List of Titles

Understanding personal use of the Internet at work: An integrated model of neutralization techniques and general deterrence theory

- Cheng, Lijiao, Li, Wenli, Zhai, Qingguo, Smyth, Russell

Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
Date: 2014
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
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Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
Description: C1

Density-ratio based clustering for discovering clusters with varying densities

- Zhu, Ye, Ting, Kaiming, Carman, Mark

Authors: Zhu, Ye , Ting, Kaiming , Carman, Mark
Date: 2016
Type: Text , Journal article
Relation: Pattern Recognition Vol. 60, no. (2016), p. 983-997
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Description: Density-based clustering algorithms are able to identify clusters of arbitrary shapes and sizes in a dataset which contains noise. It is well-known that most of these algorithms, which use a global density threshold, have difficulty identifying all clusters in a dataset having clusters of greatly varying densities. This paper identifies and analyses the condition under which density-based clustering algorithms fail in this scenario. It proposes a density-ratio based method to overcome this weakness, and reveals that it can be implemented in two approaches. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. We provide an empirical evaluation using DBSCAN, OPTICS and SNN to show the effectiveness of these two approaches. © 2016 Elsevier Ltd

Clustering in large data sets with the limited memory bundle method

- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona

Authors: Karmitsa, Napsu , Bagirov, Adil , Taheri, Sona
Date: 2018
Type: Text , Journal article
Relation: Pattern Recognition Vol. 83, no. (2018), p. 245-259
Relation: http://purl.org/au-research/grants/arc/DP140103213
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Description: The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets.

Flow and Telepresence contributing to Internet Abuse : Differences according to gender and age

- Stavropoulos, Vasileios, Alexandraki, Kyriaki, Motti-Stefanidi, Frosso

Authors: Stavropoulos, Vasileios , Alexandraki, Kyriaki , Motti-Stefanidi, Frosso
Date: 2013
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 29, no. 5 (2013), p. 1941-1948
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Description: Flow describes immersive tendencies to Internet activities, and Telepresence defines the level one is absorbed in his virtual environment. The aim of this study was twofold: (a) to test whether and how Flow and Telepresence may contribute to Internet Abuse and (b) to examine group differences in Internet Abuse, Flow and Telepresence according to gender and age among adolescents. The sample consisted of 1609 adolescents, with a mean age of 16 years old. Internet Abuse was assessed with the Internet Addiction Test (Young, 1998), Flow with the Flow Questionnaire (Chen, Wigand, & Nilan, 1999) and Telepresence with the Presence II questionnaire (Witmer & Singer, 1998). Findings revealed that Flow and Telepresence were related with Internet Abuse with Telepresence positively moderating the effect of Flow. Considering group differences, males were at higher risk of Internet Abuse and were more likely to experience Flow and Telepresence, while older adolescents scored higher only in Telepresence. © 2013 Elsevier Ltd. All rights reserved.

Grouping points by shared subspaces for effective subspace clustering

- Zhu, Ye, Ting, Kaiming, Carman, Mark

Authors: Zhu, Ye , Ting, Kaiming , Carman, Mark
Date: 2018
Type: Text , Journal article
Relation: Pattern Recognition Vol. 83, no. (2018), p. 230-244
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Description: Clusters may exist in different subspaces of a multidimensional dataset. Traditional full-space clustering algorithms have difficulty in identifying these clusters. Various subspace clustering algorithms have used different subspace search strategies. They require clustering to assess whether cluster(s) exist in a subspace. In addition, all of them perform clustering by measuring similarity between points in the given feature space. As a result, the subspace selection and clustering processes are tightly coupled. In this paper, we propose a new subspace clustering framework named CSSub (Clustering by Shared Subspaces). It enables neighbouring core points to be clustered based on the number of subspaces they share. It explicitly splits candidate subspace selection and clustering into two separate processes, enabling different types of cluster definitions to be employed easily. Through extensive experiments on synthetic and real-world datasets, we demonstrate that CSSub discovers non-redundant subspace clusters with arbitrary shapes in noisy data; and it significantly outperforms existing state-of-the-art subspace clustering algorithms.

