Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Australian feature films and distribution: Industry or cottage industry?

Feature films remain critical flagships to any national film industry. Australian feature films can be highly commercial endeavours that also perform symbolic functions by embodying the national imaginary in big screen based sound and imagery. They conduct a dialogue with domestic audiences as well as showcase key aspects of Australia in the global film festival circuit. As the pre-eminent filmmaking form, feature films also serve as important launchpads for the careers of many Australian writers, directors, actors and technical crew. In the wake of over a decade of diminished share of local box office obtained by Australian feature films, Australian Feature Films and Distribution: Industry or cottage industry, examines issues in the production sector affecting the performance of Australian feature films and some responses by the central funding and support screen agency, Screen Australia.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Feature extraction for fingerprint classification

This paper presents two algorithms for smoothing and feature extraction for fingerprint classification. Deutsch's(2) Thinning algorithm (rectangular array) is used for thinning the digitized fingerprint (binary version). A simple algorithm is also suggested for classifying the fingerprints. Experimental results obtained using such algorithms are presented.

Feature - No pain, no gain: Pleasure and suffering in technologies of leidenschaft

Training for bodybuilding competition is clearly a serious business that inflicts serious demands on the competitor. Not only did Francis commit time and money to compete, but he also arguably put winning before his physical well-being—enduring pain and suffering from his injury. Bodybuilding may seem like an extreme example, but it is not the only activity in which people suffer in pursuit of their goals. Boxers fight each other in the ring; soccer players risk knee and ankle injuries, sometimes playing despite being hurt; and mountaineers risk their lives in dangerous climbs. In the arts there are many examples of people suffering to achieve their goals: Beethoven kept composing, conducting, and performing despite his hearing loss; van Gogh grappled with depression but kept painting, finding fame only posthumously; and Mozart lived the final years of his life impoverished but still composing. These examples show that many great achievements come at a price: severe suffering...

Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Heavy metals build-up on urban road surfaces is a complex process and influenced by a diverse range of factors. Although numerous research studies have been conducted in the area of heavy metals build-up, limited research has been undertaken to rank these factors in terms of their influence on the build-up process. This results in limitations in the identification of the most critical factor/s for accurately estimating heavy metal loads and for designing effective stormwater treatment measures. The research study undertook an in-depth analysis of the factors which influence heavy metals build-up based on data generated from a number of different geographical locations around the world. Traffic volume was found to be the highest ranked factor in terms of influencing heavy metals build-up while land use was ranked the second. Proximity to arterial roads, antecedent dry days and road surface roughness has a relatively lower ranking. Furthermore, the study outcomes advances the conceptual understanding of heavy metals build-up based on the finding that with increasing traffic volume, total heavy metal build-up load increases while the variability decreases. The outcomes from this research study are expected to contribute to more accurate estimation of heavy metals build-up loads leading to more effective stormwater treatment design.

The construction of a taxonomy of early childhood educators’ work

Despite considerable research on the impact of early childhood education (ECE), there is little empirical evidence about what the everyday work of educators entails. This paper reports on the development of a tool to capture generalisable data on the everyday work of educators so as to inform effective workforce policy. This tool—a taxonomy of early childhood educators’ work—was developed by drawing on the expertise of six early years’ experts from Australia and the United States of America (USA) and includes time-use diaries, focus groups and interviews with 21 early childhood educators working in long day care and preschool services. The taxonomy, which we present here, consists of 10 domains, each with a number of sub-classes. We propose that this taxonomy is a useful codification system for ascertaining the everyday work tasks, activities and actions of early childhood educators in diverse early years’ settings. It is anticipated that the taxonomy will prove a valuable tool for subsequent research investigating the early childhood workforce.

vSpeak: Edge detection based feature extraction for sign to text conversion

This paper presents 'vSpeak', the first initiative taken in Pakistan for ICT enabled conversion of dynamic Sign Urdu gestures into natural language sentences. To realize this, vSpeak has adopted a novel approach for feature extraction using edge detection and image compression which gives input to the Artificial Neural Network that recognizes the gesture. This technique caters for the blurred images as well. The training and testing is currently being performed on a dataset of 200 patterns of 20 words from Sign Urdu with target accuracy of 90% and above.

Taxonomy and redescription of the Swamp Antechinus, Antechinus minimus (E. Geoffroy) (Marsupialia: Dasyuridae)

We provide a taxonomic redescription of the dasyurid marsupial Swamp Antechinus, Antechinus minimus (Geoffroy, 1803). In the past, A. minimus has been classified as two subspecies: the nominate A. minimus minimus (Geoffroy, 1803), which is found throughout much of Tasmania (including southern Bass Strait islands) and A. minimus maritimus (Finlayson, 1958), which is found on mainland Australia (as well as some near-coastal islands) and is patchily distributed in mostly coastal areas between South Gippsland (Victoria) and Robe (South Australia). Based on an assessment of morphology and DNA, we conclude that A. minimus is both distinctly different from all extant congeners and that the two existing subspecies of Swamp Antechinus are appropriately taxonomically characterised. In our genetic phylogenies, the Swamp Antechinus was monophyletic with respect to all 14 known extant congeners; moreover, A. minimus was well-positioned in a large clade, together with all four species in the Dusky Antechinus complex, to the exclusion of all other antechinus. Within A. minimus, between subspecies there were subtle morphological differences (A. m. maritimus skulls tend to be broader, with larger molar teeth, than A. m. minimus, but these differences were not significant); there was distinct, but only moderately deep genetic differences (3.9–4.5% at mtDNA) between A. minimus subspecies. Comparatively, across Bass Strait, the two subspecies of A. minimus are morphologically and genetically markedly less divergent than recently recognised species pairs within the Dusky Antechinus complex, found in Victoria (A. mimetes) and Tasmania (A. swainsonii) (9.4–11.6% divergent at mtDNA)

