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Articles 2731 - 2760 of 3436
Full-Text Articles in Databases and Information Systems
Information Sharing And Strategic Signaling In Supply Chains, Robert J. Kauffman, Hamid Mohtadi
Information Sharing And Strategic Signaling In Supply Chains, Robert J. Kauffman, Hamid Mohtadi
Research Collection School Of Computing and Information Systems
Information sharing in procurement occurs in rich and varied industry contexts in which managerial decisions are made and organizational strategy is formulated. We explore how information sharing ought to work in procurement contexts that involve investments in inter-organizational information systems (IOS) and collaborative planning, forecasting and replenishment (CPFR) practices. How and under what circumstances does a firm that plays the role of a supply chain buyer decide to share information on key variables, such as point-of-sale consumer demand data with its supplier, up the supply chain? This is a key issue that crosses the boundary between supply chain management and …
Nonrigid Shape Recovery By Gaussian Process Regression, Jianke Zhu, Steven C. H. Hoi, Michael R. Liu
Nonrigid Shape Recovery By Gaussian Process Regression, Jianke Zhu, Steven C. H. Hoi, Michael R. Liu
Research Collection School Of Computing and Information Systems
Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangulated mesh models heavily relying on the quadratic regularization term are difficult to accurately capture large deformations, such as severe bending. In this paper, we propose a novel Gaussian process regression approach to the nonrigid shape recovery problem, which does not require to involve a predefined triangulated mesh model. By taking advantage of our novel Gaussian process regression formulation together with a robust coarse-to-fine optimization scheme, the proposed method is fully automatic and is able to handle large …
Intentional Learning Agent Architecture, Budhitama Subagdja, Liz Sonenberg, Iyad Rahwan
Intentional Learning Agent Architecture, Budhitama Subagdja, Liz Sonenberg, Iyad Rahwan
Research Collection School Of Computing and Information Systems
Dealing with changing situations is a major issue in building agent systems. When the time is limited, knowledge is unreliable, and resources are scarce, the issue becomes more challenging. The BDI (Belief-Desire-Intention) agent architecture provides a model for building agents that addresses that issue. The model can be used to build intentional agents that are able to reason based on explicit mental attitudes, while behaving reactively in changing circumstances. However, despite the reactive and deliberative features, a classical BDI agent is not capable of learning. Plans as recipes that guide the activities of the agent are assumed to be static. …
Simplenpkl: Simple Non-Parametric Kernel Learning, Jinfeng Zhuang, Ivor Tsang, Steven C. H. Hoi
Simplenpkl: Simple Non-Parametric Kernel Learning, Jinfeng Zhuang, Ivor Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity of standard interior-point SDP solvers could be as high as O(n6.5). Such intensive computation cost prohibits NPK learning applicable to real applications, even for data sets of moderate size. In this paper, we propose an efficient approach to NPK learning from side information, referred to as SimpleNPKL, which can efficiently learn non-parametric kernels from large sets of pairwise constraints. In particular, we show that the proposed SimpleNPKL with linear loss has a closed-form solution that can …
A Revisit Of Generative Model For Automatic Image Annotation Using Markov Random Fields, Yu Xiang, Xiangdong Zhou, Tat-Seng Chua, Chong-Wah Ngo
A Revisit Of Generative Model For Automatic Image Annotation Using Markov Random Fields, Yu Xiang, Xiangdong Zhou, Tat-Seng Chua, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Much research effort on Automatic Image Annotation (AIA) has been focused on Generative Model, due to its well formed theory and competitive performance as compared with many well designed and sophisticated methods. However, when considering semantic context for annotation, the model suffers from the weak learning ability. This is mainly due to the lack of parameter setting and appropriate learning strategy for characterizing the semantic context in the traditional generative model. In this paper, we present a new approach based on Multiple Markov Random Fields (MRF) for semantic context modeling and learning. Differing from previous MRF related AIA approach, we …
