C6 : A holistic model for decision making in web services
- Sun, Zhaohao, Meredith, Grant, Jia, Long
- Authors: Sun, Zhaohao , Meredith, Grant , Jia, Long
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australasian Conference on Information Systems, Monash University, Melbourne, Victoria : 2nd-4th Dec 2009 p. 904-914
- Full Text:
- Description: Web services are playing a pivotal role in e-business, service intelligence, service science and information systems. This article will examine how the main players make decisions for activities in web service lifecycle (WSLC) and propose a holistic model for decision making in web services. More specifically, this article first examines main players in web services. It also reviews the existing web service lifecycles and proposes a demand-driven web service lifecycle for web service requesters. It will then examine six driving factors for web services, look at their interrelationships and propose a holistic model for decision making in web services, C6, which consists of six Cs: communication, competition, coordination, collaboration, cooperation and control, taking into account the main players in web services and web service lifecycle (WSLC). The proposed approach will facilitate research and development of web services, e-services, service intelligence, service science and service computing.
- Description: 2003007874
- Authors: Sun, Zhaohao , Meredith, Grant , Jia, Long
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 20th Australasian Conference on Information Systems, Monash University, Melbourne, Victoria : 2nd-4th Dec 2009 p. 904-914
- Full Text:
- Description: Web services are playing a pivotal role in e-business, service intelligence, service science and information systems. This article will examine how the main players make decisions for activities in web service lifecycle (WSLC) and propose a holistic model for decision making in web services. More specifically, this article first examines main players in web services. It also reviews the existing web service lifecycles and proposes a demand-driven web service lifecycle for web service requesters. It will then examine six driving factors for web services, look at their interrelationships and propose a holistic model for decision making in web services, C6, which consists of six Cs: communication, competition, coordination, collaboration, cooperation and control, taking into account the main players in web services and web service lifecycle (WSLC). The proposed approach will facilitate research and development of web services, e-services, service intelligence, service science and service computing.
- Description: 2003007874
Financial reporting and accountability in charitable organisations
- Dellaportas, Steven, Langton, Jonathan, West, Brian
- Authors: Dellaportas, Steven , Langton, Jonathan , West, Brian
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at 2008 AFAANZ / IAAER Conference, Sydney, New South Wales : 6th-8th July 2008
- Full Text: false
- Description: The not-for-profit sector is made up of approximately 700,000 entities employing around 600,000 people and controlling assets up to $70 billion, with the largest charities comparing favourably in size and value to big business. While it is expected that many charitable organisations are well-managed and make a significant contribution to society, a lack of good governance and/or accountability could have devastating affects on the community. Official inquiries into the not-for-profit sector have questioned the lack of transparency and accountability created by a complex legal and regulatory regime. Critics of the existing regime have called for reforms including mandatory financial reporting requirements and an independent regulator to enhance public accountability and organizational efficiency and performance. This study provides an insight into the financial reporting activities of Australia’s largest charities so as to provide a measure of the extent to which charities meet public expectations of accountability. This involved the examination of over 100 publicly available reports for charitable organizations and the administration of a questionnaire. The majority of charities examined in this study provide community, welfare or health services, employ over 100 employees, and rely on a similar number of volunteers to assist with their activities. We have attempted to shed light on a significant failure of the not-for-profit sector to date, in that although our research highlights inconsistencies in reporting formats, ambiguus financial information and a high proportion of qualified audit reports there is a strong belief from charity organisations that the public is entitled to receive quality information on financial performance, suggesting that increased financial disclosures would be beneficial to the charitable sector. Respondents supported ‘programme accountability’ (89.1%), ‘fiscal accountability’ (78.2%) and ‘profit’ (76.6%) as suitable measures of performance with the circumstances of not-for-profit organisations sufficiently different to require a specific and dedicated accounting standard for the sector.
