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Full-Text Articles in Entire DC Network
Discovering And Exploiting Causal Dependencies For Robust Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Discovering And Exploiting Causal Dependencies For Robust Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian networks (BNs) for building recommender systems that minimize context acquisition. Our learning approach iteratively trims the BN-based context model until it contains only the minimal set of context parameters that are important to a user. In addition, we show that a two-tiered context model can effectively capture the causal …
An Empirical Study On Large-Scale Content-Based Image Retrieval, Yuk Man Wong, Steven C. H. Hoi, Michael R. Lyu
An Empirical Study On Large-Scale Content-Based Image Retrieval, Yuk Man Wong, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
One key challenge in content-based image retrieval (CBIR) is to develop a fast solution for indexing high-dimensional image contents, which is crucial to building large-scale CBIR systems. In this paper, we propose a scalable content-based image retrieval scheme using locality-sensitive hashing (LSH), and conduct extensive evaluations on a large image testbed of a half million images. To the best of our knowledge, there is less comprehensive study on large-scale CBIR evaluation with a half million images. Our empirical results show that our proposed solution is able to scale for hundreds of thousands of images, which is promising for building Web-scale …
Learning Causal Models For Noisy Biological Data Mining: An Application To Ovarian Cancer Detection, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Learning Causal Models For Noisy Biological Data Mining: An Application To Ovarian Cancer Detection, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Undetected errors in the expression measurements from highthroughput DNA microarrays and protein spectroscopy could seriously affect the diagnostic reliability in disease detection. In addition to a high resilience against such errors, diagnostic models need to be more comprehensible so that a deeper understanding of the causal interactions among biological entities like genes and proteins may be possible. In this paper, we introduce a robust knowledge discovery approach that addresses these challenges. First, the causal interactions among the genes and proteins in the noisy expression data are discovered automatically through Bayesian network learning. Then, the diagnosis of a disease based on …
The Business Model Of "Software-As-A-Service", Dan Ma
The Business Model Of "Software-As-A-Service", Dan Ma
Research Collection School Of Computing and Information Systems
The emergence of the software-as-a-service (SaaS) business model has attracted great attentions from both researchers and practitioners. SaaS vendors deliver on-demand information processing services to users, and thus offer computing utility rather than the standalone software itself. In this work, the author propose an analytical model to study the competition between the SaaS and the traditional COTS (commercial off-the-shelf) solutions for software applications. The author show that when software applications become open, modulated, and standardized, the SaaS business model will take a significant market share. In addition, under certain market conditions, offering users an easy exit option through the software …
An Examination Of Online Product Comparison Service: Fit Between Product Type And Disposition Style, Fiona Fui-Hoon Nah, W. Hong, L. Chen, H. Lee
An Examination Of Online Product Comparison Service: Fit Between Product Type And Disposition Style, Fiona Fui-Hoon Nah, W. Hong, L. Chen, H. Lee
Research Collection School Of Computing and Information Systems
Horizontal and vertical disposition styles are two main formats used in product comparison service on e-commerce websites. In this research, we hypothesize that there is a fit between product type (‘think’ vs. ‘feel’ product) and disposition style (horizontal vs. vertical style), where horizontal disposition style is more appropriate for ‘feel’ products and vertical disposition style is a better fit for ‘think’ products. An experiment will be carried out to test the hypotheses.
Appraisal - Who Needs It?, M. Thulasidas
Appraisal - Who Needs It?, M. Thulasidas
Research Collection School Of Computing and Information Systems
WE GO through this ordeal every year when our boss- es appraise our performance. Our career progression, bonus and salary depend on it. So, we spend sleepless nights agonising over it.
Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Near-duplicate keyframes (NDK) play a unique role in large-scale video search, news topic detection and tracking. In this paper, we propose a novel NDK retrieval approach by exploring both visual and textual cues from the visual vocabulary and semantic context respectively. The vocabulary, which provides entries for visual keywords, is formed by the clustering of local keypoints. The semantic context is inferred from the speech transcript surrounding a keyframe. We experiment the usefulness of visual keywords and semantic context, separately and jointly, using cosine similarity and language models. By linearly fusing both modalities, performance improvement is reported compared with the …
Continuous Medoid Queries Over Moving Objects, Stavros Papadopoulos, Dimitris Sacharidis, Kyriakos Mouratidis
Continuous Medoid Queries Over Moving Objects, Stavros Papadopoulos, Dimitris Sacharidis, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
In the k-medoid problem, given a dataset P, we are asked to choose kpoints in P as the medoids. The optimal medoid set minimizes the average Euclidean distance between the points in P and their closest medoid. Finding the optimal k medoids is NP hard, and existing algorithms aim at approximate answers, i.e., they compute medoids that achieve a small, yet not minimal, average distance. Similarly in this paper, we also aim at approximate solutions. We consider, however, the continuous version of the problem, where the points in P move and our task is to maintain the medoid set on-the-fly …
On Searching Continuous Nearest Neighbors In Wireless Data Broadcast Systems, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
On Searching Continuous Nearest Neighbors In Wireless Data Broadcast Systems, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee
Research Collection School Of Computing and Information Systems
A continuous nearest neighbor (CNN) search, which retrieves the nearest neighbors corresponding to every point in a given query line segment, is important for location-based services such as vehicular navigation and tourist guides. It is infeasible to answer a CNN search by issuing a traditional nearest neighbor query at every point of the line segment due to the large number of queries generated and the overhead on bandwidth. Algorithms have been proposed recently to support CNN search in the traditional client-server systems but not in the environment of wireless data broadcast, where uplink communication channels from mobile devices to the …
Continuous Monitoring Of Top-K Queries Over Sliding Windows, Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias
Continuous Monitoring Of Top-K Queries Over Sliding Windows, Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias
Research Collection School Of Computing and Information Systems
Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace …
Cross-Lingual Query Suggestion Using Query Logs Of Different Languages, Wei Gao, Cheng Niu, Jian-Yun Nie, Ming Zhou, Jian Hu, Kam-Fai Wong, Hsiao-Wuen Hon
Cross-Lingual Query Suggestion Using Query Logs Of Different Languages, Wei Gao, Cheng Niu, Jian-Yun Nie, Ming Zhou, Jian Hu, Kam-Fai Wong, Hsiao-Wuen Hon
Research Collection School Of Computing and Information Systems
Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs. Previously, the suggested terms are mostly in the same language of the input query. In this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one …
Continuous Nearest Neighbor Queries Over Sliding Windows, Kyriakos Mouratidis, Dimitris Papadias
Continuous Nearest Neighbor Queries Over Sliding Windows, Kyriakos Mouratidis, Dimitris Papadias
Research Collection School Of Computing and Information Systems
Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as fluctuations of edge weights. The first one maintains the query results by processing only updates that may invalidate …
The Multi-Agent Data Collection In Hla-Based Simulation System, Heng-Jie Song, Zhi-Qi Shen, Chunyan Miao, Ah-Hwee Tan, Guo-Peng Zhao
The Multi-Agent Data Collection In Hla-Based Simulation System, Heng-Jie Song, Zhi-Qi Shen, Chunyan Miao, Ah-Hwee Tan, Guo-Peng Zhao
Research Collection School Of Computing and Information Systems
The High Level Architecture (HLA) for distributed simulation was proposed by the Defense Modeling and Simulation Office of the Department of Defense (DOD) in order to support interoperability among simulations as well as reuse of simulation models. One aspect of reusability is to collect and analyze data generated in simulation exercises, including a record of events that occur during the execution, and the states of simulation objects. In order to improve the performance of existing data collection mechanisms in the HLA simulation system, the paper proposes a multi-agent data collection system. The proposed approach adopts the hierarchical data management/organization mechanism …
A Multi-Scale Tikhonov Regularization Scheme For Implicit Surface Modeling, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
