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Computationally Secure Hierarchical Self-Healing Key Distribution For Heterogeneous Wireless Sensor Networks, Yanjiang Yang, Jianying Zhou, Robert H. Deng, Feng Bao Dec 2009

Computationally Secure Hierarchical Self-Healing Key Distribution For Heterogeneous Wireless Sensor Networks, Yanjiang Yang, Jianying Zhou, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Self-healing group key distribution is a primitive aimed to achieve robust key distribution in wireless sensor networks (WSNs) over lossy communication channels. However, all the existing self-healing group key distribution schemes in the literature are designed for homogenous WSNs that do not scale. In contract, heterogeneous WSNs have better scalability and performance. We are thus motivated to study self-healing group key distribution for heterogeneous WSNs. In particular, we propose the concept of hierarchical self-healing group key distribution, tailored to the heterogeneous WSN architecture; we further revisit and adapt Dutta et al.’s model to the setting of hierarchical self-healing group …

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Denial-Of-Service Attacks On Host-Based Generic Unpackers, Limin Liu, Jiang Ming, Zhi Wang, Debin Gao, Chunfu Jia Dec 2009

Denial-Of-Service Attacks On Host-Based Generic Unpackers, Limin Liu, Jiang Ming, Zhi Wang, Debin Gao, Chunfu Jia

Research Collection School Of Computing and Information Systems

With the advance of packing techniques, a few generic and automatic unpackers have been proposed. These unpackers are designed to automatically unpack packed binaries without specific knowledge of the packing techniques used. In this paper, we present an automatic packer with which packed malware forges spurious unpacking behaviors that lead to a denial-of-service attack on host-based generic unpackers. We present the design, implementation, and evaluation of the proposed packer and malware produced using the proposed packer, and show the success of denial-of-service attacks on host-based generic unpackers.

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A Robust Damage Assessment Model For Corrupted Database Systems, Ge Fu, Hong Zhu, Yingjiu Li Dec 2009

A Robust Damage Assessment Model For Corrupted Database Systems, Ge Fu, Hong Zhu, Yingjiu Li

Research Collection School Of Computing and Information Systems

An intrusion tolerant database uses damage assessment techniques to detect damage propagation scales in a corrupted database system. Traditional damage assessment approaches in a intrusion tolerant database system can only locate damages which are caused by reading corrupted data. In fact, there are many other damage spreading patterns that have not been considered in traditional damage assessment model. In this paper, we systematically analyze inter-transaction dependency relationships that have been neglected in the previous research and propose four different dependency relationships between transactions which may cause damage propagation. We extend existing damage assessment model based on the four novel dependency …

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A New Approach For Anonymous Password Authentication, Yanjiang Yang, Jianying Zhou, Jian Weng, Feng Bao Dec 2009

A New Approach For Anonymous Password Authentication, Yanjiang Yang, Jianying Zhou, Jian Weng, Feng Bao

Research Collection School Of Computing and Information Systems

Anonymous password authentication reinforces password authentication with the protection of user privacy. Considering the increasing concern of individual privacy nowadays, anonymous password authentication represents a promising privacy-preserving authentication primitive. However, anonymous password authentication in the standard setting has several inherent weaknesses, making its practicality questionable. In this paper, we propose a new and efficient approach for anonymous password authentication. Our approach assumes a different setting where users do not register their passwords to the server; rather, they use passwords to protect their authentication credentials. We present a concrete scheme, and get over a number of challenges in securing password-protected credentials …

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Duol: A Double Updating Approach For Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin Dec 2009

Duol: A Double Updating Approach For Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short. Instead of only assigning a fixed weight to the misclassified example received in current trial, the proposed online learning algorithm also tries to update the weight for one …

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Learning Bregman Distance Functions And Its Application For Semi-Supervised Clustering, Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu Dec 2009

Learning Bregman Distance Functions And Its Application For Semi-Supervised Clustering, Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu

Research Collection School Of Computing and Information Systems

Learning distance functions with side information plays a key role in many machine learning and data mining applications. Conventional approaches often assume a Mahalanobis distance function. These approaches are limited in two aspects: (i) they are computationally expensive (even infeasible) for high dimensional data because the size of the metric is in the square of dimensionality; (ii) they assume a fixed metric for the entire input space and therefore are unable to handle heterogeneous data. In this paper, we propose a novel scheme that learns nonlinear Bregman distance functions from side information using a nonparametric approach that is similar to …