- Description: 2003006345
Local contrast as an effective means to robust clustering against varying densities
- Chen, Bo, Ting, Kaiming, Washio, Takashi, Zhu, Ye
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Zhu, Ye
- Date: 2018
- Type: Text , Journal article
- Relation: Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
- Full Text:
- Reviewed:
- Description: Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. © 2018, The Author(s).
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Zhu, Ye
- Date: 2018
- Type: Text , Journal article
- Relation: Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
- Full Text:
- Reviewed:
- Description: Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. © 2018, The Author(s).
An efficient data delivery mechanism for AUV-based Ad hoc UASNs
- Karmakar, Gour, Kamruzzaman, Joarder, Nowsheen, Nusrat
- Authors: Karmakar, Gour , Kamruzzaman, Joarder , Nowsheen, Nusrat
- Date: 2018
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 86, no. (2018), p. 1193-1208
- Full Text: false
- Reviewed:
- Description: Existing 3D Underwater Acoustic Sensor Networks (UASNs) are either fixed having nodes anchored with the seabed or a combination of Autonomous Underwater Vehicles (AUVs) and a fixed UASN where AUVs are controlled to move along paths for data collection. Existing data delivery protocols for such AUV equipped networks are designed in a way where AUVs act as message ferries. These UASNs are deployed for a specific service such as asset (e.g., oil pipes, shipwreck) monitoring and event detection. For a coordinated data collection, to deploy a network for any service like information discovery in an ad hoc manner, it requires a 3D UASN consisting of only AUVs and the movement of an AUV needs to be controlled by another AUV through commands. To our knowledge, no such data delivery protocol required for a 3D UASN comprising only AUVs exists in the current literature that can efficiently handle data collection and delivery. To address this research gap, in this paper, an AUV-based technique for ad hoc underwater network, namely AUV-based Data Delivery Protocol (ADDP), is introduced which ensures data delivery within a given time-constraint by controlling node (i.e., AUV) movement at each hop through commands of a node. The performance of the proposed protocol has also been evaluated and compared with existing relevant protocols in terms of packet delivery ratio, routing overhead and energy consumption considering various network scenarios and sizes. Results exhibit outstanding performance improvement achieved by the proposed protocol for all metrics. © 2017 Elsevier B.V.
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.
- Full Text:
- Reviewed:
- 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.
- 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.
- Full Text:
- Reviewed:
- 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.
An evaluation methodology for interactive reinforcement learning with simulated users
- Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: 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 sample 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: 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 sample 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Diarrhoeal disease surveillance in Papua New Guinea : findings and challenges
- Abdad, Mohammad, Soli, Kevin, Pham, Bang, Bande, Grace, Maure, Tobias, Jonduo, Marinjo, Kisa, Debbie, Rai, Glennis, Phuanukoonnon, Suparat, Siba, Peter, Horwood, Paul, Greenhill, Andrew
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
- Full Text:
- Reviewed:
- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
- Full Text:
- Reviewed:
- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
The role of individual differences in cyber dating abuse perpetration
- March, Evita, Grieve, Rachel, Clancy, Elizabeth, Klettke, Bianca, Van Dick, Rolf, Hernandez Bark, Alina
- Authors: March, Evita , Grieve, Rachel , Clancy, Elizabeth , Klettke, Bianca , Van Dick, Rolf , Hernandez Bark, Alina
- Date: 2021
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 24, no. 7 (2021), p. 457-463
- Full Text:
- Reviewed:
- Description: There is a growing research interest in cyber dating abuse (CDA). CDA includes abusive online behavior toward a current or former intimate partner, such as aggression, control, harassment, and humiliation. Despite the potential overlap and reciprocal relationship of CDA and intimate partner violence, there remains considerable paucity in research exploring predictors of this abusive online behavior. In the current study, we adopt the General Aggression Model framework and explore the role of gender, hegemonic masculinity, vulnerable narcissism, and sexual aggression myths to predict perpetration of CDA. Participants (N = 415, 51 percent women; Mage = 32.68 years) were recruited via social media advertisements and completed an anonymous, confidential online questionnaire. The questionnaire comprised the Conformity to Masculine Roles Norms Inventory, the Hypersensitive Narcissism Scale, the Acceptance of Modern Myths About Sexual Aggression Scale, and a modified Cyber Aggression in Relationships Scale. A hierarchical regression analysis indicated that hegemonic masculinity, vulnerable narcissism, and sexual aggression myths were all significant positive predictors of perpetrating CDA. As gender was a significant predictor until the inclusion of these variables, a multiple mediation analysis was performed, indicating that both hegemonic masculinity and sexual aggression myths fully mediated the relationship between gender and perpetrating CDA. These results add to the growing body of research exploring how CDA emerges as a behavior and highlight possible implications for management and intervention. © Copyright 2021, Mary Ann Liebert, Inc., publishers 2021.