A Multi-Scale Tikhonov Regularization Scheme For Implicit Surface Modeling, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Kernel machines have recently been considered as a promising solution for implicit surface modelling. A key challenge of machine learning solutions is how to fit implicit shape models from large-scale sets of point cloud samples efficiently. In this paper, we propose a fast solution for approximating implicit surfaces based on a multi-scale Tikhonov regularization scheme. The optimization of our scheme is formulated into a sparse linear equation system, which can be efficiently solved by factorization methods. Different from traditional approaches, our scheme does not employ auxiliary off-surface points, which not only saves the computational cost but also avoids the problem …
Assessing Organizational Innovation Capability And Its Effect On E-Commerce Initiatives, Ann L. Fruhling, Keng Siau
Assessing Organizational Innovation Capability And Its Effect On E-Commerce Initiatives, Ann L. Fruhling, Keng Siau
Research Collection School Of Computing and Information Systems
This research uses a qualitative approach to study the innovative capability of two organizations and the effect of innovation on their E-Commerce initiatives, strategies, and outcomes. The Innovation Strategy Model is used in this research to analyze the innovative capability of two organizations. The case study research methodology was selected and two case studies are presented. The research results show that one organization is more innovative than the other in terms of its innovative capability. A post-study follow-up shows that the organization that was high on innovative capability was very successful in their E-Commerce initiative whereas the other organization was …
Can Uml Be Simplified? Practitioner Use Of Uml In Separate Domains, J. Erickson, Keng Siau
Can Uml Be Simplified? Practitioner Use Of Uml In Separate Domains, J. Erickson, Keng Siau
Research Collection School Of Computing and Information Systems
UML’s complexity is regularly criticized by practitioners and researchers alike, who argue that such complexity is a considerable detriment to the adoption and use of UML in the field. Attempts have been made to assess and/or measure UML’s complexity in a number of ways. Erickson and Siau proposed that a subset (kernel) of UML, composed of the most important constructs, could be equated with the complexity that practitioners face when using the modeling language. This research extends Erickson and Siau’s work by proposing a UML kernel in three application areas, real-time, webbased and enterprise systems. Compared to other modeling methods …
Designing An Experimental Gaming Platform For Trading Grid Resources, Danny Oh, Shih-Fen Cheng, Dan Ma, Ravi Bapna
Designing An Experimental Gaming Platform For Trading Grid Resources, Danny Oh, Shih-Fen Cheng, Dan Ma, Ravi Bapna
Research Collection School Of Computing and Information Systems
This paper describes our current work in designing an experimental gaming platform for simulating the trading of grid resources. The open platform allows researchers in grid economics to experiment with different market structures and pricing models. We would be using a design science approach in the implementation. Key design considerations and an overview of the functional design of the platform are presented and discussed.
A Time-And-Value Centric Provenance Model And Architecture For Medical Event Streams, Marion Bllount, John Davis, Archan Misra, Daby Sow, Min Wang
A Time-And-Value Centric Provenance Model And Architecture For Medical Event Streams, Marion Bllount, John Davis, Archan Misra, Daby Sow, Min Wang
Research Collection School Of Computing and Information Systems
Provenance becomes a critical requirement for healthcare IT infrastructures, especially when pervasive biomedical sensors act as a source of raw medical streams for large-scale, automated clinical decision support systems. Medical and legal requirements will make it obligatory for such systems to answer queries regarding the underlying data samples from which output alerts are derived, the IDs of the processing components used and the privileges of the individuals and software components accessing the medical data. Unfortunately, existing models of either annotation or process based provenance are designed for transaction-oriented systems and do not satisfy the unique requirements for systems processing high-volume, …
Innovation Engine, Arcot Desai Narasimhalu
Innovation Engine, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
This paper describes a meta-model for innovation using an automobile engine as a metaphor. This innovation meta-model is used to manage a collection of innovation models. We develop an algorithm to identify innovations with potential for success using this meta-model. This meta-model can be used by corporations and individuals to identify plausible innovations at any given point in time.
