Department of Computer and Information Science Works

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    Secure Authentication in Heterogeneous Wireless Networks
    (Hindawi, 2008-04-03) Durresi, Arjan; Durresi, Mimoza; Barolli, Leonard; Computer and Information Science, School of Science
    The convergence of cellular and IP technologies has pushed the integration of 3G and WLAN networks to the forefront. Gaining secure access to 3G services from 802.11 WLANs is a primary challenge for this new integrated wireless technology. Successful execution of 3G security algorithms can be limited to a specified area by encrypting a user's authentication challenge with spatial data defining his visited WLAN. With limited capacity to determine a user's location only to within a current cell and restrictions on accessing users' location due to privacy, 3G operators must rely on spatial data sent from visited WLANs to implement spatial authentication control. A potential risk is presented to 3G operators since no prior relationship or trust may exist with a WLAN owner. Algorithms to quantify the trust between all parties of 3G-WLAN integrated networks are presented to further secure user authentication. Ad-hoc serving networks and the trust relationships established between mobile users are explored to define stronger algorithms for 3G – WLAN user authentication.
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    ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining
    (BioMed Central, 2008-08-12) Huan, Tianxiao; Sivachenko, Andrey Y.; Harrison, Scott H.; Chen, Jake Yue; Computer and Information Science, School of Science
    Background New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. Results We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. Conclusion The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.
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    Networked Biomedical System for Ubiquitous Health Monitoring
    (Hindawi, 2008-11-20) Durresi, Arjan; Durresi, Mimoza; Merkoci, Arben; Barolli, Leonard; Computer and Information Science, School of Science
    We propose a distributed system that enables global and ubiquitous health monitoring of patients. The biomedical data will be collected by wearable health diagnostic devices, which will include various types of sensors and will be transmitted towards the corresponding Health Monitoring Centers. The permanent medical data of patients will be kept in the corresponding Home Data Bases, while the measured biomedical data will be sent to the Visitor Health Monitor Center and Visitor Data Base that serves the area of present location of the patient. By combining the measured biomedical data and the permanent medical data, Health Medical Centers will be able to coordinate the needed actions and help the local medical teams to make quickly the best decisions that could be crucial for the patient health, and that can reduce the cost of health service.
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    Advances in Mobile Communications and Computing
    (Hindawi, 2009) Durresi, Arjan; Denko, Mieso; Computer and Information Science, School of Science
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    Advances in Wireless Networks
    (Hindawi, 2009-04-13) Durresi, Arjan; Denko, Mieso; Computer and Information Science, School of Science
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    Tools for Performance Assessment of OLSR Protocol
    (Hindawi, 2009-04-29) Ikeda, Makoto; Barolli, Leonard; De Marco, Giuseppe; Yang, Tao; Durresi, Arjan; Xhafa, Fatos; Computer and Information Science, School of Science
    In this paper, we evaluate the performance of Optimized Link State Routing (OLSR) protocol by experimental and simulation results. The experiments are carried out by using our implemented testbed and the simulations by using ns-2 simulator. We also designed and implemented a new interface for the ad-hoc network testbed in order to make more easier the experiments. The comparison between experimental and simulation results shows that for the same parameters set, in the simulation we did not notice any packet loss. On the other hand, in the experiments we experienced packet loss because of the environment effects and traffic interference.
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    Simulating Real-Time Aspects of Wireless Sensor Networks
    (Springer Verlag, 2009-12-22) Pagano, Paolo; Chitnis, Mangesh; Lipari, Giuseppe; Nastasi, Christian; Liang, Yao; Computer and Information Science, School of Science
    Wireless Sensor Networks (WSNs) technology has been mainly used in the applications with low-frequency sampling and little computational complexity. Recently, new classes of WSN-based applications with different characteristics are being considered, including process control, industrial automation and visual surveillance. Such new applications usually involve relatively heavy computations and also present real-time requirements as bounded end-to- end delay and guaranteed Quality of Service. It becomes then necessary to employ proper resource management policies, not only for communication resources but also jointly for computing resources, in the design and development of such WSN-based applications. In this context, simulation can play a critical role, together with analytical models, for validating a system design against the parameters of Quality of Service demanded for. In this paper, we present RTNS, a publicly available free simulation tool which includes Operating System aspects in wireless distributed applications. RTNS extends the well-known NS-2 simulator with models of the CPU, the Real-Time Operating System and the application tasks, to take into account delays due to the computation in addition to the communication. We demonstrate the benefits of RTNS by presenting our simulation study for a complex WSN-based multi-view vision system for real-time event detection.
