Efficient one-round key exchange in the standard model

We consider one-round key exchange protocols secure in the standard model. The security analysis uses the powerful security model of Canetti and Krawczyk and a natural extension of it to the ID-based setting. It is shown how KEMs can be used in a generic way to obtain two different protocol designs with progressively stronger security guarantees. A detailed analysis of the performance of the protocols is included; surprisingly, when instantiated with specific KEM constructions, the resulting protocols are competitive with the best previous schemes that have proofs only in the random oracle model.

Efficient one-round key exchange in the efficient one-round key exchange in the standard model

We consider one-round key exchange protocols secure in the standard model. The security analysis uses the powerful security model of Canetti and Krawczyk and a natural extension of it to the ID-based setting. It is shown how KEMs can be used in a generic way to obtain two different protocol designs with progressively stronger security guarantees. A detailed analysis of the performance of the protocols is included; surprisingly, when instantiated with specific KEM constructions, the resulting protocols are competitive with the best previous schemes that have proofs only in the random oracle model.

Efficient one-round key exchange in the standard model

We consider one-round key exchange protocols secure in the standard model. The security analysis uses the powerful security model of Canetti and Krawczyk and a natural extension of it to the ID-based setting. It is shown how KEMs can be used in a generic way to obtain two different protocol designs with progressively stronger security guarantees. A detailed analysis of the performance of the protocols is included; surprisingly, when instantiated with specific KEM constructions, the resulting protocols are competitive with the best previous schemes that have proofs only in the random oracle model.

Improving the Traversal of Large Hierarchical Process Repositories

Business Process Management (BPM) has increased in popularity and maturity in recent years. Large enterprises engage use process management approaches to model, manage and refine repositories of process models that detail the whole enterprise. These process models can run to the thousands in number, and may contain large hierarchies of tasks and control structures that become cumbersome to maintain. Tools are therefore needed to effectively traverse this process model space in an efficient manner, otherwise the repositories remain hard to use, and thus are lowered in their effectiveness. In this paper we analyse a range of BPM tools for their effectiveness in handling large process models. We establish that the present set of commercial tools is lacking in key areas regarding visualisation of, and interaction with, large process models. We then present six tool functionalities for the development of advanced business process visualisation and interaction, presenting a design for a tool that will exploit the latest advances in 2D and 3D computer graphics to enable fast and efficient search, traversal and modification of process models.

State-of-the-Art Review on Opinion Mining from Online Customers’ Feedback

Dealing with the ever-growing information overload in the Internet, Recommender Systems are widely used online to suggest potential customers item they may like or find useful. Collaborative Filtering is the most popular techniques for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating about a service provider, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service providers’. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a state of the art review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and sub-categories. Each of those steps is analyzed with their strength and limitations in this paper.

Editorial

This special issue of the Journal of Community, Citizen’s and Third Sector Media and Communication (3CMedia) is based on selected presentations given at the 5th annual Making Links conference, held at The University of Melbourne from 11th to 13th November, 2008. Making Links (see also www.makinglinks.org.au) is a conference that seeks to engage interested people, organisations and groups working at the intersection of social action and information technology, including community workers, educators, trainers, not-for-profit organisations, people who work with marginalised groups, activists and researchers. One of the program streams at this conference was dedicated to the practice of digital storytelling (Lambert, 2002; Hartley & McWilliam, 2009).

HCX : an efficient hybrid clustering approach for XML documents

This paper proposes a novel Hybrid Clustering approach for XML documents (HCX) that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The empirical analysis reveals that the proposed method is scalable and accurate.

XCFS - a novel approach for clustering XML documents using both the structure and the content

XML document clustering is essential for many document handling applications such as information storage, retrieval, integration and transformation. An XML clustering algorithm should process both the structural and the content information of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. This paper introduces a novel approach that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The proposed method reduces the high dimensionality of input data by using only the structure-constrained content. The empirical analysis reveals that the proposed method can effectively cluster even very large XML datasets and outperform other existing methods.