Lily R. Liang

Analysis of payment transaction security in mobile commerce

Mobile payment is the process of two parties exchanging financial value using a mobile device in return for goods or services. This paper is an analysis of the security issues in mobile payment for m-commerce. We introduce m-commerce and mobile payment, discuss the public key infrastructure as a basis for secure mobile technologies, and study the features for different security technologies employed in current m-commerce market, including WAP, SIM application toolkit and J2M. In addition, we compare the effectiveness of these security technologies in supporting a secure mobile payment, and discuss research issues to enhance the security of mobile payment for large scale deployment of m-commerce.

FM-test: A fuzzy-set-theory-based approach to differential gene expression data analysis

Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention. In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. We further analyze the asymptotic property of FM-test, and then establish the relationship between FM d-value and p-value. We applied FM-test to a diabetes expression dataset and a lung cancer expression dataset, respectively. Within the 10 significant genes identified in diabetes dataset, six of them have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. Within the 10 significantly overexpressed genes identified in lung cancer data, most (eight) of them have been confirmed by the literatures which are related to the lung cancer. Our experiments on synthetic datasets show that FM-test is effective and robust. The results in diabetes and lung cancer datasets validated the effectiveness of FM-test. FM-test is implemented as a Web-based application and is available for free at http://database.cs.wayne.edu/bioinformatics .BackgroundMicroarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention.ResultsIn this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. We further analyze the asymptotic property of FM-test, and then establish the relationship between FM d-value and p-value. We applied FM-test to a diabetes expression dataset and a lung cancer expression dataset, respectively. Within the 10 significant genes identified in diabetes dataset, six of them have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. Within the 10 significantly overexpressed genes identified in lung cancer data, most (eight) of them have been confirmed by the literatures which are related to the lung cancer.ConclusionOur experiments on synthetic datasets show that FM-test is effective and robust. The results in diabetes and lung cancer datasets validated the effectiveness of FM-test. FM-test is implemented as a Web-based application and is available for free at http://database.cs.wayne.edu/bioinformatics.

Wavelet fuzzy classification for detecting and tracking region outliers in meteorological data

In this paper, a wavelet fuzzy classification approach is proposed to detect and track region outliers in meteorological data. First wavelet transform is applied to meteorological data to bring up distinct patterns that might be hidden within the original data. Then a powerful image processing technique, edge detection with competitive fuzzy classifier, is extended to identify the boundary of region outlier. After that, to determine the center of the region outlier, the fuzzy-weighted average of the longitudes and latitudes of the boundary locations is computed. By linking the centers of the outlier regions within consecutive frames, the movement of a region outlier can be captured and traced. Experimental evaluation was conducted on a real-world meteorological data to examine the effectiveness of the proposed approach. This work will help discover interesting and implicit information for large volume of meteorological data.

MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways

BackgroundGene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases.ResultsThis paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes.ConclusionOur experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research.

Detecting Insider Threats: Solutions and Trends

ABSTRACT Insider threats pose significant challenges to any organization. Many solutions have been proposed in the past to detect insider threats. Unfortunately, given the complexity of the problem and the human factors involved, many solutions which have been proposed face strict constraints and limitations when it comes to the working environment. As a result, many past insider threat solutions have in practice failed in their implementations. In this work, we review some of the recent insider threat detection solutions and explore their benefits and limitations. We also discuss insider threat issues for emerging areas such as cloud computing, virtualization, and social networking.

Gfba: a biclustering algorithm for discovering value-coherent biclusters

Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity of expression under different conditions. However, it is often the case that some genes behave similarly only on a subset of conditions and their behavior is uncorrelated over the rest of the conditions. As traditional clustering methods will fail to identify such gene groups, the biclustering paradigm is introduced recently to overcome this limitation. In contrast to traditional clustering, a biclustering method produces biclusters, each of which identifies a set of genes and a set of conditions under which these genes behave similarly. The boundary of a bicluster is usually fuzzy in practice as genes and conditions can belong to multiple biclusters at the same time but with different membership degrees. However, to the best of our knowledge, a method that can discover fuzzy value-coherent biclusters is still missing. In this paper, (i) we propose a new fuzzy bicluster model for value-coherent biclusters; (ii) based on this model, we define an objective function whose minimum will characterize good fuzzy value-coherent biclusters; and (iii) we propose a genetic algorithm based method, Genetic Fuzzy Biclustering Algorithm (GFBA), to identify fuzzy value-coherent biclusters. Our experiments show that GFBA is very efficient in converging to the global optimum.

