Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

An investigation of the effect of individual cognitive preferences on learning through computer-based instruction

This paper reports a study investigating the effect of individual cognitive styles on learning through computer-based instruction. The study adopted a quasi-experimental design involving four groups which were presented with instructional material that either matched or mismatched with their preferred cognitive styles. Cognitive styles were measured by cognitive style assessment software (Riding, 1991). The instructional material was designed to cater for the four cognitive styles identified by Riding. Students' learning outcomes were measured by the time taken to perform test tasks and the number of marks scored. The results indicate no significant difference between the matched and mismatched groups on both time taken and scores on test tasks. However, there was significant difference between the four cognitive styles on test score. The Wholist/Verbaliser group performed better then all other groups. There was no significant difference between the other groups. An analysis of the performance on test task by each cognitive style showed significant difference between the groups on recall, labelling and explanation. Difference between the cognitive style groups did not reach significance level for problem-solving tasks. The findings of the study indicate a potential for cognitive style to influence learning outcomes measured by performance on test tasks.

Characteristics of utility cyclists in Queensland, Australia : an examination of the associations between individual, social and environmental factors and utility cycling

Background: Initiatives to promote utility cycling in countries like Australia and the US, which have low rates of utility cycling, may be more effective if they first target recreational cyclists. This study aimed to describe patterns of utility cycling and examine its correlates, among cyclists in Queensland, Australia. Methods: An online survey was administered to adult members of a state-based cycling community and advocacy group (n=1813). The survey asked about demographic characteristics and cycling behavior, motivators and constraints. Utility cycling patterns were described, and logistic regression modeling was used to examine associations between utility cycling and other variables. Results: Forty-seven percent of respondents reported utility cycling: most did so to commute (86%). Most journeys (83%) were >5 km. Being male, younger, employed full-time, or university-educated increased the likelihood of utility cycling (p<0.05). Perceiving cycling to be a cheap or a convenient form of transport were associated with utility cycling (p<0.05). Conclusions: The moderate rate of utility cycling among recreational cyclists highlights a potential to promote utility cycling among this group. To increase utility cycling, strategies should target female and older recreational cyclists and focus on making cycling a cheap and convenient mode of transport.

Artful inquiry : an arts-based facilitation approach for individual and organisational learning and development

The research undertaken in these two major doctoral studies investigates the field of artsbased learning, a pedagogical approach to individual and organisational learning and development, my professional creative facilitation practice and development as a researcher. While the studies are stand-alone projects they are intended to build on each other in order to tell the evolving story of my research and professional practice. The first study combines The Role of Arts-based Learning in a Creative Economy; The Need for Artistry in Professional Education the art of knowing what to do when you don’t know what to do and Lines of Inquiry: Making Sense of Research and Professional Practice. The Role of Arts-based Learning in a Creative Economy provides an overview of the field of arts-based learning in business. The study focuses on the relevant literature and interviews with people working in the field. The paper argues that arts-based learning is a valuable addition to organisations for building a culture of creativity and innovation. The Need for Artistry in Professional Education continues that investigation. It explores the way artists approach their work and considers what skills and capabilities from artistic practice can be applied to other professions’ practices. From this research the Sphere of Professional Artistry model is developed and depicts the process of moving toward professional artistry. Lines of Inquiry: making sense of research and professional practice through artful inquiry is a self-reflective study. It explores my method of inquiry as a researcher and as a creative facilitation practitioner using arts-based learning processes to facilitate groups of people for learning, development and change. It discusses how my research and professional practice influence and inspire the other and draws on cased studies. The second major research study Artful Inquiry: Arts-based Learning for Inquiry, Reflection and Action in Professional Practice is a one year practice-led inquiry. It continues the research into arts-based and aesthetic learning experiences and my arts-based facilitation practice. The research is conducted with members of a Women’s Network in a large government service agency. It develops the concept of ‘Artful Inquiry’’ a creative, holistic, and embodied approach for facilitation, inquiry, learning, reflection, and action. Storytelling as Inquiry is used as a methodology for understanding participants’ experiences of being involved in arts-based learning experiences. The study reveals the complex and emergent nature of practice and research. It demonstrates what it can mean to do practice-led research with others, within an organisational context, and to what effect.

Vector quantization based Gaussian modeling for speaker verification

Gaussian mixture models (GMMs) have become an established means of modeling feature distributions in speaker recognition systems. It is useful for experimentation and practical implementation purposes to develop and test these models in an efficient manner particularly when computational resources are limited. A method of combining vector quantization (VQ) with single multi-dimensional Gaussians is proposed to rapidly generate a robust model approximation to the Gaussian mixture model. A fast method of testing these systems is also proposed and implemented. Results on the NIST 1996 Speaker Recognition Database suggest comparable and in some cases an improved verification performance to the traditional GMM based analysis scheme. In addition, previous research for the task of speaker identification indicated a similar system perfomance between the VQ Gaussian based technique and GMMs

Phenomenological modeling of cell-to-cell and beat-to-beat variability in isolated Guinea Pig ventricular myocytes

