Design-driven digital futures: The role of design-led innovation in the creation of online connected communities for new mobility solutions

Online communities have fundamentally changed how humans connected and are now so common they are fundamental to the human experience. As the Internet developed for Web 1.0 to Web 2.0, the functionality of these communities has far exceeded initial expectations. These communities have shifted from simply places to share information to ways to access products and services that bridge the online and offline worlds. This shift has led to the disruption of many industries with the transportation industry being one such sector. Both private transport providers and public transport systems face competition from online communities who are able to link services providers and customers more effectively and innovatively. These types of communities fall under what has been popularised as collaborative consumption or the sharing economy. The aim of this study is to explore the role of Design-led Innovation in the creation of digital futures, specifically online connected communities for successful new mobility solutions. To explore this proposition multiple data collection methods are proposed;Content Analysis, ii) A Comparative Qualitative Study consisting of Qualitative Interviews and Focus Groups / Design Workshops and iii) An Action Research Cycle of Embedded Practice. The multidisciplinary nature of this study grounds this research in a novel position contributing to new knowledge in both the field of design, and also a deeper understanding of the larger fast-growing online community phenomena.

An innovative online intervention: The Steering Clear First Offender Drink Driving Program

Drink driving remains a substantial public health issue warranting investigation. First offender drink drivers are seen to be less risky than repeat offenders, though the majority of first offenders report drink driving prior to detection, and many continue to drink drive following conviction. Few first offenders are offered treatment programs, and as such there is a need to address drink driving behaviour at this stage. A comprehensive approach including first offender treatment is needed to address the problem. Online interventions have demonstrated effectiveness in reducing risky behaviours such as harmful substance use. Such interventions allow for personalised tailored content to be delivered to individuals targeting specific mechanisms of behavioural change. This method also allows for targeting screening to ensure relevance of content on an individual level. However, there have been no research based online programs to date aimed at reducing repeat drink driving by first offenders. The Steering Clear First Offender Drink Driving Program is a self-guided, research based online program aimed at reducing recidivism by first time drink driving offenders. It includes a specialised web app to track drinks and build plans to prevent future drink driving. This allows for elongation of learning and encouragement of sustained behavioural change using self-monitoring after initial program completion. An outline of the program is discussed and the qualitative experience of the program on a sample of first offenders recruited at the time of court appearance is described.

A new intelligent algorithm for online voltage stability assessment and monitoring

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.

Online Researcher Profiles

Research Services Librarian, JCU and Paula Callan, Scholarly communications Librarian, QUT. Presented 16 September via Blackboard Collaborate as part of the QULOC Research Support for Library Liaison webinar series. This work is licensed under a Creative Commons

A Pattern Recognition Model of Voice-Based Personal Verification Systems for Forensic Applications

Abstract-The success of automatic speaker recognition in laboratory environments suggests applications in forensic science for establishing the Identity of individuals on the basis of features extracted from speech. A theoretical model for such a verification scheme for continuous normaliy distributed featureIss developed. The three cases of using a) single feature, b)multipliendependent measurements of a single feature, and c)multpleindependent features are explored.The number iofndependent features needed for areliable personal identification is computed based on the theoretcal model and an expklatory study of some speech featues.

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Eigennamen in Pressetexten : Ein Vergleich zweier Online-Zeitungen

