Ranking based clustering for social event detection

The problem of clustering a large document collection is not only challenged by the number of documents and the number of dimensions, but it is also affected by the number and sizes of the clusters. Traditional clustering methods fail to scale when they need to generate a large number of clusters. Furthermore, when the clusters size in the solution is heterogeneous, i.e. some of the clusters are large in size, the similarity measures tend to degrade. A ranking based clustering method is proposed to deal with these issues in the context of the Social Event Detection task. Ranking scores are used to select a small number of most relevant clusters in order to compare and place a document. Additionally,instead of conventional cluster centroids, cluster patches are proposed to represent clusters, that are hubs-like set of documents. Text, temporal, spatial and visual content information collected from the social event images is utilized in calculating similarity. Results show that these strategies allow us to have a balance between performance and accuracy of the clustering solution gained by the clustering method.

Topical pattern based document modelling and relevance ranking

For traditional information filtering (IF) models, it is often assumed that the documents in one collection are only related to one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling was proposed to generate statistical models to represent multiple topics in a collection of documents, but in a topic model, topics are represented by distributions over words which are limited to distinctively represent the semantics of topics. Patterns are always thought to be more discriminative than single terms and are able to reveal the inner relations between words. This paper proposes a novel information filtering model, Significant matched Pattern-based Topic Model (SPBTM). The SPBTM represents user information needs in terms of multiple topics and each topic is represented by patterns. More importantly, the patterns are organized into groups based on their statistical and taxonomic features, from which the more representative patterns, called Significant Matched Patterns, can be identified and used to estimate the document relevance. Experiments on benchmark data sets demonstrate that the SPBTM significantly outperforms the state-of-the-art models.

A multi-criteria analysis approach for ranking and selection of microorganisms for the production of oils for biodiesel production

Oleaginous microorganisms have potential to be used to produce oils as alternative feedstock for biodiesel production. Microalgae (Chlorella protothecoides and Chlorella zofingiensis), yeasts (Cryptococcus albidus and Rhodotorula mucilaginosa), and fungi (Aspergillus oryzae and Mucor plumbeus) were investigated for their ability to produce oil from glucose, xylose and glycerol. Multi-criteria analysis (MCA) using analytic hierarchy process (AHP) and preference ranking organization method for the enrichment of evaluations (PROMETHEE) with graphical analysis for interactive aid (GAIA), was used to rank and select the preferred microorganisms for oil production for biodiesel application. This was based on a number of criteria viz., oil concentration, content, production rate and yield, substrate consumption rate, fatty acids composition, biomass harvesting and nutrient costs. PROMETHEE selected A. oryzae, M. plumbeus and R. mucilaginosa as the most prospective species for oil production. However, further analysis by GAIA Webs identified A. oryzae and M. plumbeus as the best performing microorganisms.

Relative ranking: A biased rating

Reviewers' ratings have become one of the most influential parameters when making a decision to purchase or rent the products or services from the online vendors. Star Rating system is the de-facto standard for rating a product. It is regarded as one of the most visually appealing rating systems that directly interact with the consumers; helping them find products they will like to purchase as well as register their views on the product. It offers visual advantage to pick the popular or most rated product. Any system that is not as appealing as star system will have a chance of rejection by online business community. This paper argues that, the visual advantage is not enough to declare star rating system as a triumphant, the success of a ranking system should be measured by how effectively the system helps customers make decisions that they, retrospectively, consider correct. This paper argues and suggests a novel approach of Relative Ranking within the boundaries of star rating system to overcome a few inherent disadvantages the former system comes with. © Springer Science+Business Media B.V. 2010.

