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Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System

- Ning, Zhaolong, Dong, Peiran, Wang, Xiaojie, Rodrigues, Joel, Xia, Feng

  • Authors: Ning, Zhaolong , Dong, Peiran , Wang, Xiaojie , Rodrigues, Joel , Xia, Feng
  • Date: 2019
  • Type: Text , Journal article
  • Relation: ACM Transactions on Intelligent Systems and Technology Vol. 10, no. 6 (Dec 2019), p. 24
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  • Description: The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

Spring framework in smart proxy transaction model

- Pradhan, Sunam, Zaslavsky, Arkady, Tari, Zahir

COREG : A corner based registration technique for multimodal images

- Lv, Guohua, Teng, Shyh, Lu, Guojun

  • Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Multimedia Tools and Applications Vol. 77, no. 10 (2018), p. 12607-12634
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  • Description: This paper presents a COrner based REGistration technique for multimodal images (referred to as COREG). The proposed technique focuses on addressing large content and scale differences in multimodal images. Unlike traditional multimodal image registration techniques that rely on intensities or gradients for feature representation, we propose to use contour-based corners. First, curvature similarity between corners are for the first time explored for the purpose of multimodal image registration. Second, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the edges in the neighborhood of corners. Third, a simple yet effective way of estimating scale difference is proposed by making use of geometric relationships between corner triplets from the reference and target images. Using a set of benchmark multimodal images and multimodal microscopic images, we will demonstrate that our proposed technique outperforms a state-of-the-art multimodal image registration technique. © 2017, Springer Science+Business Media, LLC.

A simulated annealing-based maximum-margin clustering algorithm

- Seifollahi, Sattar, Bagirov, Adil, Borzeshi, Ehsan, Piccardi, Massimo

  • Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
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  • Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.

A framework for ERP post-implementation amendments : A literature analysis

- Oseni, Taiwo, Foster, Susan, Rahim, Mahbubur, Smith, Stephen Patrick

  • Authors: Oseni, Taiwo , Foster, Susan , Rahim, Mahbubur , Smith, Stephen Patrick
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p.
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  • Description: Post-implementation amendments to ERP systems (ERP-PIA) are of importance for advancing ERP research, but more importantly essential if ERP systems are to be used as a strategic and competitive business tool. For ease of clarity, we have adopted the term “amendments” to encompass the main forms of post implementation activities: maintenance, enhancements and upgrades. The term “amendments” is used to counteract one of the major findings from this research - the inconsistency of terms used by many authors to explain post implementation activities. This paper presents a review of the ERP post-implementation amendment literature in order to provide answers to two specific questions: first, what is the current state of research in the field of ERP-PIA; and second, what are the future research directions that need to be explored in the field of ERP-PIA. From the review, we develop a framework to identify: (a) major themes concerning ERP post-implementation amendments, (b) inherent gaps in the post-implementation amendments literature, and (c) specific areas that require further research attention influencing the uptake of amendments. Suggestions on empirical evaluation of research directions and their relevance in the extension of existing literature is presented.

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.

OKC-enabled online knowledge integration: role of group heterogeneity and group interaction process

- Qiu, Jiangnan, Xu, Liwei, Zuo, Min, Wang, Jingxian, Weadon, Helen

  • Authors: Qiu, Jiangnan , Xu, Liwei , Zuo, Min , Wang, Jingxian , Weadon, Helen
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Information Technology and People Vol. 34, no. 1 (2020), p. 336-359
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  • Description: Purpose: Online knowledge integration has been an important concern of the online knowledge community as it can lead to various positive outcomes of online knowledge coproduction. This paper identifies online knowledge integration factors by considering group heterogeneity and group interaction process. Design/methodology/approach: Based on the categorization-elaboration model (CEM) and interactive team cognition (ITC) theory, a research model that reflects the antecedent's factors and mediating factors of online knowledge integration was developed and empirically examined based on data collected from 2,339,836 data extracted from Wikipedia. Findings: Group interaction process plays an essential mediator role in online knowledge integration. Group knowledge heterogeneity negatively influences online knowledge integration and group experience heterogeneity positively, and they both positively promote online knowledge integration through group interaction process with different paths. Research limitations: Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms. Practical implications: The findings of the study could assist the online knowledge community's organizers to understand the motivational mechanisms of online knowledge integration. Group interaction process could be regarded as the key role to promote group wisdom and maintain group independence. Social implications: We advance the understanding of the online knowledge integration and gain a richer understanding of the importance of group interaction independence for online knowledge integration based on the agreement of group wisdom. It suggested keeping group interaction independence is an important aspect for highly online knowledge integration among heterogeneity groups. Originality/value: This study extends CEM and ITC theory to the domain of knowledge integration context and finds the mechanism between group heterogeneity and online knowledge integration by introducing the group interaction process. © 2020, Emerald Publishing Limited.

