Wanli Ma - Profile on Academia.edu (original) (raw)

Papers by Wanli Ma

Tensor Decomposition of Dense SIFT Descriptors in Object Recognition

From hypertext to flat text: a tool for document construction

Multran| A coordination programming language using multiset and transactions

On providing temporal semantics for the GAMMA programming model

A general aggregate model for improving multi-class brain-computer interface systems' performance

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013

ABSTRACT This paper proposes a general aggregate model for improving performance of multi-class B... more ABSTRACT This paper proposes a general aggregate model for improving performance of multi-class Brain-Computer Interface (BCI) systems. In BCI systems, activation and delay are well known issues in conducting experiments. The delay of meaningful brain signal depends on subjects, tasks and experimental design. Therefore, within a trial it is not easy to identify where meaningful brain signal starts and ends. Most of current methods estimate the delay and extract a portion of meaningful brain signal in a trial and use this signal as a representative for the whole trial. Instead of doing so, our proposed aggregate model divides a trial into overlapping frames and treat them equally. These frames are classified and their results are then aggregated together to form classification result of the trial. From the general aggregate model, we derive two specific aggregate models using two state-of-the-art Common Spatial Patterns (CSP)-based methods for feature extraction. We performed experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed models. This dataset was designed for motor imagery classification with 4 classes. Preliminary experimental results show that our proposed aggregate models are up to 8% better than the original CSP-based methods. Furthermore, we show that our aggregate model can be easily extended to online BCI systems.

Graphical assistance in parallel program development

Proceedings of 1994 IEEE Symposium on Visual Languages, 1994

Researchers have proposed many visualisation tools that assist the development of parallel progra... more Researchers have proposed many visualisation tools that assist the development of parallel programs. A number of graph formalisms or notations - which we will call graph models - have been used to visualise various aspects of parallel programs and their executions. This paper attempts to classify and compare these graph models which provide different information at different stages of parallel

Object recognition in a context-aware application

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.

EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features

Lecture Notes in Computer Science, 2013

Improved HOG Descriptors in Image Classification with CP Decomposition

Lecture Notes in Computer Science, 2013

Investigating the impacts of epilepsy on EEG-based person identification systems

2014 International Joint Conference on Neural Networks (IJCNN), 2014

A Study on the Feasibility of Using EEG Signals for Authentication Purpose

Lecture Notes in Computer Science, 2013

Maximal margin learning vector quantisation

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013

ABSTRACT Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalis... more ABSTRACT Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method.

EEG-Based User Authentication Using Artifacts

Advances in Intelligent Systems and Computing, 2014

ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user aut... more ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user authentication with many security advantages of being difficult to fake, impossible to observe or intercept, unique, and alive person recording require. The difficulty is that EEG signals are very weak and subject to the contamination from many artifact signals. However, for the applications in human health, true EEG signals, without the contamination, is highly desirable, but for the purposes of authentication, where stable and repeatable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose an EEG-based authentication method, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements. These tasks can be single or combined, depending on the level of security required. Our experiment showed that using EEG artifacts for user authentication in multilevel security systems is promising.

EEG-Based User Authentication in Multilevel Security Systems

Lecture Notes in Computer Science, 2013

Multi-factor EEG-based user authentication

2014 International Joint Conference on Neural Networks (IJCNN), 2014

ICTACS 2006 - Proceedings of the First International Conference on Theories and Applications of Computer Science 2006, 2007

Graph algorithms have several important applications in fields such as computer science, software... more Graph algorithms have several important applications in fields such as computer science, software engineering, mathematics, engineering, business, and bioinformatics. Researchers and practitioners in these disciplines often need to experiment with empirical data about graphs to gain deeper insights into their properties, which may lead to general proofs. Students also need to learn and be able to implement graph algorithms for their applications. However, these individuals often have varying backgrounds and training, and they may not have a working knowledge of programming tools to implement graph algorithms. The goal of our research is to create a software system which allows users to easily create and implement graph algorithms through a simple graphical user interface, without any coding. Towards this goal, we have developed several systems that can be used to draw and manipulate graphs as well as to execute graph algorithms. The development of the general system raises a number of interesting research questions that we will discuss. To illustrate the need for experimentation with different graph algorithms, we describe some examples of our research in different areas of graph theory and computational geometry. We also describe some features of our systems that we have found useful in teaching graph theory.

