Digital Commons Network^™

Cosign: A Parallel Algorithm For Coordinated Traffic Signal Control, Shih-Fen Cheng, Marina A. Epelman, Robert L. Smith

Research Collection School Of Computing and Information Systems

The problem of finding optimal coordinated signal timing plans for a large number of traffic signals is a challenging problem because of the exponential growth in the number of joint timing plans that need to be explored as the network size grows. In this paper, the game-theoretic paradigm of fictitious play to iteratively search for a coordinated signal timing plan is employed, which improves a system-wide performance criterion for a traffic network. The algorithm is robustly scalable to realistic-size networks modeled with high-fidelity simulations. Results of a case study for the city of Troy, MI, where there are 75 signalized …

Dynamic Multi-Linked Negotiations In Multi-Echelon Production Scheduling Networks, Hoong Chuin Lau, Guan Li Soh, Wee Chong Wan

Research Collection School Of Computing and Information Systems

In this paper, we are concerned with scheduling resources in a multi-tier production/logistics system for multi-indenture goods. Unlike classical production scheduling problems, the problem we study is concerned with local utilities which are private. We present an agent model and investigate an efficient scheme for handling multi-linked agent negotiations. With this scheme we attempt to overcome the drawbacks of sequential negotiations and negotiation parameter settings. Our approach is based on embedding a credit-based negotiation protocol within a local search scheduling algorithm. We demonstrate the computational efficiency and effectiveness of the approach in solving a real-life dynamic production scheduling problem which …

Plans As Products Of Learning, Samin Karim, Budhitama Subagdja, Liz Sonenberg

Research Collection School Of Computing and Information Systems

This paper presents motivations and current related work in the field of plan learning. Additionally, two approaches that achieve plan learning are presented. The two presented approaches are centred on the BDI framework of agency and have particular focus on plans, which, alongside goals, are the means to fulfil intentions in most pragmatic and theoretical realisations of the BDI framework. The first approach is a hybrid architecture that combines a BDI plan extractor and executor with a generic low-level learner. The second approach uses hypotheses to suggest incremental refinements of a priori plans. Both approaches achieve plan generation that is …

Set-Based Cascading Approaches For Magnetic Resonance (Mr) Image Segmentation (Scamis), Jiang Liu, Tze-Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang

Research Collection School Of Computing and Information Systems

This paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking. In our methodology, we describe how set operations can be utilized to better overcome these problems. To evaluate the effectiveness of this approach, Magnetic Resonance Images taken from a teaching hospital research programme have been utilised, to reflect the real world quality needed for testing in patient datasets. A comparison between the …

Supply Chain Information Sharing In A Macro Prediction Market, Zhiling Guo, Fang Fang, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

This paper aims to address supply chain partners' incentives for information sharing from an information systems design perspective. Specifically, we consider a supply chain characterized by N geographically distributed retailers who order a homogeneous product from one manufacturer. Each retailer's demand risk consists of two parts: a systematic risk part that affects all retailers and an idiosyncratic risk part that only has a local effect. We propose a macro prediction market to effectively elicit and aggregate useful information about systematic demand risk. We show that such information can be used to achieve accurate demand forecast sharing and better channel coordination …

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.

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 …

Low-Latency Broadcast In Multirate Wireless Mesh Networks, Chun Tung Chou, Archan Misra, Junaid Qadir

Research Collection School Of Computing and Information Systems

In a multirate wireless network, a node can dynamically adjust its link transmission rate by switching between different modulation schemes. In the current IEEE802.11a/b/g standards, this rate adjustment is defined for unicast traffic only. In this paper, we consider a wireless mesh network (WMN), where a node can dynamically adjust its link-layer multicast rates to its neighbors, and address the problem of realizing low-latency network-wide broadcast in such a mesh. We first show that the multirate broadcast problem is significantly different from the single-rate case. We will then present an algorithm for achieving low-latency broadcast in a multirate mesh which …

Robust Controllability In Temporal Constraint Networks Under Uncertainty, Hoong Chuin Lau, Jia Li, Roland H. C. Yap

Research Collection School Of Computing and Information Systems

Temporal constraint networks are embedded in many planning and scheduling problems. In dynamic problems, a fundamental challenge is to decide whether such a network can be executed as uncertainty is revealed over time. Very little work in this domain has been done in the probabilistic context. In this paper, we propose a Temporal Constraint Network (TCN) model where durations of uncertain activities are represented by random variables. We wish to know whether such a network is robust controllable, i.e. can be executed dynamically within a given failure probability, and if so, how one might find a feasible schedule as the …

Dcma: A Label-Switching Mac For Efficient Packet Forwarding In Multi-Hop Wireless Networks, Arup Acharya, Sachin Ganu, Archan Misra

Research Collection School Of Computing and Information Systems

This paper addresses the problem of efficient packet forwarding in a multihop, wireless "mesh" network. We present an efficient interface contained forwarding (ICF) architecture for a "wireless router," i.e., a forwarding node with a single wireless network interface card (NIC) in a multihop wireless network that allows a packet to be forwarded entirely within the NIC of the forwarding node without requiring per-packet intervention by the node's CPU. To effectively forward packets in a pipelined fashion without incurring the 802.11-related overheads of multiple independent channel accesses, we specify a slightly modified version of the 802.11 MAC, called data driven cut-through …

Design And Analysis Of A Class-Aware Recursive Loop Scheduler For Class-Based Scheduling, Raphael Rom, Moshe Sidi, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

In this paper, we consider the problem of devising a loop scheduler that allocates slots to users according to their relative weights as smoothly as possible. Instead of the existing notion of smoothness based on balancedness, we propose a variance-based metric which is more intuitive and easier to compute.

