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Articles 9241 - 9270 of 10474
Full-Text Articles in Entire DC Network
Re-Engineering Xid Technologies - From Enterprise To Consumer Markets, Arcot Desai Narasimhalu
Re-Engineering Xid Technologies - From Enterprise To Consumer Markets, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
Several studies have addressed the process of taking ideas to markets but few have shared the experiences of start up companies that have reexamined their product strategies and repositioned their products and services for better revenues and profits. This paper reports the efforts related to repositioning of XID technologies, a start up company, into new markets while continuing to exploit its core technical competencies.
Innovation Stack - Choosing Innovations For Commercialization, Arcot Desai Narasimhalu
Innovation Stack - Choosing Innovations For Commercialization, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
This paper describes a method for enterprises to order the innovations of interest according to a number of parameters including their own business strategy and core competencies. The method takes into account aspects such as ability to create entry barriers and complementary assets. Enterprises can now use this method to both filter out innovations that may not be of interest to them and then order the short listed or selected innovations according to their attractiveness.
Cost-Time Sensitive Decision Tree With Missing Values, Shichao Zhang, Xiaofeng Zhu, Jilian Zhang, Chengqi Zhang
Cost-Time Sensitive Decision Tree With Missing Values, Shichao Zhang, Xiaofeng Zhu, Jilian Zhang, Chengqi Zhang
Research Collection School Of Computing and Information Systems
Cost-sensitive decision tree learning is very important and popular in machine learning and data mining community. There are many literatures focusing on misclassification cost and test cost at present. In real world application, however, the issue of time-sensitive should be considered in cost-sensitive learning. In this paper, we regard the cost of time-sensitive in cost-sensitive learning as waiting cost (referred to WC), a novelty splitting criterion is proposed for constructing cost-time sensitive (denoted as CTS) decision tree for maximal decrease the intangible cost. And then, a hybrid test strategy that combines the sequential test with the batch test strategies is …
Towards Optimal Bag-Of-Features For Object Categorization And Semantic Video Retrieval, Yu-Gang Jiang, Chong-Wah Ngo, Jun Yang
Towards Optimal Bag-Of-Features For Object Categorization And Semantic Video Retrieval, Yu-Gang Jiang, Chong-Wah Ngo, Jun Yang
Research Collection School Of Computing and Information Systems
Bag-of-features (BoF) deriving from local keypoints has recently appeared promising for object and scene classification. Whether BoF can naturally survive the challenges such as reliability and scalability of visual classification, nevertheless, remains uncertain due to various implementation choices. In this paper, we evaluate various factors which govern the performance of BoF. The factors include the choices of detector, kernel, vocabulary size and weighting scheme. We offer some practical insights in how to optimize the performance by choosing good keypoint detector and kernel. For the weighting scheme, we propose a novel soft-weighting method to assess the significance of a visual word …
Efficient Near-Duplicate Keyframe Retrieval With Visual Language Models, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Efficient Near-Duplicate Keyframe Retrieval With Visual Language Models, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Near-duplicate keyframe retrieval is a critical task for video similarity measure, video threading and tracking. In this paper, instead of using expensive point-to-point matching on keypoints, we investigate the visual language models built on visual keywords to speed up the near-duplicate keyframe retrieval. The main idea is to estimate a visual language model on visual keywords for each keyframe and compare keyframes by the likelihood of their visual language models. Experiments on a subset of TRECVID-2004 video corpus show that visual language models built on visual keywords demonstrate promising performance for near-duplicate keyframe retrieval, which greatly speed up the retrieval …
Is Interpersonal Trust A Necessary Condition For Organisational Learning?, Siu Loon Hoe
Is Interpersonal Trust A Necessary Condition For Organisational Learning?, Siu Loon Hoe
Research Collection School Of Computing and Information Systems
The organisational behaviour and management literature has devoted a lot attention on various factors affecting organisational learning. While there has been much work done to examine trust in promoting organisational learning, there is a lack of consensus on the specific type of trust involved. The purpose of this paper is to highlight the importance of interpersonal trust in promoting organisational learning and propose a research agenda to test the extent of interpersonal trust on organisational learning. This paper contributes to the existing organisational learning literature by specifying a specific form of trust, interpersonal trust, which promotes organisational learning and proposing …