Tämä pro gradu -tutkielma vertailee korpuksen avulla erisnimien kvantitatiivista jakautumista luokkiin kahdessa saksalaisessa verkkolehdessä. Työn tavoitteena on selvittää, kuinka erisnimiä voidaan luokitella ja mitä eroja niiden avulla on havaittavissa lehtien raportoinnissa. Laajempana kehyksenä toimii kysymys siitä, voidaanko erisnimiä hyödyntäen hahmottaa lehtien sisältöjä. Korpus on kerätty Frankfurter Allgemeine Zeitungin ja Süddeutsche Zeitungin verkkolehtien http: //www.faz.net (FAZ) ja http://www.sueddeutsche.de (SZ) artikkeleista ajalta 2.11.2004-8.11.2004. Valitut sivustot edustavat Saksan arvostetuimpien päivittäisten, koko maan kattavien sanomaleh- tien verkkojulkaisuja. Näistä FAZ:ia pidetään konservatiivisena ja SZ:ia liberaalina lehtenä. Kumpikin korpus käsittelee USA:n presidentinvaaleja syksyllä 2004 ja sisältää hieman alle 30 000 sanaa noin 40 lehtiartikkelista. Aihesidonnaisen korpuksen valinta perustuu erityisesti siihen, että tutkimuksen päämääränä on saada erisnimien avulla selville, miltä osin FAZ ja SZ eroavat toisistaan käsitellessään samaa aihetta. Teoriaosassa käydään läpi saksalaisten verkkolehtien taustaa, työhön liittyviä tekstilingvistisiä teo- rioita sekä erisnimien erikoispiirteitä. Siinä käsitellään myös kolmea aiempaa, saksankielisen eris- nimitutkimuksen luokittelua ja yhtä englanninkielistä, kieliteknologian luokittelua. Näissä havaitut puutteet motivoivat yhdistelemään ja muuttamaan olemassa olevia luokitteluja tätä työtä varten. Uusi luokittelu sisältää neljä yläluokkaa (olentojen, maantieteelliset, instituutioden ja asioiden ni- met), jotka kaikki kattavat kahdesta yhdeksään alaluokkaa. Kummankin korpuksen erisnimet luo- kitellaan tämän perusteella. Kvantitatiivinen analyysi keskittyy ylä- ja alaluokkien vertailuun lehtien välillä. Lisäksi se kattaa sekä kummankin aineiston että pääluokkien frekventimpien sanojen tarkastelun. Vaikka FAZ ja SZ käyttivätkin pääosin samoja erisnimiä raportoinnissaan, voidaan lehtien välillä osoittaa selkeitä eroja alaluokkien kohdalla ja vähäisiä eroja erisnimien jakautumisessa yläluokkiin. chi2 -testin näytti kuitenkin, että erisnimien jakautuminen yläluokkiin on lehtisidonnaista. Siksi voidaan väittää, että muun muassa valittu media vaikuttaa erisnimivalintoihin. Erisnimien frekvenssit antavat ymmärtää, että SZ raportoisi monipuolisemmin kuin FAZ, joka käyttää erisnimiä keskitetymmin. SZ:in aineiston erisnimiä yhdistää eurooppalainen näkökulma vaaleihin, kun taas FAZ pyrkii tuomaan esille tapahtumia USA:n eri osavaltioissa. Niin lehdissä mainitut henkilöiden kuin instituutioden nimet tukevat tätä väitetettä. SZ korostaa maantieteellisesti kaupunkien merkitystä, FAZ osavaltioiden. Saadut tulokset osoittavat, että tämänkaltaisen erisnimitutkimuksen soveltaminen lehtiteksteihin on mahdollista. Luokitellut erisnimet heijastavat osittain käsiteltyjen aineistojen sisältöä ja paljastavat raportoinnin painopisteistä.

Fluorescent labelling of strychnine: A novel approach for recognition of strychnine binding sites on neuronal membrane

trychnine was coupled to fluorescein isothiocyanate to mark strychnine binding sites in spinal cord of rat. Specific binding of strychnine could be demonstrated in synaptosomal fraction. Addition of glycine to the strychninised membrane led to a decrease in fluorescence indicating same receptor loci.

Simplified algorithms based on Haar transforms for signal recognition in protective relays

This letter presents the development of simplified algorithms based on Haar functions for signal extraction in relaying signals. These algorithms, being computationally simple, are better suited for microprocessor-based power system protection relaying. They provide accurate estimates of the signal amplitude and phase.

