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Analysis Of Topological Characteristics Of Huge Online Social Networking Services, Yong-Yeol Ahn, Seungyeop Han, Haewoon Kwak, Sue Moon, Hawoong Jeong
Analysis Of Topological Characteristics Of Huge Online Social Networking Services, Yong-Yeol Ahn, Seungyeop Han, Haewoon Kwak, Sue Moon, Hawoong Jeong
Research Collection School Of Computing and Information Systems
Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network …
Enhancing The Performance Of Semi-Supervised Classification Algorithms With Bridging, Jason Yuk Hin Chan, Josiah Poon, Irena Koprinska
Enhancing The Performance Of Semi-Supervised Classification Algorithms With Bridging, Jason Yuk Hin Chan, Josiah Poon, Irena Koprinska
Research Collection School Of Computing and Information Systems
Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in any supervised approach such as co-training or selflearning. We empirically show that classification performance increases by improving the semi-supervised algorithm’s ability to correctly assign labels to previouslyunlabelled …
Achieving End-To-End Authentication In Intermediary-Enabled Multimedia Delivery Systems, Robert H. Deng, Yanjiang Yang
Achieving End-To-End Authentication In Intermediary-Enabled Multimedia Delivery Systems, Robert H. Deng, Yanjiang Yang
Research Collection School Of Computing and Information Systems
Considerable research and experiment results in recent years have shown that the server-proxy-user architecture represents an efficient and scalable new paradigm for multimedia content delivery. However, not much effort has been spent on the security issues in such systems. In this paper, we study data authentication in multimedia content delivery, and in particular, we focus on achieving end-to-end authentication from the multimedia server to end users in the server-proxy-user architecture where intermediary proxies transcode multimedia content dynamically. We present a formal model for the end-to-end authentication problem, and propose a basic construction for generic data modality and prove its security. …
Mining Colossal Frequent Patterns By Core Pattern Fusion, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu, Hong Cheng
Mining Colossal Frequent Patterns By Core Pattern Fusion, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu, Hong Cheng
Research Collection School Of Computing and Information Systems
Extensive research for frequent-pattern mining in the past decade has brought forth a number of pattern mining algorithms that are both effective and efficient. However, the existing frequent-pattern mining algorithms encounter challenges at mining rather large patterns, called colossal frequent patterns, in the presence of an explosive number of frequent patterns. Colossal patterns are critical to many applications, especially in domains like bioinformatics. In this study, we investigate a novel mining approach called Pattern-Fusion to efficiently find a good approximation to the colossal patterns. With Pattern-Fusion, a colossal pattern is discovered by fusing its small core patterns in one step, …
Vulnerability Analysis Of Emap: An Efficient Rfid Mutual Authentication Protocol, Tieyan Li, Robert H. Deng
Vulnerability Analysis Of Emap: An Efficient Rfid Mutual Authentication Protocol, Tieyan Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In this paper, we analyze the security vulnerabilities of EMAP, an efficient RFID mutual authentication protocol recently proposed by Peris-Lopez et al. (2006). We present two effective attacks, a de-synchronization attack and a full-disclosure attack, against the protocol. The former permanently disables the authentication capability of a RFID tag by destroying synchronization between the tag and the RFID reader. The latter completely compromises a tag by extracting all the secret information stored in the tag. The de-synchronization attack can be carried out in just round of interaction in EMAP while the full-disclosure attack is accomplished across several runs of EMAP. …
Malicious Kgc Attacks In Certificateless Cryptography, Man Ho Au, Jing Chen, Joseph K. Liu, Yi Mu, Duncan S. Wong, Guomin Yang, Guomin Yang
Malicious Kgc Attacks In Certificateless Cryptography, Man Ho Au, Jing Chen, Joseph K. Liu, Yi Mu, Duncan S. Wong, Guomin Yang, Guomin Yang
Research Collection School Of Computing and Information Systems
Identity-based cryptosystems have an inherent key escrow issue, that is, the Key Generation Center (KGC) always knows user secret key. If the KGC is malicious, it can always impersonate the user. Certificateless cryptography, introduced by Al-Riyami and Paterson in 2003, is intended to solve this problem. However, in all the previously proposed certificateless schemes, it is always assumed that the malicious KGC starts launching attacks (so-called Type II attacks) only after it has generated a master public/secret key pair honestly. In this paper, we propose new security models that remove this assumption for both certificateless signature and encryption schemes. Under …
Efficient Algorithms For Machine Scheduling Problems With Earliness And Tardiness Penalties, Guang Feng, Hoong Chuin Lau