Matching algorithms : fundamentals, applications and challenges

- Ren, Jing, Xia, Feng, Chen, Xiangtai, Liu, Jiaying, Sultanova, Nargiz

  • Authors: Ren, Jing , Xia, Feng , Chen, Xiangtai , Liu, Jiaying , Sultanova, Nargiz
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 5, no. 3 (2021), p. 332-350
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  • Description: Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren, Xia Feng, Nargiz Sultanova" is provided in this record**

Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football

- Jelinek, Herbert, Kelarev, Andrei, Robinson, Dean, Stranieri, Andrew, Cornforth, David

  • Authors: Jelinek, Herbert , Kelarev, Andrei , Robinson, Dean , Stranieri, Andrew , Cornforth, David
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Applied Soft Computing Vol. 14, no. PART A (2014), p. 81-87
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  • Description: This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05). © 2013 Published by Elsevier B.V. All rights reserved.
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Investigating the social implications of biometrics and the need for global biometric uniformity

- Leicester, Phillip, Kulkarni, Siddhivinayak

  • Authors: Leicester, Phillip , Kulkarni, Siddhivinayak
  • Date: 2013
  • Type: Text , Journal article
  • Relation: International Journal for Infonomics Vol. 6, no. 3/4 (2013), p. 731-735
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  • Description: This research paper looks at the social implications of biometrics pertaining to the ethics of privacy and the ownership of individual biometric data, including how these issues can be resolved through the establishment of a Biometric Commission by introducing global standardised biometric uniformity and the guidelines that will ensure their technological foundations. Much of the distrust that engulfs society is due to the past performances and policy implementations that governments have initiated surrounding biometric technology and its miss use beyond the realms of individual identification for security purposes. There needs to be total transparency from governments and organisations that use biometric technology for security identification in how every individuals biometric data will be used, stored and the ethical standards provided in eliminating many of the implications that every society has towards on how their biometric data will be used.

Educational big data : predictions, applications and challenges

- Bai, Xiaomei, Zhang, Fuli, Li, Jinzhou, Guo, Teng, Xia, Feng

  • Authors: Bai, Xiaomei , Zhang, Fuli , Li, Jinzhou , Guo, Teng , Xia, Feng
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: Big Data Research Vol. 26, no. (2021), p.
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  • Description: Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**

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

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

Understanding victims of identity theft: A grounded theory approach

- Turville, Kylie, Firmin, Sally, Yearwood, John, Miller, Charlynn

  • Authors: Turville, Kylie , Firmin, Sally , Yearwood, John , Miller, Charlynn
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: 5th International Conference on Qualitative Research in IT and IT in Qualitative Research, QualIT 2010
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  • Description: Being a victim of identity theft can be a devastating and life-changing event. Once the victim discovers the misuse they need to begin the process of recovery. For the "lucky" victims this may take only a couple of phone calls and a small amount of time; however, some victims may experience difficulties for many year. In order to recover, victims of crime require support and assistance; however, within Australia this support is sadly lacking. In order to identify the issues currently faced by victims of identity theft as they work through the recovery process, a Grounded Theory methodology was identified as most appropriate. This paper provides a brief overview of the history of the research project; a brief introduction of grounded theory with a focus on preconceived ideas and their implications; and a description of the research project currently being undertaken. A discussion of some issues experienced when using grounded theory within an IT department with very little experience of qualitative research will be provided, along with some preliminary results.
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Data-driven computational social science : A survey

- Zhang, Jun, Wang, Wei, Xia, Feng, Lin, Yu-Ru, Tong, Hanghang

  • Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
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  • Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.

Haptics can "lend a hand" to a bionic eye

- Richardson, Barry, Van Doorn, George

  • Authors: Richardson, Barry , Van Doorn, George
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Haptics: Generating and Perceiving Tangible Sensations p. 320-325
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  • Description: Here we argue that haptics (touch and kinaesthesis) can play a key role in the development of a bionic eye. Tactile displays can supplement and complement the incomplete information that a visual prosthetic will offer the brain in early stages of the prosthetic’s development. Kinaesthetic inputs give the brain feedback about motor activities that correlate with both visual and tactile inputs, and are critical for perceptual competency. Haptic inputs can also help “teach” the new visual sense to respond to stimuli that are initially indiscriminable and enable cross-calibration of inputs to strengthen multimodal cortical connections. By using haptics to supplement and complement inputs from a visual prosthetic, a bionic eye can develop more quickly than did the Bionic Ear.

