A parallel Euler approach for large-scale biological sequence assembly

Biological sequence assembly is an essential step for sequencing the genomes of organisms. Sequence assembly is very computing intensive especially for the large-scale sequence assembly. Parallel computing is an effective way to reduce the computing time and support the assembly for large amount of biological fragments. Euler sequence assembly algorithm is an innovative algorithm proposed recently. The advantage of this algorithm is that its computing complexity is polynomial and it provides a better solution to the notorious “repeat” problem. This paper introduces the parallelization of the Euler sequence assembly algorithm. All the Genome fragments generated by whole genome shotgun (WGS) will be assembled as a whole rather than dividing them into groups which may incurs errors due to the inaccurate group partition. The implemented system can be run on supercomputers, network of workstations or even network of PC computers. The experimental results have demonstrated the performance of our system.

A defense system against DDoS attacks by large-scale IP traceback

In this paper, we present a new approach, called Flexible Deterministic Packet Marking (FDPM), to perform a large-scale IP traceback to defend against Distributed Denial of Service (DDoS) attacks. In a DDoS attack the victim host or network is usually attacked by a large number of spoofed IP packets coming from multiple sources. IP traceback is the ability to trace the IP packets to their sources without relying on the source address field of the IP header. FDPM provides many flexible features to trace the IP packets and can obtain better tracing capability than current IP traceback mechanisms, such as Probabilistic Packet Marking (PPM), and Deterministic Packet Marking (DPM). The flexibilities of FDPM are in two ways, one is that it can adjust the length of marking field according to the network protocols deployed; the other is that it can adjust the marking rate according to the load of participating routers. The implementation and evaluation demonstrates that the FDPM needs moderately only a small number of packets to complete the traceback process; and can successfully perform a large-scale IP traceback, for example, trace up to 110,000 sources in a single incident response. It has a built-in overload prevention mechanism, therefore this scheme can perform a good traceback process even it is heavily loaded.

Security in the online e-learning environment

This paper addresses the role of security in the collaborative e-learning environment, and in particular, the social aspects of security and the importance of identity. It represents a case study, completed in Nov 2004, which was conducted to test the sense of security that students experienced whilst using the wiki platform as a means of online collaboration in the tertiary education environment. Wikis, fully editable Web sites, are easily accessible, require no software and allow its contributors (in this case students) to feel a sense of responsibility and ownership. A comparison between two wiki studies will be made whereby one group employed user login and the other maintained anonymity throughout the course of the study. The results consider the democratic participation and evolution of the work requirements over time, which in fact ascertains the nonvalidity of administrative identification.

Employing wikis for online collaboration in the e-learning environment: case study

This paper examines the various ways in which students reflect on their very recent experiences in collaborating in an online e-learning environment. Wikis, fully editable Websites, are easily accessible, require no software and allow its contributors, in these case students, to feel a sense of responsibility and ownership. Wikis are everywhere, but, unfortunately, the online literature has not yet begun to focus enough on wikis (Mattison 2003). Whereas students are used to the WebCT based university Elearning environment, Deakin Studies Online (DSO), this case study, completed in Nov 2004, was conducted to test the wiki platform as a means of online collaboration in the tertiary education environment. A full analysis of the results is presented, as are recommendations for improving the platform in an effort to employ wikis and utilize them to their full and absolute potential.

Mark-aided distributed filtering by using neural network for DDoS defense

Currently Distributed Denial of Service (DDoS) attacks have been identified as one of the most serious problems on the Internet. The aim of DDoS attacks is to prevent legitimate users from accessing desired resources, such as network bandwidth. Hence the immediate task of DDoS defense is to provide as much resources as possible to legitimate users when there is an attack. Unfortunately most current defense approaches can not efficiently detect and filter out attack traffic. Our approach is to find the network anomalies by using neural network, deploy the system at distributed routers, identify the attack packets, and then filter them. The marks in the IP header that are generated by a group of IP traceback schemes, Deterministic Packet Marking (DPM)/Flexible Deterministic Packet Marking (FDPM), assist this process of identifying attack packets. The experimental results show that this approach can be used to defend against both intensive and subtle DDoS attacks, and can catch DDoS attacks’ characteristic of starting from multiple sources to a single victim. According to results, we find the marks in IP headers can enhance the sensitivity and accuracy of detection, thus improve the legitimate traffic throughput and reduce attack traffic throughput. Therefore, it can perform well in filtering DDoS attack traffic precisely and effectively.