Business process modeling : perceived benefits

The process-centered design of organizations and information systems is globally seen as an appropriate response to the increased economic pressure on organizations. At the methodological core of process-centered management is process modeling. However, business process modeling in large initiatives can be a time-consuming and costly exercise, making it potentially difficult to convince executive management of its benefits. To date, and despite substantial interest and research in the area of process modeling, the understanding of the actual benefits of process modeling in academia and practice is limited. To address this gap, this paper explores the perception of benefits derived from process modeling initiatives, as reported through a global Delphi study. The study incorporates the views of three groups of stakeholders – academics, practitioners and vendors. Our findings lead to the first identification and ranking of 19 unique benefits associated with process modeling. The study in particular found that process modeling benefits vary significantly between practitioners and academics. We argue that these variations may point to a dangerous disconnect between research projects and practical demands.

An investigation into the barriers to the implementation of automation and robotics technologies in the construction industry

The rising problems associated with construction such as decreasing quality and productivity, labour shortages, occupational safety, and inferior working conditions have opened the possibility of more revolutionary solutions within the industry. One prospective option is in the implementation of innovative technologies such as automation and robotics, which has the potential to improve the industry in terms of productivity, safety and quality. The construction work site could, theoretically, be contained in a safer environment, with more efficient execution of the work, greater consistency of the outcome and higher level of control over the production process. By identifying the barriers to construction automation and robotics implementation in construction, and investigating ways in which to overcome them, contributions could be made in terms of better understanding and facilitating, where relevant, greater use of these technologies in the construction industry so as to promote its efficiency. This research aims to ascertain and explain the barriers to construction automation and robotics implementation by exploring and establishing the relationship between characteristics of the construction industry and attributes of existing construction automation and robotics technologies to level of usage and implementation in three selected countries; Japan, Australia and Malaysia. These three countries were chosen as their construction industry characteristics provide contrast in terms of culture, gross domestic product, technology application, organisational structure and labour policies. This research uses a mixed method approach of gathering data, both quantitative and qualitative, by employing a questionnaire survey and an interview schedule; using a wide range of sample from management through to on-site users, working in a range of small (less than AUD0.2million) to large companies (more than AUD500million), and involved in a broad range of business types and construction sectors. Detailed quantitative (statistical) and qualitative (content) data analysis is performed to provide a set of descriptions, relationships, and differences. The statistical tests selected for use include cross-tabulations, bivariate and multivariate analysis for investigating possible relationships between variables; and Kruskal-Wallis and Mann Whitney U test of independent samples for hypothesis testing and inferring the research sample to the construction industry population. Findings and conclusions arising from the research work which include the ranking schemes produced for four key areas of, the construction attributes on level of usage; barrier variables; differing levels of usage between countries; and future trends, have established a number of potential areas that could impact the level of implementation both globally and for individual countries.

Innovative Practices in the Australian Built Environment Sector : An Information Resource for Industry

PROJECT BRIEF Information provided by the Built Environment Industry Innovation Council as background to this project includes the following information on construction and innovation within the industry. • The construction industry contributes around $67 billion to GDP and employs around 970,000 and generates exports of nearly $150 million. • The industry has one of the lowest innovation rates of any industry in Australia, ranking third last across all Australian industries in terms of its proportion of business expenditure on innovation, and second last in terms of the proportion of income generated from innovation (ABS, 2006). • Key innovation challenges include addressing energy and water use efficiency, and housing costs in preparing for the implementation of the Carbon Pollution Reduction Scheme. The sector will need to build its capability and capacity to deliver the technical and operational expertise required.The broader Built Environment Innovation Project aims to address the following two objectives: 1. Identify current innovative practice across the Built Environment industry. 2. Develop a knowledge exchange strategy for this information to be disseminated to all industry stakeholders. Industry practice issues are critical to the built environment industry’s ability to innovate, and the BRITE project from the CRC for Construction Innovation has previously undertaken work to identify the key factors that drive innovation. Part 1 of the current project aims to extend this work by conducting a stocktake of current and emerging innovative practices within the built environment industry. Part 2 of the project addresses the second of these objectives, that is, to recommend a knowledge exchange strategy for promoting the wider uptake of innovative practices that makes the information identified in Part 1 of the study (on emerging innovative practices) accessible to Australian built environment industry stakeholders. The project brief was for the strategy to include a mechanism to enable this information resource to be updated as new initiatives/practices are developed. A better understanding of the built environment industry’s own knowledge infrastructure also has the potential to enhance innovation outcomes for the industry. This project will develop a coordinated knowledge exchange strategy, informed by the best available information on current innovation practices within the industry and suggest directions for gaining a better understanding of: the industry contexts that lead to innovative practices; the industry (including enterprise and individual) drivers for innovation; and appropriate knowledge exchange pathways for delivering future industry innovation. A deliverable of Part 2 will be a recommendation for a knowledge exchange strategy to accelerate adoption of innovative practices in the built environment industry, including resource implications and how such a recommendation could be taken forward as an ongoing resource.