TABLET PC APPLICATIONS IN AN ACADEMIC ENVIRONMENT

ICTACS 2006 - Proceedings of the First International Conference on Theories and Applications of Computer Science 2006, 2007

A Proposed Statistical Model for Spam Email Detection

ICTACS 2006 - Proceedings of the First International Conference on Theories and Applications of Computer Science 2006, 2007

The keyword list-based spam email detection system uses keywords in a blacklist to detect spam em... more The keyword list-based spam email detection system uses keywords in a blacklist to detect spam emails. To avoid detection, keywords are written as misspellings, for example "virrus", "vi-rus" and "viruus" instead of "virus". The system needs to update the blacklist from time ...

A study on the feature selection of network traffic for intrusion detection purpose

2008 IEEE International Conference on Intelligence and Security Informatics, 2008

Abstract—The 3 most important issues for anomaly detection based intrusion detection systems by u... more Abstract—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 ...

Using Windows Printer Drivers for Solaris Applications – An Application of Multiagent System

Lecture Notes in Computer Science, 2006

Abstract. This paper proposes using multiagent system technology to solve the problem of printing... more Abstract. This paper proposes using multiagent system technology to solve the problem of printing across heterogeneous operating systems without re-implementing printer drivers. The printing problem comes from a real world application, where we have to print from Solaris ...

Tensor Decomposition of Dense SIFT Descriptors in Object Recognition

From hypertext to flat text: a tool for document construction

Multran| A coordination programming language using multiset and transactions

On providing temporal semantics for the GAMMA programming model

A general aggregate model for improving multi-class brain-computer interface systems' performance

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013

ABSTRACT This paper proposes a general aggregate model for improving performance of multi-class B... more ABSTRACT This paper proposes a general aggregate model for improving performance of multi-class Brain-Computer Interface (BCI) systems. In BCI systems, activation and delay are well known issues in conducting experiments. The delay of meaningful brain signal depends on subjects, tasks and experimental design. Therefore, within a trial it is not easy to identify where meaningful brain signal starts and ends. Most of current methods estimate the delay and extract a portion of meaningful brain signal in a trial and use this signal as a representative for the whole trial. Instead of doing so, our proposed aggregate model divides a trial into overlapping frames and treat them equally. These frames are classified and their results are then aggregated together to form classification result of the trial. From the general aggregate model, we derive two specific aggregate models using two state-of-the-art Common Spatial Patterns (CSP)-based methods for feature extraction. We performed experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed models. This dataset was designed for motor imagery classification with 4 classes. Preliminary experimental results show that our proposed aggregate models are up to 8% better than the original CSP-based methods. Furthermore, we show that our aggregate model can be easily extended to online BCI systems.

Graphical assistance in parallel program development

Proceedings of 1994 IEEE Symposium on Visual Languages, 1994

Researchers have proposed many visualisation tools that assist the development of parallel progra... more Researchers have proposed many visualisation tools that assist the development of parallel programs. A number of graph formalisms or notations - which we will call graph models - have been used to visualise various aspects of parallel programs and their executions. This paper attempts to classify and compare these graph models which provide different information at different stages of parallel

Object recognition in a context-aware application

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.

EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features

Lecture Notes in Computer Science, 2013

Improved HOG Descriptors in Image Classification with CP Decomposition

Lecture Notes in Computer Science, 2013

Investigating the impacts of epilepsy on EEG-based person identification systems

2014 International Joint Conference on Neural Networks (IJCNN), 2014

A Study on the Feasibility of Using EEG Signals for Authentication Purpose

Lecture Notes in Computer Science, 2013

Maximal margin learning vector quantisation

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013

ABSTRACT Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalis... more ABSTRACT Kernel Generalised Learning Vector Quantisation (KGLVQ) was proposed to extend Generalised Learning Vector Quantisation into the kernel feature space to deal with complex class boundaries and thus yielded promising performance for complex classification tasks in pattern recognition. However KGLVQ does not follow the maximal margin principle, which is crucial for kernel-based learning methods. In this paper we propose a maximal margin approach (MLVQ) to the KGLVQ algorithm. MLVQ inherits the merits of KGLVQ and also follows the maximal margin principle to improve the generalisation capability. Experiments performed on the well-known data sets available in UCI repository show promising classification results for the proposed method.

EEG-Based User Authentication Using Artifacts

Advances in Intelligent Systems and Computing, 2014

ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user aut... more ABSTRACT Recently, electroencephalography (EEG) is considered as a new potential type of user authentication with many security advantages of being difficult to fake, impossible to observe or intercept, unique, and alive person recording require. The difficulty is that EEG signals are very weak and subject to the contamination from many artifact signals. However, for the applications in human health, true EEG signals, without the contamination, is highly desirable, but for the purposes of authentication, where stable and repeatable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose an EEG-based authentication method, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements. These tasks can be single or combined, depending on the level of security required. Our experiment showed that using EEG artifacts for user authentication in multilevel security systems is promising.

EEG-Based User Authentication in Multilevel Security Systems

Lecture Notes in Computer Science, 2013

Multi-factor EEG-based user authentication

2014 International Joint Conference on Neural Networks (IJCNN), 2014

ICTACS 2006 - Proceedings of the First International Conference on Theories and Applications of Computer Science 2006, 2007

Graph algorithms have several important applications in fields such as computer science, software... more Graph algorithms have several important applications in fields such as computer science, software engineering, mathematics, engineering, business, and bioinformatics. Researchers and practitioners in these disciplines often need to experiment with empirical data about graphs to gain deeper insights into their properties, which may lead to general proofs. Students also need to learn and be able to implement graph algorithms for their applications. However, these individuals often have varying backgrounds and training, and they may not have a working knowledge of programming tools to implement graph algorithms. The goal of our research is to create a software system which allows users to easily create and implement graph algorithms through a simple graphical user interface, without any coding. Towards this goal, we have developed several systems that can be used to draw and manipulate graphs as well as to execute graph algorithms. The development of the general system raises a number of interesting research questions that we will discuss. To illustrate the need for experimentation with different graph algorithms, we describe some examples of our research in different areas of graph theory and computational geometry. We also describe some features of our systems that we have found useful in teaching graph theory.

TABLET PC APPLICATIONS IN AN ACADEMIC ENVIRONMENT

ICTACS 2006 - Proceedings of the First International Conference on Theories and Applications of Computer Science 2006, 2007

A Proposed Statistical Model for Spam Email Detection

ICTACS 2006 - Proceedings of the First International Conference on Theories and Applications of Computer Science 2006, 2007

The keyword list-based spam email detection system uses keywords in a blacklist to detect spam em... more The keyword list-based spam email detection system uses keywords in a blacklist to detect spam emails. To avoid detection, keywords are written as misspellings, for example "virrus", "vi-rus" and "viruus" instead of "virus". The system needs to update the blacklist from time ...

A study on the feature selection of network traffic for intrusion detection purpose

2008 IEEE International Conference on Intelligence and Security Informatics, 2008

Abstract—The 3 most important issues for anomaly detection based intrusion detection systems by u... more Abstract—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 ...

Using Windows Printer Drivers for Solaris Applications – An Application of Multiagent System

Lecture Notes in Computer Science, 2006

Abstract. This paper proposes using multiagent system technology to solve the problem of printing... more Abstract. This paper proposes using multiagent system technology to solve the problem of printing across heterogeneous operating systems without re-implementing printer drivers. The printing problem comes from a real world application, where we have to print from Solaris ...