Tablet PCs are a new generation of notebook computers which provide multimodal input options of p... more Tablet PCs are a new generation of notebook computers which provide multimodal input options of pen, voice and keyboard. Recently, these portable and flexible Tablet PCs have attracted attention as a potential tool in academic environments. This paper reviews the current use of Tablet PCs in teaching computer science and software engineering courses, presenting lectures and papers, and creating peer-review comments. The paper also presents applications of Tablet PCs in teaching and research at our university, the University of Canberra, Australia. These applications include marking assignments and reports, and developing signature verification applications.
2008 IEEE International Conference on Intelligence and Security Informatics, 2008
The 3 most important issues for anomaly detection based intrusion detection systems by using data... more The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of the dataset into 4 groups: Group I contains the basic network traffic features; Group II is actually not network traffic related, but the features collected from hosts; Group III and IV are temporally aggregated features. In this paper, we demonstrate the different detection rates of choosing the different combinations of these groups. We also demonstrate the effectiveness and the ineffectiveness in finding anomalies by looking at the network data alone. In addition, we also briefly investigate the effectiveness of data normalization. To validate our findings, we conducted the same experiments with 3 different clustering algorithms-K-means clustering, fuzzy C means clustering (FCM), and fuzzy entropy clustering (FE).
A dynamic approach to truck scheduling using an intelligent distributed multi-agent architecture ... more A dynamic approach to truck scheduling using an intelligent distributed multi-agent architecture is presented. This paper presents an inherently distributed problem, the dynamic truck scheduling problem which has motivated the current study on an intelligent multi-agent architecture for optimal real time solutions. The .Net environment is critiqued for the proposed multi-agent architecture. A design and implementation of a prototype system is presented and some test results discussed. Possible future extensions to the system to cater for communication through hand-held devices for mobile agents required in the truck scheduling problem are also summarized.
Adaptable Term Weighting Framework for Text Classification
Lecture Notes in Computer Science, 2011
In text classification, term frequency and term co-occurrence factors are dominantly used in weig... more In text classification, term frequency and term co-occurrence factors are dominantly used in weighting term features. Category relevance factors have recently been used to propose term weighting approaches. However, these approaches are mainly based on their own-...
Grammatical Dependency-Based Relations for Term Weighting in Text Classification
Lecture Notes in Computer Science, 2011
Term frequency and term co-occurrence are currently used to estimate term weightings in a documen... more Term frequency and term co-occurrence are currently used to estimate term weightings in a document. However these methods do not employ relations based on grammatical dependency among terms to measure dependency between word features. In this paper, ...
2009 Third International Conference on Network and System Security, 2009
This paper presents a multi-agent security architecture, which utilizes the agent characteristics... more This paper presents a multi-agent security architecture, which utilizes the agent characteristics to cater for security processes in online communications. The Multilayer Communication approach (MLC) [1] is used to determine the security processes, which uses cryptography protocols to secure data and communication channel. Agents are skilled to perform certain tasks. At the Sender's host, agents interact with each other to secure a message to be sent to the Recipient, including encryption, digital signature, and hash code. A mobile agent is used to carry the encrypted messages as well as the agent's code to the Recipient's host. Our approach also provides mechanisms to verify the authenticity, confidentiality and the integrity of the code and data that arrived at the Recipient's host. The message and the code are authenticated, the code is executed to perform tasks to recover the plaintext.
2009 IEEE-RIVF International Conference on Computing and Communication Technologies, 2009
This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extrac... more This paper presents a novel fuzzy subspace-based approach to hidden Markov model. Features extracted from patterns are considered as feature vectors in a multi-dimensional feature space. Current hidden Markov modeling techniques treat features equally, however this assumption may not be true. We propose to consider subspaces in the feature space and assign a weight to each feature to determine the contribution of that feature in different subspaces to modeling and recognizing patterns. Weights can be computed if a learning estimation method such as maximum likelihood is given. Experimental results in network intrusion detection based on the proposed approach show promising results.
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
ABSTRACT In a dynamic operational environment such as robotic or an autonomous navigation system,... more ABSTRACT In a dynamic operational environment such as robotic or an autonomous navigation system, the interactions between humans and objects around them play an important role (context-awareness). The task of recognizing and tracking such objects introduces many challenges in the machine vision research field. In this paper, we propose a novel method that combines the information from modern depth sensors with conventional machine vision techniques such as Scale-invariant Feature Transform (SIFT) to produce a system that is capable of performing object recognition and tracking with a satisfactory level of accuracy in real-time. A prototype is implemented and tested to confirm that the proposed method does provide better performance comparing with currently used methods in image processing.