Cases from the technology disciplines

This document is a collection of ‘cases’ adapted from interviews with supervisors of higher degree research students from the technology disciplines. The supervisors come from a wide range of sub disciplines and represent many levels of experience. We follow in this document Hammond and Ryland’s (2009)2 suggested ranking of supervision experience:  No completions – No experience or new supervisors, with no doctoral completions as principal supervisor  Experienced – 1 to 5 doctoral completions as principal supervisor  Very experienced – over 6 doctoral completions as principal supervisor The cases focus attention on thinking about supervision as a teaching and learning practice; a dimension of higher degree research supervision that is increasingly being recognized as important. They are offered as prompts for individuals and groups of supervisors in thinking about their supervision as a teaching and learning practice.

Host searching behaviour of Diachasmimorpha kraussii (Fullaway) (Hymenoptera: Braconidae: Opiinae), a polyphagous parasitoid of Dacinae fruit flies (Diptera: Tephritidae)

Diachasmimorpha kraussii (Hymenoptera: Braconidae: Opiinae) is a koinobiont larval parasitoid of dacine fruit flies of the genus Bactrocera (Diptera: Tephritidae) in its native range (Australia, Papua New Guinea, Solomon Islands). The wasp is a potentially important control agent for pest fruit flies, having been considered for both classical and inundative biological control releases. I investigated the host searching, selection and utilisation mechanisms of the wasp against native host flies within its native range (Australia). Such studies are rare in opiine research where the majority of studies, because of the applied nature of the research, have been carried out using host flies and environments which are novel to the wasps. Diachasmimorpha kraussii oviposited equally into maggots of four fruit fly species, all of which coexist with the wasp in its native range (Australia), when tested in a choice trial using a uniform artificial diet media. While eggs laid into Bactrocera tryoni and B. jarvisi developed successfully through to adult wasps, eggs laid into B. cucumis and B. cacuminata were encapsulated. These results suggest that direct larval cues are not an important element in host selection by D. kraussii. Further exploring how D. kraussii locates suitable host larvae, I investigated the role of plant cues in host searching and selection. This was examined in a laboratory choice trial using uninfested fruit or fruit infested with either B. tryoni or B. jarvisi maggots. The results showed a consistent preference ranking among infested fruits by the wasp, with guava and peach most preferred, but with no response to uninfested fruits. Thus, it appears the wasp uses chemical cues emitted in response to fruit fly larval infestation for host location, but does not use cues from uninfested fruits. To further tease apart the role of (i) suitable and non-suitable maggots, (ii) infested and uninfested fruits of different plant species, and (iii) adult flies, in wasp host location and selection, I carried out a series of behavioural tests where I manipulated these attributes in a field cage. These trials confirmed that D. kraussii did not respond to cues in uninfested fruits, that there were consistent preferences by the wasps for different maggot infested fruits, that fruit preference did not vary depending on whether the maggots were physiologically suitable or not suitable for wasp offspring development, and finally, that adult flies appear to play a secondary role as indicators of larval infestation. To investigate wasp behaviour in an unrestrained environment, I concurrently observed diurnal foraging behaviours of both the wasp and one of its host fly in a small nectarine orchard. Wasp behaviour, both spatially and temporally, was not correlated with adult fruit fly behaviour or abundance. This study reinforced the point that infested fruit seems to be the primary cue used by foraging wasps. Wasp and fly feeding and mating was not observed in the orchard, implying these activities are occurring elsewhere. It is highly unlikely that these behaviours were happening within the orchard during the night as both insects are diurnal. As the final component of investigating host location, I carried out a habitat preference study for the wasp at the landscape scale. Using infested sentinel fruits, I tested the parasitism rate of B. tryoni in eucalyptus sclerophyll forest, rainforest and suburbia in South East Queensland. Although, rainforest is the likely endemic habitat of both B. tryoni and D. kraussii, B. tryoni abundance is significantly greater in suburban environments followed by eucalyptus sclerophyll forest. Parasitism rate was found to be higher in suburbia than in the eucalyptus sclerophyll forest, while no parasitism was recorded in the rainforest. This result suggests that wasps orient within the landscape towards areas of high host density and are not restricted by habitat types. Results from the different experiments suggest that host searching, selection and utilisation behaviour of D. kraussii are strongly influenced by cues associated with fruit fly larval feeding. Cues from uninfested fruits, the host larvae themselves, and the adult host flies play minimal roles. The discussion focuses on the fit of D. kraussii to Vinson’s classical parasitoid host location model and the implications of results for biological control, including recommendations for host and plant preference screening protocols and release regimes.

