Establishing statistical foundations early : data modeling with young learners

This paper addresses research from a three-year longitudinal study that engaged children in data modeling experiences from the beginning school year through to third year (6-8 years). A data modeling approach to statistical development differs in several ways from what is typically done in early classroom experiences with data. In particular, data modeling immerses children in problems that evolve from their own questions and reasoning, with core statistical foundations established early. These foundations include a focus on posing and refining statistical questions within and across contexts, structuring and representing data, making informal inferences, and developing conceptual, representational, and metarepresentational competence. Examples are presented of how young learners developed and sustained informal inferential reasoning and metarepresentational competence across the study to become “sophisticated statisticians”.

An Investigation into the Options for Increasing the Extent of Energy Efficiency Knowledge and Skills in Engineering Education

Society is increasingly calling for professionals across government, industry, business and civil society to be able to problem-solve issues related to climate change and sustainable development as part of their work. In particular there is an emerging realisation of the fundamental need to swiftly reduce the growing demand for energy across society, and to then meet the demand with low emissions options. A key ingredient to addressing such issues is equipping professionals with emerging knowledge and skills to address energy challenges in all aspects of their work. The Council of Australian Governments has recognised this need, signing the National Partnership Agreement on Energy Efficiency in July 2009, which included a commitment to assist business and industry obtain the knowledge, skills and capacity to pursue cost-effective energy efficiency opportunities.2 Engineering will play a critical part among the professions, with Engineers Australia acknowledging that, ‘The need to make changes in the way energy is used and supplied throughout the world represents the greatest challenge to engineers in moving toward sustainability.’

Energy Efficiency Resources for Undergraduate Engineering Education: Final Report

This report presents the findings of an investigation of energy efficiency resources for undergraduate engineering education, undertaken by web-based research, conversations with educators, and a university survey. The investigation draws on the results of a number of previous investigations undertaken by the research team for NFEE related to energy efficiency education and presents the following findings and recommendations, as explained in greater detail in the body of the report. The findings suggest that even though certain EE concepts and principles have been identified by lecturers as being important there is little to no coverage of a number of these concepts in some programs/courses. Similarly, many topics relating to the most important EE workforce skills and significant shortages as identified in industry research, do not rate highly in terms of both perceived importance by lecturers, or coverage within existing courses. Overall, these findings suggest that despite growing awareness of the importance of EE in both industry and academia, the current depth and breadth of EE content in courses does not reflect this. It confirms that efforts in these areas can be better supported.

Energy Efficiency Graduate Attributes Project: Final Report

The Energy Efficiency (EE) Graduate Attributes Project focuses on engineering as a priority profession that has a significant role to play in addressing energy demand and supply issues in Australia. Specifically, this project aims to support embedding EE knowledge and skills throughout the engineering undergraduate curriculum, to help build capacity within the Australian workforce across major sectors of the economy, from mining, manufacturing and industrial applications to design, construction, maintenance and retrofitting built environments. The resultant report is intended to assist in future consultation with key groups such as Engineers Australia (EA), the Australian Council of Engineering Deans (ACED) and the eight EA colleges, to support systemic curriculum renewal and promote the design and development of high quality EE engineering education resources. The project is based on a whole-of-program outcomes-based approach to curriculum renewal, creating a transparent framework for integrating EE. This comprises collaborative consideration by academics and professional engineers who have experience in teaching and practising EE, to identify what students should learn to be equipped with relevant competencies by the time they graduate.

Microalgal growth characteristics and subsequent influence on dewatering efficiency

Dewatering of microalgal culture is a major bottleneck towards the industrial-scale processing of microalgae for bio-diesel production. The dilute nature of harvested microalgal cultures poses a huge operation cost to dewater; thereby rendering microalgae-based fuels less economically attractive. This study explores the influence of microalgal growth phases and intercellular interactions during cultivation on dewatering efficiency of microalgae cultures. Experimental results show that microalgal cultures harvested during a low growth rate phase (LGRP) of 0.03 d-1 allowed a higher rate of settling than those harvested during a high growth rate phase (HGRP) of 0.11 d-1, even though the latter displayed a higher average differential biomass concentration of 0.2 g L-1 d-1. Zeta potential profile during the cultivation process showed a maximum electronegative value of -43.2 ± 0.7 mV during the HGRP which declined to stabilization at -34.5 ± 0.4 mV in the LGRP. The lower settling rate observed for HGRP microalgae is hence attributed to the high stability of the microalgal cells which electrostatically repel each other during this growth phase. Tangential flow filtration of 20 L HGRP culture concentrated 23 times by consuming 0.51 kWh/m3 of supernatant removed whilst 0.38 kWh/m3 was consumed to concentrate 20 L of LGRP by 48 times.