Efficient Algorithms For Machine Scheduling Problems With Earliness And Tardiness Penalties, Guang Feng, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we study the multi-machine scheduling problem with earliness and tardiness penalties and sequence dependent setup times. This problem can be decomposed into two subproblems—sequencing and timetabling. Sequencing focuses on assigning each job to a fixed machine and determine the job sequence on each machine. We call such assignment a semi-schedule. Timetabling focuses on finding an executable schedule from the semi-schedule via idle-time insertion. Sequencing is strongly NP-hard in general. Although timetabling is polynomial-time solvable, it can become a computational bottleneck if the procedure is executed many times within a larger framework. This paper makes two contributions. We …
Towards Efficient Planning For Real World Partially Observable Domains, Pradeep R. Varakantham
Towards Efficient Planning For Real World Partially Observable Domains, Pradeep R. Varakantham
Research Collection School Of Computing and Information Systems
My research goal is to build large-scale intelligent systems (both single- and multi-agent) that reason with uncertainty in complex, real-world environments. I foresee an integration of such systems in many critical facets of human life ranging from intelligent assistants in hospitals to offices, from rescue agents in large scale disaster response to sensor agents tracking weather phenomena in earth observing sensor webs, and others. In my thesis, I have taken steps towards achieving this goal in the context of systems that operate in partially observable domains that also have transitional (non-deterministic outcomes to actions) uncertainty. Given this uncertainty, Partially Observable …
Clustering And Combinatorial Optimization In Recursive Supervised Learning, Kiruthika Ramanathan, Sheng Uei Guan
Clustering And Combinatorial Optimization In Recursive Supervised Learning, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In …
Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang
Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang
Research Collection School Of Computing and Information Systems
Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (resource description framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied …
Lecture Video Enhancement And Editing By Integrating Posture, Gesture, And Text, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Lecture Video Enhancement And Editing By Integrating Posture, Gesture, And Text, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Research Collection School Of Computing and Information Systems
This paper describes a novel framework for automatic lecture video editing by gesture, posture, and video text recognition. In content analysis, the trajectory of hand movement is tracked and the intentional gestures are automatically extracted for recognition. In addition, head pose is estimated through overcoming the difficulties due to the complex lighting conditions in classrooms. The aim of recognition is to characterize the flow of lecturing with a series of regional focuses depicted by human postures and gestures. The regions of interest (ROIs) in videos are semantically structured with text recognition and the aid of external documents. By tracing the …
Moving-Object Detection, Association, And Selection In Home Videos, Zailiang Pan, Chong-Wah Ngo
Moving-Object Detection, Association, And Selection In Home Videos, Zailiang Pan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Due to the prevalence of digital video camcorders, home videos have become an important part of life-logs of personal experiences. To enable efficient video parsing, a critical step is to automatically extract objects, events and scene characteristics present in videos. This paper addresses the problem of extracting objects from home videos. Automatic detection of objects is a classical yet difficult vision problem, particularly for videos with complex scenes and unrestricted domains. Compared with edited and surveillant videos, home videos captured in uncontrolled environment are usually coupled with several notable features such as shaking artifacts, irregular motions, and arbitrary settings. These …
Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan
Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Decision tree (DT) has great potential in image semantic learning due to its simplicity in implementation and its robustness to incomplete and noisy data. Decision tree learning naturally requires the input attributes to be nominal (discrete). However, proper discretization of continuous-valued image features is a difficult task. In this paper, we present a decision tree based image semantic learning method, which avoids the difficult image feature discretization problem by making use of semantic template (ST) defined for each concept in our database. A ST is the representative feature of a concept, generated from the low-level features of a collection of …
Real-Time Supply Chain Control Via Multi-Agent Adjustable Autonomy, Hoong Chuin Lau, Lucas Agussurja, Ramesh Thangarajoo
Real-Time Supply Chain Control Via Multi-Agent Adjustable Autonomy, Hoong Chuin Lau, Lucas Agussurja, Ramesh Thangarajoo
Research Collection School Of Computing and Information Systems
