A feedback-based approach for segmenting handwritten legal amounts on bank cheques
The proposed feedback-based approach is implemented in two steps. In the first step, segmentation is done according to the structural features between the connected components in the legal amounts. In the second step, a feedback process is introduced to re-segment the parts that could not be identified in the first step. Then a multiple neural network classifier is used to verify the re-segmentation result. The confidence value produced by the classifier is used to determine the best segmentation points. This approach is tested on a CENPARMI database and the result indicates that the correct segmentation rate increased by 13.4% from the previous approach.

title: Understanding Human-Computer Interactions in Map Revision
abstract: It is difficult to track, parse and model human-computer interactions during editing and revising of documents, but it is necessary if we are to develop automated technologies that will aid or replace humans. This paper introduces a system for accessing and recording a stream of events related to human actions in a real-time cartographic map revision system. The recorded events are parsed into a sequence of meaningful user actions and an action representation in XML format is generated. We also report results of experiments on predicting user actions such as view changes, edits, road tracking/production using hidden Markov models.

title: Authenticating Query Results in Data Publishing
abstract: We propose a communication-efficient authentication scheme to authenticate query results disseminated by untrusted data publishing servers. In our scheme, signatures of multiple tuples in the result set are aggregated into one and thus the communication overhead incurred by the signature keeps constant. Next attr-MHTs (tuple based Merkle Hash Tree) are built to further reduce the communication overhead incurred by auxiliary authentication information (AAI). Besides the property of communication-efficiency, our scheme also supports dynamic SET operations (UNION, INTERSECTION) and dynamic JOIN with immunity to reordering attack.

title: An Authentication Protocol for Pervasive Computing
abstract: Authentication protocols are essential for security in many systems. However, authentication protocols are error-prone and difficult to design. In pervasive computing, the inherent characteristics such as mobility and restricted resources make it even harder to design suitable authentication protocols. In this paper we propose an authentication protocol to solve an open problem in pervasive computing, that is secure use of public information utilities without accessing a trusted third party (TTP). Our solution not only provides authentication, but also establishes a secure communication channel between the user and the service provider without the participation of TTP. The authentication protocol can be built with any secure symmetric and asymmetric cryptographic algorithm. We show the protocol can resist passive and active attacks. We also discuss how the protocol can be extended to an applicable scheme with payment support.

title: Further analysis of the Internet key exchange protocol
abstract: Secure communication over the Internet becomes an essential requirement for any value-added Internet application. The use of cryptography for secure communication brings out the requirement of cryptographic key management. This paper examines some security issues on the Internet Key Exchange (IKE) protocol specified in RFC 2409. The investigation is intended for clarifying some specification ambiguities in RFC 2409 and facilitating a correct implementation of the IKE protocol.

title: Regularized B-spline network and its application to heart arrhythmia classification
abstract: This paper presents an effective learning scheme that combines B-spline modeling and regularized neural networks. Essential issues of structural design and learning process are discussed. Regularization theory is leveraged to design the topological structure of the network. A training algorithm is derived for the learning of both synaptic weights and B-spline coefficients. The approach is then applied to the medical problem of heart arrhythmia detection, particularly the detection of premature ventricular contraction. Promising results demonstrate the potential benefits of the proposed method.

title: Algorithm Selectors for Providing Location Estimation Services within a Cellular Radio Network
abstract: Mobile location estimation is becoming an important value-added service for mobile phone operators. Many mobile location estimation algorithms based on the cellular radio networks have been proposed but there exists no general solution since each algorithm has its own advantage depending on specific terrain and environmental factors. In this paper, we propose and investigate three algorithm selectors, one with a LDA classifier and the other two with Bayes classifiers using either a Naive Bayes probabilistic model or a Bayes probabilistic model, to select the best mobile location estimation algorithms from our previous work in order to combine their merits, and provide a more accurate estimation for location services. We have tested these three algorithm selectors with real data taken in Hong Kong. Experiment results have shown that they are all useful in particular, and the one with a Bayes probabilistic model outperforms all other existing location algorithms among different kinds of terrains in terms of average errors