Improving behaviour classification consistency: a technique from biological taxonomy

Quantitative behaviour analysis requires the classification of behaviour to produce the basic data. In practice, much of this work will be performed by multiple observers, and maximising inter-observer consistency is of particular importance. Another discipline where consistency in classification is vital is biological taxonomy. A classification tool of great utility, the binary key, is designed to simplify the classification decision process and ensure consistent identification of proper categories. We show how this same decision-making tool - the binary key - can be used to promote consistency in the classification of behaviour. The construction of a binary key also ensures that the categories in which behaviour is classified are complete and non-overlapping. We discuss the general principles of design of binary keys, and illustrate their construction and use with a practical example from education research.

Assessing the structure of learning outcomes from counselling using the SOLO taxonomy: an exploratory study

Thirty-five clients who had received counselling completed a letter to a friend describing in as much detail as possible what they had learned from counselling. The participants' written responses were analysed and classified using the Structure of Learning Outcomes (SOLO) taxonomy. The results suggested that an expanded SOLO offers a promising and exciting way to view the outcomes of counselling within a learning framework. If the SOLO taxonomy is found to be stable in subsequent research, and clients are easily able to be classified using the taxonomy, then this approach may have implications for the process of counselling. To maximise the learning outcomes, counsellors could use strategies and techniques to enhance their clients' learning.

Personalized recommender systems integrating social tags and item taxonomy

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Taxonomy and diagnostics of fruit fly infesting Opiine Braconids in Australia and the South Pacific

Opiine wasps (Hymenoptera: Braconidae: Opiinae) are parasitoids of dacine fruit flies (Diptera: Tephritidae: Dacinae), the primary horticultural pests of Australia and the South Pacific. Effective use of opiines for biological control of fruit flies is limited by poor taxonomy and identification difficulties. To overcome these problems, this thesis had two aims: (i) to carry out traditional taxonomic research on the fruit fly infesting opine braconids of Australia and the South Pacific; and (ii) to transfer the results of the taxonomic research into user friendly diagnostic tools. Curated wasp material was borrowed from all major Australian museum collections holding specimens. This was supplemented by a large body of material gathered as part of a major fruit fly project in Papua New Guinea: nearly 4000 specimens were examined and identified. Each wasp species was illustrated using traditional scientific drawings, full colour photomicroscopy and scanning electron microscopy. An electronic identification key was developed using Lucid software and diagnostic images were loaded on the web-based Pest and Diseases Image Library (PaDIL). A taxonomic synopsis and distribution and host records for each of the 15 species of dacine-parasitising opiine braconids found in the South Pacific is presented. Biosteres illusorius Fischer (1971) was formally transferred to the genus Fopius and a new species, Fopius ferrari Carmichael and Wharton (2005), was described. Other species dealt with were Diachasmimorpha hageni (Fullaway, 1952), D. kraussii (Fullaway, 1951), D. longicaudata (Ashmead, 1905), D. tryoni (Cameron, 1911), Fopius arisanus (Sonan, 1932), F. deeralensis (Fullaway, 1950), F. schlingeri Wharton (1999), Opius froggatti Fullaway (195), Psyttalia fijiensis (Fullaway, 1936), P. muesebecki (Fischer, 1963), P. novaguineensis (Szépliget, 1900i) and Utetes perkinsi (Fullaway, 1950). This taxonomic component of the thesis has been formally published in the scientific literature. An interactive diagnostics package (“OpiineID”) was developed, the centre of which is a Lucid based multi-access key. Because the diagnostics package is computer based, without the space limitations of the journal publication, there is no pictorial limit in OpiineID and so it is comprehensively illustrated with SEM photographs, full colour photographs, line drawings and fully rendered illustrations. The identification key is only one small component of OpiineID and the key is supported by fact sheets with morphological descriptions, host associations, geographical information and images. Each species contained within the OpiineID package has also been uploaded onto the PaDIL website (www.padil.gov.au). Because the identification of fruit fly parasitoids is largely of concern to fruit fly workers, rather than braconid specialists, this thesis deals directly with an area of growing importance to many areas of pure and applied biology; the nexus between taxonomy and diagnostics. The Discussion chapter focuses on this area, particularly the opportunities offered by new communication and information tools as new ways delivering the outputs of taxonomic science.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.

On a taxonomy of delegation

Delegation, from the technical point of view, is widely considered as a potential approach in addressing the problem of providing dynamic access control decisions in activities with a high level of collaboration, either within a single security domain or across multiple security domains. Although delegation continues to attract significant attention from the research community, presently, there is no published work that presents a taxonomy of delegation concepts and models. This paper intends to address this gap by presenting a set of taxonomic criteria relevant to the concept of delegation and applies the taxonomy to a selection of significant delegation models published in the literature.

On a taxonomy of delegation

Delegation, from a technical point of view, is widely considered as a potential approach in addressing the problem of providing dynamic access control decisions in activities with a high level of collaboration, either within a single security domain or across multiple security domains. Although delegation continues to attract significant attention from the research community, presently, there is no published work that presents a taxonomy of delegation concepts and models. This article intends to address this gap by presenting a set of taxonomic criteria relevant to the concept of delegation. This article also applies the taxonomy to a selection of significant delegation models published in the literature.

A Taxonomy of image matching techniques for stereo vision

Stereo vision is a method of depth perception, in which depth information is inferred from two (or more) images of a scene, taken from different perspectives. Applications of stereo vision include aerial photogrammetry, autonomous vehicle guidance, robotics, industrial automation and stereomicroscopy. A key issue in stereo vision is that of image matching, or identifying corresponding points in a stereo pair. The difference in the positions of corresponding points in image coordinates is termed the parallax or disparity. When the orientation of the two cameras is known, corresponding points may be projected back to find the location of the original object point in world coordinates. Matching techniques are typically categorised according to the nature of the matching primitives they use and the matching strategy they employ. This report provides a detailed taxonomy of image matching techniques, including area based, transform based, feature based, phase based, hybrid, relaxation based, dynamic programming and object space methods. A number of area based matching metrics as well as the rank and census transforms were implemented, in order to investigate their suitability for a real-time stereo sensor for mining automation applications. The requirements of this sensor were speed, robustness, and the ability to produce a dense depth map. The Sum of Absolute Differences matching metric was the least computationally expensive; however, this metric was the most sensitive to radiometric distortion. Metrics such as the Zero Mean Sum of Absolute Differences and Normalised Cross Correlation were the most robust to this type of distortion but introduced additional computational complexity. The rank and census transforms were found to be robust to radiometric distortion, in addition to having low computational complexity. They are therefore prime candidates for a matching algorithm for a stereo sensor for real-time mining applications. A number of issues came to light during this investigation which may merit further work. These include devising a means to evaluate and compare disparity results of different matching algorithms, and finding a method of assigning a level of confidence to a match. Another issue of interest is the possibility of statistically combining the results of different matching algorithms, in order to improve robustness.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

A Taxonomy for Homework Used by Mental Health Case Managers When Working with Individuals Diagnosed with Severe Mental Illness

A survey was completed by 122 case managers describing the types of homework assignments commonly used with individuals diagnosed with severe mental illness (SMI). Homework types were categorized using a 12-item homework description taxonomy and in relation to the 22 domains of the Camberwell Assessment of Need (CAN). Case managers predominately reported using behaviourally based homework tasks such as scheduling activities and the development of personal hygiene skills. Homework focused on CAN areas of need in relation to Company, Psychological Distress, Psychotic Symptoms and Daytime Activities. The applications of the taxonomy for both researchers and case managers are discussed.