On work in progress optimisation

In this paper, we investigate how to best optimise the level of work in progress (WIP) in a real world factory. Using a simulation model of the factory, we show that an optimum level of WIP can be attained. By systematically varying the maximum allowable level of WIP within different model runs, results show that the throughput reaches a high level very quickly and then tapers off. The production lead times, in contrast, begin at relatively low levels and increase after the optimum WIP level has been reached.

The pubstro phenomenon : Robin Hoods of the internet

This paper presents a case study of a compromised Web server that was being used to distribute illegal 'warez'. The mechanism by which the server was compromised is discussed as if the way in which it was found. The hacker organisations that engage in these activities are viewed as a Virtual Community and their rules and code of ethics investigated.

Hybrid ant colony algorithm for texture classification

We present a novel ant colony algorithm integrating genetic algorithms and simplex algorithms. This method is able to not only speed up searching process for optimal solutions, but also improve the quality of the solutions. The proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on real world images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

A computational framework for nucleic acid sub-sequence identification

Identification of nucleic acid sub-sequences within larger background sequences is a fundamental need of the biology community. The applicability correlates to research studies looking for homologous regions, diagnostic purposes and many other related activities. This paper serves to detail the approaches taken leading to sub-sequence identification through the use of hidden Markov models and associated scoring optimisations. The investigation of techniques for locating conserved basal promoter elements correlates to promoter thus gene identification techniques. The case study centred on the TATA box basal promoter element, as such the background is a gene sequence with the TATA box the target. Outcomes from the research conducted, highlights generic algorithms for sub-sequence identification, as such these generic processes can be transposed to any case study where identification of a target sequence is required. Paths extending from the work conducted in this investigation have led to the development of a generic framework for the future applicability of hidden Markov models to biological sequence analysis in a computational context.

Concept learning of text documents

Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A naïve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods.

Classification of self-consumable highlights for soccer video summaries

An effective scheme for soccer summarization is significant to improve the usage of this massively growing video data. The paper presents an extension to our recent work which proposed a framework to integrate highlights into play-breaks to construct more complete soccer summaries. The current focus is to demonstrate the benefits of detecting some specific audio-visual features during play-break sequences in order to classify highlights contained within them. The main purpose is to generate summaries which are self-consumable individually. To support this framework, the algorithms for shot classification and detection of near-goal and slow-motion replay scenes is described. The results of our experiment using 5 soccer videos (20 minutes each) show the performance and reliability of our framework.

The cost of recovery protocols in web-based database systems

The cost of recovery protocols is important with respect to system performance during normal operation and failure in terms of overhead, and time taken to recover failed transactions. The cost of recovery protocols for web database systems has not been addressed much. In this paper, we present a quantitative study of cost of recovery protocols. For this purpose, we use an experiment setup to evaluate the performance of two recovery algorithms, namely the, two-phase commit algorithm and log-based algorithm. Our work is a step towards building reliable protocols for web database systems.

Unifying manufacturing simulation models using HLA

The increasing use of simulation in manufacturing has seen an increase in simulation models created using many simulation package. This use of different simulators can create simulation islands in a manufacturing factory, making it difficult to get a true simulated overview of the factory. At present, there are only a few cases where manufacturing simulations have been linked to enable multiple simulation models to run as one. This research expands upon these cases. For this paper the topic of discussion is the research in connecting different 'Commercial Off The Shelf' simulators together to allow flow of all information through the connected models using high level architecture.

A novel middleware based web database model

In this paper, we propose a novel model for web-based database systems based on the multicast and anycast' protocols. In the model, we design a middleware, castway, which locates between the database server and the Web server. Every castway in a distributed system operates as a multicast node and an anycast node independently, respectively. The proposed mechanism can balance the workload among the distributed database servers, and offers the "best" server to serve for a query. Three algorithms are employed for the model: the requirement-based probing algorithm for anycast routing, the atomic multicast update algorithm for database synchronization, and the job deviation algorithm for system workload balance. The simulations and experiments show that the proposed model works very well.

Load balance algorithms for anycast

Increasingly, replicated anycast servers are being used to deliver network applications and service ever increasing user requests. Therefore, the strategies used to guarantee network bandwidth prerequisites and perform load balancing across the nodes of an anycast group are critical to the performance of online applications. In this paper, we model user requests, network congestion and latency, and server load using a combination of hydro-dynamics and queuing theory to develop an efficient job distribution strategy. Current, anycast research does not explicitly consider the system load of nodes within an anycast groups when distributing requests. Therefore, the performance of a heavily loaded anycast system can quickly become congested and uneven as jobs are routed to closely linked nodes which are already saturated with requests. In comparison, the nodes of further away systems remain relatively unused because of other issues such as network bandwidth and latency during these times. Our system redirects requests from busy systems to the idle, remotely linked nodes, to process requests faster in spite of slower network access. Using an empirical study, we show this technique can improve request performance, and throughput with minimal network probing overhead.

Applying a reconfigurable multi-agent scheduler to product distribution

More intelligent scheduling methods are required for manufacturing scheduling due to the move to more agile systems. Multi-agent methods are one such approach. This paper describes the application of a reconfigurable multi-agent scheduler to the problem of allocating orders to warehouses in a distribution supply chain. This multi-agent system was originally developed for allocation of orders to machines in a highly reconfigurable manufacturing system and this work was aimed at investigating the ease of applying this same scheduler to other problems. It was found that this new application was readily achieved because of the modular structure of the scheduler. This paper shows how the application to the new problem was achieved.

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.

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

Supporting disconnected operations in mobile computing

Mobile computing has enabled users to seamlessly access databases even when they are on the move. However, in the absence of readily available high-quality communication, users are often forced to operate disconnected from the network. As a result, software applications have to be redesigned to take advantage of this environment while accommodating the new challenges posed by mobility. In particular, there is a need for replication and synchronization services in order to guarantee availability of data and functionality, (including updates) in disconnected mode. To this end we propose a scalable and highly available data replication and management service. The proposed replication technique is compared with a baseline replication technique and shown to exhibit high availability, fault tolerance and minimal access times of the data and services, which are very important in an environment with low-quality communication links.

Novel data management algorithms in peer-to-peer content distribution networks

The peer-to-peer content distribution network (PCDN) is a hot topic recently, and it has a huge potential for massive data intensive applications on the Internet. One of the challenges in PCDN is routing for data sources and data deliveries. In this paper, we studied a type of network model which is formed by dynamic autonomy area, structured source servers and proxy servers. Based on this network model, we proposed a number of algorithms to address the routing and data delivery issues. According to the highly dynamics of the autonomy area, we established dynamic tree structure proliferation system routing, proxy routing and resource searching algorithms. The simulations results showed that the performance of the proposed network model and the algorithms are stable.