Classification of gender and face based on gradient faces
3rd European Workshop on Visual Information Processing, 2011
This paper presents a new method for solving face gender identification and face classification p... more This paper presents a new method for solving face gender identification and face classification problems. The proposed method uses gradient features for feature extraction and support vector machine for classification. Experiments for the proposed method have been conducted on two public data sets CalTech and AT&T. The results show that the proposed method could improve the classification rates.
Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems
Lecture Notes in Computer Science, 2012
ABSTRACT We investigate time domain parameters called high order moments in functional Near Infra... more ABSTRACT We investigate time domain parameters called high order moments in functional Near Infrared Spectroscopy (fNIRS) signal and propose to use them as new brain features in fNIRS-based Brain Computer Interface (BCI) research. These high order moments are well appropriate with fNIRS data without any special preprocessing or filtering step. Therefore, they could be used to guide users in feedback fNIRS-based BCI experiments. We performed experiments on motor imagery and person identification problems with the 2nd order moment, 4th order moment and a combination of these moments. Experimental results showed that these features provided high accuracy. For motor imagery problem, our system could achieve accuracy up to 99.5% for subject independent problem and varies between 86.5α5.4% and 97.0α2.1% for subject dependent problem. For person identification problem, our system could achieve accuracy nearly 100%. Comparing with other systems that used non-filtered raw signal as feature, these features are more stable than the raw signal because of noise reduction. We also found that the 2nd order moment alone could be an excellent and efficient feature for fNIRS-based BCI systems.
Multi-sphere Support Vector Data Description (MS-SVDD) has been proposed in our previous work. MS... more Multi-sphere Support Vector Data Description (MS-SVDD) has been proposed in our previous work. MS-SVDD aims to build a set of spherically shaped boundaries that provide a better data description to the normal dataset and an iterative learning algorithm that determines the set of spherically shaped boundaries. MS-SVDD could improve classification rate for oneclass classification problems comparing with SVDD. However MS-SVDD requires a small abnormal data set to build the spherically shaped boundaries for the normal data set. In this paper, we propose a new fuzzy MS-SVDD that can be used when only the normal data set is available. Experimental results on 14 well-known datasets and a comparison between fuzzy MS-SVDD and SVDD are also presented.
Fuzzy feature weighting techniques for vector quantisation
International Conference on Fuzzy Systems, 2010
ABSTRACT Vector quantization (VQ) is a simple but effective modelling technique in pattern recogn... more ABSTRACT Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ employs a clustering technique to convert a feature vector set in to a cluster center set to model the feature vector set. Some clustering techniques have been applied to improve VQ. However VQ is not always effective because data features are treated equally although their importance may not be the same. Some automated feature weighting techniques have been proposed to overcome this drawback. This paper reviews those weighting techniques and proposes a general scheme for selecting any pair of clustering and feature weighting techniques to form a fuzzy feature weighting-based VQ modelling technique. Besides the current techniques, a number of new feature weighting-based VQ techniques is proposed and their evaluations are also presented.
2014 IEEE Fifth International Conference on Communications and Electronics (ICCE), 2014
In this paper, design of chaos-based 4 × 4-bit substitution box (S-box) is presented. The chaotic... more In this paper, design of chaos-based 4 × 4-bit substitution box (S-box) is presented. The chaotic 4 × 4-bit S-box provides good cryptographic properties and has hardware efficiency. The proposed chaotic 4×4-bit S-box is used for design of chaotic S-boxes chaining layer, which offers a highly secure level. The result of implementation shows that the proposed chaotic 4×4-bit S-box is suitable for the lightweight block cipher due to low resource utilization.
In state mixture modelling (SMM), the temporal structure of the observation sequences is represen... more In state mixture modelling (SMM), the temporal structure of the observation sequences is represented by the state joint probability distribution where mixtures of states are considered. This technique is considered in an iterative scheme via maximum likelihood estimation. A fuzzy estimation approach is also introduced to cooperate with the SMM model. This new approach not only saves calculations from 2x (HMM direct calculation) and x 2 (Forward± backward algorithm) to just only 2NT calculations, but also achieves a better recognition result.
13th Australasian International Conference on Speech Science and Technology, 2010
Under an ARC Linkage Infrastructure, Equipment and Facilities (LIEF) grant, speech science and te... more Under an ARC Linkage Infrastructure, Equipment and Facilities (LIEF) grant, speech science and technology experts from across Australia have joined forces to organise the recording of audio-visual (AV) speech data from representative speakers of Australian English in all capital cities and some regional centres. The Big Australian Speech Corpus (the Big ASC) will provide a standard recording setup and a collaboratively-designed elicitation protocol to create a corpus of AV speech data incorporating annotations and ...
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