- Authors: March, Evita , Grieve, Rachel , Clancy, Elizabeth , Klettke, Bianca , Van Dick, Rolf , Hernandez Bark, Alina
- Date: 2021
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 24, no. 7 (2021), p. 457-463
- Full Text:
- Reviewed:
- Description: There is a growing research interest in cyber dating abuse (CDA). CDA includes abusive online behavior toward a current or former intimate partner, such as aggression, control, harassment, and humiliation. Despite the potential overlap and reciprocal relationship of CDA and intimate partner violence, there remains considerable paucity in research exploring predictors of this abusive online behavior. In the current study, we adopt the General Aggression Model framework and explore the role of gender, hegemonic masculinity, vulnerable narcissism, and sexual aggression myths to predict perpetration of CDA. Participants (N = 415, 51 percent women; Mage = 32.68 years) were recruited via social media advertisements and completed an anonymous, confidential online questionnaire. The questionnaire comprised the Conformity to Masculine Roles Norms Inventory, the Hypersensitive Narcissism Scale, the Acceptance of Modern Myths About Sexual Aggression Scale, and a modified Cyber Aggression in Relationships Scale. A hierarchical regression analysis indicated that hegemonic masculinity, vulnerable narcissism, and sexual aggression myths were all significant positive predictors of perpetrating CDA. As gender was a significant predictor until the inclusion of these variables, a multiple mediation analysis was performed, indicating that both hegemonic masculinity and sexual aggression myths fully mediated the relationship between gender and perpetrating CDA. These results add to the growing body of research exploring how CDA emerges as a behavior and highlight possible implications for management and intervention. © Copyright 2021, Mary Ann Liebert, Inc., publishers 2021.
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
- Full Text:
- Reviewed:
- 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**
- 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
- Full Text:
- Reviewed:
- 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**
A survey on context awareness in big data analytics for business applications
- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 62, no. 9 (2020), p. 3387-3415
- Full Text:
- Reviewed:
- Description: The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
The spectrum of big data analytics
- Authors: Sun, Zhaohao , Huo, Yanxia
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 61, no. 2 (2021), p. 154-162
- Full Text:
- Reviewed:
- Description: Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems.
- Authors: Sun, Zhaohao , Huo, Yanxia
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 61, no. 2 (2021), p. 154-162
- Full Text:
- Reviewed:
- Description: Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems.