We propose a recursive loop scheduler for a class-based scheduling scenario based on an optimal weighted round-robin scheduler. We show that it achieves very good allocation smoothness with almost no degradation in intra-class fairness. In addition, we also demonstrate the equivalence between our proposed metric and the balancedness-based metric.

Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The identification of near-duplicate keyframe (NDK) pairs is a useful task for a variety of applications such as news story threading and content-based video search. In this paper, we propose a novel approach for the discovery and tracking of NDK pairs and threads in the broadcast domain. The detection of NDKs in a large data set is a challenging task due to the fact that when the data set increases linearly, the computational cost increases in a quadratic speed, and so does the number of false alarms. This paper explores the symmetric and transitive nature of near-duplicate for the effective …

Critical Success Factors For Erp Implementation And Upgrade, Fiona Fui-Hoon Nah, Santiago Delgado

Research Collection School Of Computing and Information Systems

Seven categories of critical success factors were identified from the ERP literature: (1) business plan and vision; (2) change management, (3) communication; (4) ERP team composition, skills and compensation; (5) management support and championship; (6) project management, (7) system analysis, selection and technical implementation. We conducted a case study of two organizations that had implemented and upgraded ERP systems. We adopted Markus and Tanis' four-phase model and compared the importance of these critical success factors across the phases of ERP implementation and upgrade. The importance of these factors across the phases of ERP implementation and upgrade is very similar. 'Business …

Service Pattern Discovery Of Web Service Mining In Web Service Registry-Repository, Qianhui Althea Liang, Jen-Yao Chung, Steven M. Miller, Yang Ouyang

Research Collection School Of Computing and Information Systems

This paper presents and elaborates the concept of Web service usage patterns and pattern discovery through service mining. We define three different levels of service usage data: i) user request level, ii) template level and iii) instance level. At each level, we investigate patterns of service usage data and the discovery of these patterns. An algorithm for service pattern discovery at the template level is presented. We show the system architecture of a service-mining enabled service registry repository. Web service patterns, pattern discovery and pattern mining supports the discovery and composition of complex services, which in turn supports the application …

Two-Instant Reallocation In Two-Echelon Spare Parts Inventory Systems, Huawei Song, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of deciding when and how to perform reallocation of existing spare parts in a multi-echelon reparable item inventory system. We present a mathematical model that solves the problem when there are two reallocation instants, in response to the open challenge post by Cao and Silver(2005) to consider two or more possible reallocations within a replenishment cycle.

Natural Document Clustering By Clique Percolation In Random Graphs, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Document clustering techniques mostly depend on models that impose explicit and/or implicit priori assumptions as to the number, size, disjunction characteristics of clusters, and/or the probability distribution of clustered data. As a result, the clustering effects tend to be unnatural and stray away more or less from the intrinsic grouping nature among the documents in a corpus. We propose a novel graph-theoretic technique called Clique Percolation Clustering (CPC). It models clustering as a process of enumerating adjacent maximal cliques in a random graph that unveils inherent structure of the underlying data, in which we unleash the commonly practiced constraints in …

Extracting Link Chains Of Relationship Instances From A Website, Myo-Myo Naing, Ee Peng Lim, Roger Hsiang-Li Chiang

Research Collection School Of Computing and Information Systems

Web pages from a Web site can often be associated with concepts in an ontology, and pairs of Web pages also can be associated with relationships between concepts. With such associations, the Web site can be searched, browsed, or even reorganized based on the concept and relationship labels of its Web pages. In this article, we study the link chain extraction problem that is critical to the extraction of Web pages that are related. A link chain is an ordered list of anchor elements linking two Web pages related by some semantic relationship. We propose a link chain extraction method …

Privacy Enhanced Superdistribution Of Layered Content With Trusted Access Control, Daniel J. T. Chong, Robert H. Deng

Research Collection School Of Computing and Information Systems

Traditional superdistribution approaches do not address consumer privacy issues and also do not reliably prevent the malicious consumer from indiscriminately copying and redistributing the decryption keys or the decrypted content. The layered nature of common digital content can also be exploited to efficiently provide the consumer with choices over the quality of the content, allowing him/her to pay less for lower quality consumption and vice versa. This paper presents a system that superdistributes encrypted layered content and (1) allows the consumer to select a quality level at which to decrypt and consume the content; (2) prevents the merchant from knowing …