Eye tracking to investigate cue processing in medical decision making : A scoping review

- Al-Moteri, Modi, Symmons, Mark, Plummer, Virginia, Cooper, Simon J.

Authors: Al-Moteri, Modi , Symmons, Mark , Plummer, Virginia , Cooper, Simon J.
Date: 2017
Type: Text , Journal article , Review
Relation: Computers in Human Behavior Vol. 66, no. (2017), p. 52-66
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Description: Eye-tracking techniques have been adopted as a research tool for a wide range of applications in healthcare studies. Recently, healthcare researchers have started to show interest in using eye-tracking techniques to study medical decision-making. Mapping the literature pertaining to eye tracking using a systematic approach is valuable at this point to bring together all the studies to date on how medical decision-makers make decisions, and the results may contribute to clinical training. This review follows Arksey and O'Malley's scoping review framework to improve our understanding of visual cue processing in medical decision-making. A diverse range of studies was identified, and the results are presented descriptively to develop a more coherent understanding of different aspects of cue processing and errors in medical decision-making. The review shows the need for more extensive investigations of cue processing and medical decision-making. Crown Copyright (C) 2016 Published by Elsevier Ltd. All rights reserved.

Dangerous dating in the digital age : jealousy, hostility, narcissism, and psychopathy as predictors of cyber dating abuse

- Branson, Molly, March, Evita

Authors: Branson, Molly , March, Evita
Date: 2021
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 119, no. (2021), p.
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Description: Cyber Dating Abuse includes perpetration of aggressive and controlling behaviours against an intimate partner via technology. Despite the significant physical and psychological outcomes, there is a paucity in research exploring predictors of Cyber Dating Abuse. In the current study, we replicate and extend previous research by exploring the utility of jealousy, hostility, narcissism (grandiose and vulnerable), and psychopathy (primary and secondary) as predictors of Cyber Dating Abuse. Participants (N = 817; 78.2% women; Mage = 28.16, SD = 10.49) were recruited via social media and completed an online questionnaire which comprised measures of jealousy, hostility, narcissism, psychopathy, and perpetration of Cyber Dating Abuse behaviours. Results supported the hypotheses that jealousy, vulnerable narcissism, and secondary psychopathy would be significant, positive predictors of Cyber Dating Abuse perpetration. No other predictor reached significance, and gender (control variable) was also a non-significant predictor. Results are discussed through the lens of evolutionary theory and the General Aggression Model and indicate that the perpetration of Cyber Dating Abuse may be better attributed to reactive, emotional aggression, compared to proactive, instrumental aggression. These results have important clinical and practical implications and may inform management and prevention of online intimate partner abuse and violence. © 2021 Elsevier Ltd

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Integrated generalized zero-shot learning for fine-grained classification

- Shermin, Tasfia, Teng, Shyh, Sohel, Ferdous, Murshed, Manzur, Lu, Guojun

Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
Date: 2022
Type: Text , Journal article
Relation: Pattern Recognition Vol. 122, no. (2022), p.
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Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd

Siamese network for object tracking with multi-granularity appearance representations

- Zhang, Zhuoyi, Zhang, Yifeng, Cheng, Xu, Lu, Guojun

Authors: Zhang, Zhuoyi , Zhang, Yifeng , Cheng, Xu , Lu, Guojun
Date: 2021
Type: Text , Journal article
Relation: Pattern Recognition Vol. 118, no. (2021), p.
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Description: A reliable tracker has the ability to adapt to change of objects over time, and is robust and accurate. We build such a tracker by extracting semantic features using robust Siamese networks and multi-granularity color features. It incorporates a semantic model that can capture high quality semantic features and an appearance model that can describe object at pixel, local and global levels effectively. Furthermore, we propose a novel selective traverse algorithm to allocate weights to semantic models and appearance models dynamically for better tracking performance. During tracking, our tracker updates appearance representations for objects based on the recent tracking results. The proposed tracker operates at speeds that exceed the real-time requirement, and outperforms nearly all other state-of-the-art trackers on OTB-2013/2015 and VOT-2016/2017 benchmarks. © 2021 Elsevier Ltd

