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Articles 7651 - 7680 of 8458
Full-Text Articles in Computer Sciences
Batch Mode Active Learning And Its Applications To Medical Image Classification, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Batch Mode Active Learning And Its Applications To Medical Image Classification, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu
Research Collection School Of Computing and Information Systems
The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher …
Learning Distance Metrics With Contextual Constraints For Image Retrieval, Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma
Learning Distance Metrics With Contextual Constraints For Image Retrieval, Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma
Research Collection School Of Computing and Information Systems
Relevant Component Analysis (RCA) has been proposed for learning distance metrics with contextual constraints for image retrieval. However, RCA has two important disadvantages. One is the lack of exploiting negative constraints which can also be informative, and the other is its incapability of capturing complex nonlinear relationships between data instances with the contextual information. In this paper, we propose two algorithms to overcome these two disadvantages, i.e., Discriminative Component Analysis (DCA) and Kernel DCA. Compared with other complicated methods for distance metric learning, our algorithms are rather simple to understand and very easy to solve. We evaluate the performance of …
On The Release Of Crls In Public Key Infrastructure, Chengyu Ma, Nan Hu, Yingjiu Li
On The Release Of Crls In Public Key Infrastructure, Chengyu Ma, Nan Hu, Yingjiu Li
Research Collection School Of Computing and Information Systems
Public key infrastructure provides a promising foundation for verifying the authenticity of communicating parties and transferring trust over the internet. The key issue in public key infrastructure is how to process certificate revocations. Previous research in this aspect has concentrated on the tradeoffs that can be made among different revocation options. No rigorous efforts have been made to understand the probability distribution of certificate revocation requests based on real empirical data. In this study, we first collect real empirical data from VeriSign and derive the probability function for certificate revocation requests. We then prove that a revocation system will become …
Security Analysis On A Conference Scheme For Mobile Communications, Zhiguo Wan, Feng Bao, Robert H. Deng, A. L. Ananda
Security Analysis On A Conference Scheme For Mobile Communications, Zhiguo Wan, Feng Bao, Robert H. Deng, A. L. Ananda
Research Collection School Of Computing and Information Systems
The conference key distribution scheme (CKDS) enables three or more parties to derive a common conference key to protect the conversation content in their conference. Designing a conference key distribution scheme for mobile communications is a difficult task because wireless networks are more susceptible to attacks and mobile devices usually obtain low power and limited computing capability. In this paper we study a conference scheme for mobile communications and find that the scheme is insecure against the replay attack. With our replay attack, an attacker with a compromised conference key can cause the conferees to reuse the compromised conference key, …
Can Online Reviews Reveal A Product's True Quality? Empirical Findings Analytical Modeling Of Online Word-Of-Mouth Communication, Nan Hu, Paul Pavlou, Jennifer Zhang
Can Online Reviews Reveal A Product's True Quality? Empirical Findings Analytical Modeling Of Online Word-Of-Mouth Communication, Nan Hu, Paul Pavlou, Jennifer Zhang
Research Collection School Of Computing and Information Systems
As a digital version of word-of-mouth, online review has become a major information source for consumers and has very important implications for a wide range of management activities. While some researchers focus their studies on the impact of online product review on sales, an important assumption remains unexamined, that is, can online product review reveal the true quality of the product? To test the validity of this key assumption, this paper first empirically tests the underlying distribution of online reviews with data from Amazon. The results show that 53% of the products have a bimodal and non-normal distribution. For these …
Multilearner Based Recursive Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan, Laxmi R. Iyer
Multilearner Based Recursive Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan, Laxmi R. Iyer
Research Collection School Of Computing and Information Systems
In supervised learning, most single solution neural networks such as constructive backpropagation give good results when used with some datasets but not with others. Others such as probabilistic neural networks (PNN) fit a curve to perfection but need to be manually tuned in the case of noisy data. Recursive percentage based hybrid pattern training (RPHP) overcomes this problem by recursively training subsets of the data, thereby using several neural networks. MultiLearner based recursive training (MLRT) is an extension of this approach, where a combination of existing and new learners are used and subsets are trained using the weak learner which …