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Wake Up Or Fall Asleep: Value Implication Of Trusted Computing, Nan Hu, Jianhui Huang, Ling Liu, Yingjiu Li, Dan Ma Dec 2009

Wake Up Or Fall Asleep: Value Implication Of Trusted Computing, Nan Hu, Jianhui Huang, Ling Liu, Yingjiu Li, Dan Ma

Research Collection School Of Computing and Information Systems

More than 10 years have passed since trusted computing (TC) technology was introduced to the market; however, there is still no consensus about its value. The increasing importance of user and enterprise security and the security promised by TC, coupled with the increasing tension between the proponents and the opponents of TC, make it timely to investigate the value relevance of TC in terms of both capital market and accounting performance. Based on both price and volume studies, we found that news releases related to the adoption of the TC technology had no information content. All investors, regardless of whether …

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Cyber Attacks: Does Physical Boundary Matter?, Qiu-Hong Wang, Seung-Hyun Kim Dec 2009

Cyber Attacks: Does Physical Boundary Matter?, Qiu-Hong Wang, Seung-Hyun Kim

Research Collection School Of Computing and Information Systems

Information security issues are characterized with interdependence. Particularly, cyber criminals can easily cross national boundaries and exploit jurisdictional limitations between countries. Thus, whether cyber attacks are spatially autocorrelated is a strategic issue for government authorities and a tactic issue for insurance companies. Through an empirical study of cyber attacks across 62 countries during the period 2003-2007, we find little evidence on the spatial autocorrelation of cyber attacks at any week. However, after considering economic opportunity, IT infrastructure, international collaboration in enforcement and conventional crimes, we find strong evidence that cyber attacks were indeed spatially autocorrelated as they moved over time. …

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Managing Supply Uncertainty With An Information Market, Zhiling Guo, Fang Fang, Andrew B. Whinston Dec 2009

Managing Supply Uncertainty With An Information Market, Zhiling Guo, Fang Fang, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

We propose a market-based information aggregation mechanism to manage the supply side uncertainty in the supply chain. In our analytical model, a simple supply chain consists of a group of retailers who order a homogeneous product from two suppliers. The two suppliers differ in their ability to fulfill orders – one always delivers orders and the other fulfills orders probabilistically. We model the supply chain decisions as a Stackelberg game where the supplier who has uncertain reliability decides a wholesale price before the retailers who independently receive signals about the supplier’s reliability determine their sourcing strategies. We then propose an …

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On Strategies For Imbalanced Text Classification Using Svm: A Comparative Study, Aixin Sun, Ee Peng Lim, Ying Liu Dec 2009

On Strategies For Imbalanced Text Classification Using Svm: A Comparative Study, Aixin Sun, Ee Peng Lim, Ying Liu

Research Collection School Of Computing and Information Systems

Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study on the effectiveness of these strategies in the context of imbalanced text classification using Support Vector Machines (SVM) classifier. SVM is the interest in this study for its good classification accuracy reported in many text classification tasks. We propose a taxonomy to organize all proposed strategies following the training and the test phases in text classification tasks. Based on the taxonomy, …

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Adaptive Type-2 Fuzzy Maintenance Advisor For Offshore Power Systems, Zhaoxia Wang, C. S. Chang, Fan Yang, W. W. Tan Dec 2009

Adaptive Type-2 Fuzzy Maintenance Advisor For Offshore Power Systems, Zhaoxia Wang, C. S. Chang, Fan Yang, W. W. Tan

Research Collection School Of Computing and Information Systems

Proper maintenance strategies are very desirable for minimizing the operational and maintenance costs of power systems without sacrificing reliability. Condition-based maintenance has largely replaced time-based maintenance because of the former's potential economic benefits. As offshore substations are often remotely located, they experience more adverse environments, higher failures, and therefore need more powerful analytical tools than their onshore counterpart. As reliability information collected during operation of an offshore substation can rarely avoid uncertainties, it is essential to obtain consistent estimates of reliability measures under changing environmental and operating conditions. Some attempts with type-1 fuzzy logic were made with limited success in …

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The Cartography Of National Humiliation And The Emergence Of China’S Geobody, William A. Callahan Dec 2009

The Cartography Of National Humiliation And The Emergence Of China’S Geobody, William A. Callahan

