Digital Commons Network^™

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.

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 …

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 …

Scrutinising The Art Of Kata: How Toyota Does It, Knowledge@Smu

Knowledge@SMU

Toyota is, arguably, the most famous Japanese car maker and a model of manufacturing and production excellence. While there have been some recent setbacks, the company’s continuously-refined work process, ideas, and philosophies offer timeless lessons and insights. In his book Toyota Kata, the result of a multi-year study, engineer, management consultant and researcher Mike Rother shares with Toyota’s admirers and sceptics alike how this company does it.

A Bayesian Inventory Model Using Real-Time Condition Monitoring Information, Rong Li, Jennifer Ryan

Research Collection Lee Kong Chian School Of Business

No abstract provided.

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 …

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 …

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 …

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 …

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 …

Economic Transition And Growth, Peter C. B. Phillips, Donggyu Sul

Research Collection School Of Economics

Some extensions of neoclassical growth models are discussed that allow for cross-section heterogeneity among economies and evolution in rates of technological progress over time. The models offer a spectrum of transitional behavior among economies that includes convergence to a common steady-state path as well as various forms of transitional divergence and convergence. Mechanisms for modeling such transitions, measuring them econometrically, assessing group behavior and selecting subgroups are developed in the paper. Some econometric issues with the commonly used augmented Solow regressions are pointed out, including problems of endogeneity and omitted variable bias which arise under conditions of transitional heterogeneity. Alternative …

The Trans-Pacific Strategic Economic Partnership Agreement: High Standard Or Missed Opportunity?, Shuchao Henry Gao

Research Collection Yong Pung How School Of Law

When the Trans-Pacific Strategic Economic Partnership Agreement (P4 Agreement) was signed in 2005, it was hailed as a “high-standard” agreement that could serve as a model within the Asia-Pacific region and attract other countries in the region to join as members. This claim seems to have received support from recent events, such as the launch of the accession negotiation by the US and the expression of interests from Australia, Peru and Vietnam.

This article provides a critical analysis on whether the P4 Agreement is a “high-standard” agreement as its members have claimed. In the view of the author, the P4 …

Modeling Change Over Time: Conceptualization, Measurement, Analysis, And Interpretation, David Chan

Research Collection School of Social Sciences

The objective of this research report is to provide a state-of-the art review of the issues and methods relating to the modeling of change over time. The focus is on the quantitative assessment of the concept of change of time, which includes issues of conceptualization, measurement, data analysis, and interpretation. The rationale and motivation for this research originated from the author?s observations (specifically in the past decade in the capacities of journal editor, reviewer, symposium discussant, and workshop leader) on the need for a comprehensive and relatively non-technical reference on and integration of various issues in the modeling of change …

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, …

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 …

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, …

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 …

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.

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 …

Inventory Flexibility Through Adjustment Contracts, Rong Li, Jennifer Ryan

Research Collection Lee Kong Chian School Of Business

No abstract provided.

Analyzing The Video Popularity Characteristics Of Large-Scale User Generated Content Systems, Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, Sue Moon

Research Collection School Of Computing and Information Systems

User generated content (UGC), now with millions of video producers and consumers, is re-shaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world's largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show …

Analyzing And Forecasting Business Cycles In A Small Open Economy: A Dynamic Factor Model For Singapore, Hwee Kwan Chow, Keen Meng Choy

Research Collection School Of Economics

A dynamic factor model is applied to a large panel dataset of Singapore’s macroeconomic variables and global economic indicators with the initial objective of analysing business cycles in a small open economy. The empirical results suggest that four common factors – which can broadly be interpreted as world, regional, electronics and domestic economic cycles – capture a large proportion of the co-variation in the quarterly time series. The estimated factor model also explains well the observed fluctuations in real economic activity and price inflation, leading us to use it in forecasting Singapore’s business cycles. We find that the forecasts generated …

Distance Metric Learning From Uncertain Side Information With Application To Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu

Research Collection School Of Computing and Information Systems

Automated photo tagging is essential to make massive unlabeled photos searchable by text search engines. Conventional image annotation approaches, though working reasonably well on small testbeds, are either computationally expensive or inaccurate when dealing with large-scale photo tagging. Recently, with the popularity of social networking websites, we observe a massive number of user-tagged images, referred to as "social images", that are available on the web. Unlike traditional web images, social images often contain tags and other user-generated content, which offer a new opportunity to resolve some long-standing challenges in multimedia. In this work, we aim to address the challenge of …

Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose. However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only. Also, the few algorithms that find frequent subgraphs in a single labeled graph typically identify subgraphs appearing regionally in the input graph. In contrast, for real-world applications, it is commonly required that the identified frequent subgraphs in a single labeled graph should also be globally distributed. This paper thus fills this crucial void by proposing a new measure, termed G-Measure, to find globally …

Wissen Und Entwicklung In Singapur: Trends Und Thesen / Knowledge And Development In Singapore: Trends And Propositions, Thomas Menkhoff, Solvay Gerke, Hans-Dieter Evers, Yue-Wah Chay

Research Collection Lee Kong Chian School Of Business

This paper addresses the question how knowledge is used to benefit the economic development of Singapore. The country has followed strict science policies to establish knowledge governance regimes for a knowledge-based economy. On the basis of empirical studies the authors show, how cultural diversity and social capital impact on the ability to develop an epistemic culture of knowledge sharing and ultimately an innovative knowledge-based economy.

Analysis Of Tradeoffs Between Buffer And Qos Requirements In Wireless Networks, Raphael Rom, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

In this paper, we consider the scheduling problem where data packets from K input-flows need to be delivered to K corresponding wireless receivers over a heterogeneous wireless channel. Our objective is to design a wireless scheduler that achieves good throughput and fairness performance while minimizing the buffer requirement at each wireless receiver. This is a challenging problem due to the unique characteristics of the wireless channel. We propose a novel idea of exploiting both the long-term and short-term error behavior of the wireless channel in the scheduler design. In addition to typical first-order Quality of Service (QoS) metrics such as …

First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King

Research Collection School Of Computing and Information Systems

The ACM SIGMM International Workshop on Social Media(WSM’09) is the first workshop held in conjunction withthe ACM International Multimedia Conference (MM’09) atBejing, P.R. China, 2009. This workshop provides a forumfor researchers and practitioners from all over the world toshare information on their latest investigations on social mediaanalysis, exploration, search, mining, and emerging newsocial media applications.

A Service Choice Model For Optimizing Taxi Service Delivery, Shih-Fen Cheng, Xin Qu

Research Collection School Of Computing and Information Systems

Taxi service has undergone radical revamp in recent years. In particular, significant investments in communication system and GPS devices have improved quality of taxi services through better dispatches. In this paper, we propose to leverage on such infrastructure and build a service choice model that helps individual drivers in deciding whether to serve a specific taxi stand or not. We demonstrate the value of our model by applying it to a real-world scenario. We also highlight interesting new potential approaches that could significantly improve the quality of taxi services.

Sharing Mobile Multimedia Annotations To Support Inquiry-Based Learning Using Mobitop, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin-Leng Theng, Quang Minh Nguyen, Thi Nhu Quynh Kim, Ee Peng Lim, Chew-Hung Chang, Kalyani Chatterjea, Aixin Sun

Research Collection School Of Computing and Information Systems

Mobile devices used in educational settings are usually employed within a collaborative learning activity in which learning takes place in the form of social interactions between team members while performing a shared task. We introduce MobiTOP (Mobile Tagging of Objects and People), a geospatial digital library system which allows users to contribute and share multimedia annotations via mobile devices. A key feature of MobiTOP that is well suited for collaborative learning is that annotations are hierarchical, allowing annotations to be annotated by other users to an arbitrary depth. A group of student-teachers involved in an inquiry-based learning activity in geography …

Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu

Research Collection School Of Computing and Information Systems

The Bag-of-Words (BoW) model is a promising image representation for annotation. One critical limitation of existing BoW models is the semantic loss during the codebook generation process, in which BoW simply clusters visual words in Euclidian space. However, distance between two visual words in Euclidean space does not necessarily reflect the semantic distance between the two concepts, due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme for learning a codebook such that semantically related features will be mapped to the same visual word. In particular, we consider the distance between …