Comparison and modification of car following models toward a more accurate microscopic simulation parameters reproduction

Traffic safety studies demand more than what current micro-simulation models can provide as they presume that all drivers of motor vehicles exhibit safe behaviours. Several car-following models are used in various micro-simulation models. This research compares the mainstream car following models’ capabilities of emulating precise driver behaviour parameters such as headways and Time to Collisions. The comparison firstly illustrates which model is more robust in the metric reproduction. Secondly, the study conducted a series of sensitivity tests to further explore the behaviour of each model. Based on the outcome of these two steps exploration of the models, a modified structure and parameters adjustment for each car-following model is proposed to simulate more realistic vehicle movements, particularly headways and Time to Collision, below a certain critical threshold. NGSIM vehicle trajectory data is used to evaluate the modified models performance to assess critical safety events within traffic flow. The simulation tests outcomes indicate that the proposed modified models produce better frequency of critical Time to Collision than the generic models, while the improvement on the headway is not significant. The outcome of this paper facilitates traffic safety assessment using microscopic simulation.

Reputation modelling in citizen science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, as the gathered information is from the crowd, the data quality is always hard to manage. There are many ways to manage data quality, and reputation management is one of the common approaches. In recent year, many research teams have deployed many audio or image sensors in natural environment in order to monitor the status of animals or plants. The collected data will be analysed by ecologists. However, as the amount of collected data is exceedingly huge and the number of ecologists is very limited, it is impossible for scientists to manually analyse all these data. The functions of existing automated tools to process the data are still very limited and the results are still not very accurate. Therefore, researchers have turned to recruiting general citizens who are interested in helping scientific research to do the pre-processing tasks such as species tagging. Although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Therefore, this research aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we aim to investigate how to use reputation management to enhance data reliability. Reputation systems have been used to solve the uncertainty and improve data quality in many marketing and E-Commerce domains. The commercial organizations which have chosen to embrace the reputation management and implement the technology have gained many benefits. Data quality issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. However, research on reputation management in this area is relatively new. We therefore start our investigation by examining existing reputation systems in different domains. Then we design novel reputation management approaches for Citizen Science projects to categorise participants and data. We have investigated some critical elements which may influence data reliability in Citizen Science projects. These elements include personal information such as location and education and performance information such as the ability to recognise certain bird calls. The designed reputation framework is evaluated by a series of experiments involving many participants for collecting and interpreting data, in particular, environmental acoustic data. Our research in exploring the advantages of reputation management in Citizen Science (or crowdsourcing in general) will help increase awareness among organizations that are unacquainted with its potential benefits.

Identities and transformational experiences for quantitative problem solving : gender comparisons of first-year university science students

Women are underrepresented in science, technology, engineering and mathematics (STEM) areas in university settings; however this may be the result of attitude rather than aptitude. There is widespread agreement that quantitative problem-solving is essential for graduate competence and preparedness in science and other STEM subjects. The research question addresses the identities and transformative experiences (experiential, perception, & motivation) of both male and female university science students in quantitative problem solving. This study used surveys to investigate first-year university students’ (231 females and 198 males) perceptions of their quantitative problem solving. Stata (statistical analysis package version 11) analysed gender differences in quantitative problem solving using descriptive and inferential statistics. Males perceived themselves with a higher mathematics identity than females. Results showed that there was statistical significance (p<0.05) between the genders on 21 of the 30 survey items associated with transformative experiences. Males appeared to have a willingness to be involved in quantitative problem solving outside their science coursework requirements. Positive attitudes towards STEM-type subjects may need to be nurtured in females before arriving in the university setting (e.g., high school or earlier). Females also need equitable STEM education opportunities such as conversations or activities outside school with family and friends to develop more positive attitudes in these fields.

CCI-CCI Creative City Index 2012

The CCI-Creative City Index was commissioned in 2010 by the Beijing Academy of Science & Technology's Beijing Research Center for the Science of Science. John Hartley was asked to develop a new creative global city index. The brief was to improve on the existing indexes with a specific focus on creative industries and the sources of creative development. This report, by John Hartley, Jason Potts, Trent MacDonald, with Chris Erkunt and Carl Kufleitner, sets out the new model we have developed, which we call the CCI Creative City Index (CCI-CCI). It presents the results of a pilot application of the index to six cities: London, Cardiff, Berlin, Bremen, Melbourne and Brisbane. The index incorporates many elements from other global and creative city indexes, but also adds several new dimensions relating to creative industries scope, micro-productivity, and the economy of attention. The report and Excel spreadsheets of index calculations can be found on this site.