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Block-based feature detection and matching for mosaicing of camera-captured document images

In this paper, we present a new feature-based approach for mosaicing of camera-captured document images. A novel block-based scheme is employed to ensure that corners can be reliably detected over a wide range of images. 2-D discrete cosine transform is computed for image blocks defined around each of the detected corners and a small subset of the coefficients is used as a feature vector A 2-pass feature matching is performed to establish point correspondences from which the homography relating the input images could be computed. The algorithm is tested on a number of complex document images casually taken from a hand-held camera yielding convincing results.

Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

On Guo and Nixon's Criterion for Feature Subset Selection: Assumptions, Implications,and Alternative Options

Guo and Nixon proposed a feature selection method based on maximizing I(x; Y),the multidimensional mutual information between feature vector x and class variable Y. Because computing I(x; Y) can be difficult in practice, Guo and Nixon proposed an approximation of I(x; Y) as the criterion for feature selection. We show that Guo and Nixon's criterion originates from approximating the joint probability distributions in I(x; Y) by second-order product distributions. We remark on the limitations of the approximation and discuss computationally attractive alternatives to compute I(x; Y).

Taxonomy and phylogeny of white-rot polypores : case studies in Hymenochaetales and Polyporales (Basidiomycota)

Tutkimus käsittelee kääpien sukulaisuussuhteita. Käävät ovat kantasienten (Basidiomycota) muotoryhmä, joiden itiöemien alapinta muodostuu yhteensulautuneista pilleistä. Muotoryhmänä kääpiä voi verrata vaikka puihin siinä mielessä, että käävät kuten puutkaan eivät ole samankaltaisuudestaan huolimatta kaikki sukua toisilleen. DNA:n käyttö sukulaisuussuhteiden selvittämisessä on aloittanut mullistuksen kääpien luokittelussa. Aiemmin käytetty, itiöemien ominaisuuksiin perustunut luokittelu on osoittautunut keinotekoiseksi sukulaisuussuhteiden kannalta. Tutkimuksessani syvennyttiin useamman kääpäsuvun polveutumishistoriaan hyödyntäen DNA:ta ja perinteisiä menetelmiä. Tutkimuksen keskeisimmät tulokset liittyvät sitkokääpien sukuun (Antrodiella). Tämä noin 70 lajia sisältävä suku osoittautui rikkonaiseksi - sitkokääpiin luetut lajit kuuluvat kahteen sienilahkoon ja oikesti vähintään 13 sukuun. Tutkimuksessa löytyi kaksi Suomelle uutta sitkokääpää, leppikääpä (A. ichnusana) ja nipukkakääpä (A. leucoxantha). Uudet suvut kuvattiin Suomessa esiintyville sirppikääville (Sidera) ja talikääville (Obba). Uusi kääpäsuku ja -laji kuvattiin myös Indonesiasta (Sebipora aquosa). Valtaosa sitkokääpiin luetuista lajeista kuuluu orakarakoiden heimoon (Steccherinaceae), joka rajattiin tässä tutkimuksessa uudelleen. Heimoon kuuluvat mm. karakäävät (Junghuhnia) ja orakasmaiset orakarakat (Steccherinum). Sen sisällä selvitettiin kääpien ja orakkaiden sukulaisuussuhteita. Perinteisesti käävät ja orakkaat on viety eri sukuihin riippumatta niiden mikroskooppisesta samankaltaisuudesta. Tulosten valossa orakarakoiden heimossa käävät ja orakkaat pysyvät pääosin erillisissä suvuissa, mutta tästä on myös poikkeuksia (Antrodiella, Metuloidea ja Steccherinum). Lähes kaikki DNA:n perusteella määriteltävissä olevat suvut ovat tunnistettavissa itiöemien ominaisuuksiensa perusteella. Tulokset antavat eväitä kääpien luokitteluun laajemminkin osoittamalla, mitkä ominaisuudet ovat luokittelun kannalta merkityksellisiä. Tarkentunut tieto lajimäärästä ja lajien sukulaisuussuhteista hyödyttää ekologista tutkimusta sekä arvioita lajien uhanalaisuudesta. Tutkimuksen aikana luotua DNA-kirjastoa käytetään lajien tunnistamiseen. Tuloksia voidaan hyödyntää myös etsittäessä bioteknologisia sovelluksia käävistä, sillä sovellusten kannalta kiinnostavat ominaisuudet seuraavat usein sienten sukupuuta.