Usage-Based Versus Flat Pricing For E-Business Services With Differentiated Qos, Zhen Liu, Laura Wynter, Cathy Xia
Usage-Based Versus Flat Pricing For E-Business Services With Differentiated Qos, Zhen Liu, Laura Wynter, Cathy Xia
Research Collection School Of Computing and Information Systems
Design of e-commerce services that are competitive, in such a quickly responding market, requires the analyses of prices and price structures. We present a general model of an e-commerce market that allows us to analyze optimal price structures, both flat and usage-based. Based on the price structure of a major web hosting provider, we consider both single-tier and two-tier (burst-rate) pricing, and our result suggests that the more complex two-tier structure may not be worth the marketing effort, as the firm?s equilibrium profits will not increase through the use of this structure. An essential feature of our approach is that …
Teaching "Global Project Management" With Distributed Team Projects, Randy Weinberg, Benjamin Gan
Teaching "Global Project Management" With Distributed Team Projects, Randy Weinberg, Benjamin Gan
Research Collection School Of Computing and Information Systems
The education of rising professionals must keep pace with changing forces of globalization and the realities of distributed work. Students who understand the basics of global project management, teamwork and collaboration are likely to find themselves at a competitive advantage over those who do not. This article describes the experiences in an undergraduate course called Global Project Management offered concurrently at two universities, one in the U.S. and one in Singapore, and incorporating collaborative student projects.
An Efficient Identity-Based Key Exchange Protocol With Kgs Forward Secrecy For Low-Power Devices, Robert W. Zhu, Guomin Yang, Duncan S. Wong
An Efficient Identity-Based Key Exchange Protocol With Kgs Forward Secrecy For Low-Power Devices, Robert W. Zhu, Guomin Yang, Duncan S. Wong
Research Collection School Of Computing and Information Systems
For an ID-based key exchange (KE) protocol, KGS forward secrecy is about the protection of previously established session keys after the master secret key of the Key Generation Server (KGS) is compromised. This is the strongest notion of forward secrecy that one can provide for an ID-based KE protocol. Among all the comparable protocols, there are only a few of them that provide this level of forward secrecy, and all of these protocols require expensive bilinear pairing operations and map-to-point hash operations that may not be suitable for implementation on low-power devices such as sensors. In this paper, we propose …
Simplifying Cyber Foraging For Mobile Devices, Rajesh Krishna Balan, Darren Gergle, Mahadev Satyanarayanan, James Herbsleb
Simplifying Cyber Foraging For Mobile Devices, Rajesh Krishna Balan, Darren Gergle, Mahadev Satyanarayanan, James Herbsleb
Research Collection School Of Computing and Information Systems
Cyber foraging is the transient and opportunistic use of compute servers by mobile devices. The short market life of such devices makes rapid modification of applications for remote execution an important problem. We describe a solution that combines a "little language" for cyber foraging with an adaptive runtime system. We report results from a user study showing that even novice developers are able to successfully modify large, unfamiliar applications in just a few hours. We also show that the quality of novice-modified and expert-modified applications are comparable in most cases.
Intelligence Through Interaction: Towards A Unified Theory For Learning, Ah-Hwee Tan, Gail A. Carpenter, Stephen Grossberg
Intelligence Through Interaction: Towards A Unified Theory For Learning, Ah-Hwee Tan, Gail A. Carpenter, Stephen Grossberg
Research Collection School Of Computing and Information Systems
Machine learning, a cornerstone of intelligent systems, has typically been studied in the context of specific tasks, including clustering (unsupervised learning), classification (supervised learning), and control (reinforcement learning). This paper presents a learning architecture within which a universal adaptation mechanism unifies a rich set of traditionally distinct learning paradigms, including learning by matching, learning by association, learning by instruction, and learning by reinforcement. In accordance with the notion of embodied intelligence, such a learning theory provides a computational account of how an autonomous agent may acquire the knowledge of its environment in a real-time, incremental, and continuous manner. Through a …
Learning Nonparametric Kernel Matrices From Pairwise Constraints, Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Learning Nonparametric Kernel Matrices From Pairwise Constraints, Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Some kernel learning methods assume the target kernel matrix to be a linear combination of parametric kernel matrices. This assumption again importantly limits the flexibility of the target kernel matrices. The key challenge with nonparametric kernel learning arises from the difficulty in linking the nonparametric kernels to the input patterns. In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with …