Real-time supply chain management in a rapidly changing environment requires reactive and dynamic collaboration among participating entities. In this work, we model supply chain as a multi-agent system where agents are subject to an adjustable autonomy. The autonomy of an agent refers to its capability to make and influence decisions within a multi-agent system. Adjustable autonomy means changing the autonomy of the agents during runtime as a response to changes in the environment. In the context of a supply chain, different entities will have different autonomy levels and objective functions as the environment changes, and the goal is to design …
Value-At-Risk In It Services Contracts, Robert J. Kauffman, Ryan Sougstad
Value-At-Risk In It Services Contracts, Robert J. Kauffman, Ryan Sougstad
Research Collection School Of Computing and Information Systems
As information systems (IS) and technology solutions become increasingly service-driven, managers are faced with the task of choosing parameters such as service-levels, pricing, and contract duration. Information technology (IT) services vendors manage portfolios of contracts in which parameters, decided at inception, are often subject to future risks. The contract profit maximization decision may adversely affect the risk position of the firm's portfolio of services contracts. We propose a model to inform vendors on setting optimal parameters for IS contracts subject to acceptable levels of risk. The analytic model presented draws from IS economics research and the principles of value-at-risk (VaR) …
Towards Efficient Computation Of Quality Bounded Solutions In Pomdps: Expected Value Approximation And Dynamic Disjunctive Beliefs, Pradeep Reddy Varakantham, Rajiv Maheswaran, Tapana Gupta, Milind Tambe
Towards Efficient Computation Of Quality Bounded Solutions In Pomdps: Expected Value Approximation And Dynamic Disjunctive Beliefs, Pradeep Reddy Varakantham, Rajiv Maheswaran, Tapana Gupta, Milind Tambe
Research Collection School Of Computing and Information Systems
While POMDPs (partially observable markov decision problems) are a popular computational model with wide-ranging applications, the computational cost for optimal policy generation is prohibitive. Researchers are investigating ever-more efficient algorithms, yet many applications demand such algorithms bound any loss in policy quality when chasing efficiency. To address this challenge, we present two new techniques. The first approximates in the value space to obtain solutions efficiently for a pre-specified error bound. Unlike existing techniques, our technique guarantees the resulting policy will meet this bound. Furthermore, it does not require costly computations to determine the quality loss of the policy. Our second …
Image Segmentation Using Multi-Coloured Active Illumination, Tze Ki (Xu Shuqi) Koh, Nicholas Miles, Barrie Hayes-Gill
Image Segmentation Using Multi-Coloured Active Illumination, Tze Ki (Xu Shuqi) Koh, Nicholas Miles, Barrie Hayes-Gill
Research Collection School Of Computing and Information Systems
In this paper, the use of active illumination is extended to image segmentation, specifically in the case of overlapping particles. This work is based on Multi-Flash Imaging (MFI), originally developed by Mitsubishi Electric Labs, to detect depth discontinuities. Illuminations of different wavelengths are projected from multiple positions, providing additional information about a scene compared to conventional segmentation techniques. Shadows are used to identify true object edges. The identification of non- occluded particles is made possible by exploiting the fact that shadows are cast on underlying particles. Implementation issues such as selecting the appropriate colour model and number of illuminations are …
Anticipatory Event Detection Via Classification, He Qi, Kuiyu Chang, Ee Peng Lim
Anticipatory Event Detection Via Classification, He Qi, Kuiyu Chang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The idea of event detection is to identify interesting patterns from a constant stream of incoming news documents. Previous research in event detection has largely focused on identifying the first event or tracking subsequent events belonging to a set of pre-assigned topics such as earthquakes, airline disasters, etc. In this paper, we describe a new problem, called anticipatory event detection (AED), which aims to detect if a user-specified event has transpired. AED can be viewed as a personalized combination of event tracking and new event detection. We propose using sentence-level and document-level classification approaches to solve the AED problem for …
Mining Multiple Visual Appearances Of Semantics For Image Annotation, Hung-Khoon Tan, Chong-Wah Ngo
Mining Multiple Visual Appearances Of Semantics For Image Annotation, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper investigates the problem of learning the visual semantics of keyword categories for automatic image annotation. Supervised learning algorithms which learn only a single concept point of a category are limited in their effectiveness for image annotation. We propose to use data mining techniques to mine multiple concepts, where each concept may consist of one or more visual parts, to capture the diverse visual appearances of a single keyword category. For training, we use the Apriori principle to efficiently mine a set of frequent blobsets to capture the semantics of a rich and diverse visual category. Each concept is …