Spam filtering based on preference ranking

When the average number of spam messages received is continually increasing exponentially, both the Internet service provider and the end user suffer. The lack of an efficient solution may threaten the usability of the email as a communication means. In this paper we present a filtering mechanism applying the idea of preference ranking. This filtering mechanism will distinguish spam emails from other email on the Internet. The preference ranking gives the similarity values for nominated emails and spam emails specified by users, so that the ISP/end users can deal with spam emails at filtering points. We designed three filtering points to classify nominated emails into spam email, unsure email and legitimate email. This filtering mechanism can be applied on both middleware and at the client-side. The experiments show that high precision, recall and TCR (total cost ratio) of spam emails can be predicted for the preference based filtering mechanisms.

A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

Inhibitors and facilitators for mobile payment adoption in Australia: a preliminary study

With the seamless diffusion and acceptance of mobile phones into people's everyday life as trusted communication devices, businesses have begun exploring their potential as payment devices. In this paper, we report on the findings from a pioneering study conducted in Australia, which explored inhibitors and facilitators in the adoption of mobile phones as payment devices.

Intelligent headrest

In this paper we discuss the design and development of a novel intelligent headrest system. Developed to reduce neck injuries resulting from up to 63% of rear end accidents, this system uses inductive sensing technology to establish the position of a driver or passenger's skull in a vehicle. Once detected, the system autonomously places the vehicle's headrest in a position that best support an occupant's head in the case of an accident. Sensor construction, mechatronic design and controller selection and real world tests of the system under various conditions are covered.

Linking discrete event simulation models using HLA

The increasing usage of discrete event simulation packages for modeling and analyzing manufacturing and logistics has led to a need for connecting simulation models together at runtime. One such methodology for linking discrete event simulation models together has been developed for this research and this paper demonstrates the usage of this linking method. A unified simulation model is developed from two submodels developed using different simulation packages.

Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

Robust parallel filtering for mobile agent tracking

In this paper we develop a robust method of target/mobile agent tracking involving two independent estimators with separate measurement systems. The outputs of the two estimators are combined using simple trigonometry (post-estimation data fusion) and provide a robust and reliable tracking path. We demonstrate that through the use of recent advances in robust set-value state estimation, our robust parallel filter approach performs well even when the individual filters do not. Brief comparisons with common data fusion methods are conducted in order to demonstrate the advantages of our parallel (post-estimation fusion) approach

A new blind signal separation algorithm for instantaneous MIMO system

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. In this paper, we propose a new constant modulus (CM)-based algorithm which employ nonlinear function as the de-correlation term. Moreover, it is shown by theoretical analysis that the proposed algorithm has less mean square error (MSE), i.e., better separation performance, in steady state than the cross-correlation and constant modulus algorithm (CC-CMA). Numerical simulations show the effectiveness of the proposed result.

Location based power control for energy critical sensors in a disconnected network

This paper provides a location based power control strategy for disconnected sensory nodes deployed for long term service. Power conservation is of importance particularly when sensors communicate with a mobile robot used for data collection. The proposed algorithm uses estimations from a Robust Extended Kalman Filter (REKF) with RSSI measurements, in implementing a sigmoid function based power control algorithm which essentially approaches a desired power emission trajectory based on carrier-to-interference ratios(CIR) to ensure interferenceless reception. The more realistic modelling we use incorporates physical dynamics between the mobile robot and the sensors together with the wireless propagation parameters between the transmitter and receiver to formulate a sophisticated and effective power control strategy for the exclusive usage of energy critical disconnected nodes in a sensory network increasing their life span.