ORCID Profile
0000-0002-6199-9685
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 04-2021
Publisher: Cambridge University Press (CUP)
Date: 08-10-2013
DOI: 10.1017/S1351324913000272
Abstract: Aliases play an important role in online environments by facilitating anonymity, but also can be used to hide the identity of cybercriminals. Previous studies have investigated this alias matching problem in an attempt to identify whether two aliases are shared by an author, which can assist with identifying users. Those studies create their training data by randomly splitting the documents associated with an alias into two sub-aliases. Models have been built that can regularly achieve over 90% accuracy for recovering the linkage between these ‘random sub-aliases’. In this paper, random sub-alias generation is shown to enable these high accuracies, and thus does not adequately model the real-world problem. In contrast, creating sub-aliases using topic-based splitting drastically reduces the accuracy of all authorship methods tested. We then present a methodology that can be performed on non-topic controlled datasets, to produce topic-based sub-aliases that are more difficult to match. Finally, we present an experimental comparison between many authorship methods to see which methods better match aliases under these conditions, finding that local n -gram methods perform better than others.
Publisher: Springer Science and Business Media LLC
Date: 06-03-2023
DOI: 10.1007/S00521-023-08423-1
Abstract: Broad-XAI moves away from interpreting in idual decisions based on a single datum and aims to provide integrated explanations from multiple machine learning algorithms into a coherent explanation of an agent’s behaviour that is aligned to the communication needs of the explainee. Reinforcement Learning (RL) methods, we propose, provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. However, these algorithms operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) aims to develop techniques to extract concepts from the agent’s: perception of the environment intrinsic/extrinsic motivations/beliefs Q-values, goals and objectives. This paper aims to introduce the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. CXF is designed to incorporate many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes its decisions. This paper aims to: establish XRL as a distinct branch of XAI introduce a conceptual framework for XRL review existing approaches explaining agent behaviour and identify opportunities for future research. Finally, this paper discusses how additional information can be extracted and ultimately integrated into models of communication, facilitating the development of Broad-XAI.
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 11-2017
Publisher: American Chemical Society (ACS)
Date: 21-12-2022
Publisher: Cambridge University Press (CUP)
Date: 28-09-2012
DOI: 10.1017/S1351324912000241
Abstract: Unsupervised Authorship Analysis (UAA) aims to cluster documents by authorship without knowing the authorship of any documents. An important factor in UAA is the method for calculating the distance between documents. This choice of the authorship distance method is considered more critical to the end result than the choice of cluster analysis algorithm. One method for measuring the correlation between a distance metric and a labelling (such as class values or clusters) is the Silhouette Coefficient (SC). The SC can be leveraged by measuring the correlation between the authorship distance method and the true authorship, evaluating the quality of the distance method. However, we show that the SC can be severely affected by outliers. To address this issue, we introduce the Positive Silhouette Coefficient, given as the proportion of instances with a positive SC value. This metric is not easily altered by outliers and produces a more robust metric. A large number of authorship distance methods are then compared using the PSC, and the findings are presented. This research provides an insight into the efficacy of methods for UAA and presents a framework for testing authorship distance methods.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 15-11-2021
DOI: 10.1038/S41598-021-01476-Z
Abstract: The purpose of this study was to characterize the alterations in microstructural organization of arterial tissue using higher-order diffusion magnetic resonance schemes. Three porcine carotid artery models namely native, collagenase treated and decellularized, were used to estimate the contribution of collagen and smooth muscle cells (SMC) on diffusion signal attenuation using gaussian and non-gaussian schemes. The s les were imaged in a 7 T preclinical scanner. High spatial and angular resolution diffusion weighted images (DWIs) were acquired using two multi-shell (max b-value = 3000 s/mm 2 ) acquisition protocols. The processed DWIs were fitted using monoexponential, stretched-exponential, kurtosis and bi-exponential schemes. Directionally variant and invariant microstructural parametric maps of the three artery models were obtained from the diffusion schemes. The parametric maps were used to assess the sensitivity of each diffusion scheme to collagen and SMC composition in arterial microstructural environment. The inter-model comparison showed significant differences across the considered models. The bi-exponential scheme based slow diffusion compartment (Ds) was highest in the absence of collagen, compared to native and decellularized microenvironments. In intra-model comparison, kurtosis along the radial direction was the highest. Overall, the results of this study demonstrate the efficacy of higher order dMRI schemes in mapping constituent specific alterations in arterial microstructure.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: World Scientific Pub Co Pte Ltd
Date: 03-2009
DOI: 10.1142/S1793557109000042
Abstract: Optimization of multiple classifiers is an important problem in data mining. We introduce additional structure on the class sets of the classifiers using string rewriting systems with a convenient matrix representation. The aim of the present paper is to develop an efficient algorithm for the optimization of the number of errors of in idual classifiers, which can be corrected by these multiple classifiers.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 10-2012
DOI: 10.1109/CTC.2012.13
Publisher: Elsevier BV
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 04-10-2017
Publisher: Springer Science and Business Media LLC
Date: 10-2021
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 22-12-2011
Publisher: MDPI AG
Date: 12-08-2020
DOI: 10.3390/APP10165574
Abstract: Robots are extending their presence in domestic environments every day, it being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely used in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a Human–Robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects the proposed methods are (i) deep reinforcement learning (DeepRL) (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent–IDeepRL) and (iii) interactive deep reinforcement learning using a human advisor (human–IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent–IDeepRL or human–IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.
Publisher: Springer Science and Business Media LLC
Date: 26-08-2008
Publisher: Center for Open Science
Date: 17-09-2023
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-60960-091-4.CH020
Abstract: The highly sophisticated and rapidly evolving area of internet commerce security presents many novel challenges for the organization of discourse in reasoning communities. This chapter suggests appropriate reasoning methods and demonstrates how establishing reasoning communities of security experts and enabling productive group discourse among them can play a crucial role in successful resolution of problems concerning the implementation, integration, deployment and maintenance of flexible local security systems for defense against malware threats in internet security. Local security systems of this sort may combine several ready open source or commercial software packages behind a common front-end and may enhance and supplement their facilities with additional plug-ins. To illustrate the erse character of challenges the reasoning communities in internet security are likely to be faced with, this chapter concentrates on defense against phishing attacks. This ex le was selected as it is one of the newest and most rapidly changing application domains for the principles of organizing reasoning communities. The major group discourse methods suggested for the reasoning communities of security experts in this chapter include the Delphi Method, the Wideband Delphi Process, the Generic/Actual Argument Model of Structured Reasoning, Brainstorming, Reverse Brainstorming, Consensus Decision Making, Voting, Open Delphi and Open Brainstorming Methods. The Delphi Method and Wideband Delphi Process are suggested as tools for organizing a cohesive reasoning architecture, for coordinating other methods, and for preparing and allocating other methods to particular issues.
Publisher: Ubiquity Press, Ltd.
Date: 2023
DOI: 10.5334/JORS.444
Publisher: Springer Berlin Heidelberg
Date: 2009
Location: Australia
No related grants have been discovered for Richard Dazeley.