Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Adaptable interface model for intuitively learnable interfaces : an approach to address diversity in older users' capabilities

This study started with the aim to develop an approach that will help designers create interfaces that are more intuitive for older adults to use. Two objectives were set for this study: 1) to investigate one of the possible strategies for developing intuitive interfaces for older people, and; 2) to investigate factors that could interfere with intuitive use. This paper briefly presents the outcome of the two experiments and how it has lead to the development of an adaptable interface design model that will help designers develop interfaces that are intuitive to learn and, over time, intuitive to use for users with diverse technology prior experience and cognitive abilities.

Make-or-break : work and family interface and wellbeing among small business owners

This study responds to calls for research on work-family aspects in entrepreneurship research. Our study examined the role of work-family conflict and enhancement on small business owners’ (SBOs) wellbeing. We found work-family has negative direct effect on mental health, job and family satisfactions. Furthermore, we found that under high level of work-family conflict condition, SBOs who perceive a greater level of work-family enhancement would feel more satisfy with their life, job as well as family aspects. Interestingly, under high level of conflict, even SBOs perceive greater level of enhancement, it would not lessen the negative impact of the conflict on their mental health. These results suggest that once psychological health is harmed by work-family conflict, its negative consequences remain unchanged.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Refactoring Service Interface to Support Artefact-centric Service interaction

The growth of APIs and Web services on the Internet, especially through larger enterprise systems increasingly being leveraged for Cloud and software-as-a-service opportunities, poses challenges for improving the efficiency of integration with these services. Interfaces of enterprise systems are typically larger, more complex and overloaded, with single operations having multiple data entities and parameter sets, supporting varying requests, and reflecting versioning across different system releases, compared to fine-grained operations of contemporary interfaces. We propose a technique to support the refactoring of service interfaces by deriving business entities and their relationships. In this paper, we focus on the behavioural aspects of service interfaces, aiming to discover the sequential dependencies of operations (otherwise known as protocol extraction) based on the entities and relationships derived. Specifically, we propose heuristics according to these relationships, and in turn, deriving permissible orders in which operations are invoked. As a result of this, service operations can be refactored on business entity CRUD lines, with explicit behavioural protocols as part of an interface definition. This supports flexible service discovery, composition and integration. A prototypical implementation and analysis of existing Web services, including those of commercial logistic systems (Fedex), are used to validate the algorithms proposed through the paper.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Detection of rain in acoustic recordings of the environment using machine learning techniques

This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.

Mini-Orb : A personal indoor climate preference feedback interface

The control of environmental factors in open-office environments, such as lighting and temperature is becoming increasingly automated. This development means that office inhabitants are losing the ability to manually adjust environmental conditions according to their needs. In this paper we describe the design, use and evaluation of MiniOrb, a system that employs ambient and tangible interaction mechanisms to allow inhabitants of office environments to maintain awareness of environmental factors, report on their own subjectively perceived office comfort levels and see how these compare to group average preferences. The system is complemented by a mobile application, which enables users to see and set the same sensor values and preferences, but using a screen-based interface. We give an account of the system’s design and outline the results of an in-situ trial and user study. Our results show that devices that combine ambient and tangible interaction approaches are well suited to the task of recording indoor climate preferences and afford a rich set of possible interactions that can complement those enabled by more conventional screen-based interfaces.

Fabrication of macro–mesoporous titania/alumina core–shell materials in oil/water interface

A series of macro–mesoporous TiO2/Al2O3 nanocomposites with different morphologies were synthesized. The materials were calcined at 723 K and were characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscope (TEM), N2 adsorption/desorption, Infrared Emission Spectroscopy (IES), X-ray photoelectron spectroscopy (XPS) and UV–visible spectroscopy (UV–visible). A modified approach was proposed for the synthesis of 1D (fibrous) nanocomposite with higher Ti/Al molar ratio (2:1) at lower temperature (<100 °C), which makes it possible to synthesize such materials on industrial scale. The performance–morphology relationship of as-synthesized TiO2/Al2O3 nanocomposites was investigated by the photocatalytic degradation of a model organic pollutant under UV irradiation. The samples with 1D (fibrous) morphology exhibited superior catalytic performance than the samples without, such as titania microspheres.

