ORCID Profile
0000-0002-8687-4424
Current Organisation
Federation University
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Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11553939_80
Publisher: Wiley
Date: 20-04-2012
DOI: 10.1002/ASI.22627
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/SNPD.2011.39
Publisher: IEEE
Date: 06-2008
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Singapore
Date: 2019
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Netherlands
Date: 2008
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 12-03-2022
Publisher: Springer Netherlands
Date: 15-12-2010
Publisher: Springer Berlin Heidelberg
Date: 2005
Publisher: Emerald
Date: 25-09-2009
DOI: 10.1108/14684520911001882
Abstract: The purpose of this paper is to examine the usefulness of fusion as a means of improving the precision of automated opinion detection. Five system fusion methods are proposed and tested using runs submitted by the Text REtrieval Conference (TREC) Blog06 participants as input. The methods include a voting method, an inverse rank method (IRM), a linear‐normalised score method and two weighted methods that use a weighted IRM score to rank the document. Mean average precision (MAP) is used as an indicator of the performance of the runs in this study. The best system fusion method achieves a 55.5 percent higher MAP result compared with the highest MAP result of any in idual run submitted by the Blog06 participants. This equates to an increase in detection of 2,398 relevant opinion documents (21 percent). System fusion can be used to improve upon the results achieved by existing in idual opinion detection systems. On the other hand, multiple opinion detection approaches can be combined into one system and fusion used to combine the results to build in ersity. Diversity within fusion inputs can increase the improvements achieved by fusion methods. The improved output from a erse opinion detection system will then contain a higher number of relevant documents and reduce the incidence of high‐ranking non‐relevant documents and low‐ranking relevant documents. The fusion methods proposed in this study demonstrate that simple fusion of opinion detection systems can improve performance.
Publisher: Springer Netherlands
Date: 15-12-2010
Publisher: MDPI AG
Date: 23-10-2019
DOI: 10.3390/ELECTRONICS8111210
Abstract: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and erse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.
Publisher: IEEE
Date: 2003
Publisher: Cambridge University Press (CUP)
Date: 04-2012
DOI: 10.1017/S1446181112000016
Abstract: The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K -complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K -complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K -complexes. It is also found that most classification methods perform significantly better on the extracted features.
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 19-05-2017
Publisher: IEEE
Date: 10-2012
DOI: 10.1109/CTC.2012.13
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 11-2018
Publisher: Hindawi Limited
Date: 2005
DOI: 10.1002/INT.20105
Publisher: Springer International Publishing
Date: 2018
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1833-6.CH008
Abstract: This chapter describes a novel multistage method for linguistic clustering of large collections of texts available on the Internet as a precursor to linguistic analysis of these texts. This method addresses the practicalities of applying clustering operations to a very large set of text documents by using a combination of unsupervised clustering and supervised classification. The method relies on creating a multitude of independent clusterings of a randomized s le selected from the International Corpus of Learner English. Several consensus functions and sophisticated algorithms are applied in two substages to combine these independent clusterings into one final consensus clustering, which is then used to train fast classifiers in order to enable them to perform the profiling of very large collections of text and web data. This approach makes it possible to apply advanced highly accurate and sophisticated clustering techniques by combining them with fast supervised classification algorithms. For the effectiveness of this multistage method it is crucial to determine how well the supervised classification algorithms are going to perform at the final stage, when they are used to process large data sets available on the Internet. This performance may also serve as an indication of the quality of the combined consensus clustering obtained in the preceding stages. The authors’ experimental results compare the performance of several classification algorithms incorporated in this multistage scheme and demonstrate that several of these classification algorithms achieve very high precision and recall and can be used in practical implementations of their method.
Publisher: Springer Science and Business Media LLC
Date: 05-06-2015
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 04-10-2017
Publisher: Springer Science and Business Media LLC
Date: 16-07-2022
DOI: 10.1007/S10458-022-09575-5
Abstract: The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial, and provides a suitable basis for the creation of artificial general intelligence. We contest the underlying assumption of Silver et al. that such reward can be scalar-valued. In this paper we explain why scalar rewards are insufficient to account for some aspects of both biological and computational intelligence, and argue in favour of explicitly multi-objective models of reward maximisation. Furthermore, we contend that even if scalar reward functions can trigger intelligent behaviour in specific cases, this type of reward is insufficient for the development of human-aligned artificial general intelligence due to unacceptable risks of unsafe or unethical behaviour.
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11941439_18
Publisher: Springer Science and Business Media LLC
Date: 10-2021
Publisher: Elsevier BV
Date: 05-2010
Publisher: MDPI AG
Date: 09-02-2021
DOI: 10.3390/BIOMIMETICS6010013
Abstract: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient s le size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_14
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_149
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer International Publishing
Date: 2017
Publisher: ACM
Date: 29-01-2019
Publisher: Springer Science and Business Media LLC
Date: 22-12-2011
Publisher: Cambridge University Press (CUP)
Date: 13-03-2009
DOI: 10.1017/S0004972708001111
Abstract: Drensky and Lakatos (Lecture Notes in Computer Science, 357 (Springer, Berlin, 1989), pp. 181–188) have established a convenient property of certain ideals in polynomial quotient rings, which can now be used to determine error-correcting capabilities of combined multiple classifiers following a standard approach explained in the well-known monograph by Witten and Frank ( Data Mining: Practical Machine Learning Tools and Techniques (Elsevier, Amsterdam, 2005)). We strengthen and generalise the result of Drensky and Lakatos by demonstrating that the corresponding nice property remains valid in a much larger variety of constructions and applies to more general types of ideals. Ex les show that our theorems do not extend to larger classes of ring constructions and cannot be simplified or generalised.
Publisher: Ubiquity Press, Ltd.
Date: 2023
DOI: 10.5334/JORS.444
No related grants have been discovered for Peter Vamplew.