Intelligent energy prediction techniques for fog computing networks

- Farooq, Umar, Shabir, Muhammad, Javed, Muhammad, Imran, Muhammad

Authors: Farooq, Umar , Shabir, Muhammad , Javed, Muhammad , Imran, Muhammad
Date: 2021
Type: Text , Journal article
Relation: Applied Soft Computing Vol. 111, no. (2021), p.
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Description: Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.

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Community-diversified influence maximization in social networks

- Li, Jianxin, Cai, Taotao, Deng, Ke, Wang, Xinjue, Sellis, Timos, Xia, Feng

Authors: Li, Jianxin , Cai, Taotao , Deng, Ke , Wang, Xinjue , Sellis, Timos , Xia, Feng
Date: 2020
Type: Text , Journal article
Relation: Information Systems Vol. 92, no. (2020), p.
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Description: To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. © 2020 Elsevier Ltd

Insta-identity: dark personality traits as predictors of authentic self-presentation on Instagram

- Geary, Christina, March, Evita, Grieve, Rachel

Authors: Geary, Christina , March, Evita , Grieve, Rachel
Date: 2021
Type: Text , Journal article
Relation: Telematics and Informatics Vol. 63, no. (2021), p.
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Description: People vary in the extent to which they present themselves authentically online. This study applied Uses and Gratifications Theory to investigate the utility of grandiose narcissism, vulnerable narcissism, primary psychopathy, secondary psychopathy, and Machiavellianism in predicting authentic self-presentation on Instagram. Participants (N = 542; 62.9% women; 60.1% Australian) with a currently active Instagram account were recruited via social media advertisements and completed an online questionnaire assessing personality and authenticity. Instagram authenticity was operationalised as Euclidean distances between the true self and the Instagram self. Results partially supported hypotheses, with higher levels of trait vulnerable narcissism and Machiavellianism predicting less congruence between the true self and the Instagram self (i.e., inauthenticity). Grandiose narcissism, primary psychopathy, and secondary psychopathy were not significant predictors of authentic self-presentation on Instagram. Findings of the current study extend previous research exploring authentic self-presentation on social media and are interpreted in line with uses and gratifications. We recommend further exploration of variation in authentic self-presentation on Instagram, from normative forms to outright deception. © 2021 Elsevier Ltd

Malware detection in edge devices with fuzzy oversampling and dynamic class weighting

- Khoda, Mahbub, Kamruzzaman, Joarder, Gondal, Iqbal, Imam, Tasadduq, Rahman, Ashfaqur

Authors: Khoda, Mahbub , Kamruzzaman, Joarder , Gondal, Iqbal , Imam, Tasadduq , Rahman, Ashfaqur
Date: 2021
Type: Text , Journal article
Relation: Applied Soft Computing Vol. 112, no. (2021), p.
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Description: In Internet-of-things (IoT) domain, edge devices are used increasingly for data accumulation, preprocessing, and analytics. Intelligent integration of edge devices with Artificial Intelligence (AI) facilitates real-time analysis and decision making. However, these devices simultaneously provide additional attack opportunities for malware developers, potentially leading to information and financial loss. Machine learning approaches can detect such attacks but their performance degrades when benign samples substantially outnumber malware samples in training data. Existing approaches for such imbalanced data assume samples represented as continuous features and thus can generate invalid samples when malware applications are represented by binary features. We propose a novel malware oversampling technique that addresses this issue. Further, we propose two approaches for malware detection. Our first approach uses fuzzy set theory, while the second approach dynamically assigns higher priority to malware samples using a novel loss function. Combining our oversampling technique with these approaches, the proposed approach attains over 9% improvement over competing methods in terms of F1_score. Our approaches can, therefore, result in enhanced privacy and security in edge computing services. © 2021 Elsevier B.V.

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