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Articles 7621 - 7650 of 9003

Full-Text Articles in Computer Sciences

Analysis Of Tradeoffs Between Buffer And Qos Requirements In Wireless Networks, Raphael Rom, Hwee-Pink Tan Oct 2009

Analysis Of Tradeoffs Between Buffer And Qos Requirements In Wireless Networks, Raphael Rom, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

In this paper, we consider the scheduling problem where data packets from K input-flows need to be delivered to K corresponding wireless receivers over a heterogeneous wireless channel. Our objective is to design a wireless scheduler that achieves good throughput and fairness performance while minimizing the buffer requirement at each wireless receiver. This is a challenging problem due to the unique characteristics of the wireless channel. We propose a novel idea of exploiting both the long-term and short-term error behavior of the wireless channel in the scheduler design. In addition to typical first-order Quality of Service (QoS) metrics such as …

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Secure Mobile Agents With Designated Hosts, Qi Zhang, Yi Mu, Minji Zhang, Robert H. Deng Oct 2009

Secure Mobile Agents With Designated Hosts, Qi Zhang, Yi Mu, Minji Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Mobile agents often travel in a hostile environment where their security and privacy could be compromised by any party including remote hosts in which agents visit and get services. It was proposed in the literature that the host visited by an agent should jointly sign a service agreement with the agent's home, where a proxy-signing model was deployed and every host in the agent system can sign. We observe that this actually poses a serious problem in that a host that should be excluded from an underlying agent network could also send a signed service agreement. In order to solve …

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Domain Adaptive Semantic Diffusion For Large Scale Context-Based Video Annotation, Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah Ngo Oct 2009

Domain Adaptive Semantic Diffusion For Large Scale Context-Based Video Annotation, Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Learning to cope with domain change has been known as a challenging problem in many real-world applications. This paper proposes a novel and efficient approach, named domain adaptive semantic diffusion (DASD), to exploit semantic context while considering the domain-shift-of-context for large scale video concept annotation. Starting with a large set of concept detectors, the proposed DASD refines the initial annotation results using graph diffusion technique, which preserves the consistency and smoothness of the annotation over a semantic graph. Different from the existing graph learning methods which capture relations among data samples, the semantic graph treats concepts as nodes and the …

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Verifying Stateful Timed Csp Using Implicit Clocks And Zone Abstraction, Jun Sun, Yang Liu, Jin Song Dong, Xian Zhang Sep 2009

Verifying Stateful Timed Csp Using Implicit Clocks And Zone Abstraction, Jun Sun, Yang Liu, Jin Song Dong, Xian Zhang

Research Collection School Of Computing and Information Systems

In this work, we study model checking of compositional real-time systems. A system is modeled using mutable data variables as well as a compositional timed process. Instead of explicitly manipulating clock variables, a number of compositional timed behavioral patterns are used to capture quantitative timing requirements, e.g. delay, timeout, deadline, timed interrupt, etc. A fully automated abstraction technique is developed to build an abstract finite state machine from the model. The idea is to dynamically create/delete clocks, and maintain/solve a constraint on the clocks. The abstract machine weakly bi-simulates the model and, therefore, LTL model checking or trace-refinement checking are …

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Why Quants Fail, M. Thulasidas Sep 2009

Why Quants Fail, M. Thulasidas

Research Collection School Of Computing and Information Systems

Mathematical finance is built on a couple of assumptions. The most fundamental of them is the one on ma ket efficiency. It states that the market prices every asset fairly, and that the prices contain all the information available in the market.

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Multi-View Ear Recognition Based On Moving Least Square Pose Interpolation, Heng Liu, David Zhang, Zhiyuan Zhang Sep 2009

Multi-View Ear Recognition Based On Moving Least Square Pose Interpolation, Heng Liu, David Zhang, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

Based on moving least square, a multi-view ear pose interpolation and corresponding recognition approach is proposed. This work firstly analyzes the shape characteristics of actual trace caused by ear pose varying in feature space. Then according to training samples pose projection, we manage to recover the complete multi-view ear pose manifold by using moving least square pose interpolation. The constructed multi-view ear pose manifolds can be easily utilized to recognize ear images captured under different views based on finding the minimal projection distance to the manifolds. The experimental results and some comparisons show the new method is superior to manifold …