Experimental action potential (AP) recordings in isolated ventricular myoctes display significant temporal beat-to-beat variability in morphology and duration. Furthermore, significant cell-to-cell differences in AP also exist even for isolated cells originating from the same region of the same heart. However, current mathematical models of ventricular AP fail to replicate the temporal and cell-to-cell variability in AP observed experimentally. In this study, we propose a novel mathematical framework for the development of phenomenological AP models capable of capturing cell-to-cell and temporal variabilty in cardiac APs. A novel stochastic phenomenological model of the AP is developed, based on the deterministic Bueno-Orovio/Fentonmodel. Experimental recordings of AP are fit to the model to produce AP models of individual cells from the apex and the base of the guinea-pig ventricles. Our results show that the phenomenological model is able to capture the considerable differences in AP recorded from isolated cells originating from the location. We demonstrate the closeness of fit to the available experimental data which may be achieved using a phenomenological model, and also demonstrate the ability of the stochastic form of the model to capture the observed beat-to-beat variablity in action potential duration.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Modelling passenger flow at airport terminals : individual agent decision model for stochastic passenger behaviour

Airport system is complex. Passenger dynamics within it appear to be complicate as well. Passenger behaviours outside standard processes are regarded more significant in terms of public hazard and service rate issues. In this paper, we devised an individual agent decision model to simulate stochastic passenger behaviour in airport departure terminal. Bayesian networks are implemented into the decision making model to infer the probabilities that passengers choose to use any in-airport facilities. We aim to understand dynamics of the discretionary activities of passengers.

Modeling train delays in urban networks

The reliability of urban passenger trains is a critical performance measure for passenger satisfaction and ultimately market share. A delay to one train in a peak period can have a severe effect on the schedule adherence of other trains. This paper presents an analytically based model to quantify the expected positive delay for individual passenger trains and track links in an urban rail network. The model specifically addresses direct delay to trains, knock-on delays to other trains, and delays at scheduled connections. A solution to the resultant system of equations is found using an iterative refinement algorithm. Model validation, which is carried out using a real-life suburban train network consisting of 157 trains, shows the model estimates to be on average within 8% of those obtained from a large scale simulation. Also discussed, is the application of the model to assess the consequences of increased scheduled slack time as well as investment strategies designed to reduce delay.

Institutional and individual labor market discrimination based on hukou status : an artefactual field experiment

We study discrimination based on the hukou system that segregates citizens in groups of migrants and locals in urban China. We use an artefactual field experiment with a labor market framing. We recruit workers on their real labor market as experimental participants and investigate if official discrimination motivates individual discrimination based on hukou status. In our experimental results we observe discrimination based on the hukou characteristic: however, statistical discrimination does not seem to be the source of this, as status is exogeneous for our participants and migrants and locals behave similarly. Furthermore, discrimination increases between two experimental frameworks when motives for statistical discrimination are removed.

Eigenvoice modeling for cross likelihood ratio based speaker clustering : a Bayesian approach

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modeling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modeling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modeling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Neighbourhood disadvantage, individual-level socioeconomic position, and self-reported chronic arthritis : a cross-sectional multilevel study

Objective: To examine the association between individual- and neighborhood-level disadvantage and self-reported arthritis. Methods: We used data from a population-based cross-sectional study conducted in 2007 among 10,757 men and women ages 40–65 years, selected from 200 neighborhoods in Brisbane, Queensland, Australia using a stratified 2-stage cluster design. Data were collected using a mail survey (68.5% response). Neighborhood disadvantage was measured using a census-based composite index, and individual disadvantage was measured using self-reported education, household income, and occupation. Arthritis was indicated by self-report. Data were analyzed using multilevel modeling. Results: The overall rate of self-reported arthritis was 23% (95% confidence interval [95% CI] 22–24). After adjustment for sociodemographic factors, arthritis prevalence was greatest for women (odds ratio [OR] 1.5, 95% CI 1.4–1.7) and in those ages 60–65 years (OR 4.4, 95% CI 3.7–5.2), those with a diploma/associate diploma (OR 1.3, 95% CI 1.1–1.6), those who were permanently unable to work (OR 4.0, 95% CI 3.1–5.3), and those with a household income <$25,999 (OR 2.1, 95% CI 1.7–2.6). Independent of individual-level factors, residents of the most disadvantaged neighborhoods were 42% (OR 1.4, 95% CI 1.2–1.7) more likely than those in the least disadvantaged neighborhoods to self-report arthritis. Cross-level interactions between neighborhood disadvantage and education, occupation, and household income were not significant. Conclusion: Arthritis prevalence is greater in more socially disadvantaged neighborhoods. These are the first multilevel data to examine the relationship between individual- and neighborhood-level disadvantage upon arthritis and have important implications for policy, health promotion, and other intervention strategies designed to reduce the rates of arthritis, indicating that intervention efforts may need to focus on both people and places.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.

Electronic functionality in graphene-based nanoarchitectures : discovery and design via first-principles modeling

Graphene has promised many novel applications in nanoscale electronics and sustainable energy due to its novel electronic properties. Computational exploration of electronic functionality and how it varies with architecture and doping presently runs ahead of experimental synthesis yet provides insights into types of structures that may prove profitable for targeted experimental synthesis and characterization. We present here a summary of our understanding on the important aspects of dimension, band gap, defect, and interfacial engineering of graphene based on state-of-the-art ab initio approaches. Some most recent experimental achievements relevant for future theoretical exploration are also covered.