Research Collection School of Social Sciences

Maps are an important site of the production and consumption of the national image. This essay examines modern Chinese maps to show how the very material borders between foreign and domestic space are the outgrowth of the symbolic workings of historical geography and the conventions of Chinese cartography. These maps do much more than celebrate the extent of Chinese sovereignty; they also mourn the loss of national territories through a cartography of national humiliation. The goal of this essay is to shift our attention from the diplomatic issues of international borders to examine what Chinese maps of China can tell …

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Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung Dec 2009

Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung

Research Collection School Of Economics

Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz–Thompson method, however, is sensitive to the inverse weights and may suffer from loss of efficiency. In this paper, we propose a unified empirical likelihood approach to missing data problems and explore the use …

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Coherent Phrase Model For Efficient Image Near-Duplicate Retrieval, Yiqun Hu, Xiangang Cheng, Liang-Tien Chia, Xing Xie, Deepu Rajan, Ah-Hwee Tan Dec 2009

Coherent Phrase Model For Efficient Image Near-Duplicate Retrieval, Yiqun Hu, Xiangang Cheng, Liang-Tien Chia, Xing Xie, Deepu Rajan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents an efficient and effective solution for retrieving image near-duplicate (IND) from image database. We introduce the coherent phrase model which incorporates the coherency of local regions to reduce the quantization error of the bag-of-words (BoW) model. In this model, local regions are characterized by visual phrase of multiple descriptors instead of visual word of single descriptor. We propose two types of visual phrase to encode the coherency in feature and spatial domain, respectively. The proposed model reduces the number of false matches by using this coherency and generates sparse representations of images. Compared to other method, the …

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On The Accuracy And Stability Of A Variety Of Differential Quadrature Formulations For The Vibration Analysis Of Beams, C. H. W. Ng, Yibao Zhao, Y. Xiang, G. W. Wei Dec 2009

On The Accuracy And Stability Of A Variety Of Differential Quadrature Formulations For The Vibration Analysis Of Beams, C. H. W. Ng, Yibao Zhao, Y. Xiang, G. W. Wei

Research Collection Lee Kong Chian School Of Business

The occurrence of spurious complex eigenvalues is a serious stability problem in many differential quadrature methods (DQMs). This paper studies the accuracy and stability of a variety of different differential quadrature formulations. Special emphasis is given to two local DQMs. One utilizes both fictitious grids and banded matrices, called local adaptive differential quadrature method (LaDQM). The other has banded matrices without using fictitious grids to facilitate boundary conditions, called finite difference differential quadrature methods (FDDQMs). These local DQMs include the classic DQMs as special cases given by extending their banded matrices to full matrices. LaDQMs and FDDQMs are implemented on …

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Personality Scale Validities Increase Throughout Medical School, Filip Lievens, Deniz S. Ones, Stephan Dilchert Nov 2009

Personality Scale Validities Increase Throughout Medical School, Filip Lievens, Deniz S. Ones, Stephan Dilchert

Research Collection Lee Kong Chian School Of Business

Admissions and personnel decisions rely on stable predictor-criterion relationships. The authors studied the validity of Big Five personality factors and their facets for predicting academic performance in medical school across multiple years, investigating whether criterion-related validities change over time. In this longitudinal investigation, an entire European country's 1997 cohort of medical students was studied throughout their medical school career (Year 1, N = 627; Year 7, N = 306). Over time, extraversion, openness, and conscientiousness factor and facet scale scores showed increases in operational validity for predicting grade point averages. Although there may not be any advantages to being open …

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Automated Likelihood Based Inference For Stochastic Volatility Models, H. Skaug, Jun Yu Nov 2009

Automated Likelihood Based Inference For Stochastic Volatility Models, H. Skaug, Jun Yu

Research Collection School Of Economics

The Laplace approximation is used to perform maximum likelihood estimation of univariate and multivariate stochastic volatility (SV) models. It is shown that the implementation of the Laplace approximation is greatly simplified by the use of a numerical technique known as automatic differentiation (AD). Several algorithms are proposed and compared with some existing maximum likelihood methods using both simulated data and actual data. It is found that the new methods match the statistical efficiency of the existing methods while significantly reducing the coding effort. Also proposed are simple methods for obtaining the filtered, smoothed and predictive values for the latent variable. …