CM-test: An Innovative Divergence Measurement and Its Application in Diabetes Gene Expression Data Analysis

One important problem in data analysis is to effec- tively measure the divergence of two sets of values of a feature, each from a group of samples with a particular condition. Such a measurement is the foundation for identifying critical features that contribute to the difference between the two conditions. The two traditional methods t-test and Wilcoxon rank sum test measure this divergence indirectly, using the difference of the means of the two groups and the sum of the ranks from one of the groups, respectively. In this paper, we propose an innovative approach based on fuzzy set theory, the Cluster Misclassification test (CM-test), to quantify the divergence directly and robustly. To validate our approach, we conducted experiments on both synthetic and real diabetes gene expression datasets. On the synthetic datasets, we observed that CM-test effectively quantifies the divergence of two sets. On the real diabetes dataset, we observed that in the top ten genes identified by CM-test, eight of them have been confirmed to be associated with diabetes in the literature. We suggest the remaining two genes, M95610 and M88461, as two potential diabetic genes for further biological investigation. Therefore, we recommend that CM-test be another effective method for measuring the divergence of two sets, complementing t-test and Wilcoxon rank sum test in practice.

An Adaptive Fuzzy Classifier Approach to Edge Detection in Latent Fingerprint Images

This paper proposes an Adaptive Fuzzy Classifier Approach (AFCA) to local edge detection in order to address the challenges of detecting latent fingerprint in severely degraded images. The proposed approach adapts classifier parameters to different parts of input images using the concept of reference neighborhood. Three variants of AFCAs, namely K-means-clustering AFCA, Entropy-based AFCA, and Statistical AFCA, were developed. Experiments were conducted both on synthetic images and on real fingerprint images to compare these AFCAs and Canny edge detection. The presented results show that Statistical AFCA is the best performer with latent images.

Fuzzy-inferenced decisionmaking under uncertainty and incompleteness

An outstanding problem is how to make decisions with uncertain and incomplete data from disparate sources without NP-hard algorithms. Here we introduce a new reasoning methodology, fuzzy-inferenced decisionmaking (FIND), to solve this problem in polynomial time. In this methodology, a fuzzy-belief-state base (FBSB) is created from historical data of the states of a system by clustering the set of values for each state variable into three clusters upon whose center fuzzy set membership functions LOW, MEDIUM and HIGH are defined. The FBSB is mined for fuzzy association rules using the fuzzy set memberships to infer values for the missing data via these rules. When given an incomplete and uncertain observation of the system state, the observed state is completed via fuzzy association rules. Then each case in the FBSB is matched against the inference-completed observation to retrieve the best matching fuzzy belief state record that contains a decision as an extra variable. The process is analogous to case-based reasoning, but it uses fuzzification to ameliorate uncertainty and to complete missing data. The test results on real world data prove the effectiveness of this methodology.

FM-test: A Fuzzy Set Theory Based Approach for Discovering Diabetes Genes

Diabetes is a disorder of metabolism that has affected 18.2 million people in the United States. In recent years, researchers have identified many genes that play important roles in the onset, development and progression of diabetes. Identification of these diabetes genes offers better understanding of the molecular mechanisms underlying pathogenesis, which is essential for developing preventative and therapeutic methods. In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify diabetes associated genes from micro array gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. Experiments were conducted to study the distribution of d-values and the relationship between the d-value and the significance level of p-value. We applied FM-test to a gene expression dataset obtained from insulin-sensitive and insulin-resistant people and identified ten significant genes. Six of the ten have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. The remaining three genes, D85181, M95610 and U06452, are suggested as potential diabetes genes for further biological investigation