Fuzzy Cognitive Goal Net For Interactive Storytelling Plot Design, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen
Fuzzy Cognitive Goal Net For Interactive Storytelling Plot Design, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen
Research Collection School Of Computing and Information Systems
Interactive storytelling attracts a lot of research interests among the interactive entertainments in recent years. Designing story plot for interactive storytelling is currently one of the most critical problems of interactive storytelling. Some traditional AI planning methods, such as Hierarchical Task Network, Heuristic Searching Method are widely used as the planning tool for the story plot design. This paper proposes a model called Fuzzy Cognitive Goal Net as the story plot planning tool for interactive storytelling, which combines the planning capability of Goal net and reasoning ability of Fuzzy Cognitive Maps. Compared to conventional methods, the proposed model shows a …
Cognitive Mapping Techniques For User-Database Interaction, Keng Siau, X. Tan
Cognitive Mapping Techniques For User-Database Interaction, Keng Siau, X. Tan
Research Collection School Of Computing and Information Systems
In this paper, we first develop a framework of user-database interaction. Based on this framework, we then provide a discussion on how notable human factors influence various dimensions of user-database interaction. Following that, we propose using cognitive mapping techniques to overcome some cognitive and behavioral biases during user-database interaction. Three popular cognitive mapping techniques-causal mapping, semantic mapping, and concept mapping-are introduced as techniques to elicit an individual's belief systems regarding a problem domain. Through an example database application, we demonstrate how to use these cognitive mapping techniques to improve user-database interaction. Finally, we discuss the implications of this research for …
Exploiting Domain Structure For Named Entity Recognition, Jing Jiang, Chengxiang Zhai
Exploiting Domain Structure For Named Entity Recognition, Jing Jiang, Chengxiang Zhai
Research Collection School Of Computing and Information Systems
Named Entity Recognition (NER) is a fundamental task in text mining and natural language understanding. Current approaches to NER (mostly based on supervised learning) perform well on domains similar to the training domain, but they tend to adapt poorly to slightly different domains. We present several strategies for exploiting the domain structure in the training data to learn a more robust named entity recognizer that can perform well on a new domain. First, we propose a simple yet effective way to automatically rank features based on their generalizabilities across domains. We then train a classifier with strong emphasis on the …
Discovering Causal Dependencies In Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Discovering Causal Dependencies In Mobile Context-Aware Recommenders, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Mobile context-aware recommender systems face unique challenges in acquiring context. Resource limitations make minimizing context acquisition a practical need, while the uncertainty inherent to the mobile environment makes missing context values a major concern. This paper introduces a scalable mechanism based on Bayesian network learning in a tiered context model to overcome both of these challenges. Extensive experiments on a restaurant recommender system showed that our mechanism can accurately discover causal dependencies among context, thereby enabling the effective identification of the minimal set of important context for a specific user and task, as well as providing highly accurate recommendations even …
Winning Back The Cup For Distributed Pomdps: Planning Over Continuous Belief Spaces, Pradeep Varakantham, Ranjit Nair, Milind Tambe, Makoto Yokoo
Winning Back The Cup For Distributed Pomdps: Planning Over Continuous Belief Spaces, Pradeep Varakantham, Ranjit Nair, Milind Tambe, Makoto Yokoo
Research Collection School Of Computing and Information Systems
Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms have been proposed to obtain locally or globally optimal policies. Unfortunately, most of these algorithms have either been explicitly designed or experimentally evaluated assuming knowledge of a starting belief point, an assumption that often does not hold in complex, uncertain domains. Instead, in such domains, it is important for agents to explicitly plan over continuous belief spaces. This paper provides a novel algorithm to explicitly compute finite horizon policies over continuous belief spaces, without restricting the space of …
Time-Dependent Semantic Similarity Measure Of Queries Using Historical Click-Through Data, Qiankun Zhao, Steven C. H. Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, Wei-Ying Ma
Time-Dependent Semantic Similarity Measure Of Queries Using Historical Click-Through Data, Qiankun Zhao, Steven C. H. Hoi, Tie-Yan Liu, Sourav S. Bhowmick, Michael R. Lyu, Wei-Ying Ma