The trials and tribulations of a BYOD science classroom

The increase in the availability and use of portable mobile devices has had a number of impacts on society. In particular, this impact has been seen within Higher Education Institutions where staff and students are using these devices for both simple and complex tasks. Within undergraduate teacher education courses there is an expectation that students will be fully prepared for teaching their respective areas of expertise as well as having the ability to use ICT, and in particular portable mobile devices, to support teaching and learning. This paper reports on a small case study into the use of portable mobile devices in a science unit, where the students (N=16) bring their own devices into the classroom and use them in lectures, tutorials and workshops. The study highlights the changing nature of classroom practice within the university setting and the challenges faced by teaching staff and students when using these devices.

Australia at the crossroads : a review of school science practical work

In Australia we are at a crossroad in science education. We have come from a long history of adopting international curricula, through to blending international and Australian developed materials, to the present which is a thoroughly unique Australian curriculum in science. This paper documents Australia’s journey over the past 200 years, as we prepare for the unveiling of our first truly Australian National Curriculum. One of the unique aspects of this curriculum is the emphasis on practical work and inquiry-based learning. This paper identifies seven forms of practical work currently used in Australian schools and the purposes aligned with each form by 138 pre-service and experienced in-service teachers. The paper explores the question “What does the impending national curriculum, with its emphasis on practical inquiry mean to the teachers now, are they ready?” The study suggests that practical work in Australian schools is multifaceted, and the teacher aligned purposes are dependent not only upon the age of the student, but also on the type of practical work being undertaken. It was found that most teachers are not ready to teach using inquiry-based pedagogy and cite lack of content knowledge, behaviour management, and lack of physical resources and availability of classroom space as key issues which will hinder their implementation of the inquiry component of Australia’s pending curriculum in science.

Timed and Probabilistic Automata for Automatic Animal Call Recognition

Automatic Call Recognition is vital for environmental monitoring. Patten recognition has been applied in automatic species recognition for years. However, few studies have applied formal syntactic methods to species call structure analysis. This paper introduces a novel method to adopt timed and probabilistic automata in automatic species recognition based upon acoustic components as the primitives. We demonstrate this through one kind of birds in Australia: Eastern Yellow Robin.

Sequential slotted amplify-and-forward with partial relay isolation

In this paper, we propose a novel relay ordering and scheduling strategy for the sequential slotted amplify-and-forward (SAF) protocol and evaluate its performance in terms of diversity-multiplexing trade-off (DMT). The relays between the source and destination are grouped into two relay clusters based on their respective locations. The proposed strategy achieves partial relay isolation and decreases the decoding complexity at the destination. We show that the DMT upper bound of sequential-SAF with the proposed strategy outperforms other amplify and forward protocols and is more practical compared to the relay isolation assumption made in the original paper [1]. Simulation result shows that the sequential-SAF protocol with the proposed strategy has better outage performance compared to the existing AF and non-cooperative protocols in high SNR regime.

Sequential slotted amplify-decode-and-forward

In this paper, we propose a novel slotted hybrid cooperative protocol named the sequential slotted amplify-decodeand-forward (SADF) protocol and evaluate its performance in terms of diversity-multiplexing trade-off (DMT). The relays between the source and destination are divided into two different groups and each relay either amplifies or decodes the received signal. We first compute the optimal DMT of the proposed protocol with the assumption of perfect decoding at the DF relays. We then derive the DMT closed-form expression of the proposed sequential-SADF and obtain the proximity gain bound for achieving the optimal DMT. With the proximity gain bound, we then found the distance ratio to achieve the optimal DMT performance. Simulation result shows that the proposed protocol with high proximity gain outperforms other cooperative communication protocols in high SNR regime.

When digital native meets analogue reality : a case study of ICT skills in first year university students

Recently claims have been made that all universities will in coming decades merge to become just a few mega-institutions offering online degrees to the world. This assumes a degree of literacy with ICT (information and communication technology) amongst potential students, who are often regarded as 'digital natives'. Far from being digital natives, many students have considerable trouble using ICT beyond the ubiquitous Facebook. While some students are computer literate, a substantial proportion lack the skills to prosper under their own devices in an online tertiary education environment. For these students a blended learning experience is needed to develop skills to effectively interact in the virtual environment. This paper presents a case study that specifically examined the ICT capabilities of first-year university students enrolled in the School of Civil Engineering and the Built Environment at Queensland University of Technology (QUT). Empirical data are presented and curriculum strategies articulated to develop ICT skills in university undergraduates.

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.