Algorithm Selectors for Providing Location Estimation Services within a Cellular Radio Network
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

title: An improved ellipse propagation model for location estimation in facilitating ubiquitous computing
abstract: Positioning is a crucial technology for ubiquitous computing. A directional propagation model - the ellipse propagation model (EPM) is proposed by our research group for locating a mobile station (MS) within a radio cellular network with an accuracy that can enable a number of location based services to realize ubiquitous computing. By using a geometric algorithm, the location of the mobile station can be estimated. However, since one parameter in our geometric algorithm is fixed, errors may be induced as the surrounding environment changes. In view of this, we would like to propose a new algorithm - the iterative algorithm to provide the positioning based on EPM. With the technical support of two local mobile phone operators, we have conducted a series of experiments using real data and experiment results showed that the proposed iterative algorithm outperforms the geometric algorithm by a good margin of 18% in terms of average error.

title: A selector method for providing mobile location estimation services within a radio cellular network
abstract: Mobile location estimation or mobile positioning is becoming an important service for a mobile phone network. It is well-known that GPS can provide accurate location estimation, but it is also a known fact that GPS does not perform well in urban areas like downtown New York and cities like Hong Kong. Then many mobile location estimation approaches based on radio cellular networks have been proposed to compensate the problem of the lost of GPS signals in providing location services to mobile users in metropolitan areas. In this paper, we present a selector method with the linear discriminant analysis (LDA) among different kinds of mobile location estimation technologies we had proposed in previous work in order to combine their merits, then provide a more accurate estimation for location services. We build up a three-level binary tree to classify these four algorithms. These three levels are named as Stat-Geo level, CG-nonCG level and CT-EPM level. And these success ratios of these three levels are 85.22%, 88.45% and 88.89% respectively. We have tested our selector method with real data taken in Hong Kong and it is proven that it outperforms other existing location estimation algorithms among different kinds of terrains.

title: Undeniable Mobile Billing Schemes
abstract: An undeniable mobile billing system allows a mobile network service provider to bill its subscribers with trustworthy evidences. Chen, Jan and Chen proposed such a billing system by introducing a trusted third party - Observer and exploiting a hash chain mechanism. In their system, the Observer provides call time evidence to both cellular carriers and subscribers for billing. In this paper, we first identify some vulnerabilities in their mobile billing system. Then, we propose an undeniable billing scheme based on a proper combination of digital signature and hash chain mechanism. The proposed scheme can achieve authentication, non-repudiation, and fairness, which are desirable security requirements for an undeniable mobile billing system.

title: On the Security of a Multi-party Certified Email Protocol
abstract: As a value-added service to deliver important data over the Internet with guaranteed receipt for each successful delivery, certified email has been discussed for years and a number of research papers appeared in the literature. But most of them deal with the two-party scenarios, i.e., there are only one sender and one recipient. In some applications, however, the same certified message may need to be sent to a set of recipients. In ISC’02, Ferrer-Gomila et. al presented a multi-party certified email protocol . It has two major features. A sender could notify multiple recipients of the same information while only those recipients who acknowledged are able to get the information. In addition, its exchange protocol is optimized, which has only three steps. In this paper, we demonstrate some flaws and weaknesses in that protocol, and propose an improved version which is robust against the identified attacks while preserving the features of the original protocol.

title: An Overlay Subscription Network for Live Internet TV Broadcast
abstract: We propose a framework, called overlay subscription network (OSN), for live Internet TV broadcast, where a subscriber can choose to watch at any time. This framework allows the source server to incrementally build a topology graph that contains the network connections not only from the server to each subscriber, but also among the subscribers themselves. With such a topology graph in place, we consider efficient overlay multicast for scalable OSN services. We first show that idling nodes, which do not receive video data for their own playback, can actually be used for data forwarding to significantly reduce the cost of overlay multicast. In light of this observation, we then propose a novel overlay multicast technique that distinguishes itself from existing schemes with these three aspects. First, the proposed technique is centered on the topology graph and can take advantage of the actual network connections among the subscribing nodes. Second, the new scheme is able to find and incorporate appropriate idling nodes in multicast to reduce network traffic. Third, with our approach, a node can be used in multiple multicast trees for data forwarding to improve the overall system performance. We evaluate the performance of the proposed technique through simulation. Our extensive studies show that the proposed framework has the potential to enable the Internet, a vehicle up to date mainly for transferring text and image data, for large-scale and cost-effective TV broadcast

title: Two-Party Privacy-Preserving Agglomerative Document Clustering
abstract: Document clustering is a powerful data mining technique to analyze the large amount of documents and structure large sets of text or hypertext documents. Many organizations or companies want to share their documents in a similar theme to get the joint benefits. However, it also brings the problem of sensitive information leakage without consideration of privacy. In this paper, we propose a cryptography-based framework to do the privacy-preserving document clustering among the users under the distributed environment: two parties, each having his private documents, want to collaboratively execute agglomerative document clustering without disclosing their private contents.

title: Toward automatic annotation of in situ mRNA expression patterns of drosophila embryos
abstract: The in situ mRNA hybridization gene expression pattern images of Drosophila melanogaster in the course of embryogenesis provide spatial-temporal information of the expression patterns of a target gene. This information is critical for understanding the roles of genes during the development of embryos. Currently, the annotation of these images is often done by manually assigning a subset of image ontology terms to the images. This approach is time consuming and depends heavily on the consistency of the experts. Alternatively, if the annotation process can be automated or semi-automated, efficiency and consistency are likely to be greatly enhanced. We formulate this task as a pattern classification problem, and present preliminary results. We consider both the template-based global matching and the multi-objective classification based on neural networks (e.g. multi-layer perceptron). We develop a method to combine them to optimize efficiency and accuracy. This method has been applied to the gene expression pattern image database generated by the Berkeley Drosophila Genome Project.