Modelling user profiles for recommender systems

Recommender systems assist users in finding what they want. The challenging issue is how to efficiently acquire user preferences or user information needs for building personalized recommender systems. This research explores the acquisition of user preferences using data taxonomy information to enhance personalized recommendations for alleviating cold-start problem. A concept hierarchy model is proposed, which provides a two-dimensional hierarchy for acquiring user preferences. The language model is also extended for the proposed hierarchy in order to generate an effective recommender algorithm. Both Amazon.com book and music datasets are used to evaluate the proposed approach, and the experimental results show that the proposed approach is promising.

A geometric approach to stationary defect solutions in one space dimension

In this manuscript, we consider the impact of a small jump-type spatial heterogeneity on the existence of stationary localized patterns in a system of partial dierential equations in one spatial dimension...

Reengaging, reenacting and reenvisaging discourses of disaster through public space performance: Austerity, art, and the art of protest

This paper offers a mediation on disaster, recovery, resilience, and restoration of balance, in both a material and a metaphorical sense, when ‘disaster’ befalls not the body politic of the nation but the body personal. In the past few decades, of course, artists, activists and scholars have deliberately tried to avoid describing personal, physical and phenomenological experiences of the disabled body in terms of difficulty and disaster. This has been part of a political move, from a medical model, in which disability, disease and illness are positioned as personal catastrophes, to a social model, in which disability is positioned as a social construct that comes from systems, institutions and infrastructure designed to exclude different bodies. It is a move that is responsible for a certain discomfort people with disabilities, and artists with disabilities, today feel towards performances that deploy disability as a metaphor for disaster, from Hijikata, to Theatre Hora. In the past five years, though, this particular discourse has begun rising again, particularly as people with disabilities fact their own anything but natural disasters as a result of the austerity measures now widespread across the US, UK, Europe and elsewhere. Measures that threaten people’s ability to live, and take part in social and institutional life, in any meaningful way. Measures that, as artist Katherine Araniello notes, also bring additional difficulty, danger, and potential for disaster as they ripple outwards across the tides of familial ties, threatening family, friends, and careers who become bound up in the struggle to do more with less. In this paper, I consider how people with disabilities use performance, particularly public space interventionalist performance, to reengage, renact and reenvisage the discourse of national, economic, environmental or other forms of disaster, the need for austerity, the need to avoid providing people with support for desires and interests as well as basic daily needs, particularly when fraud and corruption is so right, and other such ideas that have become an all too unpleasant reality for many people. Performances, for instance, like Liz Crow’s Bedding Out, where she invited people into her bed – for people with disabilities a symbolic space, which necessarily becomes more a public living room restaurant, office and so forth than a private space when poor mobility means they spend much time it in – to talk about their lives, their difficulties, and dealing with austerity. Or, for instance, like the Bolshy Divas, who mimic public and political policy, reports and advertising paranoia to undermine their discourses about austerity. I examine the effects, politics and ethics of such interventions, including examination of the comparative effect of highly bodied interventions (like Crow’s) and highly disembodied interventions (like the Bolshy Diva’s) in discourses of difficulty, disaster and austerity on a range of target spectator communities.

Performing immobility / protesting immobility: The risks & rewards of public space protest performances

Though there is much interest in mobilities and performing mobilities as a characteristic of modern, urban, social life today, this is not always matched by attention to immobilities, as the flipside of mobility in modern life. In this paper, I investigate public space performances designed to draw attention to precisely this counterpoint to current discourses of mobilities – performances about the socially produced immobilities many people with disabilities find a more fundamental feature of day-to-day life, the fight for mobility, and the freedom found when accommodations for alternative mobilities are made available. Although public policy is increasingly aligned with a social model of disability, which sees disability as socially constructed through systems, institutions and infrastructure deliberately designed to exclude specific bodies – stairs, curbs, queues and so forth – and although governments in the US, UK, and to a lesser degree Australia, New Zealand and other Commonwealth nations aim to address these inequalities, the experience of immobility is still every-present for many people. This often comes not just from pain, or from impairment, or event from lack of accommodations for alternative mobilities, but from fellow social performers’ antipathy to, appropriation of, or destruction of accommodations designed to facilitate access for a range of different bodies in public space, and thus the public sphere. The archetypal instance of this tension between the mobile, and those needing accommodations to allow mobility, is, of course, the antipathy many able bodied people feel towards the provision of disabled parking spaces. A cursory search online shows thousands of accounts of antagonism, vitriol, and even violence prompted by disputes which began when a disabled person asked an able person to exit a designated disabled parking space. For many, it seems, expecting them to pass by such parks so others can experience the mobility they take for granted is too much. In this paper, I examine a number of protest performances in public space in which activist present actions – for example, placing wheelchairs in every regular parking space in a precinct – to give bystanders, passersby and spectators, as well as antagonistic fellow social performers, a sense of what socially produced immobility feels like. I examine responses to such protest performances, and what they say about the potential social, political and ethical impacts of such protests, in terms of their potential to produce new attitudes to mobility, alternative mobility, and access to alternative modes of mobility.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Simulation design in information systems research: example of studying it value cocreation with NK model

As an emerging research method that has showed promising potential in several research disciplines, simulation received relatively few attention in information systems research. This paper illustrates a framework for employing simulation to study IT value cocreation. Although previous studies identified factors driving IT value cocreation, its underlying process remains unclear. Simulation can address this limitation through exploring such underlying process with computational experiments. The simulation framework in this paper is based on an extended NK model. Agent-based modeling is employed as the theoretical basis for the NK model extensions.