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User Interfaces For Visual Analysis And Monitoring In Business Intelligence, Lars Grammel, Margaret-Anne Storey, Christoph Treude Nov 2009

User Interfaces For Visual Analysis And Monitoring In Business Intelligence, Lars Grammel, Margaret-Anne Storey, Christoph Treude

Research Collection School Of Computing and Information Systems

Business intelligence is concerned with understanding and leveraging the vast amounts of information stored in the databases of modern enterprises. Visualization techniques have been used to make sense of this data for a long time, first in the form of simple charts, and nowadays in the form of interactive visualizations. By leveraging the strengths of the human perceptual system and incorporating user interaction, they support the flexible analysis of data as well as data monitoring by users. The recent progress in the fields of information and data visualization as well as new hardware developments and trends in business intelligence have …

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Ensemble And Individual Noise Reduction Method For Induction-Motor Signature Analysis, Zhaoxia Wang, C.S. Chang, Tw Chua, W.W Tan Nov 2009

Ensemble And Individual Noise Reduction Method For Induction-Motor Signature Analysis, Zhaoxia Wang, C.S. Chang, Tw Chua, W.W Tan

Research Collection School Of Computing and Information Systems

Unlike a fixed-frequency power supply, the voltagesupplying an inverter-fed motor is heavily corrupted by noises,which are produced from high-frequency switching leading tonoisy stator currents. To extract useful information from statorcurrentmeasurements, a theoretically sound and robust denoisingmethod is required. The effective filtering of these noisesis difficult with certain frequency-domain techniques, such asFourier transform or Wavelet analysis, because some noises havefrequencies overlapping with those of the actual signals, andsome have high noise-to-frequency ratios. In order to analyze thestatistical signatures of different types of signals, a certainnumber is required of the individual signals to be de-noisedwithout sacrificing the individual characteristic and quantity ofthe …

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Online Fault Detection Of Induction Motors Using Independent Component Analysis And Fuzzy Neural Network, Zhaoxia Wang, C. S. Chang, X. German, W.W. Tan Nov 2009

Online Fault Detection Of Induction Motors Using Independent Component Analysis And Fuzzy Neural Network, Zhaoxia Wang, C. S. Chang, X. German, W.W. Tan

Research Collection School Of Computing and Information Systems

This paper proposes the use of independent component analysis and fuzzy neural network for online fault detection of induction motors. The most dominating components of the stator currents measured from laboratory motors are directly identified by an improved method of independent component analysis, which are then used to obtain signatures of the stator current with different faults. The signatures are used to train a fuzzy neural network for detecting induction-motor problems such as broken rotor bars and bearing fault. Using signals collected from laboratory motors, the robustness of the proposed method for online fault detection is demonstrated for various motor …

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Mining Hierarchical Scenario-Based Specifications, David Lo, Shahar Maoz Nov 2009

Mining Hierarchical Scenario-Based Specifications, David Lo, Shahar Maoz

Research Collection School Of Computing and Information Systems

Scalability over long traces, as well as comprehensibility and expressivity of results, are major challenges for dynamic analysis approaches to specification mining. In this work we present a novel use of object hierarchies over traces of inter-object method calls, as an abstraction/refinement mechanism that enables user-guided, top-down or bottom-up mining of layered scenario-based specifications, broken down by hierarchies embedded in the system under investigation. We do this using data mining methods that provide statistically significant sound and complete results modulo user-defined thresholds, in the context of Damm and Harel’s live sequence charts (LSC); a visual, modal, scenario-based, inter-object language. Thus, …

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What Makes Categories Difficult To Classify?, Aixin Sun, Ee Peng Lim, Ying Liu Nov 2009

What Makes Categories Difficult To Classify?, Aixin Sun, Ee Peng Lim, Ying Liu

Research Collection School Of Computing and Information Systems

In this paper, we try to predict which category will be less accurately classified compared with other categories in a classification task that involves multiple categories. The categories with poor predicted performance will be identified before any classifiers are trained and additional steps can be taken to address the predicted poor accuracies of these categories. Inspired by the work on query performance prediction in ad-hoc retrieval, we propose to predict classification performance using two measures, namely, category size and category coherence. Our experiments on 20-Newsgroup and Reuters-21578 datasets show that the Spearman rank correlation coefficient between the predicted rank of …