Measuring Qualities Of Articles Contributed By Online Communities, Ee Peng Lim, Ba-Quy Vuong, Hady W. Lauw, Aixin Sun
Measuring Qualities Of Articles Contributed By Online Communities, Ee Peng Lim, Ba-Quy Vuong, Hady W. Lauw, Aixin Sun
Research Collection School Of Computing and Information Systems
Using open source Web editing software (e.g., wiki), online community users can now easily edit, review and publish articles collaboratively. While much useful knowledge can be derived from these articles, content users and critics are often concerned about their qualities. In this paper, we develop two models, namely basic model and peer review model, for measuring the qualities of these articles and the authorities of their contributors. We represent collaboratively edited articles and their contributors in a bipartite graph. While the basic model measures an article's quality using both the authorities of contributors and the amount of contribution from each …
Rapid Identification Of Column Heterogeneity, Bing Tian Dai, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh Venkatasubramanian
Rapid Identification Of Column Heterogeneity, Bing Tian Dai, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh Venkatasubramanian
Research Collection School Of Computing and Information Systems
No abstract provided.
Online Advertising By The Comparison Challenge Approach, Jae Wong Lee, Jae Kyu Lee
Online Advertising By The Comparison Challenge Approach, Jae Wong Lee, Jae Kyu Lee
Research Collection School Of Computing and Information Systems
To enhance the effectiveness of online comparisons from a manufacturer's point of view, we develop a framework for the comparison challenge approach. To develop the comparison challenge framework, we analyze 12 factors that determine the characteristics of comparison and propose models of valuable comparison challenges using the CompareMe and CompareThem strategies. We demonstrate the approaches with the example of PC selection. To help plan the comparison challenges, we formulate a mathematical programming model that maximizes the total value of comparison under the constraints of comparison opportunity and budgetary limitation. The model is applied to eight comparison scenarios, and its performance …
Identifying Difficulties In Learning Uml, Keng Siau, Poi-Peng Loo
Identifying Difficulties In Learning Uml, Keng Siau, Poi-Peng Loo
Research Collection School Of Computing and Information Systems
Despite its recognition as a standard object-oriented modeling language, Unified Modeling Language (UML) has been criticized for such deficiencies as semantic inconsistencies, vagueness, and conflicting notations. the relationship between these deficiencies and the difficulties in the learning process is the focus of this study. A concept mapping technique is used to unveil the learning difficulties and suggestions for alleviating them are provided.
Query-Based Watermarking For Xml Data, Xuan Zhou, Hwee Hwa Pang, Kian-Lee Tan
Query-Based Watermarking For Xml Data, Xuan Zhou, Hwee Hwa Pang, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
As increasing amount of XML data is exchanged over the internet, copyright protection of this type of data is becoming an important requirement for many applications. In this paper, we introduce a rights protection scheme for XML data based on digital watermarking. One of the main challenges for watermarking XML data is that the data could be easily reorganized by an adversary in an attempt to destroy any embedded watermark. To overcome it, we propose a query-based watermarking scheme, which creates queries to identify available watermarking capacity, such that watermarks could be recovered from reorganized data through query rewriting. The …
Formal Analysis And Systematic Construction Of Two-Factor Authentication Scheme, Guomin Yang, Duncan S. Wong, Huaxiong Wang, Xiaotie Deng
Formal Analysis And Systematic Construction Of Two-Factor Authentication Scheme, Guomin Yang, Duncan S. Wong, Huaxiong Wang, Xiaotie Deng
Research Collection School Of Computing and Information Systems
One of the most commonly used two-factor authentication mechanisms is based on smart card and user’s password. Throughout the years, there have been many schemes proposed, but most of them have already been found flawed due to the lack of formal security analysis. On the cryptanalysis of this type of schemes, in this paper, we further review two recently proposed schemes and show that their security claims are invalid. To address the current issue, we propose a new and simplified property set and a formal adversarial model for analyzing the security of this type of schemes. We believe that the …
On The Lower Bound Of Local Optimums In K-Means Algorithms, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung
On The Lower Bound Of Local Optimums In K-Means Algorithms, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung
Research Collection School Of Computing and Information Systems
No abstract provided.
