THE quest for alpha : can artificial neural networks help?

The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

An algebraic approach for determination of DG parameters to support voltage profiles in radial distribution networks

Rapidly increasing electricity demands and capacity shortage of transmission and distribution facilities are the main driving forces for the growth of Distributed Generation (DG) integration in power grids. One of the reasons for choosing a DG is its ability to support voltage in a distribution system. Selection of effective DG characteristics and DG parameters is a significant concern of distribution system planners to obtain maximum potential benefits from the DG unit. This paper addresses the issue of improving the network voltage profile in distribution systems by installing a DG of the most suitable size, at a suitable location. An analytical approach is developed based on algebraic equations for uniformly distributed loads to determine the optimal operation, size and location of the DG in order to achieve required levels of network voltage. The developed method is simple to use for conceptual design and analysis of distribution system expansion with a DG and suitable for a quick estimation of DG parameters (such as optimal operating angle, size and location of a DG system) in a radial network. A practical network is used to verify the proposed technique and test results are presented.

Optimising offsets and bandwidths in vehicle traffic networks

Bandwidths and offsets are important components in vehicle traffic control strategies. This article proposes new methods for quantifying and selecting them. Bandwidth is the amount of green time available for vehicles to travel through adjacent intersections without the requirement to stop at the second traffic light. The offset is the difference between the starting-time of ``green'' periods at two adjacent intersections, along a given route. The core ideas in this article were developed during the 2013 Maths and Industry Study Group in Brisbane, Australia. Analytical expressions for computing bandwidth, as a function of offset, are developed. An optimisation model, for selecting offsets across an arterial, is proposed. Arterial roads were focussed upon, as bandwidth and offset have a greater impact on these types of road as opposed to a full traffic network. A generic optimisation-simulation approach is also proposed to refine an initial starting solution, according to a specified metric. A metric that reflects the number of stops, and the distance between stops, is proposed to explicitly reduce the dissatisfaction of road users, and to implicitly reduce fuel consumption and emissions. Conceptually the optimisation-simulation approach is superior as it handles real-life complexities and is a global optimisation approach. The models and equations in this article can be used in road planning and traffic control.

Out of their minds: legal theory in neural networks

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.

Privacy by design for social networks

This research has established a new privacy framework, privacy model, and privacy architecture to create more transparent privacy for social networking users. The architecture is designed into three levels: Business, Data, and Technology, which is based on The Open Group Architecture Framework (TOGAF®). This framework and architecture provides a novel platform for investigating privacy in Social Networks (SNs). This approach mitigates many current SN privacy issues, and leads to a more controlled form of privacy assessment. Ultimately, more privacy will encourage more connections between people across SN services.

Knowledge ecosystems of early career academics : a grounded theory of experiencing information use for learning in developmental networks

This study explored early career academics' experiences in using information to learn while building their networks for professional development. A 'knowledge ecosystem' model was developed consisting of informal learning interactions such as relating to information to create knowledge and engaging in mutually supportive relationships. Findings from this study present an alternative interpretation of information use for learning that is focused on processes manifesting as human interactions with informing entities revolving around the contexts of reciprocal human relationships.

The Arab Spring and its social media audiences : English and Arabic Twitter users and their networks

2011 ‘Arab Spring’ are likely to overstate the impact of Facebook and Twitter on these uprisings, it is nonetheless true that protests and unrest in countries from Tunisia to Syria generated a substantial amount of social media activity. On Twitter alone, several millions of tweets containing the hashtags #libya or #egypt were generated during 2011, both by directly affected citizens of these countries, and by onlookers from further afield. What remains unclear, though, is the extent to which there was any direct interaction between these two groups (especially considering potential language barriers between them). Building on hashtag datasets gathered between January and November 2011, this paper compares patterns of Twitter usage during the popular revolution in Egypt and the civil war in Libya. Using custom-made tools for processing ‘big data’, we examine the volume of tweets sent by English-, Arabic-, and mixed-language Twitter users over time, and examine the networks of interaction (variously through @replying, retweeting, or both) between these groups as they developed and shifted over the course of these uprisings. Examining @reply and retweet traffic, we identify general patterns of information flow between the English- and Arabic-speaking sides of the Twittersphere, and highlight the roles played by users bridging both language spheres.

Professional development and social networks in digital content industry micro businesses

This study explores the professional development strategies of digital content professionals in Australian micro businesses. This thesis presents the argument that as these professionals are working in cutting edge creative fields where digital technology drives ongoing change, formal education experiences may be less important than for other professionals, and that specific types of online and face-to-face socially mediated informal learning strategies may be critical to currency. This thesis documents the findings of a broad survey of industry professionals' learning needs and development strategies, in conjunction with rich data from in-depth interviews and social network analyses.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Mechanics of microfilaments networks : a cross-scales study

The mechanical properties of microfilament networks are systematically summarized at different special scales in this paper. We have presented the mechanical models of single microfilaments and microfilament networks at microscale. By adopting a coarse-grained simulation strategy, the mechanical stability of microfilaments related cellular structures are analysed. Structural analysis is conducted to microfilament networks to understand the stress relaxation under compression. The nanoscale molecular mechanisms of the microfilaments deformation is also summarized from the viewpoint of molecular dynamics simulation. This paper provides the fundaments of multiscale modelling framework for the mechanical behaviours simulation of hierarchical microfilament networks.

Concurrent validity of accelerations measured using a tri-axial inertial measurement unit while walking on firm, compliant and uneven surfaces

Although accelerometers are extensively used for assessing gait, limited research has evaluated the concurrent validity of these devices on less predictable walking surfaces or the comparability of different methods used for gravitational acceleration compensation. This study evaluated the concurrent validity of trunk accelerations derived from a tri-axial inertial measurement unit while walking on firm, compliant and uneven surfaces and contrasted two methods used to remove gravitational accelerations: i) subtraction of the best linear fit from the data (detrending), and; ii) use of orientation information (quaternions) from the inertial measurement unit. Twelve older and twelve younger adults walked at their preferred speed along firm, compliant and uneven walkways. Accelerations were evaluated for the thoracic spine (T12) using a tri-axial inertial measurement unit and an eleven-camera Vicon system. The findings demonstrated excellent agreement between accelerations derived from the inertial measurement unit and motion analysis system, including while walking on uneven surfaces that better approximate a real-world setting (all differences <0.16 m.s−2). Detrending produced slightly better agreement between the inertial measurement unit and Vicon system on firm surfaces (delta range: −0.05 to 0.06 vs. 0.00 to 0.14 m.s−2), whereas the quaternion method performed better when walking on compliant and uneven walkways (delta range: −0.16 to −0.02 vs. −0.07 to 0.07 m.s−2). The technique used to compensate for gravitational accelerations requires consideration in future research, particularly when walking on compliant and uneven surfaces. These findings demonstrate trunk accelerations can be accurately measured using a wireless inertial measurement unit and are appropriate for research that evaluates healthy populations in complex environments.

The role and future challenges of wireless communication networks for cooperative autonomous city vehicles

This paper elaborates on the use of future wireless communication networks for autonomous city vehicles. After addressing the state of technology, the paper explains the autonomous vehicle control system architecture and the Cybercars-2 communication framework; it presents experimental tests of communication-based real-time decision making; and discusses potential applications for communication in order to improve the localization and perception abilities of autonomous vehicles in urban environments.