Service Interface Synthesis in Business Networks

Mismatches between services needing to interoperate have been addressed through the adaptation of structural and behavioural interfaces of services, which in practice incur long lead time through manual, coding effort. We propose a framework, complementary to con- ventional service adaptation, to synthesise service interfaces in the open setting of business networks, allowing consumers to introspect service interfaces and formulate service invocations. The framework also allows evolved service requests, as new features of service capabilities are discov- ered, through interactions with other, similar services. Finally the frame- work fosters reuse of adaptation efforts through normalisation of struc- tural and behavioural interfaces of similar services. This paper provides a first exposition of the service interface synthesis framework, describing patterns containing novel requirements for unilateral service adaptation and detailing the interface synthesis technique. Complex examples of ser- vices drawn from commercial logistic systems are then used to validate the synthesis technique and identify open challenges and future research directions.

Bringing the farmer perspective to agricultural robots

The research reported in this paper explores autonomous technologies for agricultural farming application and is focused on the development of multiple-cooperative agricultural robots (AgBots). These are highly autonomous, small, lightweight, and unmanned machines that operate cooperatively (as opposed to a traditional single heavy machine) and are suited to work on broadacre land (large-scale crop operations on land parcels greater than 4,000m2). Since this is a new, and potentially disruptive technology, little is yet known about farmer attitudes towards robots, how robots might be incorporated into current farming practice, and how best to marry the capability of the robot with the work of the farmer. This paper reports preliminary insights (with a focus on farmer-robot control) gathered from field visits and contextual interviews with farmers, and contributes knowledge that will enable further work toward the design and application of agricultural robotics.

Social networking sites and participatory reluctance: A case study of Gaydar, user resistance and interface rejection

This article conceptualises ‘participatory reluctance’ as a particular orientation to social media that problematises binarised notions of connection and disconnection in social networking sites. It qualitatively examines how the concept has functioned within gay men’s social networking service, Gaydar, among 18- to 28-year-old users of the site in Brisbane, Australia. Participatory reluctance is shown to be a central aspect of the culture of this space, fostered among the studied demographic by the convergence of the growing global push for marriage equality and increasing normalisation of the kinds of gay male identities commonly adopted among this group, with three key factors rooted primarily in Gaydar’s design: (1) young users’ perceptions of the site as a space for procuring casual sex; (2) their perceptions of the imagined user as embodying existing stereotypes of gay masculinity, and; (3) a lack of genuine alternatives in terms of niche digital spaces for gay men’s social networking.

Applications of real-time machine vision to the control of mining equipment

The mining industry presents us with a number of ideal applications for sensor based machine control because of the unstructured environment that exists within each mine. The aim of the research presented here is to increase the productivity of existing large compliant mining machines by retrofitting with enhanced sensing and control technology. The current research focusses on the automatic control of the swing motion cycle of a dragline and an automated roof bolting system. We have achieved: * closed-loop swing control of an one-tenth scale model dragline; * single degree of freedom closed-loop visual control of an electro-hydraulic manipulator in the lab developed from standard components.

Who owns Superman? The Man of Steel fights trademark law

Who is Superman’s greatest threat? Evil genius Lex Luthor? General Zod from the Phantom Zone? The doppelganger Bizarro? Super-villain Brainiac? Kryptonite? Or is it intellectual property law?

The High Court of Australia and the Marlboro Man: The Battle Over the Plain Packaging of Tobacco Products

In 2012, the High Court of Australia handed down a landmark decision on the plain packaging of tobacco products. This chapter considers the historic ruling in the case of JT International SA v Commonwealth; British American Tobacco Australasia Ltd v Commonwealth. This chapter explores several themes in the decision. First, it highlights the historical work by the High Court of Australia on the role of health regulation, the use of health warnings, and tobacco control. Second, the chapter considers the High Court of Australia's view that intellectual property law promotes the public interest.Third, it explores the High Court of Australia’s analysis of the constitutional law on acquisition of property on just terms. Finally, this chapter contends that the High Court of Australia's ruling on plain packaging of tobacco products will spark an 'Olive Revolution' — and will encourage superior courts and policy-makers to follow suit.