On Mining Rating Dependencies In Online Collaborative Rating Networks, Hady W. Lauw, Ee Peng Lim, Ke Wang
On Mining Rating Dependencies In Online Collaborative Rating Networks, Hady W. Lauw, Ee Peng Lim, Ke Wang
Research Collection School Of Computing and Information Systems
The trend of social information processing sees e-commerce and social web applications increasingly relying on user-generated content, such as rating, to determine the quality of objects and to generate recommendations for users. In a rating system, a set of reviewers assign to a set of objects different types of scores based on specific evaluation criteria. In this paper, we seek to determine, for each reviewer and for each object, the dependency between scores on any two given criteria. A reviewer is said to have high dependency between a pair of criteria when his or her rating scores on objects based …
Zeros And Ones, M. Thulasidas
Zeros And Ones, M. Thulasidas
Research Collection School Of Computing and Information Systems
Computers are notorious for their infuriatingly literal obedience. I am sure anyone who has ever worked with a computer has come across the lack of empathy on its part – it follows our instructions to the dot, yet ends up accomplishing something altogether different from what we intend. Let’s spare a thought for the way your glorified adding machine makes sense of things
Predicting Outcome For Collaborative Featured Article Nomination In Wikipedia, Meiqun Hu, Ee Peng Lim, Ramayya Krishnan
Predicting Outcome For Collaborative Featured Article Nomination In Wikipedia, Meiqun Hu, Ee Peng Lim, Ramayya Krishnan
Research Collection School Of Computing and Information Systems
In Wikipedia, good articles are wanted. While Wikipedia relies on collaborative effort from online volunteers for quality checking, the process of selecting top quality articles is time consuming. At present, the duty of decision making is shouldered by only a couple of administrators. Aiming to assist in the quality checking cycles so as to cope with the exponential growth of online contributions to Wikipedia, this work studies the task of predicting the outcome of featured article (FA) nominations. We analyze FA candidate (FAC) sessions collected over a period of 3.5 years, and examine the extent to which consensus has been …
Semisupervised Svm Batch Mode Active Learning With Applications To Image Retrieval, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Semisupervised Svm Batch Mode Active Learning With Applications To Image Retrieval, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to …
Sharing Hierarchical Mobile Multimedia Content Using The Mobitop System, Quang Minh Nguyen, Thi Nhu Quynh Kim, Dion Hoe-Lian Goh, Ee-Peng Lim, Yin-Leng Theng, Kalyani Chatterjea, Chew-Hung Chang, Aixin Sun, Khasfariyati Razikin
Sharing Hierarchical Mobile Multimedia Content Using The Mobitop System, Quang Minh Nguyen, Thi Nhu Quynh Kim, Dion Hoe-Lian Goh, Ee-Peng Lim, Yin-Leng Theng, Kalyani Chatterjea, Chew-Hung Chang, Aixin Sun, Khasfariyati Razikin
Research Collection School Of Computing and Information Systems
We introduce MobiTOP (Mobile Tagging of Objects and People), a map-based application which allows users to contribute and share geo-referenced multimedia annotations via mobile devices. An important feature of MobiTOP is that annotations are hierarchical, allowing annotations to be annotated to an arbitrary depth. MobiTOP's interface was designed using a participatory design methodology to ensure that the user interface meets the needs of potential users. In an evaluation, a group of student-teachers involved in a geographical field study were tasked to collaboratively identify rock formations using the MobiTOP system. The students who were in the field were guided by their …
A Self-Organizing Neural Network Architecture For Intentional Planning Agents, Budhitama Subagdja, Ah-Hwee Tan