Modeling Local Interest Points For Semantic Detection And Video Search At Trecvid 2006, Yu-Gang Jiang, Xiaoyong Wei, Chong-Wah Ngo, Hung-Khoon Tan, Wanlei Zhao, Xiao Wu
Modeling Local Interest Points For Semantic Detection And Video Search At Trecvid 2006, Yu-Gang Jiang, Xiaoyong Wei, Chong-Wah Ngo, Hung-Khoon Tan, Wanlei Zhao, Xiao Wu
Research Collection School Of Computing and Information Systems
Local interest points (LIPs) and their features have been shown to obtain surprisingly good results in object detection and recognition. Its effectiveness and scalability, however, have not been seriously addressed in large-scale multimedia database, for instance TRECVID benchmark. The goal of our works is to investigate the role and performance of LIPs, when coupling with multi-modality features, for high-level feature extraction and automatic video search.
Integration Of Wikipedia And A Geography Digital Library, Ee Peng Lim, Zhe Wang, Darwin Sadeli, Yuanyuan Li, Chew-Hung Chang, Kalyani Chatterjea, Dion Hoe-Lian Goh, Yin-Leng Theng, Jun Zhang, Aixin Sun
Integration Of Wikipedia And A Geography Digital Library, Ee Peng Lim, Zhe Wang, Darwin Sadeli, Yuanyuan Li, Chew-Hung Chang, Kalyani Chatterjea, Dion Hoe-Lian Goh, Yin-Leng Theng, Jun Zhang, Aixin Sun
Research Collection School Of Computing and Information Systems
In this paper, we address the problem of integrating Wikipedia, an online encyclopedia, and G-Portal, a web-based digital library, in the geography domain. The integration facilitates the sharing of data and services between the two web applications that are of great value in learning. We first present an overall system architecture for supporting such an integration and address the metadata extraction problem associated with it. In metadata extraction, we focus on extracting and constructing metadata for geo-political regions namely cities and countries. Some empirical performance results will be presented. The paper will also describe the adaptations of G-Portal and Wikipedia …
Smartic: Towards Building An Accurate, Robust And Scalable Specification Miner, David Lo, Siau-Cheng Khoo
Smartic: Towards Building An Accurate, Robust And Scalable Specification Miner, David Lo, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Improper management of software evolution, compounded by imprecise, and changing requirements, along with the “short time to market ” requirement, commonly leads to a lack of up-to-date specifications. This can result in software that is characterized by bugs, anomalies and even security threats. Software specification mining is a new technique to address this concern by inferring specifications automatically. In this paper, we propose a novel API specification mining architecture called SMArTIC (Specification Mining Architecture with Trace fIltering and Clustering) to improve the accuracy, robustness and scalability of specification miners. This architecture is constructed based on two hypotheses: (1) Erroneous traces …
New Paradigm Of Inference Control With Trusted Computing, Yanjiang Yang, Yingjiu Li, Robert H. Deng
New Paradigm Of Inference Control With Trusted Computing, Yanjiang Yang, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
The database server is a crucial bottleneck in traditional inference control architecture, as it enforces highly computation-intensive auditing for all users who query the protected database. As a result, most auditing methods, though rigorously studied, can never be implemented in practice for protecting largescale real-world database systems. To shift this paradigm, we propose a new inference control architecture that will entrust inference control to each users platform, provided that the platform is equipped with trusted computing technology. The trusted computing technology is designed to attest the state of a users platform to the database server, so as to assure the …