Risk-assessment practices in the private rental sector : implications for low-income renters (Final Report)

This report focuses on risk-assessment practices in the private rental market, with particular consideration of their impact on low-income renters. It is based on the fieldwork undertaken in the second stage of the research process that followed completion of the Positioning Paper. The key research question this study addressed was: What are the various factors included in ‘risk-assessments’ by real estate agents in allocating ‘affordable’ tenancies? How are these risks quantified and managed? What are the key outcomes of their decision-making? The study builds on previous research demonstrating that a relatively large proportion of low-cost private rental accommodation is occupied by moderate- to high-income households (Wulff and Yates 2001; Seelig 2001; Yates et al. 2004). This is occurring in an environment where the private rental sector is now the de facto main provider of rental housing for lower-income households across Australia (Seelig et al. 2005) and where a number of factors are implicated in patterns of ‘income–rent mismatching’. These include ongoing shifts in public housing assistance; issues concerning eligibility for rent assistance; ‘supply’ factors, such as loss of low-cost rental stock through upgrading and/or transfer to owner-occupied housing; patterns of supply and demand driven largely by middle- to high-income owner-investors and renters; and patterns of housing need among low-income households for whom affordable housing is not appropriate. In formulating a way of approaching the analysis of ‘risk-assessment’ in rental housing management, this study has applied three sociological perspectives on risk: Beck’s (1992) formulation of risk society as entailing processes of ‘individualisation’; a socio-cultural perspective which emphasises the situated nature of perceptions of risk; and a perspective which has drawn attention to different modes of institutional governance of subjects, as ‘carriers of specific indicators of risk’. The private rental market was viewed as a social institution, and the research strategy was informed by ‘institutional ethnography’ as a method of enquiry. The study was based on interviews with property managers, real estate industry representatives, tenant advocates and community housing providers. The primary focus of inquiry was on ‘the moment of allocation’. Six local areas across metropolitan and regional Queensland, New South Wales, and South Australia were selected as case study localities. In terms of the main findings, it is evident that access to private rental housing is not just a matter of ‘supply and demand’. It is also about assessment of risk among applicants. Risk – perceived or actual – is thus a critical factor in deciding who gets housed, and how. Risk and its assessment matter in the context of housing provision and in the development of policy responses. The outcomes from this study also highlight a number of salient points: 1.There are two principal forms of risk associated with property management: financial risk and risk of litigation. 2. Certain tenant characteristics and/or circumstances – ability to pay and ability to care for the rented property – are the main factors focused on in assessing risk among applicants for rental housing. Signals of either ‘(in)ability to pay’ and/or ‘(in)ability to care for the property’ are almost always interpreted as markers of high levels of risk. 3. The processing of tenancy applications entails a complex and variable mix of formal and informal strategies of risk-assessment and allocation where sorting (out), ranking, discriminating and handing over characterise the process. 4. In the eyes of property managers, ‘suitable’ tenants can be conceptualised as those who are resourceful, reputable, competent, strategic and presentable. 