A Self-Organizing Neural Network Architecture For Intentional Planning Agents, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents a model of neural network embodiment of intentions and planning mechanisms for autonomous agents. The model bridges the dichotomy of symbolic and non-symbolic representation in developing agents. Some novel techniques are introduced that enables the neural network to process and manipulate sequential and hierarchical structures of information. It is suggested that by incorporating intentional agent model which relies on explicit symbolic description with self-organizing neural networks that are good at learning and recognizing patterns, the best from both sides can be exploited. This paper demonstrates that plans can be represented as weighted connections and reasoning processes can …
A Novel Framework For Efficient Automated Singer Identification In Large Music Databases, Jialie Shen, John Shepherd, Bin Cui, Kian-Lee Tan
A Novel Framework For Efficient Automated Singer Identification In Large Music Databases, Jialie Shen, John Shepherd, Bin Cui, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Over the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for …
Joint Ranking For Multilingual Web Search, Wei Gao, Cheng Niu, Ming Zhou, Kam-Fai Wong
Joint Ranking For Multilingual Web Search, Wei Gao, Cheng Niu, Ming Zhou, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Ranking for multilingual information retrieval (MLIR) is a task to rank documents of different languages solely based on their relevancy to the query regardless of query’s language. Existing approaches are focused on combining relevance scores of different retrieval settings, but do not learn the ranking function directly. We approach Web MLIR ranking within the learning-to-rank (L2R) framework. Besides adopting popular L2R algorithms to MLIR, a joint ranking model is created to exploit the correlations among documents, and induce the joint relevance probability for all the documents. Using this method, the relevant documents of one language can be leveraged to improve …
Exploring Hierarchically Organized Georeferenced Multimedia Annotations In The Mobitop System, Thi Nhu Quynh Kim, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin Leng Theng, Quang Minh Nguyen, Ee-Peng Lim, Aixin Sun, Chew Hung Chang, Kalyani Chatterjea
Exploring Hierarchically Organized Georeferenced Multimedia Annotations In The Mobitop System, Thi Nhu Quynh Kim, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin Leng Theng, Quang Minh Nguyen, Ee-Peng Lim, Aixin Sun, Chew Hung Chang, Kalyani Chatterjea
Research Collection School Of Computing and Information Systems
We introduce MobiTOP, a map-based interface for accessing hierarchically organized georeferenced annotations. Each annotation contains multimedia content associated with a location, and users are able to annotate existing annotations, in effect creating a hierarchy. MobiTOPs interface was designed using a participatory design methodology to ensure that the user interface meets the needs of potential users. A pilot study to compare the MobiTOP interface with a space-filling thumbnail (SFT) interface suggested that participants preferred the MobiTOP design for accessing annotations even though the SFT interface was conceptually easier to understand resources.
An Incremental Threshold Method For Continuous Text Search Queries, Kyriakos Mouratidis, Hwee Hwa Pang
An Incremental Threshold Method For Continuous Text Search Queries, Kyriakos Mouratidis, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
A text filtering system monitors a stream of incoming documents, to identify those that match the interest profiles of its users. The user interests are registered at a server as continuous text search queries. The server constantly maintains for each query a ranked result list, comprising the recent documents (drawn from a sliding window) with the highest similarity to the query. Such a system underlies many text monitoring applications that need to cope with heavy document traffic, such as news and email monitoring. In this paper, we propose the first solution for processing continuous text queries efficiently. Our objective is …
Efficient Evaluation Of Multiple Preference Queries, Hou U Leong, Nikos Mamaoulis, Kyriakos Mouratidis
