Coherence based histograms for shape retrieval

Histograms have been used for Shape Representation and Retrieval. The drawback of the histograms method is that histograms can be same for dissimilar shapes, which renders the method less effective for retrieval of shapes. In this paper, we describe the concept of coherence. We show how coherence can be used with distance and angular histograms. We perform experiments to test the effectiveness of the proposed method. It is found that coherence improves accuracy of retrieval significantly.

Australian mechanical engineers : industries and attributes

The US and UK have had significant influence on the delivery and content of engineering programs both globally and particularly on Australia and yet the industrial profile of Australia has historically been significantly different from that of the US and UK. This paper first presents a study into the differences in industrial profile in the employment of mechanical engineers in the US, UK and Australia. This paper then presents key findings from a role-based study that identified the relative significance of a broad range of attributes for the most common mechanical engineering roles in the six industries that employ the greatest number of Australian mechanical engineers.

Challenges relating to RFID implementation within the electronic supply chain management - a practical approach

The use of RHFID (Radio Frequency ldentification) technology can be employed for not only reducing companies management costs but also to track uniquely each shipping container, pallet, case, and product being manufactured, shipped and sold, to increase visibility and accountability in the supply chain. RFID technology connects the supply chain players (i.e., suppliers, manifacturers, distributors, retailers and customers) and allows them to exchange data and product information. Despite these potential benefits, there are challenges and obstacles with the deployment of a RFID-enabled system in the global supply chain. The paper outlines the major RFID issues faced by supply chain management. In this paper, we also present a case study on pharmaceutical supply chain management (SCM) applications by addressing and examining the issues of RFID implementation in a SCM system.

Incorporating design explanation within formal object-oriented method (FOOM)

Requirements engineering is a commencing phase in the development of either software applications or information systems. It is concerned with understanding and specifying the customer's requirements of the system to be delivered. Throughout the literature, this is agreed to be one of the most crucial and, unfortunately, problematic phases in development. Despite the diversity of research directions, approaches and methods, the question of process understanding and management is still limited. Among contemporary approaches to the improvement of the current practice of Requirements Engineering, Formal Object-Oriented Method (FOOM) has been introduced as a new promising solution. The FOOM approach to requirements engineering is based on a synthesis of socio-organisational theory, the object-oriented approach, and mathematical formal specification. The entire FOOM specification process is evolutionary and involves a large volume of changes in requirements. During this process, requirements evolve through various forms of informal, semi-formal, and formal while maintaining a semantic link between these forms and, most importantly, conforming to the customer's requirements. A deep understanding of the complexity of the requirements model and its dynamics is critical in improving requirements engineering process management. This thesis investigates the benefits of documenting both the evolution of the requirements model and the rationale for that evolution. Design explanation explains and justifies the deliberations of, and decisions made during, the design activity. In this thesis, design explanation is used to describe the requirements engineering process in order to improve understandability of, and traceability within, the evolving requirements specification. The design explanation recorded during this research project is also useful in assisting the researcher in gaining insights into the creativity and opportunistic characteristics of the requirements engineering process. This thesis offers an interpretive investigation into incorporating design explanation within FOOM in order to extend and advantage the method. The researcher's interpretation and analysis of collected data highlight an insight-driven and opportunistic process rather than a strictly and systematically predefined one. In fact, the process was not smoothly evolutionary, but involved occasional 'crisis' points at which the model was reconceptualised, simplified and restructured. Therefore, contributions of the thesis lie not only in an effective incorporation of design explanation within FOOM, but also a deep understanding of the dynamic process of requirements engineering. The new understanding of the complexity of the requirements model and its dynamics suggests new directions for future research and forms a basis for a new approach to process management.

Key success factors in real-time industrial system assurance

The thesis analyses the issues of implementing real-time software systems in industrial applications. The benefit is the development of the Integrated Mega Project Development Model, to improve the effectiveness of planning and timely delivery of software, the quality of the delivered software and reducing problems associated with integrating software systems.

A performance testing framework for digital forensic tools

This thesis surveys the latest development of digital forensic tools designed for anti-cybercrime purposes. It discusses the necessity of testing the digital forensics tools, and presents a novel testing framework. This new testing framework takes the viewpoint of software vendors rather than traditional software engineering approaches.

Malicious code detection architecture inspired by human immune system

Malicious code is a threat to computer systems globally. In this paper, we outline the evolution of malicious code attacks. The threat is evolving, leaving challenges for attackers to improve attack techniques and for researchers and security specialists to improve detection accuracy. We present a novel architecture for an effective defense against malicious code attack, inspired by the human immune system. We introduce two phases of program execution: Adolescent and Mature Phase. The first phase uses a malware profile matching mechanism, whereas the second phase uses a program profile matching mechanism. Both mechanisms are analogous to the innate immune system

An efficient short certificate-based signature scheme

 FEBRUARY SPECIAL ISSUE : with selected papers from the 23rd Brazilian Symposium on Software Engineering

Factorial multi-task learning : a Bayesian nonparametric approach

Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods. Copyright 2013 by the author(s).

Project and Module Based Teaching and Learning

This paper proposes a new teaching and learning approach-project and module based teaching and learning (PMBTL). The PMBTL approach incorporates the merits of project/problem based and module based learning methods, and overcomes the limitations of these methods. The correlation between teaching, learning, practice and assessment is emphasized in this approach, and new methods have been proposed accordingly. The distinct features of these new methods differentiate the PMBTL approach from conventional teaching approaches. Evaluation of this approach on practical teaching and learning activities demonstrates the effectiveness and stability of the approach in improving the performance and quality of teaching and learning. The approach proposed in this paper is also intuitive to the design of other teaching units.

A genetic fuzzy system to model pedestrian walking path in a built environment

A study on the pedestrian's steering behaviour through a built environment in normal circumstances is presented in this paper. The study focuses on the relationship between the environment and the pedestrian's walking trajectory. Owing to the ambiguity and vagueness of the relationship between the pedestrians and the surrounding environment, a genetic fuzzy system is proposed for modelling and simulation of the pedestrian's walking trajectory confronting the environmental stimuli. We apply the genetic algorithm to search for the optimum membership function parameters of the fuzzy model. The proposed system receives the pedestrian's perceived stimuli from the environment as the inputs, and provides the angular change of direction in each step as the output. The environmental stimuli are quantified using the Helbing social force model. Attractive and repulsive forces within the environment represent various environmental stimuli that influence the pedestrian's walking trajectory at each point of the space. To evaluate the effectiveness of the proposed model, three experiments are conducted. The first experimental results are validated against real walking trajectories of participants within a corridor. The second and third experimental results are validated against simulated walking trajectories collected from the AnyLogic® software. Analysis and statistical measurement of the results indicate that the genetic fuzzy system with optimised membership functions produces more accurate and stable prediction of heterogeneous pedestrians' walking trajectories than those from the original fuzzy model. © 2014 Elsevier B.V. All rights reserved.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.