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    Architecture for Mobile Heterogeneous Multi Domain Networks
    (Hindawi, 2010-04-01) Durresi, Arjan; Zhang, Ping; Durresi, Mimoza; Barolli, Leonard; Computer and Information Science, School of Science
    Multi domain networks can be used in several scenarios including military, enterprize networks, emergency networks and many other cases. In such networks, each domain might be under its own administration. Therefore, the cooperation among domains is conditioned by individual domain policies regarding sharing information, such as network topology, connectivity, mobility, security, various service availability and so on. We propose a new architecture for Heterogeneous Multi Domain (HMD) networks, in which one the operations are subject to specific domain policies. We propose a hierarchical architecture, with an infrastructure of gateways at highest-control level that enables policy based interconnection, mobility and other services among domains. Gateways are responsible for translation among different communication protocols, including routing, signalling, and security. Besides the architecture, we discuss in more details the mobility and adaptive capacity of services in HMD. We discuss the HMD scalability and other advantages compared to existing architectural and mobility solutions. Furthermore, we analyze the dynamic availability at the control level of the hierarchy.
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    Hyper-structure mining of frequent patterns in uncertain data streams
    (Springer Nature, 2013) HewaNadungodage, Chandima; Xia, Yuni; Lee, Jaehwan John; Tu, Yi-Cheng; Computer and Information Science, Purdue School of Science
    Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been done in discovering frequent patterns in uncertain data streams. The current solutions for frequent pattern mining in uncertain streams take a FP-tree-based approach; however, recent studies have shown that FP-tree-based algorithms do not perform well in the presence of data uncertainty. In this paper, we propose two hyper-structure-based false-positive-oriented algorithms to efficiently mine frequent itemsets from streams of uncertain data. The first algorithm, UHS-Stream, is designed to find all frequent itemsets up to the current moment. The second algorithm, TFUHS-Stream, is designed to find frequent itemsets in an uncertain data stream in a time-fading manner. Experimental results show that the proposed hyper-structure-based algorithms outperform the existing tree-based algorithms in terms of accuracy, runtime, and memory usage.
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    A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer
    (Springer Nature, 2013) Zhang, Fan; Chen, Jake; Wang, Mu; Drabier, Renee; Computer and Information Science, Purdue School of Science
    Background: In the past several years, there has been increasing interest and enthusiasm in molecular biomarkers as tools for early detection of cancer. Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for early detection of cancer. Factors such as inherent variability, protein detectability limitation, and peptide discovery biases among LC/MS/MS platforms have made the classification and prediction of proteomics profiles challenging. Developing proteomics data analysis methods to identify multi-protein biomarker panels for breast cancer diagnosis based on neural networks provides hope for improving both the sensitivity and the specificity of candidate cancer biomarkers for early detection. Results: In our previous method, we developed a Feed Forward Neural Network-based method to build the classifier for plasma samples of breast cancer and then applied the classifier to predict blind dataset of breast cancer. However, the optimal combination C* in our previous method was actually determined by applying the trained FFNN on the testing set with the combination. Therefore, in this paper, we applied a three way data split to the Feed Forward Neural Network for training, validation and testing based. We found that the prediction performance of the FFNN model based on the three way data split outperforms our previous method and the prediction performance is improved from (AUC = 0.8706, precision = 82.5%, accuracy = 82.5%, sensitivity = 82.5%, specificity = 82.5% for the testing set) to (AUC = 0.895, precision = 86.84%, accuracy = 85%, sensitivity = 82.5%, specificity = 87.5% for the testing set). Conclusions: Further pathway analysis showed that the top three five-marker panels are associated with complement and coagulation cascades, signaling, activation, and hemostasis, which are consistent with previous findings. We believe the new approach is a better solution for multi-biomarker panel discovery and it can be applied to other clinical proteomics.