The Boundary Spanner’S Role In Organisational Learning: Unleashing Untapped Potential, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

PurposeThe organizational behavior and management literature has devoted a lot attention on processes and technology in organizational learning. There has been little work to examine the role of boundary spanners in organizational learning. The purpose of this paper is to highlight the importance of boundary spanners in enhancing organizational learning.Design/methodology/approachThe existing boundary spanning literature was reviewed and the salient points on their role in organizational learning discussed.Practical implicationsThe paper offers a view on tapping the hidden potential of boundary spanners in organizations in order to enhance the organization's learning capacity form the external environment. Practicing manager would then be able …

Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan

Research Collection School Of Computing and Information Systems

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …

Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan

Research Collection School Of Computing and Information Systems

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …

Masking Page Reference Patterns In Encryption Databases On Untrusted Storage, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

To support ubiquitous computing, the underlying data have to be persistent and available anywhere-anytime. The data thus have to migrate from devices that are local to individual computers, to shared storage volumes that are accessible over open network. This potentially exposes the data to heightened security risks. In particular, the activity on a database exhibits regular page reference patterns that could help attackers learn logical links among physical pages and then launch additional attacks. We propose two countermeasures to mitigate the risk of attacks initiated through analyzing the shared storage server’s activity for those page patterns. The first countermeasure relocates …

Three Architectures For Trusted Data Dissemination In Edge Computing, Shen-Tat Goh, Hwee Hwa Pang, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Edge computing pushes application logic and the underlying data to the edge of the network, with the aim of improving availability and scalability. As the edge servers are not necessarily secure, there must be provisions for users to validate the results—that values in the result tuples are not tampered with, that no qualifying data are left out, that no spurious tuples are introduced, and that a query result is not actually the output from a different query. This paper aims to address the challenges of ensuring data integrity in edge computing. We study three schemes that enable users to check …

Wireless Indoor Positioning System With Enhanced Nearest Neighbors In Signal Space Algorithm, Quang Tran, Juki Wirawan Tantra, Ah-Hwee Tan, Ah-Hwee Tan, Kin-Choong Yow, Dongyu Qiu

Research Collection School Of Computing and Information Systems

With the rapid development and wide deployment of wireless Local Area Networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common Nearest Neighbor in Signal Space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some …

Disclosure Analysis For Two-Way Contingency Tables, Haibing Lu, Yingjiu Li, Xintao Wu

Research Collection School Of Computing and Information Systems

Disclosure analysis in two-way contingency tables is important in categorical data analysis. The disclosure analysis concerns whether a data snooper can infer any protected cell values, which contain privacy sensitive information, from available marginal totals (i.e., row sums and column sums) in a two-way contingency table. Previous research has been targeted on this problem from various perspectives. However, there is a lack of systematic definitions on the disclosure of cell values. Also, no previous study has been focused on the distribution of the cells that are subject to various types of disclosure. In this paper, we define four types of …

Human-Computer Interaction Research In The Management Information Systems Discipline, Fiona Fui-Hoon Nah, Ping Zhang, Scott Mccoy, Munyong Yi

Research Collection School Of Computing and Information Systems

No abstract provided.

Rights Protection For Data Cubes, Jie Guo, Yingjiu Li, Robert H. Deng, Kefei Chen

Research Collection School Of Computing and Information Systems

We propose a rights protection scheme for data cubes. The scheme embeds ownership information by modifying a set of selected cell values. The embedded message will not affect the usefulness of data cubes in the sense that the sum queries at any aggregation level are not affected. At the same time, the errors introduced to individual cell values are under control. The embedded message can be detected with a high probability even in the presence of typical data cube attacks. The proposed scheme can thus be used for protecting data cubes from piracy in an open, distributed environment.

Collaborative Image Retrieval Via Regularized Metric Learning, Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring …

An Experimental Study On U-Commerce Adoption: The Impact Of Personalization And Privacy Concerns, H. Sheng, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

U-commerce represents “anytime, anywhere” commerce, which is believed to be the ultimate form of commerce. Ucommerce can provide a high level of personalization, which can bring additional benefits and values to customers. However, despite these promises and potential benefits, customers’ privacy is a major concern and obstacle to the adoption of ucommerce. As customers’ intention to adopt u-commerce is based on the aggregate effect of perceived benefits and risk exposure (e.g., privacy concerns), this research examines how personalization and context can impact on customers’ perceived benefits and privacy concerns, and how this aggregated effect in turn affects u-commerce adoption intention. …

Use Of A Classroom Response System To Enhance Classroom Interactivity, Keng Siau, H. Sheng, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Classroom interactivity is a critical component of teaching and learning. This paper reports on the use of a classroom response system to enhance classroom interactivity in a systems analysis and design course. The success of the project was assessed using both quantitative and qualitative data. A pretest/posttest design was used to examine the effects of a classroom respons system on interactivity. The results show that a classroom response system can significantly improve classroom interactivity. Qualitative data was also collected to identify the strengths and weaknesses of using a classroom response system to enhance classroom interaction. Based on the quantitative and …