High esteem and hurting others online : trait sadism moderates the relationship between self-esteem and internet trolling

- March, Evita, Steele, Genevieve

  • Authors: March, Evita , Steele, Genevieve
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Cyberpsychology, behavior and social networking Vol. 23, no. 7 (2020), p. 441-446
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  • Description: Internet trolling is commonly defined as disruptive online behavior, intended to provoke and distress others for amusement. Previous research has shown that gender (specifically, male), trait psychopathy, and trait sadism significantly predict engaging in trolling. In this study, we sought to replicate and extend previous research by exploring the role of self-esteem in predicting trolling, and possible interactions between self-esteem and personality. Participants (n = 400, 67.5 percent women, average age = 24.97 years [SD = 8.84]) completed an online questionnaire, including measures of psychopathy, sadism, self-esteem, and trolling behaviors. Results corroborated previous research showing gender (male) to be a significant predictor of trolling, and trait psychopathy and sadism to be significant positive predictors. Although self-esteem had no additional value on top of trait psychopathy and sadism in explaining trolling, there was a significant interaction between self-esteem and trait sadism. A moderation analysis indicated a positive relationship between self-esteem and trolling, but only when trait sadism was high. These results portray the troll as a callous individual may enjoy causing psychological harm, particularly if their self-esteem is high. These results contribute to building the psychological profile of trolls and provide future directions for research exploring trolling behaviors.

Current programs and future needs in health literacy for older people : a literature review

- Lê, Quynh, Terry, Daniel, Woodroffe, Jess

  • Authors: Lê, Quynh , Terry, Daniel , Woodroffe, Jess
  • Date: 2013
  • Type: Text , Journal article , Review
  • Relation: Journal of Consumer Health on the Internet Vol. 17, no. 4 (2013), p. 369-388
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  • Description: Inadequate health literacy occurs more amongst older adults and can result in difficulties synthesising information and communicating with health professionals, increased emergency visits and hospitalizations, poor uptake of preventative interventions, increased mortality, and ultimately greater health care costs. A literature review was conducted that identified 12 articles that discussed and examined health literacy interventions among older adults. It revealed few papers exist which highlight programs that examine health literacy outcomes for older adults. The review identified evidence-based best-practice models of health literacy interventions need to be further developed to meet the health literacy needs of aging population. © 2013 Copyright Quynh Le, Daniel R. Terry, and Jess Woodroffe.

A fuzzy logic model for scalable trust in e-commerce

- Sun, Zhaohao, Guo, Xifeng, Zaoh, Sh

  • Authors: Sun, Zhaohao , Guo, Xifeng , Zaoh, Sh
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: The 8th IFIP Conference on e-Business, e-Services, and e-Society p. 87-97
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  • Description: With the rapid development of e-commerce and the expansion of e-commerce system scale, scalable trust in e-commerce becomes critical. This article examines scalable trust of multiagent e-commerce system (MECS) and proposes a fuzzy model for scalable trust in e-commerce. It also discusses the realization of scalable trust from the viewpoint of engineering. The proposed approach will facilitate research and development of trust, multiagent systems, e-commerce and e-services

Self-disclosure in Chinese micro-blogging : A social exchange theory perspective

- Liu, Zilong, Min, Qingfei, Zhai, Qingguo, Smyth, Russell

  • Authors: Liu, Zilong , Min, Qingfei , Zhai, Qingguo , Smyth, Russell
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Information and Management Vol. 53, no. 1 (2016), p. 53-63
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  • Description: This paper examines the influence of social benefits and costs on self-disclosure in the context of micro-blogging in China. Data were collected from 441 micro-blogging users, employing an online survey. Self-disclosure is measured in terms of amount, depth, honesty, intent, and valence of disclosure. The study found that relationship building, enjoyment and trust in service providers are positively related to self-disclosure, while perceived anonymity of self and perceived risk are negatively related to self-disclosure. However, convenience of relationship maintenance and self-presentation are not related to self-disclosure. © 2015 Elsevier B.V. All rights reserved.