Generic one round group key exchange in the standard model

Minimizing complexity of group key exchange (GKE) protocols is an important milestone towards their practical deployment. An interesting approach to achieve this goal is to simplify the design of GKE protocols by using generic building blocks. In this paper we investigate the possibility of founding GKE protocols based on a primitive called multi key encapsulation mechanism (mKEM) and describe advantages and limitations of this approach. In particular, we show how to design a one-round GKE protocol which satisfies the classical requirement of authenticated key exchange (AKE) security, yet without forward secrecy. As a result, we obtain the first one-round GKE protocol secure in the standard model. We also conduct our analysis using recent formal models that take into account both outsider and insider attacks as well as the notion of key compromise impersonation resilience (KCIR). In contrast to previous models we show how to model both outsider and insider KCIR within the definition of mutual authentication. Our analysis additionally implies that the insider security compiler by Katz and Shin from ACM CCS 2005 can be used to achieve more than what is shown in the original work, namely both outsider and insider KCIR.

Efficient certificateless KEM in the standard model

We give a direct construction of a certificateless key encapsulation mechanism (KEM) in the standard model that is more efficient than the generic constructions proposed before by Huang and Wong \cite{DBLP:conf/acisp/HuangW07}. We use a direct construction from Kiltz and Galindo's KEM scheme \cite{DBLP:conf/acisp/KiltzG06} to obtain a certificateless KEM in the standard model; our construction is roughly twice as efficient as the generic construction. We also address the security flaw discovered by Selvi et al. \cite{cryptoeprint:2009:462}.

Using Association Rules to Solve the Cold-Start Problem in Recommender Systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus it is difficult for a recommender system to make quality recommendations. This problem is known as the cold-start problem. Here we investigate using association rules as a source of information to expand a user profile and thus avoid this problem. Our experiments show that it is possible to use association rules to noticeably improve the performance of a recommender system under the cold-start situation. Furthermore, we also show that the improvement in performance obtained can be achieved while using non-redundant rule sets. This shows that non-redundant rules do not cause a loss of information and are just as informative as a set of association rules that contain redundancy.

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

The concept of 'Organismic Asymmetry' in sport science

The concept of organismic asymmetry refers to an inherent bias for seeking explanations of human performance and behaviour based on internal mechanisms and referents. A weakness in this tendency is a failure to consider the performer–environment relationship as the relevant scale of analysis. In this paper we elucidate the philosophical roots of the bias and discuss implications of organismic asymmetry for sport science and performance analysis, highlighting examples in psychology, sports medicine and biomechanics.

A design science research roadmap

This paper proposes and synthesizes from previous design science(DS) methodological literature a structured and detailed DS Roadmap for the conduct of DS research. The Roadmap is a general guide for researchers to carry out DS research by suggesting reasonably detailed activities.Though highly tentative, it is believed the Roadmap usefully inter-relates many otherwise seemingly disparate, overlapping or conflicting concepts. It is hoped the DS Roadmap will aid in the planning, execution and communication of DS research,while also attracting constructive criticism, improvements and extensions. A key distinction of the Roadmap from other DS research methods is its breadth of coverage of DS research aspects and activities; its detail and scope. We demonstrate and evaluate the Roadmap by presenting two case studies in terms of the DS Roadmap.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Geometry vs appearance for discriminating between posed and spontaneous emotions

Spontaneous facial expressions differ from posed ones in appearance, timing and accompanying head movements. Still images cannot provide timing or head movement information directly. However, indirectly the distances between key points on a face extracted from a still image using active shape models can capture some movement and pose changes. This information is superposed on information about non-rigid facial movement that is also part of the expression. Does geometric information improve the discrimination between spontaneous and posed facial expressions arising from discrete emotions? We investigate the performance of a machine vision system for discrimination between posed and spontaneous versions of six basic emotions that uses SIFT appearance based features and FAP geometric features. Experimental results on the NVIE database demonstrate that fusion of geometric information leads only to marginal improvement over appearance features. Using fusion features, surprise is the easiest emotion (83.4% accuracy) to be distinguished, while disgust is the most difficult (76.1%). Our results find different important facial regions between discriminating posed versus spontaneous version of one emotion and classifying the same emotion versus other emotions. The distribution of the selected SIFT features shows that mouth is more important for sadness, while nose is more important for surprise, however, both the nose and mouth are important for disgust, fear, and happiness. Eyebrows, eyes, nose and mouth are important for anger.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.

Inquiry teaching in primary science : a phenomenographic study

In spite of having a long history in education, inquiry teaching (the teaching in ways that foster inquiry based learning in students) in science education is still a highly problematic issue. However, before teacher educators can hope to effectively influence teacher implementation of inquiry teaching in the science classroom, educators need to understand teachers’ current conceptions of inquiry teaching. This study describes the qualitatively different ways in which 20 primary school teachers experienced inquiry teaching in science education. A phenomenographic approach was adopted and data sourced from interviews of these teachers. The three categories of experiences that emerged from this study were; Student Centred Experiences (Category 1), Teacher Generated Problems (Category 2), and Student Generated Questions (Category 3). In Category 1 teachers structure their teaching around students sensory experiences, expecting that students will see, hear, feel and do interesting things that will focus their attention, have them asking science questions, and improve their engagement in learning. In Category 2 teachers structure their teaching around a given problem they have designed and that the students are required to solve. In Category 3 teachers structure their teaching around helping students to ask and answer their own questions about phenomena. These categories describe a hierarchy with the Student Generated Questions Category as the most inclusive. These categories were contrasted with contemporary educational theory, and it was found that when given the chance to voice their own conceptions without such comparison teachers speak of inquiry teaching in only one of the three categories mentioned. These results also help inform our theoretical understanding of teacher conceptions of inquiry teaching. Knowing what teachers actually experience as inquiry teaching, as opposed to understand theoretically, is a valuable contribution to the literature. This knowledge provides a valuable contribution to educational theory, which helps policy, curriculum development, and the practicing primary school teachers to more fully understand and implement the best educative practices in their daily work. Having teachers experience the qualitatively different ways of experiencing inquiry teaching uncovered in this study is expected to help teachers to move towards a more student-centred, authentic inquiry outcome for their students and themselves. Going beyond this to challenge teacher epistemological beliefs regarding the source of knowledge may also assist them in developing more informed notions of the nature of science and of scientific inquiry during professional development opportunities. The development of scientific literacy in students, a high priority for governments worldwide, will only to benefit from these initiatives.