Enabling situational awareness of business processes
- Zhao, Xiaohui, Yongchareon, Sira, Cho, Nam-Wook
- Authors: Zhao, Xiaohui , Yongchareon, Sira , Cho, Nam-Wook
- Date: 2021
- Type: Text , Journal article
- Relation: Business Process Management Journal Vol. 27, no. 3 (2021), p. 779-795
- Full Text:
- Reviewed:
- Description: Purpose: The purpose of this research is to explore the ways of integrating situational awareness into business process management for the purpose of realising hyper automated business processes. Such business processes will help improve their customer experiences, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. Design/methodology/approach: Ontology has been deployed to establish the context modelling method, and the event handling mechanisms are developed on the basis of event calculus. An approach on performance of the proposed approach has been evaluation by checking the cost savings from the simulation of a large number of business processes. Findings: In this research, the authors have formalised the context presentation for a business process with a focus on rules and entities to support context perception; proposed a system architecture to illustrate the structure and constitution of a supporting system for intelligent and situation aware business process management; developed real-time event elicitation and interpretation mechanisms to operationalise the perception of contextual dynamics and real-time responses; and evaluated the applicability of the proposed approaches and the performance improvement to business processes. Originality/value: This paper presents a framework covering process context modelling, system architecture and real-time event handling mechanisms to support situational awareness of business processes. The reported research is based on our previous work on radio frequency identification-enabled applications and context-aware business process management with substantial extension to process context modelling and process simulation. © 2021, Emerald Publishing Limited.
- Authors: Zhao, Xiaohui , Yongchareon, Sira , Cho, Nam-Wook
- Date: 2021
- Type: Text , Journal article
- Relation: Business Process Management Journal Vol. 27, no. 3 (2021), p. 779-795
- Full Text:
- Reviewed:
- Description: Purpose: The purpose of this research is to explore the ways of integrating situational awareness into business process management for the purpose of realising hyper automated business processes. Such business processes will help improve their customer experiences, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business. Design/methodology/approach: Ontology has been deployed to establish the context modelling method, and the event handling mechanisms are developed on the basis of event calculus. An approach on performance of the proposed approach has been evaluation by checking the cost savings from the simulation of a large number of business processes. Findings: In this research, the authors have formalised the context presentation for a business process with a focus on rules and entities to support context perception; proposed a system architecture to illustrate the structure and constitution of a supporting system for intelligent and situation aware business process management; developed real-time event elicitation and interpretation mechanisms to operationalise the perception of contextual dynamics and real-time responses; and evaluated the applicability of the proposed approaches and the performance improvement to business processes. Originality/value: This paper presents a framework covering process context modelling, system architecture and real-time event handling mechanisms to support situational awareness of business processes. The reported research is based on our previous work on radio frequency identification-enabled applications and context-aware business process management with substantial extension to process context modelling and process simulation. © 2021, Emerald Publishing Limited.
Scholar2vec : vector representation of scholars for lifetime collaborator prediction
- Wang, Wei, Xia, Feng, Wu, Jian, Gong, Zhiguo, Tong, Hanghang, Davison, Brian
- Authors: Wang, Wei , Xia, Feng , Wu, Jian , Gong, Zhiguo , Tong, Hanghang , Davison, Brian
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 15, no. 3 (2021), p.
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- Description: While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery.
- Authors: Wang, Wei , Xia, Feng , Wu, Jian , Gong, Zhiguo , Tong, Hanghang , Davison, Brian
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 15, no. 3 (2021), p.
- Full Text:
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- Description: While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery.
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.
- 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.
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
- 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
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.
- 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
- Full Text:
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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.
Position sensing of industrial robots-A survey
- Poplawski, Jaroslaw, Sultan, Ibrahim
- Authors: Poplawski, Jaroslaw , Sultan, Ibrahim
- Date: 2007
- Type: Text , Journal article
- Relation: Information Technology Journal Vol. 6, no. 1 (2007), p. 14-25
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- Description: This study offers a comprehensive coverage for many methods, systems and applications employed for robot positioning. The scope of material found reflects the width and breadth of what has been achieved in robot positioning and forms a basis for further research into possible new designs and applications. © 2007 Asian Network for Scientific Information.
- Description: C1
- Description: 2003004742
- Authors: Poplawski, Jaroslaw , Sultan, Ibrahim
- Date: 2007
- Type: Text , Journal article
- Relation: Information Technology Journal Vol. 6, no. 1 (2007), p. 14-25
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- Description: This study offers a comprehensive coverage for many methods, systems and applications employed for robot positioning. The scope of material found reflects the width and breadth of what has been achieved in robot positioning and forms a basis for further research into possible new designs and applications. © 2007 Asian Network for Scientific Information.
