Software reliability growth models based on local polynomial modeling with kernel smoothing

Software reliability growth models (SRGMs) are extensively employed in software engineering to assess the reliability of software before their release for operational use. These models are usually parametric functions obtained by statistically fitting parametric curves, using Maximum Likelihood estimation or Least–squared method, to the plots of the cumulative number of failures observed N(t) against a period of systematic testing time t. Since the 1970s, a very large number of SRGMs have been proposed in the reliability and software engineering literature and these are often very complex, reflecting the involved testing regime that often took place during the software development process. In this paper we extend some of our previous work by adopting a nonparametric approach to SRGM modeling based on local polynomial modeling with kernel smoothing. These models require very few assumptions, thereby facilitating the estimation process and also rendering them more relevant under a wide variety of situations. Finally, we provide numerical examples where these models will be evaluated and compared.

Defending against large-scale and coordinated attacks in the ubiquitous environments

Ubiquitous computing is an exciting paradigm shift where technology becomes virtually invisible in our lives. In the increasingly interconnected world, threats to our daily lives can come from unexpected sources and universal directions. Criminals and terrorists have recognized the value of leveraging the ubiquitous computing environments to facilitate the commission of crimes. The cyber criminals typically launch different forms of large-scale and coordinated attacks, causing huge financial loss and potential life hazard. In this talk, we report two innovative approaches to defend against large-scale and coordinated attacks in the ubiquitous environments: 1) Inferring the cyber crime's intent through network traffic classification to enable the early warning of potential attacks, and 2) Profiling the large-scale and coordinated cyber attacks through both microscopic and macroscopic modeling to provide better control of such attacks. These approaches are effective in finding weak symptoms caused by the attacks thus can successfully defend against the large-scale and coordinated attacks at their early stages.

Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model

The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and Inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.

Factor analysis of partners' commitment to risk management in public-private partnership projects

Purpose – The purpose of this paper is to investigate and uncover key determinants that could explain partners' commitment to risk management in public-private partnership projects so that partners' risk management commitment is taken into the consideration of optimal risk allocation strategies.

Design/methodology/approach – Based on an extensive literature review and an examination of the purchasing power parity (PPP) market, an industry-wide questionnaire survey was conducted to collect the data for a confirmatory factor analysis. Necessary statistical tests are conducted to ensure the validity of the analysis results.

Findings – The factor analysis results show that the procedure of confirmatory factor analysis is statistically appropriate and satisfactory. As a result, partners' organizational commitment to risk management in public-private partnerships can now be determined by a set of components, namely general attitude to a risk, perceived one's own ability to manage a risk, and the perceived reward for bearing a risk.

Practical implications – It is recommended, based on the empirical results shown in this paper, that, in addition to partners' risk management capability, decision-makers, both from public and private sectors, should also seriously consider partners' risk management commitment. Both factors influence the formation of optimal risk allocation strategies, either by their individual or interacting effects. Future research may therefore explore how to form optimal risk allocation strategies by integrating organizational capability and commitment, the determinants and measurement of which have been established in this study.

Originality/value – This paper makes an original contribution to the general body of knowledge on risk allocation in large-scale infrastructure projects in Australia adopting the procurement method of public-private partnership. In particular, this paper has innovatively established a measurement model of organisational commitment to risk management, which is crucial to determining optimal risk allocation strategies and in turn achieving project success. The score coefficients of all obtained components can be used to construct components by linear combination so that commitment to risk management can be measured. Previous research has barely focused on this topic.

Lessons from large-scale conservation networks in Australia

Australia has seen a rapid growth in the establishment of networks of lands managed for connectivity conservation across tenures, at landscape and sub-continental scales. Such networks go under a variety of names, including biosphere reserves, biolinks, wildlife corridors and conservation management networks. Their establishment has varied from state government-led initiatives to those initiated by non-government organizations and interested landholders. We surveyed existing major landscape scale conservation initiatives for successes, failures and future directions and synthesized common themes. These themes included scale, importance of social and economic networks, leadership, governance, funding, conservation planning, the role of protected areas and communication. We discuss the emergence of national policy relating to National Wildlife Corridors in Australia and the relationship of this policy to the long standing commitment to build a comprehensive, adequate and representative National Reserve System. Finally we outline areas for further research for connectivity conservation projects in Australia.

Community-based efforts to prevent obesity: Australia-wide survey of projects

Issues addressed: 

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.

Nonlinear large deformation dynamic analysis of electroactive polymer actuators

Electroactive polymers have attracted considerable attention in recent years due to their sensing and actuating properties which make them a material of choice for a wide range of applications including sensors, biomimetic robots, and biomedical micro devices. This paper presents an effective modeling strategy for nonlinear large deformation (small strains and moderate rotations) dynamic analysis of polymer actuators. Considering that the complicated electro-chemo-mechanical dynamics of these actuators is a drawback for their application in functional devices, establishing a mathematical model which can effectively predict the actuator's dynamic behavior can be of paramount importance. To effectively predict the actuator's dynamic behavior, a comprehensive mathematical model is proposed correlating the input voltage and the output bending displacement of polymer actuators. The proposed model, which is based on the rigid finite element (RFE) method, consists of two parts, namely electrical and mechanical models. The former is comprised of a ladder network of discrete resistive-capacitive components similar to the network used to model transmission lines, while the latter describes the actuator as a system of rigid links connected by spring-damping elements (sdes). Both electrical and mechanical components are validated through experimental results.