermediate and expert, have dominated the work of user interface (UI) designers, researchers and educators for decades. These categories were created to conceptualise user's needs, strategies and goals around the 80s. Since then, UI paradigm shifts, such as direct manipulation and touch, along with other advances in technology, gave new access to people with little computer knowledge. This fact produced a diversification of the existing user categories not observed in the literature review of traditional classification of users. The findings of this work include a new characterisation of the occasional user, distinguished by user's uncertainty of repetitive use of an interface and little knowledge about its functioning. In addition, the specification of the OU, together with principles and recommendations will help UI community to informatively design for users without requiring a prospective use and previous knowledge of the UI. The OU is an essential type of user to apply user-centred design approach to understand the interaction with technology as universal, accessible and transparent for the user, independently of accumulated experience and technological era that users live in

Planet Hunters: Assessing the Kepler Inventory of Short Period Planets

We present the results from a search of data from the first 33.5 days of the Kepler science mission (Quarter 1) for exoplanet transits by the Planet Hunters citizen science project. Planet Hunters enlists members of the general public to visually identify transits in the publicly released Kepler light curves via the World Wide Web. Over 24,000 volunteers reviewed the Kepler Quarter 1 data set. We examine the abundance of \geq 2 R\oplus planets on short period (< 15 days) orbits based on Planet Hunters detections. We present these results along with an analysis of the detection efficiency of human classifiers to identify planetary transits including a comparison to the Kepler inventory of planet candidates. Although performance drops rapidly for smaller radii, \geq 4 R\oplus Planet Hunters \geq 85% efficient at identifying transit signals for planets with periods less than 15 days for the Kepler sample of target stars. Our high efficiency rate for simulated transits along with recovery of the majority of Kepler \geq 4 R\oplus planets suggest suggests the Kepler inventory of \geq 4 R\oplus short period planets is nearly complete.Comment: 41 pages,13 figures, 8 tables, accepted to Ap

Engineering Advanced Training Environment for Crisis Management: The Pandora Project

The paper describes the technical framework of a near real-life training environment for learning activities suitable for training in crisis scenarios. The underlying architecture features a design that makes provision for a learning environment capable of training collaborative, as well as independent, decision making skills among crisis managers in potential crisis situations. Modelling the training scenarios takes into consideration both the pragmatic nature of responding to crisis, as well as the human behavioural factors involved in dealing with situations of chaos and uncertainty. This work is part of ongoing research on the Pandora1 project, which aims to provide a near-real training environment at affordable cost

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

Where's that sound? Exploring arbitrary user classifications of sounds for audio collection management

Proceedings of the 9th International Conference on Auditory Display (ICAD), Boston, MA, July 7-9, 2003.Collections of sound and music of increasing size and diversity are used both by general personal computer users and multimedia designers. Browsing audio collections poses several challenges to the design of effective user interfaces. In this paper, we report results from a new version of the Sonic Browser for managing general sound resources on personal computers. In particular, we have evaluated browsing of everyday sounds. The investigation was directed at comparing browsing of audio resources with arbitrary classifications. The problem of sound resource browsing for multimedia designers is the specific area of focus for our experiment. Finally, we conclude with current trends of our approach for further improvement of the system