- Description: C1
- Description: 2003004742
Differences in personality and the sharing of managerial tacit knowledge: an empirical analysis of public sector managers in Malaysia
- Abdul Manaf, Halimah, Harvey, William, Armstrong, Steven, Lawton, Alan
- Authors: Abdul Manaf, Halimah , Harvey, William , Armstrong, Steven , Lawton, Alan
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Knowledge Management Vol. 24, no. 5 (2020), p. 1177-1199
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- Description: Purpose: This study aims to identify differences in knowledge-sharing mechanisms and personality among expert, typical and novice managers within the Malaysian public sector. Strengthening knowledge sharing function is essential for enabling public institutions around the world to be more productive. Design/methodology/approach: This quantitative study involves 308 employees from management and professional groups within 98 local authorities in the Malaysian local government. Stratified random sampling techniques were used and the sampling frame comprised 1,000 staff using postal surveys. Data analyses were carried out using analysis of variance and correlations to test the research hypotheses. Findings: The findings reveal that expert managers are more proactive in sharing their knowledge, particularly those with the personality traits of conscientiousness and openness. These two personality traits were also related to expert behaviours such as thoroughness, responsibility and persistence, which led to work competency and managerial success. Originality/value: This study provides theoretical insights into how managerial tacit knowledge differs and can accumulate, depending on the personality traits of middle managers. The paper shows the different mechanisms of knowledge sharing, tacit knowledge and personality among expert, typical and novice managers. Practically, this study is important for guiding senior managers in their attempts to identify the most appropriate personalities of their middle managers. This study found that the expert group was higher in conscientiousness, openness and overall personality traits compared with the typical and novice groups. The paper also highlights the value of sharing managerial tacit knowledge effectively. © 2020, Emerald Publishing Limited.
- Authors: Abdul Manaf, Halimah , Harvey, William , Armstrong, Steven , Lawton, Alan
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Knowledge Management Vol. 24, no. 5 (2020), p. 1177-1199
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- Description: Purpose: This study aims to identify differences in knowledge-sharing mechanisms and personality among expert, typical and novice managers within the Malaysian public sector. Strengthening knowledge sharing function is essential for enabling public institutions around the world to be more productive. Design/methodology/approach: This quantitative study involves 308 employees from management and professional groups within 98 local authorities in the Malaysian local government. Stratified random sampling techniques were used and the sampling frame comprised 1,000 staff using postal surveys. Data analyses were carried out using analysis of variance and correlations to test the research hypotheses. Findings: The findings reveal that expert managers are more proactive in sharing their knowledge, particularly those with the personality traits of conscientiousness and openness. These two personality traits were also related to expert behaviours such as thoroughness, responsibility and persistence, which led to work competency and managerial success. Originality/value: This study provides theoretical insights into how managerial tacit knowledge differs and can accumulate, depending on the personality traits of middle managers. The paper shows the different mechanisms of knowledge sharing, tacit knowledge and personality among expert, typical and novice managers. Practically, this study is important for guiding senior managers in their attempts to identify the most appropriate personalities of their middle managers. This study found that the expert group was higher in conscientiousness, openness and overall personality traits compared with the typical and novice groups. The paper also highlights the value of sharing managerial tacit knowledge effectively. © 2020, Emerald Publishing Limited.
Tracing the Pace of COVID-19 research : topic modeling and evolution
- Liu, Jiaying, Nie, Hansong, Li, Shihao, Ren, Jing, Xia, Feng
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
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- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 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 “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
- Full Text:
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- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 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 “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
Random walks : a review of algorithms and applications
- Xia, Feng, Liu, Jiaying, Nie, Hansong, Fu, Yonghao, Wan, Liangtian, Kong, Xiangjie
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
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- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
- Full Text:
- Reviewed:
- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.