Time Capsule Signature: Efficient And Provably Secure Constructions, Bessie C. Hu, Duncan S. Wong, Qiong Huang, Guomin Yang, Xiaotie Deng
Time Capsule Signature: Efficient And Provably Secure Constructions, Bessie C. Hu, Duncan S. Wong, Qiong Huang, Guomin Yang, Xiaotie Deng
Research Collection School Of Computing and Information Systems
Time Capsule Signature, first formalized by Dodis and Yum in Financial Cryptography 2005, is a digital signature scheme which allows a signature to bear a (future) time t so that the signature will only be valid at time t or later, when a trusted third party called time server releases time-dependent information for checking the validity of a time capsule signature. Also, the actual signer of a time capsule signature has the privilege to make the signature valid before time t.In this paper, we provide a new security model of time capsule signature such that time server is not required …
A More Natural Way To Construct Identity-Based Identification Schemes, Guomin Yang, Jing Chen, Duncan S. Wong, Xiaotie Deng, Dongsheng Wang
A More Natural Way To Construct Identity-Based Identification Schemes, Guomin Yang, Jing Chen, Duncan S. Wong, Xiaotie Deng, Dongsheng Wang
Research Collection School Of Computing and Information Systems
Constructing identification schemes is one of the fundamental problems in cryptography, and is very useful in practice. An identity-based identification (IBI) scheme allows a prover to identify itself to a public verifier who knows only the claimed identity of the prover and some common information. In this paper, we propose a simple and efficient framework for constructing IBI schemes. Unlike some related framework which constructs IBI schemes from some standard identification schemes, our framework is based on some more fundamental assumptions on intractable problems. Depending on the features of the underlying intractable problems presumed in our framework, we can derive …
A Hybrid Of Plot-Based And Character-Based Interactive Storytelling, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen
A Hybrid Of Plot-Based And Character-Based Interactive Storytelling, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen
Research Collection School Of Computing and Information Systems
Interactive storytelling in the virtual environment attracts a lot of research interests in recent years. Story plot and character are two most important elements of a story. Based on these two elements, currently there are two research directions: plot-based and character-based interactive storytelling. However, plot-based approach lacks the refinement of character behaviors as character-based approach. On the other side, character-based approach does not follow a well organized story plot so that the moral of the story might be distorted. Therefore, there is a need to develop an integrated framework to achieve the balance between conveying story moral and enhancing the …
Instance Weighting For Domain Adaptation In Nlp, Jing Jiang, Chengxiang Zhai
Instance Weighting For Domain Adaptation In Nlp, Jing Jiang, Chengxiang Zhai
Research Collection School Of Computing and Information Systems
Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting per- spective. We formally analyze and charac- terize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, cor- responding to the different distributions of instances and classification functions in the source and the target domains. We then propose a general instance weighting frame- work for domain adaptation. Our empir- ical results on three NLP tasks show that …
Mobile G-Portal Supporting Collaborative Sharing And Learning In Geography Fieldwork: An Empirical Study, Yin-Leng Theng, Kuah-Li Tan, Ee Peng Lim, Jun Zhang, Dion Hoe-Lian Goh, Kalyani Chatterjea, Chew-Hung Chang, Aixin Sun, Han Yu, Nam Hai Dang, Yuanyuan Li, Minh Chanh Vo
Mobile G-Portal Supporting Collaborative Sharing And Learning In Geography Fieldwork: An Empirical Study, Yin-Leng Theng, Kuah-Li Tan, Ee Peng Lim, Jun Zhang, Dion Hoe-Lian Goh, Kalyani Chatterjea, Chew-Hung Chang, Aixin Sun, Han Yu, Nam Hai Dang, Yuanyuan Li, Minh Chanh Vo
Research Collection School Of Computing and Information Systems
Integrated with G-Portal, a Web-based geospatial digital library of geography resources, this paper describes the implementation of Mobile G-Portal, a group of mobile devices as learning assistant tools supporting collaborative sharing and learning for geography fieldwork. Based on a modified Technology Acceptance Model and a Task-Technology Fit model, an initial study with Mobile G-Portal was conducted involving 39 students in a local secondary school. The findings suggested positive indication of acceptance of Mobile G-Portal for geography fieldwork. The paper concludes with a discussion on technological challenges, recommendations for refinement of Mobile G-Portal, and design implications in general for digital libraries …