Research Collection School Of Computing and Information Systems
It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between …
Efficient Querying And Resource Management Using Distributed Presence Information In Converged Networks, Dipanjan Chakraborty, Koustuv Dasgupta, Archan Misra
Efficient Querying And Resource Management Using Distributed Presence Information In Converged Networks, Dipanjan Chakraborty, Koustuv Dasgupta, Archan Misra
Research Collection School Of Computing and Information Systems
Next-generation converged networks shall deliver many innovative services over the standardized SIPbased IMS signaling infrastructure. Several such services exploit the joint presence information of a consumer, i.e. SIP entity requesting a service, and a vendor, i.e. SIP resource providing a service. Presence information is a collection of contextual attributes (e.g. location, availability, reputation), some of which change dynamically. Moreover, this collective presence information is distributed across multiple presence servers. While performing query matching based on joint presence information, a server usually routes each query to a locally available resource. However, skews in the spatio-temporal distribution of queries and resources may …
Large-Scale Text Categorization By Batch Mode Active Learning, Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Large-Scale Text Categorization By Batch Mode Active Learning, Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Large-scale text categorization is an important research topic for Web data mining. One of the challenges in large-scale text categorization is how to reduce the human efforts in labeling text documents for building reliable classification models. In the past, there have been many studies on applying active learning methods to automatic text categorization, which try to select the most informative documents for labeling manually. Most of these studies focused on selecting a single unlabeled document in each iteration. As a result, the text categorization model has to be retrained after each labeled document is solicited. In this paper, we present …
Real-Time Non-Rigid Shape Recovery Via Active Appearance Models For Augmented Reality, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Real-Time Non-Rigid Shape Recovery Via Active Appearance Models For Augmented Reality, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
One main challenge in Augmented Reality (AR) applications is to keep track of video objects with their movement, orientation, size, and position accurately. This poses a challenging task to recover nonrigid shape and global pose in real-time AR applications. This paper proposes a novel two-stage scheme for online non-rigid shape recovery toward AR applications using Active Appearance Models (AAMs). First, we construct 3D shape models from AAMs offline, which do not involve processing of the 3D scan data. Based on the computed 3D shape models, we propose an efficient online algorithm to estimate both 3D pose and non-rigid shape parameters …
Clip-Based Similarity Measure For Query-Dependent Clip Retrieval And Video Summarization, Yuxin Peng, Chong-Wah Ngo
Clip-Based Similarity Measure For Query-Dependent Clip Retrieval And Video Summarization, Yuxin Peng, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to visual and granularity factors. We apply the similarity measure for two tasks: retrieval and summarization. In video retrieval, a hierarchical retrieval framework is constructed based on MM and OM. The validity of the framework is theoretically proved and empirically verified on a video database of 21 …
Gestalt-Based Feature Similarity Measure In Trademark Database, Hui Jiang, Chong-Wah Ngo, Hung-Khoon Tan
Gestalt-Based Feature Similarity Measure In Trademark Database, Hui Jiang, Chong-Wah Ngo, Hung-Khoon Tan
Research Collection School Of Computing and Information Systems
Motivated by the studies in Gestalt principle, this paper describes a novel approach on the adaptive selection of visual features for trademark retrieval. We consider five kinds of visual saliencies: symmetry, continuity, proximity, parallelism and closure property. The first saliency is based on Zernike moments, while the others are modeled by geometric elements extracted illusively as a whole from a trademark. Given a query trademark, we adaptively determine the features appropriate for retrieval by investigating its visual saliencies. We show that in most cases, either geometric or symmetric features can give us good enough accuracy. To measure the similarity of …
Mining Rdf Metadata For Generalized Association Rules: Knowledge Discovery In The Semantic Web Era, Tao Jiang, Ah-Hwee Tan
Mining Rdf Metadata For Generalized Association Rules: Knowledge Discovery In The Semantic Web Era, Tao Jiang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this paper, we present a novel frequent generalized pattern mining algorithm, called GP-Close, for mining generalized associations from RDF metadata. To solve the over-generalization problem encountered by existing methods, GP-Close employs the notion of emphgeneralization closure for systematic over-generalization reduction.