5. Property managers clearly articulated concern about risks entailed in a number of characteristics or situations. Being on a low income was the principal and overarching factor which agents considered. Others included: - unemployment - ‘big’ families; sole parent families - domestic violence - marital breakdown - shift from home ownership to private rental - Aboriginality and specific ethnicities - physical incapacity - aspects of ‘presentation’. The financial vulnerability of applicants in these groups can be invoked, alongside expressed concerns about compromised capacities to manage income and/or ‘care for’ the property, as legitimate grounds for rejection or a lower ranking. 6. At the level of face-to-face interaction between the property manager and applicants, more intuitive assessments of risk based upon past experience or ‘gut feelings’ come into play. These judgements are interwoven with more systematic procedures of tenant selection. The findings suggest that considerable ‘risk’ is associated with low-income status, either directly or insofar as it is associated with other forms of perceived risk, and that such risks are likely to impede access to the professionally managed private rental market. Detailed analysis suggests that opportunities for access to housing by low-income householders also arise where, for example: - the ‘local experience’ of an agency and/or property manager works in favour of particular applicants - applicants can demonstrate available social support and financial guarantors - an applicant’s preference or need for longer-term rental is seen to provide a level of financial security for the landlord - applicants are prepared to agree to specific, more stringent conditions for inspection of properties and review of contracts - the particular circumstances and motivations of landlords lead them to consider a wider range of applicants - In particular circumstances, property managers are prepared to give special consideration to applicants who appear worthy, albeit ‘risky’. The strategic actions of demonstrating and documenting on the part of vulnerable (low-income) tenant applicants can improve their chances of being perceived as resourceful, capable and ‘savvy’. Such actions are significant because they help to persuade property managers not only that the applicant may have sufficient resources (personal and material) but that they accept that the onus is on themselves to show they are reputable, and that they have valued ‘competencies’ and understand ‘how the system works’. The parameters of the market do shape the processes of risk-assessment and, ultimately, the strategic relation of power between property manager and the tenant applicant. Low vacancy rates and limited supply of lower-cost rental stock, in all areas, mean that there are many more tenant applicants than available properties, creating a highly competitive environment for applicants. The fundamental problem of supply is an aspect of the market that severely limits the chances of access to appropriate and affordable housing for low-income rental housing applicants. There is recognition of the impact of this problem of supply. The study indicates three main directions for future focus in policy and program development: providing appropriate supports to tenants to access and sustain private rental housing, addressing issues of discrimination and privacy arising in the processes of selecting suitable tenants, and addressing problems of supply.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Wear mechanisms and scale effects in two-body abrasion

The ‘particle size effect’ and its manifestation in abrasion still attracts considerable debate as to its origins and the ranking of its likely causes. Experiments have been conducted to study the important contribution that the formation of wear debris can have on the progression of wear. The experiments consist of unlubricated (dry) pin-on-disk tests with silicon carbide coated paper of varying particle size, with different pin material, diameter and loads. It has been observed that the influence of debris formation on wear rate is more pronounced for fine abrasives and soft-wearing materials. Consequently, it is proposed that the particle size effect can be explained in terms of geometrical scaling and the evolution of third-body effects with diminishing particle diameter.