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Industry type and business size on economic growth: Comparing Australia's Regional and Metropolitan areas

- Mardaneh, Karim

  • Authors: Mardaneh, Karim
  • Date: 2011
  • Type: Text , Conference proceedings
  • Relation: 56th Annual ICSB World Conference; Back to the Future - Changes in Perspectives of Global Entrepreneurship and Innovation,Stockholm, Sweden, 15-18 June, 2011
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  • Description: While the main body of literature regarding small-to-medium enterprises is focused on formation and growth, there is insufficient research about the role of both (a) firm size and (b) location on economic growth. The role of firm size and industrial structure on economic growth has been examined by some researchers. Pagano (2003) and Pagano and Schivardi (2000) identified a positive association between average firm size and growth and Carree and Thurik (1999) found evidence that the low number of large firms in an industry could lead to a higher value added growth. The current study attempts to investigate the impact of industry structure and businesses operating within these industries on economic growth. This paper uses “k-means” clustering algorithm to cluster Statistical Local Areas. Regression analysis is utilised to identify drivers of economic growth. Preliminary results suggest that size of business may act as a driver of economic growth but the impact could vary based on location.

Small businesses, Institutions, and Regional Incomes

- Mardaneh, Karim, O'Malley, Tony

  • Authors: Mardaneh, Karim , O'Malley, Tony
  • Date: 2014
  • Type: Text , Conference proceedings
  • Relation: 59th ISCB World Conference, Entrepreneurship and sustainability, Dublin, 11th June, 2014
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  • Description: Regional small businesses may rely on customers who earn income in local and global markets. Small business must transact with suppliers of knowledge and resources, transform those resources into innovative and saleable products or services, and transact with customers. Transformation, transaction and social activities, and the institutions which support them, are necessary for successful small businesses. Regional income and small businesses depend on innovation and trade provided by social and transaction institutions. In this paper we demonstrate this proposition empirically using a model and by investigating the relationship between regional income, transaction institutions, transformation institutions, and social institutions for 140 functional economic regions (FERs) in Australia. The model suggests that social institutions create a macro-environment in which transaction institutions and the transformation and trading activities of businesses can thrive, and help to generate regional income and prosperity. We follow others (Cooke et al., 2007) in arguing that strong transaction institutions are a necessary condition for regional innovation. Social institutions complement transaction institutions by providing education and training, arts and recreation, health care and social services. In the studies reported in this paper the capacity for search and intermediation of exchanges of all kinds (goods, services, knowledge etc.) is measured by the share of transaction institutions in regional employment. The capacity of social institutions is measured by the share of employment in social institutions. We argue that the market failures which cause regional failures to thrive may be made solvable by mobilising market making services to extend and provide governance for regional transactions with faraway markets.

THCluster: herb supplements categorization for precision traditional Chinese medicine

- Ruan, Chunyang, Wang, Ye, Zhang, Yanchun, Ma, Jiangang, Chen, Huijuan, Aickelin, Uwe, Zhu, Shanfeng, Zhang, Ting

  • Authors: Ruan, Chunyang , Wang, Ye , Zhang, Yanchun , Ma, Jiangang , Chen, Huijuan , Aickelin, Uwe , Zhu, Shanfeng , Zhang, Ting
  • Date: 2020
  • Type: Text , Conference proceedings
  • Relation: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);Kansas City, MO, USA; 13-16 Nov. 2017 p. 417-424
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  • Description: There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.

Partial undersampling of imbalanced data for cyber threats detection

- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal

  • Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
  • Description: E1

Influence of clustering on the opinion formation dynamics in online social networks

- Das, Rajkumar, Kamruzzaman, Joarder, Karmakar, Gour

Data sharing in secure multimedia wireless sensor networks

- Usman, Muhammad, Jan, Mian, Xiangjian, He, Nanda, Priyadarsi

  • Authors: Usman, Muhammad , Jan, Mian , Xiangjian, He , Nanda, Priyadarsi
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 IEEE Trustcom/BigDataSE/ISPA;Tianjin, China; 23-26 August 2016 p. 590-597
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  • Description: The use of Multimedia Wireless Sensor Networks (MWSNs) is becoming common nowadays with a rapid growth in communication facilities. Similar to any other WSNs, these networks face various challenges while providing security, trust and privacy for user data. Provisioning of the aforementioned services become an uphill task especially while dealing with real-time streaming data. These networks operates with resource-constrained sensor nodes for days, months and even years depending on the nature of an application. The resource-constrained nature of these networks makes it difficult for the nodes to tackle real-time data in mission-critical applications such as military surveillance, forest fire monitoring, health-care and industrial automation. For a secured MWSN, the transmission and processing of streaming data needs to be explored deeply. The conventional data authentication schemes are not suitable for MWSNs due to the limitations imposed on sensor nodes in terms of battery power, computation, available bandwidth and storage. In this paper, we propose a novel quality-driven clustering-based technique for authenticating streaming data in MWSNs. Nodes with maximum energy are selected as Cluster Heads (CHs). The CHs collect data from member nodes and forward it to the Base Station (BS), thus preventing member nodes with low energy from dying soon and increasing life span of the underlying network. The proposed approach not only authenticates the streaming data but also maintains the quality of transmitted data. The proposed data authentication scheme coupled with an Error Concealment technique provides an energy-efficient and distortion-free real-time data streaming. The proposed scheme is compared with an unsupervised resources scenario. The simulation results demonstrate better network lifetime along with 21.34 dB gain in Peak Signal-to-Noise Ratio (PSNR) of received video data streams.
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