On In-Network Synopsis Join Processing For Sensor Networks, Hai Yu, Ee Peng Lim, Jun Zhang
On In-Network Synopsis Join Processing For Sensor Networks, Hai Yu, Ee Peng Lim, Jun Zhang
Research Collection School Of Computing and Information Systems
The emergence of sensor networks enables applications that deploy sensors to collaboratively monitor environment and process data collected. In some scenarios, we are interested in using join queries to correlate data stored in different regions of a sensor network, where the data volume is large, making it prohibitive to transmit all data to a central server for joining. In this paper, we present an in-network synopsis join strategy for evaluating join queries in sensor networks with communication efficiency. In this strategy, we prune data that do not contribute to the join results in the early stage of the join processing, …
Osprey: A Practical Type System For Validating Dimensional Unit Correctness Of C Programs, Lingxiao Jiang, Zhendong Su
Osprey: A Practical Type System For Validating Dimensional Unit Correctness Of C Programs, Lingxiao Jiang, Zhendong Su
Research Collection School Of Computing and Information Systems
Misuse of measurement units is a common source of errors in scientific applications, but standard type systems do not prevent such errors. Dimensional analysis in physics can be used to manually detect such errors in physical equations. It is, however, not feasible to perform such manual analysis for programs computing physical equations because of code complexity. In this paper, we present a type system to automatically detect potential errors involving measurement units. It is constraint-based: we model units as types and flow of units as constraints. However, standard type checking algorithms are not powerful enough to handle units because of …
Anonymous Signature Schemes, Guomin Yang, Duncan S. Wong, Xiaotie Deng, Huaxiong Wang
Anonymous Signature Schemes, Guomin Yang, Duncan S. Wong, Xiaotie Deng, Huaxiong Wang
Research Collection School Of Computing and Information Systems
Digital signature is one of the most important primitives in public key cryptography. It provides authenticity, integrity and non-repudiation to many kinds of applications. On signer privacy however, it is generally unclear or suspicious of whether a signature scheme itself can guarantee the anonymity of the signer. In this paper, we give some affirmative answers to it. We formally define the signer anonymity for digital signature and propose some schemes of this type. We show that a signer anonymous signature scheme can be very useful by proposing a new anonymous key exchange protocol which allows a client Alice to establish …
Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim
Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead …
Effect Of Changing Requirements: A Tracking Mechanism For The Analysis Workflow, Subhajit Datta, Robert Van Engelen
Effect Of Changing Requirements: A Tracking Mechanism For The Analysis Workflow, Subhajit Datta, Robert Van Engelen
Research Collection School Of Computing and Information Systems
Managing the effects of changing requirements remains one of the greatest challenges of enterprise software development. The iterative and incremental model provides an expedient framework for addressing such concerns. This paper presents a set of metrics - Mutation Index, Component Set, Dependency Index - and a methodology to measure the effects of requirement changes in the analysis workflow from one iteration to another. Results from a sample case study are included to highlight a usage scenario. Future directions of our work based on this mechanism are also discussed.
