Very Massive Stars and the Eddington Limit

We use contemporary evolutionary models for Very Massive Stars (VMS) to assess whether the Eddington limit constrains the upper stellar mass limit. We also consider the interplay between mass and age for the wind properties and spectral morphology of VMS, with reference to the recently modified classification scheme for O2-3.5If*/WN stars. Finally, the death of VMS in the local universe is considered in the context of pair instability supernovae.Comment: 6 pages, 4 figures, from "Four Decades of Massive Star Research" (Quebec, Jul 2011), ASP Conf Ser, in press (L. Drissen, C. Robert, N. St-Louis, A.F.J. Moffat, eds.

Practical Training and the Audit Expectations Gap:The Case of Accounting Undergraduates of Universiti Utara Malaysia

The accounting profession has long faced the issue of an audit expectations gap; being the gap between the quality of the profession’s performance, its objectives and results, and that which the society expects. The profession believes that the gap could be reduced over time through education. Studies have been carried out overseas and in Malaysia to determine the effect of education in narrowing the audit expectations gap. Extending the knowledge acquired, this paper investigates whether academic internship programs could reduce the audit expectations gap in Malaysia. Using a pre-post method, the research instrument adapted from Ferguson et al. (2000) is administered to the Universiti Utara Malaysia’s accounting students at the beginning and end of their internship program. The results show there is a significant change in perceptions among students after the internship program. However, changes in perceptions do not warrant an internship program as a means of reducing the audit expectations gap as misperceptions are still found among respondents on issues of auditing after the completion of the internship program. Nevertheless, an internship program can still be used to complement audit education in a university as it is an ideal way to expose students to professional issues and enables them to have a better insight of the actual performance and duties of auditors

A modified kohonen self-organizing map (KSOM) clustering for four categorical data

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified

Traffic sign detection based on simple XOR and discriminative features

Traffic Sign Detection (TSD) is an important application in computer vision. It plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. In this paper, in addition to detecting Traffic Signs (TSs), the proposed technique also recognizes the shape of the TS. The proposed technique consist of two stages. The first stage is an image segmentation technique that is based on Learning Vector Quantization (LVQ), which divides the image into six different color regions. The second stage is based on discriminative features (area, color, and aspect ratio) and the exclusive OR logical operator (XOR). The output is the location and shape of the TS. The proposed technique is applied on the German Traffic Sign Detection Benchmark (GTSDB), and achieves overall detection and shape matching of around 97% and 100% respectively. The testing speed is around 0.8 seconds per image on a mainstream PC, and the technique is coded using the Matlab toolbox

Modeling And Simulation Of Micro-Manipulator Robotic System For Neurosurgery.

This research focuses on modeling and control simulation of a micro-manipulator robotic system model for neurosurgery application

Mapping Process of Digital Forensic Investigation Framework

Digital forensics is essential for the successful prosecution of digital criminals which involve diverse digital devices such as computer system devices, network devices, mobile devices and storage devices. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court of law. Therefore, for digital forensic investigation to be performed successfully, there are a number of important steps that have to be taken into consideration. The aim of this paper is to produce the mapping process between the processes/activities and output for each phase in Digital Forensic Investigation Framework (DFIF). Existing digital forensic frameworks will be reviewed and then the mapping is constructed. The result from the mapping process will provide a new framework to optimize the whole investigation process

Implementation of Identity Based Encryption in e-Voting System

This paper explains about the design and implementation of Identity Based Encryption (IBE) in web-based Voting application. IBE is a completely new approach to the problem of encryption which was found on the traditional Public Key Infrastructure. It can be used on any arbitrary string as a public key, enabling data to be protected without the need for certificates and reduction of infrastructure cost due to certificates database maintenance. Protection implemented in this application is a key server that controls the mapping of identities to decryption keys where the key is only one-time pad implementation. By using the IBE technique, authentication and security can be preserved in the web-based Voting application where it provides integrity, authenticity, anonymity and confidentiality in this application

Intrusion Alert Correlation Technique Analysis for Heterogeneous Log

Intrusion alert correlation is multi-step processes that receives alerts from heterogeneous log resources as input and produce a high-level description of the malicious activity on the network. The objective of this study is to analyse the current alert correlation technique and identify the significant criteria in each technique that can improve the Intrusion Detection System(IDS) problem such as prone to alert flooding, contextual problem, false alert and scalability. The existing alert correlation techniques had been reviewed and analysed. From the analysis, six capability criteria have been identified to improve the current alert correlation technique. They are capability to do alert reduction, alert clustering,identify multistep attack, reduce false alert, detect known attack and detect unknown attack