A proposed approach to monitor private-sector policies and practices related to food environments, obesity and non-communicable disease prevention

Private-sector organizations play a critical role in shaping the food environments of individuals and populations. However, there is currently very limited independent monitoring of private-sector actions related to food environments. This paper reviews previous efforts to monitor the private sector in this area, and outlines a proposed approach to monitor private-sector policies and practices related to food environments, and their influence on obesity and non-communicable disease (NCD) prevention. A step-wise approach to data collection is recommended, in which the first (‘minimal’) step is the collation of publicly available food and nutrition-related policies of selected private-sector organizations. The second (‘expanded’) step assesses the nutritional composition of each organization's products, their promotions to children, their labelling practices, and the accessibility, availability and affordability of their products. The third (‘optimal’) step includes data on other commercial activities that may influence food environments, such as political lobbying and corporate philanthropy. The proposed approach will be further developed and piloted in countries of varying size and income levels. There is potential for this approach to enable national and international benchmarking of private-sector policies and practices, and to inform efforts to hold the private sector to account for their role in obesity and NCD prevention.

Monitoring the impacts of trade agreements on food environments

The liberalization of international trade and foreign direct investment through multilateral, regional and bilateral agreements has had profound implications for the structure and nature of food systems, and therefore, for the availability, nutritional quality, accessibility, price and promotion of foods in different locations. Public health attention has only relatively recently turned to the links between trade and investment agreements, diets and health, and there is currently no systematic monitoring of this area. This paper reviews the available evidence on the links between trade agreements, food environments and diets from an obesity and non-communicable disease (NCD) perspective. Based on the key issues identified through the review, the paper outlines an approach for monitoring the potential impact of trade agreements on food environments and obesity/NCD risks. The proposed monitoring approach encompasses a set of guiding principles, recommended procedures for data collection and analysis, and quantifiable ‘minimal’, ‘expanded’ and ‘optimal’ measurement indicators to be tailored to national priorities, capacity and resources. Formal risk assessment processes of existing and evolving trade and investment agreements, which focus on their impacts on food environments will help inform the development of healthy trade policy, strengthen domestic nutrition and health policy space and ultimately protect population nutrition.

Perdido : the first Smart Field in the western hemisphere

Located in the Gulf of Mexico in nearly 8,000 feet of water, the Perdido development is the world’s deepest spar and Shell’s first Smart Field in the Western hemisphere. Jointly developed by Shell, BP, and Chevron, the spar and the subsea equipment connected to it will eventually capture approximately an order of magnitude more data than is collected from any other Shell-designed and managed development currently operating in the Gulf of Mexico. This paper will describe Shell’s Smart Fields design philosophy, briefly explain the five design elements that underpin “smartness” in Shell’s North and South American operations—specifically, remote assisted operations, exception-based surveillance, collaborative work environments, hydrocarbon development tools and workflows, and Smart Fields Foundation IT infrastructure—and shed light on the process by which a highly customized Smart Fields development and management plan was put together for Perdido.

Business value from intelligent fields

The Smart Fields programme has been active in Shell over the last decade and has given large benefits. In order to understand the value and to underpin strategies for the future implementation programme, a study was carried out to quantify the benefits to date. This focused on actually achieved value, through increased production or lower costs. This provided an estimate of the total value achieved to date. Future benefits such as increased reserves or continued production gain were recorded separately. The paper describes the process followed in the benefits quantification. It identifies the key solutions and technologies and describes the mechanism used to understand the relation between solutions and value. Examples have been given of value from various assets around the world, in both existing fields and in green fields. Finally, the study provided the methodology for tracking of value. This helps Shell to estimate and track the benefits of the Smart Fields programme at company scale.

Poisoned GOOSE : exploiting the GOOSE protocol

This paper presents a vulnerability within the generic object oriented substation event (GOOSE) communication protocol. It describes an exploit of the vulnerability and proposes a number of attack variants. The attacks sends GOOSE frames containing higher status numbers to the receiving intelligent electronic device (IED). This prevents legitimate GOOSE frames from being processed and effectively causes a hijacking of the communication channel, which can be used to implement a denial–of–service (DoS) or manipulate the subscriber (unless a status number roll-over occurs). The authors refer to this attack as a poisoning of the subscriber. A number of GOOSE poisoning attacks are evaluated experimentally on a test bed and demonstrated to be successful.

Robust recognition of human behaviour in challenging environments

Novel techniques have been developed for the automatic recognition of human behaviour in challenging environments using information from visual and infra-red camera feeds. The techniques have been applied to two interesting scenarios: Recognise drivers' speech using lip movements and recognising audience behaviour, while watching a movie, using facial features and body movements. Outcome of the research in these two areas will be useful in the improving the performance of voice recognition in automobiles for voice based control and for obtaining accurate movie interest ratings based on live audience response analysis.

Odometry-driven inference to link multiple exemplars of a location

A major challenge for robot localization and mapping systems is maintaining reliable operation in a changing environment. Vision-based systems in particular are susceptible to changes in illumination and weather, and the same location at another time of day may appear radically different to a system using a feature-based visual localization system. One approach for mapping changing environments is to create and maintain maps that contain multiple representations of each physical location in a topological framework or manifold. However, this requires the system to be able to correctly link two or more appearance representations to the same spatial location, even though the representations may appear quite dissimilar. This paper proposes a method of linking visual representations from the same location without requiring a visual match, thereby allowing vision-based localization systems to create multiple appearance representations of physical locations. The most likely position on the robot path is determined using particle filter methods based on dead reckoning data and recent visual loop closures. In order to avoid erroneous loop closures, the odometry-based inferences are only accepted when the inferred path's end point is confirmed as correct by the visual matching system. Algorithm performance is demonstrated using an indoor robot dataset and a large outdoor camera dataset.

Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms

In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.

Dealing with shadows : capturing intrinsic scene appearance for image-based outdoor localisation

In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision-based localisation approaches. In this paper we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying material property not lighting. We summarise the theory of shadow invariant images and discuss the modelling and calibration issues which are important for non-ideal off-the-shelf colour cameras. We evaluate the technique with a commonly used robotic camera and an autonomous car operating in an outdoor environment, and show that it can outperform the use of ordinary greyscale images for the task of visual localisation.

Autonomous forced landing system for light general aviation aircraft in unknown environments

This paper presents a system which enhances the capabilities of a light general aviation aircraft to land autonomously in case of an unscheduled event such as engine failure. The proposed system will not only increase the level of autonomy for the general aviation aircraft industry but also increase the level of dependability. Safe autonomous landing in case of an engine failure with a certain level of reliability is the primary focus of our work as both safety and reliability are attributes of dependability. The system is designed for a light general aviation aircraft but can be extended for dependable unmanned aircraft systems. The underlying system components are computationally efficient and provides continuous situation assessment in case of an emergency landing. The proposed system is undergoing an evaluation phase using an experimental platform (Cessna 172R) in real world scenarios.

Female game developers wanted low pay, long hours, inflexible work environments

Almost half of all game players are now women. However, women only represent a small proportion of game developers. There is a lack of previous research to suggest why women don't pursue careers in games and how we can attract more women to the industry. In this paper, we investigate the issues and barriers that prevent women from entering the games industry, as well as the solutions and steps that can be taken to attract more women to the industry. We draw on the lessons learned by the information technology industry and report on a program of events that was conducted at the Queensland University of Technology in 2011. These events provided some insight into the issues surrounding the lack of women in the games industry, as well as some initial steps that we can take as an industry to attract and support more female developers.

Datasets for the evaluation of multi-sensor perception in natural environments with challenging conditions

This paper presents large, accurately calibrated and time-synchronised datasets, gathered outdoors in controlled environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. It discusses how the data collection process was designed, the conditions in which these datasets have been gathered, and some possible outcomes of their exploitation, in particular for the evaluation of performance of sensors and perception algorithms for UGVs.

Mobility and out-of-home activities of older people living in suburban environments : ‘Because I'm a driver, I don't have a problem'

Governments are challenged by the need to ensure that ageing populations stay active and engaged as they age. Therefore, it is critical to investigate the role of mobility in older people's engagement in out-of-home activities, and to identify the experiences they have within their communities. This research investigates the use of transportation by older people and its implications for their out-of-home activities within suburban environments. The qualitative, mixed-method approach employs data collection methods which include a daily travel diary (including a questionnaire), Global Positioning System (GPS) tracking and semi-structured interviews with older people living in suburban environments in Brisbane, Australia. Results show that older people are mobile throughout the city, and their car provides them with that opportunity to access desired destinations. This ability to drive allows older people to live independently and to assist others who do not drive, particularly where transport alternatives are not as accessible. The ability to transport goods and other people is a significant advantage of the private car over other transport options. People with no access to private transportation who live in low-density environments are disadvantaged when it comes to participation within the community. Further research is needed to better understand the relationship between transportation and participation within the community environment, to assist policy makers and city and transportation planners to develop strategies for age-friendly environments within the community.

Incorporating anchored learning in a C# Intelligent Tutoring System

Learning programming is known to be difficult. One possible reason why students fail programming is related to the fact that traditional learning in the classroom places more emphasis on lecturing the material instead of applying the material to a real application. For some students, this teaching model may not catch their interest. As a result they may not give their best effort to understand the material given. Seeing how the knowledge can be applied to real life problems can increase student interest in learning. As a consequence, this will increase their effort to learn. Anchored learning that applies knowledge to solve real life problems may be the key to improving student performance. In anchored learning, it is necessary to provide resources that can be accessed by the student as they learn. These resources can be provided by creating an Intelligent Tutoring System (ITS) that can support the student when they need help or experience a problem. Unfortunately, there is no ITS developed for the programming domain that has incorporated anchored learning in its teaching system. Having an ITS that supports anchored learning will not only be able to help the student learn programming effectively but will also make the learning process more enjoyable. This research tries to help students learn C# programming using an anchored learning ITS named CSTutor. Role playing is used in CSTutor to present a real world situation where they develop their skills. A knowledge base using First Order Logic is used to represent the student's code and to give feedback and assistance accordingly.

Kelp detection in highly dynamic environments using texture recognition

This paper describes a texture recognition based method for segmenting kelp from images collected in highly dynamic shallow water environments by an Autonomous Underwater Vehicle (AUV). A particular challenge is image quality that is affected by uncontrolled lighting, reduced visibility, significantly varying perspective due to platform egomotion, and kelp sway from wave action. The kelp segmentation approach uses the Mahalanobis distance as a way to classify Haralick texture features from sub-regions within an image. The results illustrate the applicability of the method to classify kelp allowing construction of probability maps of kelp masses across a sequence of images.