How to develop an online community for pre-service and early career teachers

This paper contributes a number of design principles for developing large-scale online communities of pre-service and early career teachers (PS&ECTs). It presents the paradigms of connected learning, networked learning and communities of practice and contrasts them. It describes the potential for online communities to meet the needs of PS&ECTs and it identifies gaps that exist within certain types of existing online communities that currently support PS&ECTs. The paper proposes design principles for a new type of online community for PS&ECTs. These principles are drawn from the literature and from the preliminary outcomes of a pilot study.

Host recognition by a polyphagous lepidopteran (Helicoverpa armigera): Primary host plants, host produced volatiles and neurosensory stimulation

An important question in the host-finding behaviour of a polyphagous insect is whether the insect recognizes a suite or template of chemicals that are common to many plants? To answer this question, headspace volatiles of a subset of commonly used host plants (pigeon pea, tobacco, cotton and bean) and nonhost plants (lantana and oleander) of Helicoverpa armigera Hübner (Lepidoptera: Noctuidae) are screened by gas chromatography (GC) linked to a mated female H. armigera electroantennograph (EAG). In the present study, pigeon pea is postulated to be a primary host plant of the insect, for comparison of the EAG responses across the test plants. EAG responses for pigeon pea volatiles are also compared between females of different physiological status (virgin and mated females) and the sexes. Eight electrophysiologically active compounds in pigeon pea headspace are identified in relatively high concentrations using GC linked to mass spectrometry (GC-MS). These comprised three green leaf volatiles [(2E)-hexenal, (3Z)-hexenylacetate and (3Z)-hexenyl-2-methylbutyrate] and five monoterpenes (α-pinene, β-myrcene, limonene, E-β-ocimene and linalool). Other tested host plants have a smaller subset of these electrophysiologically active compounds and even the nonhost plants contain some of these compounds, all at relatively lower concentrations than pigeon pea. The physiological status or sex of the moths has no effect on the responses for these identified compounds. The present study demonstrates how some host plants can be primary targets for moths that are searching for hosts whereas the other host plants are incidental or secondary targets.

Repeatable Condition-Invariant Visual Odometry for Sequence-Based Place Recognition

This paper describes a vision-only system for place recognition in environments that are tra- versed at different times of day, when chang- ing conditions drastically affect visual appear- ance, and at different speeds, where places aren’t visited at a consistent linear rate. The ma- jor contribution is the removal of wheel-based odometry from the previously presented algo- rithm (SMART), allowing the technique to op- erate on any camera-based device; in our case a mobile phone. While we show that the di- rect application of visual odometry to our night- time datasets does not achieve a level of perfor- mance typically needed, the VO requirements of SMART are orthogonal to typical usage: firstly only the magnitude of the velocity is required, and secondly the calculated velocity signal only needs to be repeatable in any one part of the environment over day and night cycles, but not necessarily globally consistent. Our results show that the smoothing effect of motion constraints is highly beneficial for achieving a locally consis- tent, lighting-independent velocity estimate. We also show that the advantage of our patch-based technique used previously for frame recogni- tion, surprisingly, does not transfer to VO, where SIFT demonstrates equally good performance. Nevertheless, we present the SMART system us- ing only vision, which performs sequence-base place recognition in extreme low-light condi- tions where standard 6-DOF VO fails and that improves place recognition performance over odometry-less benchmarks, approaching that of wheel odometry.

An Adaptive Scheme for Learning the Probability Threshold in Pattern Recognition

The statistical minimum risk pattern recognition problem, when the classification costs are random variables of unknown statistics, is considered. Using medical diagnosis as a possible application, the problem of learning the optimal decision scheme is studied for a two-class twoaction case, as a first step. This reduces to the problem of learning the optimum threshold (for taking appropriate action) on the a posteriori probability of one class. A recursive procedure for updating an estimate of the threshold is proposed. The estimation procedure does not require the knowledge of actual class labels of the sample patterns in the design set. The adaptive scheme of using the present threshold estimate for taking action on the next sample is shown to converge, in probability, to the optimum. The results of a computer simulation study of three learning schemes demonstrate the theoretically predictable salient features of the adaptive scheme.