Analyzing Feature Trajectories For Event Detection, Qi He, Kuiyu Chang, Ee Peng Lim
Analyzing Feature Trajectories For Event Detection, Qi He, Kuiyu Chang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
We consider the problem of analyzing word trajectories in both time and frequency domains, with the specific goal of identifying important and less-reported, periodic and aperiodic words. A set of words with identical trends can be grouped together to reconstruct an event in a completely un-supervised manner. The document frequency of each word across time is treated like a time series, where each element is the document frequency - inverse document frequency (DFIDF) score at one time point. In this paper, we 1) first applied spectral analysis to categorize features for different event characteristics: important and less-reported, periodic and aperiodic; …
Improving Memory-Based Collaborative Filtering Using A Factor-Based Approach, Zhenxue Zhang, Dongsong Zhang, Zhiling Guo
Improving Memory-Based Collaborative Filtering Using A Factor-Based Approach, Zhenxue Zhang, Dongsong Zhang, Zhiling Guo
Research Collection School Of Computing and Information Systems
Collaborative Filtering (CF) systems generate recommendations for a user by aggregating item ratings of other like-minded users. The memory-based approach is a common technique used in CF. This approach first uses statistical methods such as Pearson’s Correlation Coefficient to measure user similarities based on their previous ratings on different items. Users will then be grouped into different neighborhood depending on the calculated similarities. Finally, the system will generate predictions on how a user would rate a specific item by aggregating ratings on the item cast by the identified neighbors of his/her. However, current memory-based CF method only measures user similarities …
Generating Job Schedules For Vessel Operations In A Container Terminal, Thin Yin Leong, Hoong Chuin Lau
Generating Job Schedules For Vessel Operations In A Container Terminal, Thin Yin Leong, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
No abstract provided.
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 …
Estimation Of Impulse Response Functions Using Long Autoregression, Pao Li Chang, Shinichi Sakata
Estimation Of Impulse Response Functions Using Long Autoregression, Pao Li Chang, Shinichi Sakata
Research Collection School Of Economics
This article proposes an alternative methodology to estimate impulse response functions without imposing parametric restrictions. The impulse responses are estimated by regressing the series of interest on estimated innovations, which are the residuals obtained from a prior-stage ‘long autoregression.’ We establish the consistency and asymptotic normality of the proposed estimator. The proposed estimator is closely related to the estimator of Jordà (2005, American Economic Review 95, 161–182). Our large sample analysis, as a byproduct, establishes the asymptotic equivalence between Jordà's estimator and our estimator, and provides justifications for the statistical inference method used in Jordà (2005).
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 …
Researching Situated Learning: Participation, Identity And Practices In Client-Consultant Relationships, Karen Handley, Timothy Adrian Robert Clark, Robin Fincham, Andrew Sturdy
Researching Situated Learning: Participation, Identity And Practices In Client-Consultant Relationships, Karen Handley, Timothy Adrian Robert Clark, Robin Fincham, Andrew Sturdy
Research Collection Lee Kong Chian School Of Business
Situated learning theory has emerged as a radical alternative to conventional cognitivist theories of knowledge and learning, emphasizing the relational and structural aspects of learning as well as the dynamics of identity construction. However, although many researchers have embraced the theoretical strengths of this perspective, methodological and operational issues remain undeveloped in the literature. This article seeks to address these deficiencies by developing a conceptual framework informed by situated learning theory and by investigating the methodological implications. The framework is applied in the context of an empirical study of how management consultants learn the practices and identities appropriate to clientconsultant …
A Structuralist Model Of The Small Open Economy In The Short, Medium And Long Run, Hian Teck Hoon, Edmund S. Phelps
A Structuralist Model Of The Small Open Economy In The Short, Medium And Long Run, Hian Teck Hoon, Edmund S. Phelps
Research Collection School Of Economics
In open-economy macroeconomics there is a monetary model in the Keynesian tradition that is deemed serviceable for analyzing the short run and there is a nonmonetary neoclassical theory thought capable of handling the long run. But do the Keynesian and neoclassical models meet the challenges thrown out by the main events of the past few decades¡ªthe '80s shock to Europe taking the form of an external jump in real interest rates; the sort of shock experienced in the U.S. and parts of northern Europe in the second half of the '90s: the emerging prospect of new industries in the future …
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, …