Plasmonic effect of annealed gold islands for improving efficiency of organic solar cells

Embedding metallic nanoparticles in organic solar cells can enhance the photoabsorption through light trapping processes. This paper investigates how gold islands obtained by annealing 1–5 nm thick Au layers affect the photoabsorption. Using finite-difference time-domain simulations, the cell efficiency for various island geometries and thicknesses are analyzed and the properties of the islands for maximal photocurrent are discussed. It is shown that a careful choice of size and concentration of gold islands could contribute to enhance the power conversion efficiencies when compared to standard organic solar cell devices. The conclusions are then compared to experimental data for thermally annealed gold islands in bulk heterojunction solar cells. The results of this paper will contribute to the optimization of plasmonic organic solar cell systems and will pave the way for the development of highly efficient organic solar cell devices.

Exploring variation in measurement as a foundation for statistical thinking in the elementary school

This study was based on the premise that variation is the foundation of statistics and statistical investigations. The study followed the development of fourth-grade students' understanding of variation through participation in a sequence of two lessons based on measurement. In the first lesson all students measured the arm span of one student, revealing pathways students follow in developing understanding of variation and linear measurement (related to research question 1). In the second lesson each student's arm span was measured once, introducing a different aspect of variation for students to observe and contrast. From this second lesson, students' development of the ability to compare their representations for the two scenarios and explain differences in terms of variation was explored (research question 2). Students' documentation, in both workbook and software formats, enabled us to monitor their engagement and identify their increasing appreciation of the need to observe, represent, and contrast the variation in the data. Following the lessons, a written student assessment was used for judging retention of understanding of variation developed through the lessons and the degree of transfer of understanding to a different scenario (research question 3).

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Sealing the leak on classical NTRU signatures

Initial attempts to obtain lattice based signatures were closely related to reducing a vector modulo the fundamental parallelepiped of a secret basis (like GGH [9], or NTRUSign [12]). This approach leaked some information on the secret, namely the shape of the parallelepiped, which has been exploited on practical attacks [24]. NTRUSign was an extremely efficient scheme, and thus there has been a noticeable interest on developing countermeasures to the attacks, but with little success [6]. In [8] Gentry, Peikert and Vaikuntanathan proposed a randomized version of Babai’s nearest plane algorithm such that the distribution of a reduced vector modulo a secret parallelepiped only depended on the size of the base used. Using this algorithm and generating large, close to uniform, public keys they managed to get provably secure GGH-like lattice-based signatures. Recently, Stehlé and Steinfeld obtained a provably secure scheme very close to NTRUSign [26] (from a theoretical point of view). In this paper we present an alternative approach to seal the leak of NTRUSign. Instead of modifying the lattices and algorithms used, we do a classic leaky NTRUSign signature and hide it with gaussian noise using techniques present in Lyubashevky’s signatures. Our main contributions are thus a set of strong NTRUSign parameters, obtained by taking into account latest known attacks against the scheme, a statistical way to hide the leaky NTRU signature so that this particular instantiation of CVP-based signature scheme becomes zero-knowledge and secure against forgeries, based on the worst-case hardness of the O~(N1.5)-Shortest Independent Vector Problem over NTRU lattices. Finally, we give a set of concrete parameters to gauge the efficiency of the obtained signature scheme.

A meshless method based on Point Interpolation Method (PIM) for the space fractional diffusion equation

This paper aims to develop a meshless approach based on the Point Interpolation Method (PIM) for numerical simulation of a space fractional diffusion equation. Two fully-discrete schemes for the one-dimensional space fractional diffusion equation are obtained by using the PIM and the strong-forms of the space diffusion equation. Numerical examples with different nodal distributions are studied to validate and investigate the accuracy and efficiency of the newly developed meshless approach.