A Unified Log-Based Relevance Feedback Scheme For Image Retrieval, Steven Hoi, Michael R. Lyu, Rong Jin
A Unified Log-Based Relevance Feedback Scheme For Image Retrieval, Steven Hoi, Michael R. Lyu, Rong Jin
Research Collection School Of Computing and Information Systems
Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback …
Efficient Client-To-Server Assignments For Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou
Efficient Client-To-Server Assignments For Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou
Research Collection School Of Computing and Information Systems
Distributed Virtual Environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign …
Experimental And Empirical Perspectives On Grid Resource Allocation For The Singapore Market, Danny Oh, Steven Miller, Nan Hu
Experimental And Empirical Perspectives On Grid Resource Allocation For The Singapore Market, Danny Oh, Steven Miller, Nan Hu
Research Collection School Of Computing and Information Systems
In this paper, we describe our work on using the Tycoon system developed by HP Labs to provide a market-based resource allocation and bidding framework for a grid. We discuss how we intend to evaluate the feasibility of the Tycoon system by measuring its economic performance using agent-based simulation experiments for a particular type of grid usage scenario, namely, the digital media market scenario. We will also discuss a related effort in collecting and using real grid data from the National Grid Pilot Platform in Singapore and how we will be using real data collected to derive actual usage patterns …
Enterprise Agility And The Enabling Role Of Information Technology, Eric Overby, Anandhi S. Bharadwaj, V. Sambamurthy
Enterprise Agility And The Enabling Role Of Information Technology, Eric Overby, Anandhi S. Bharadwaj, V. Sambamurthy
Research Collection School Of Computing and Information Systems
In turbulent environments, enterprise agility, that is, the ability of firms to sense environmental change and respond readily, is an important determinant of firm success. We define and deconstruct enterprise agility, delineate enterprise agility from similar concepts in the business research literature, explore the underlying capabilities that support enterprise agility, explicate the enabling role of information technology (IT) and digital options, and propose a method for measuring enterprise agility. The concepts in this paper are offered as foundational building blocks for the overall research program on enterprise agility and the enabling role of IT.
Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan
Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan
Research Collection School Of Computing and Information Systems
Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (Feature-based Instance Selection Algorithm), is proposed to select only a subset of negative training documents for training a SVM classifier. With a smaller carefully selected training set, a SVM classifier can be more efficiently trained while delivering comparable or better classification accuracy. In our experiments on the 20-Newsgroups dataset, using only 35% negative training examples and 60% learning …
Searching Substructures With Superimposed Distance, Xifeng Yan, Feida Zhu, Jiawei Han, Philip S. Yu
Searching Substructures With Superimposed Distance, Xifeng Yan, Feida Zhu, Jiawei Han, Philip S. Yu
Research Collection School Of Computing and Information Systems
Efficient indexing techniques have been developed for the exact and approximate substructure search in large scale graph databases. Unfortunately, the retrieval problem of structures with categorical or geometric distance constraints is not solved yet. In this paper, we develop a method called PIS (Partition-based Graph Index and Search) to support similarity search on substructures with superimposed distance constraints. PIS selects discriminative fragments in a query graph and uses an index to prune the graphs that violate the distance constraints. We identify a criterion to distinguish the selectivity of fragments in multiple graphs and develop a partition method to obtain a …
In-Network Processing Of Nearest Neigbor Queries For Wireless Sensor Networks, Yuxia Yao, Xueyan Tang, Ee Peng Lim
In-Network Processing Of Nearest Neigbor Queries For Wireless Sensor Networks, Yuxia Yao, Xueyan Tang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. The sensor nodes in the network have the abilities to sense, store, compute and communicate. To enable object tracking applications, spatial queries such as nearest neighbor queries are to be supported in these networks. The queries can be injected by the user at any sensor node. Due to the limited power supply for sensor nodes, energy efficiency is the major concern in query processing. Centralized data storage and query processing schemes do not favor energy efficiency. In this paper, we propose …