Efficient Evaluation Of Multiple Preference Queries, Hou U Leong, Nikos Mamaoulis, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Consider multiple users searching for a hotel room, based on size, cost, distance to the beach, etc. Users may have variable preferences expressed by different weights on the attributes of the searched objects. Although individual preference queries can be evaluated by selecting the object in the database with the highest aggregate score, in the case of multiple requests at the same time, a single object cannot be assigned to more than one users. The challenge is to compute a fair 1-1 matching between the queries and a subset of the objects. We model this as a stable-marriage problem and propose …
Opaque: Protecting Path Privacy In Directions Search, Ken C. K. Lee, Wang-Chien Lee, Hong Va Leong, Baihua Zheng
Opaque: Protecting Path Privacy In Directions Search, Ken C. K. Lee, Wang-Chien Lee, Hong Va Leong, Baihua Zheng
Research Collection School Of Computing and Information Systems
Directions search returns the shortest path from a source to a destination on a road network. However, the search interests of users may be exposed to the service providers, thus raising privacy concerns. For instance, a path query that finds a path from a resident address to a clinic may lead to a deduction about "who is related to what disease". To protect user privacy from accessing directions search services, we introduce the OPAQUE system, which consists of two major components: (1) an obfuscator that formulates obfuscated path queries by mixing true and fake sources/destinations; and (2) an obfuscated path …
Fast Object Search On Road Networks, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng
Fast Object Search On Road Networks, Ken C. K. Lee, Wang-Chien Lee, Baihua Zheng
Research Collection School Of Computing and Information Systems
In this paper, we present ROAD, a general framework to evaluate Location-Dependent Spatial Queries (LDSQ)s that searches for spatial objects on road networks. By exploiting search space pruning technique and providing a dynamic object mapping mechanism, ROAD is very efficient and flexible for various types of queries, namely, range search and nearest neighbor search, on objects over large-scale networks. ROAD is named after its two components, namely, Route Overlay and Association Directory, designed to address the network traversal and object access aspects of the framework. In ROAD, a large road network is organized as a hierarchy of interconnected regional sub-networks …
Web Query Recommendation Via Sequential Query Prediction, Qi He, Daxin Jiang, Zhen Liao, Steven C. H. Hoi, Kuiyu Chang, Ee Peng Lim, Hang Li
Web Query Recommendation Via Sequential Query Prediction, Qi He, Daxin Jiang, Zhen Liao, Steven C. H. Hoi, Kuiyu Chang, Ee Peng Lim, Hang Li
Research Collection School Of Computing and Information Systems
Web query recommendation has long been considered a key feature of search engines. Building a good Web query recommendation system, however, is very difficult due to the fundamental challenge of predicting users' search intent, especially given the limited user context information. In this paper, we propose a novel "sequential query prediction" approach that tries to grasp a user's search intent based on his/her past query sequence and its resemblance to historical query sequence models mined from massive search engine logs. Different query sequence models were examined, including the naive variable length N-gram model, Variable Memory Markov (VMM) model, and our …
Continuous Visible Nearest Neighbour Queries, Yunjun Gao, Baihua Zheng, Wang-Chien Lee, Gencai Chen
Continuous Visible Nearest Neighbour Queries, Yunjun Gao, Baihua Zheng, Wang-Chien Lee, Gencai Chen
Research Collection School Of Computing and Information Systems
In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q, a CVNN query returns a set of (p, R) tuples such that p ? P is the nearest neighbor (NN) to every point r along the interval R ? q as well as p is visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R, due to the obstruction of some obstacles in …
House Of Cards, M. Thulasidas
House Of Cards, M. Thulasidas
Research Collection School Of Computing and Information Systems
We are in dire straits - no doubt about it. Our banks and financial edifices are collapsing. Those left standing also look shaky. The financial industry as a whole is battling to survive. And, as its frontline warriors, we will bear the brunt of the blood- bath sure to ensue any minute now. A good opportunity to play solitaire?
