A hierarchical security assessment model for object-oriented programs

We present a hierarchical model for assessing an object-oriented program's security. Security is quantified using structural properties of the program code to identify the ways in which `classified' data values may be transferred between objects. The model begins with a set of low-level security metrics based on traditional design characteristics of object-oriented classes, such as data encapsulation, cohesion and coupling. These metrics are then used to characterise higher-level properties concerning the overall readability and writability of classified data throughout the program. In turn, these metrics are then mapped to well-known security design principles such as `assigning the least privilege' and `reducing the size of the attack surface'. Finally, the entire program's security is summarised as a single security index value. These metrics allow different versions of the same program, or different programs intended to perform the same task, to be compared for their relative security at a number of different abstraction levels. The model is validated via an experiment involving five open source Java programs, using a static analysis tool we have developed to automatically extract the security metrics from compiled Java bytecode.

Using text-spotting to query the world

The world we live in is well labeled for the benefit of humans but to date robots have made little use of this resource. In this paper we describe a system that allows robots to read and interpret visible text and use it to understand the content of the scene. We use a generative probabilistic model that explains spotted text in terms of arbitrary search terms. This allows the robot to understand the underlying function of the scene it is looking at, such as whether it is a bank or a restaurant. We describe the text spotting engine at the heart of our system that is able to detect and parse wild text in images, and the generative model, and present results from images obtained with a robot in a busy city setting.

Participation and e-democracy how to utilize web 2.0 for policy decision-making

This paper investigates in how to utilize ICT and Web 2.0 technologies and e-democracy software for policy decision-making. It introduces a cutting edge decision-making system that integrates the practice of e-petitions, e-consultation, e-rulemaking, e-voting, and proxy voting. The paper demonstrates how under precondition of direct democracy through the use this system the collective intelligence (CI) of a population would be gathered and used throughout the policy process.

Building a software architecture for a human-robot team using the ORCA framework

This paper considers the problem of building a software architecture for a human-robot team. The objective of the team is to build a multi-attribute map of the world by performing information fusion. A decentralized approach to information fusion is adopted to achieve the system properties of scalability and survivability. Decentralization imposes constraints on the design of the architecture and its implementation. We show how a Component-Based Software Engineering approach can address these constraints. The architecture is implemented using Orca – a component-based software framework for robotic systems. Experimental results from a deployed system comprised of an unmanned air vehicle, a ground vehicle, and two human operators are presented. A section on the lessons learned is included which may be applicable to other distributed systems with complex algorithms. We also compare Orca to the Player software framework in the context of distributed systems.

Validation Gating for Non-Linear Non-Gaussian Target Tracking

This paper develops a general theory of validation gating for non-linear non-Gaussian mod- els. Validation gates are used in target tracking to cull very unlikely measurement-to-track associa- tions, before remaining association ambiguities are handled by a more comprehensive (and expensive) data association scheme. The essential property of a gate is to accept a high percentage of correct associ- ations, thus maximising track accuracy, but provide a su±ciently tight bound to minimise the number of ambiguous associations. For linear Gaussian systems, the ellipsoidal vali- dation gate is standard, and possesses the statistical property whereby a given threshold will accept a cer- tain percentage of true associations. This property does not hold for non-linear non-Gaussian models. As a system departs from linear-Gaussian, the ellip- soid gate tends to reject a higher than expected pro- portion of correct associations and permit an excess of false ones. In this paper, the concept of the ellip- soidal gate is extended to permit correct statistics for the non-linear non-Gaussian case. The new gate is demonstrated by a bearing-only tracking example.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

A study of laughter in science lessons

Laughter is a fundamental human phenomenon. Yet there is little educational research on the potential functions of laughter on the enacted (lived) curriculum. In this study, we identify the functions of laughter in a beginning science teacher’s classroom throughout her first year of teaching. Our study shows that laughter is more than a gratuitous phenomenon. It is the result of a collective interactive achievement of the classroom participants that offsets the seriousness of science as a discipline. Laughter, whereas it challenges the seriousness of science, also includes the dialectical inversion of the challenge: it simultaneously reinforces the idea of science as serious business.

Calibration and operational analysis of variable speed limits for high flow condition

Variable Speed Limits (VSL) is a control tool of Intelligent Transportation Systems (ITS) which can enhance traffic safety and which has the potential to contribute to traffic efficiency. This study presents the results of a calibration and operational analysis of a candidate VSL algorithm for high flow conditions on an urban motorway of Queensland, Australia. The analysis was done using a framework consisting of a microscopic simulation model combined with runtime API and a proposed efficiency index. The operational analysis includes impacts on speed-flow curve, travel time, speed deviation, fuel consumption and emission.

Lumia : Art | light | motion. The Art of Kuuki.

The State Library of Queensland is delighted to present Lumia: art/light/motion, a culmination of many years of collaboration by the Kuuki collective led by Priscilla Bracks and Gavin Sade. This extraordinary exhibition not only showcases the unique talent of these Queenslanders, it also opens up a world of future possibilities while re-presenting the past and present. These contemporary new media installations sit comfortably within the walls of the library as they are the distinctive products of inquisitive and philosophical minds. In a sense the exhibition highlights the longevity and purposefulness of a cultural learning institution, through the non-traditional use of data, information, research and collection interpretation. The exhibition simultaneously articulates one of our key objectives – to progress the state’s digital agenda. Two academic essays have been commissioned for this joint Kuuki and State Library of Queensland publication. The first is by artist and writer Paul Brown, who has specialised in art, science and technology since the late 1960s and in computational and generative art since the mid 1970s. Brown investigates the history of new media, which is celebrating its 60th anniversary, and clearly places Sade and Bracks at the forefront of this genre nationally. The second essay is by arts writer Linda Carroli, who has delved deeply into the thoughts and processes of the artists to bring to light the complex workings of the artists’ minds. The publication also features an interview Carroli conducted with the artists. This exhibition is playful, informative and contemplative. The audience is invited to play, and consequently to ponder the way we live and the environmental and social implications of our choices. The exhibition tempts us to travel deep into the Antarctic, plunge into the Great Barrier Reef, be swamped by an orchestra of crickets, enter the Charmed world and travel back in time to a Victorian parlour where you can interact with a ‘new-world’ lyrebird and consider a brave new world where our only link to the animal world is with robotic representations. In essence this exhibition is about ideas and knowledge and what better institution than the State Library of Queensland to partner such a project?. State Library is committed to preserving culture, exploring new media and creating new content as a lasting legacy of Queensland for all Queenslanders.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

Deterministic diffraction loss modelling for novel broadband communication in rural environments

This paper presents a deterministic modelling approach to predict diffraction loss for an innovative Multi-User-Single-Antenna (MUSA) MIMO technology, proposed for rural Australian environments. In order to calculate diffraction loss, six receivers have been considered around an access point in a selected rural environment. Generated terrain profiles for six receivers are presented in this paper. Simulation results using classical diffraction models and diffraction theory are also presented by accounting the rural Australian terrain data. Results show that in an area of 900 m by 900 m surrounding the receivers, path loss due to diffraction can range between 5 dB and 35 dB. Diffraction loss maps can contribute to determine the optimal location for receivers of MUSA-MIMO systems in rural areas.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.