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Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun Nov 2009

Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun

Research Collection School Of Computing and Information Systems

Trust between users is an important piece of knowledge that can be exploited in search and recommendation.Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments …

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Trust-Oriented Composite Services Selection And Discovery, Lei Li, Yan Wang, Ee Peng Lim Nov 2009

Trust-Oriented Composite Services Selection And Discovery, Lei Li, Yan Wang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In Service-Oriented Computing (SOC) environments, service clients interact with service providers for consuming services. From the viewpoint of service clients, the trust level of a service or a service provider is a critical issue to consider in service selection and discovery, particularly when a client is looking for a service from a large set of services or service providers. However, a service may invoke other services offered by different providers forming composite services. The complex invocations in composite services greatly increase the complexity of trust-oriented service selection and discovery. In this paper, we propose novel approaches for composite service representation, …

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Static Validation Of C Preprocessor Macros, Andreas Saebjornsen, Lingxiao Jiang, Daniel Quinlan, Zhendong Su Nov 2009

Static Validation Of C Preprocessor Macros, Andreas Saebjornsen, Lingxiao Jiang, Daniel Quinlan, Zhendong Su

Research Collection School Of Computing and Information Systems

The widely used C preprocessor (CPP) is generally considered a source of difficulty for understanding and maintaining C/C++ programs. The main reason for this difficulty is CPP’s purely lexical semantics, i.e., its treatment of both input and output as token streams. This can easily lead to errors that are difficult to diagnose, and it has been estimated that up to 20% of all macros are erroneous. To reduce such errors, more restrictive, replacement languages for CPP have been proposed to limit expanded macros to be valid C syntactic units. However, there is no practical tool that can effectively validate CPP …

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Udel/Smu At Trec 2009 Entity Track, Wei Zheng, Swapna Gottipati, Jing Jiang, Hui Fang Nov 2009

Udel/Smu At Trec 2009 Entity Track, Wei Zheng, Swapna Gottipati, Jing Jiang, Hui Fang

Research Collection School Of Computing and Information Systems

We report our methods and experiment results from the collaborative participation of the InfoLab group from University of Delaware and the school of Information Systems from Singapore Management University in the TREC 2009 Entity track. Our general goal is to study how we may apply language modeling approaches and natural language processing techniques to the task. Specically, we proposed to find supporting information based on segment retrieval, to extract entities using Stanford NER tagger, and to rank entities based on a previously proposed probabilistic framework for expert finding.

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Mining Communities In Networks: A Solution For Consistency And Its Evaluation, Haewoon Kwak, Yoonchan Choi, Young-Ho Eom, Hawoong Jeong, Sue Moon Nov 2009

Mining Communities In Networks: A Solution For Consistency And Its Evaluation, Haewoon Kwak, Yoonchan Choi, Young-Ho Eom, Hawoong Jeong, Sue Moon

Research Collection School Of Computing and Information Systems

Online social networks pose significant challenges to computer scientists, physicists, and sociologists alike, for their massive size, fast evolution, and uncharted potential for social computing. One particular problem that has interested us is community identification. Many algorithms based on various metrics have been proposed for communities in networks [18, 24], but a few algorithms scale to very large networks. Three recent community identification algorithms, namely CNM [16], Wakita [59], and Louvain [10], stand out for their scalability to a few millions of nodes. All of them use modularity as the metric of optimization. However, all three algorithms produce inconsistent communities …

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Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim Nov 2009

Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim

Research Collection School Of Computing and Information Systems

We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study …

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Bias In The Estimation Of The Mean Reversion Parameter In Continuous Time Models, Jun Yu Nov 2009

Bias In The Estimation Of The Mean Reversion Parameter In Continuous Time Models, Jun Yu

Research Collection School Of Economics

It is well known that for continuous time models with a linear drift standard estimation methods yield biased estimators for the mean reversion parameter both in nite discrete samples and in large in-…ll samples. In this paper, we obtain two expressions to approximate the bias of the least squares/maximum likelihood estimator of the mean reversion parameter in the Ornstein-Uhlenbeck process with a known long run mean when discretely sampled data are available. The first expression mimics the bias formula of Marriott and Pope (1954) for the discrete time model. Simulations show that this expression does not work satisfactorily when the …

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Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu Nov 2009

Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu

Research Collection School Of Economics

In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact ¯nite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion …

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