Learning Image‐Text Associations, Tao Jiang, Ah-Hwee Tan
Learning Image‐Text Associations, Tao Jiang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Web information fusion can be defined as the problem of collating and tracking information related to specific topics on the World Wide Web. Whereas most existing work on Web information fusion has focused on text-based multidocument summarization, this paper concerns the topic of image and text association, a cornerstone of cross-media Web information fusion. Specifically, we present two learning methods for discovering the underlying associations between images and texts based on small training data sets. The first method based on vague transformation measures the information similarity between the visual features and the textual features through a set of predefined domain-specific …
An Examination Of Perceptions Of Male And Female Avatars, D. Dewester, Fiona Fui-Hoon Nah, S. Gervais, Keng Siau
An Examination Of Perceptions Of Male And Female Avatars, D. Dewester, Fiona Fui-Hoon Nah, S. Gervais, Keng Siau
Research Collection School Of Computing and Information Systems
Virtual worlds are three-dimensional, computer-generated worlds in which users take the form of avatars. Through their avatars, users can interact with objects and other avatars in the virtual world. Virtual worlds are growing in importance in both educational institutions and businesses. Educational institutions have adopted virtual worlds as a medium for instructional delivery whereas businesses are using virtual worlds for recruitment, training, collaboration, and marketing. Given these emerging phenomena, a better understanding of behavioral and perceptual issues in virtual worlds is warranted. In this paper, we propose a research model to study gender stereotypicality of male and female avatars and …
Team Collaboration In Virtual Worlds: The Role Of Task Complexity, Fiona Fui-Hoon Nah, B. Mennecke, S. Schiller
Team Collaboration In Virtual Worlds: The Role Of Task Complexity, Fiona Fui-Hoon Nah, B. Mennecke, S. Schiller
Research Collection School Of Computing and Information Systems
Virtual worlds are three-dimensional, computer-generated worlds where team collaboration is facilitated through the use of shared virtual space. In this research, we are interested in studying the effect of task complexity on team collaboration. We use a puzzle as the collaboration task and manipulate task complexity using the number of puzzle pieces. We hypothesize that task complexity will influence team cohesion as well as satisfaction with team process and outcome, increase the time taken to complete the task, and increase the relative unevenness in team members' contributions in terms of physical effort to accomplish the task due to the increased …
Stochastic Modeling Western Paintings For Effective Classification, Jialie Shen
Stochastic Modeling Western Paintings For Effective Classification, Jialie Shen
Research Collection School Of Computing and Information Systems
As one of the most important cultural heritages, classical western paintings have always played a special role in human live and been applied for many different purposes. While image classification is the subject of a plethora of related publications, relatively little attention has been paid to automatic categorization of western classical paintings which could be a key technique of modern digital library, museums and art galleries. This paper studies automatic classification on large western painting image collection. We propose a novel framework to support automatic classification on large western painting image collections. With this framework, multiple visual features can be …
Web Social Mining, Hady W. Lauw, Ee Peng Lim
Web Social Mining, Hady W. Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
No abstract provided.
Continuous Monitoring Of Spatial Queries, Kyriakos Mouratidis
Continuous Monitoring Of Spatial Queries, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
No abstract provided.
Chaos And Uncertainty, M. Thulasidas
Chaos And Uncertainty, M. Thulasidas
Research Collection School Of Computing and Information Systems
The end of 2008 in the finance industry can be summarized in two words – chaos and uncertainty. The subprime crisis, where everybody lost; the dizzying commodity price movements; the pink slip syndrome; the spectacular bank busts; and the gargantuan bail-outs all vouch for it.
Tuning On-Air Signatures For Balancing Performance And Confidentiality, Baihua Zheng, Wang-Chien Lee, Peng Liu, Dik Lun Lee, Xuhua Ding
Tuning On-Air Signatures For Balancing Performance And Confidentiality, Baihua Zheng, Wang-Chien Lee, Peng Liu, Dik Lun Lee, Xuhua Ding
Research Collection School Of Computing and Information Systems
In this paper, we investigate the trade off between performance and confidentiality in signature-based air indexing schemes for wireless data broadcast. Two metrics, namely, false drop probability and false guess probability, are defined to quantify the filtering efficiency and confidentiality loss of a signature scheme. Our analysis reveals that false drop probability and false guess probability share a similar trend as the tuning parameters of a signature scheme change and it is impossible to achieve a low false drop probability and a high false guess probability simultaneously. In order to balance the performance and confidentiality, we perform an analysis to …
Modelling Situation Awareness For Context‐Aware Decision Support, Yu-Hong Feng, Teck-Hou Teng, Ah-Hwee Tan
Modelling Situation Awareness For Context‐Aware Decision Support, Yu-Hong Feng, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Situation awareness modelling is popularly used in the command and control domain for situation assessment and decision support. However, situation models in real-world applications are typically complex and not easy to use. This paper presents a Context-aware Decision Support (CaDS) system, which consists of a situation model for shared situation awareness modelling and a group of entity agents, one for each individual user, for focused and customized decision support. By incorporating a rule-based inference engine, the entity agents provide functions including event classification, action recommendation, and proactive decision making. The implementation and the performance of the proposed system are demonstrated …