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Efficient Conditional Proxy Re-Encryption With Chosen-Ciphertext Security, Jian Weng, Yanjiang Yang, Qiang Tang, Robert H. Deng Sep 2009

Efficient Conditional Proxy Re-Encryption With Chosen-Ciphertext Security, Jian Weng, Yanjiang Yang, Qiang Tang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Recently, a variant of proxy re-encryption, named conditional proxy re-encryption (C-PRE), has been introduced. Compared with traditional proxy re-encryption, C-PRE enables the delegator to implement fine-grained delegation of decryption rights, and thus is more useful in many applications. In this paper, based on a careful observation on the existing definitions and security notions for C-PRE, we re-formalize more rigorous definition and security notions for C-PRE. We further propose a more efficient C-PRE scheme, and prove its chosen-ciphertext security under the decisional bilinear Diffie-Hellman (DBDH) assumption in the random oracle model. In addition, we point out that a recent C-PRE scheme …

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Visible Reverse K-Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Wang-Chien Lee, Ken C. K. Lee, Qing Li Sep 2009

Visible Reverse K-Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Wang-Chien Lee, Ken C. K. Lee, Qing Li

Research Collection School Of Computing and Information Systems

Reverse nearest neighbor (RNN) queries have a broad application base such as decision support, profile-based marketing, resource allocation, etc. Previous work on RNN search does not take obstacles into consideration. In the real world, however, there are many physical obstacles (e.g., buildings) and their presence may affect the visibility between objects. In this paper, we introduce a novel variant of RNN queries, namely, visible reverse nearest neighbor (VRNN) search, which considers the impact of obstacles on the visibility of objects. Given a data set P, an obstacle set O, and a query point q in a 2D space, a VRNN …

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Localized Matching Using Earth Mover's Distance Towards Discovery Of Common Patterns From Small Image Samples, Hung-Khoon Tan, Chong-Wah Ngo Sep 2009

Localized Matching Using Earth Mover's Distance Towards Discovery Of Common Patterns From Small Image Samples, Hung-Khoon Tan, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper proposes a new approach for the discovery of common patterns in a small set of images by region matching. The issues in feature robustness, matching robustness and noise artifact are addressed to delve into the potential of using regions as the basic matching unit. We novelly employ the many-to-many (M2M) matching strategy, specifically with the Earth Mover's Distance (EMD), to increase resilience towards the structural inconsistency from improper region segmentation. However, the matching pattern of M2M is dispersed and unregulated in nature, leading to the challenges of mining a common pattern while identifying the underlying transformation. To avoid …

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A Latent Model For Visual Disambiguation Of Keyword-Based Image Search, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia, Sujoy Roy Sep 2009

A Latent Model For Visual Disambiguation Of Keyword-Based Image Search, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia, Sujoy Roy

Research Collection School Of Computing and Information Systems

The problem of polysemy in keyword-based image search arises mainly from the inherent ambiguity in user queries. We propose a latent model based approach that resolves user search ambiguity by allowing sense specific diversity in search results. Given a query keyword and the images retrieved by issuing the query to an image search engine, we first learn a latent visual sense model of these polysemous images. Next, we use Wikipedia to disambiguate the word sense of the original query, and issue these Wiki-senses as new queries to retrieve sense specific images. A sense-specific image classifier is then learnt by combining …

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Accelerating Sequence Searching: Dimensionality Reduction Method, Guojie Song, Bin Cui, Baihua Zheng, Kunqing Xie, Dongqing Yang Sep 2009

Accelerating Sequence Searching: Dimensionality Reduction Method, Guojie Song, Bin Cui, Baihua Zheng, Kunqing Xie, Dongqing Yang

Research Collection School Of Computing and Information Systems

Similarity search over long sequence dataset becomes increasingly popular in many emerging applications, such as text retrieval, genetic sequences exploring, etc. In this paper, a novel index structure, namely Sequence Embedding Multiset tree (SEM − tree), has been proposed to speed up the searching process over long sequences. The SEM-tree is a multi-level structure where each level represents the sequence data with different compression level of multiset, and the length of multiset increases towards the leaf level which contains original sequences. The multisets, obtained using sequence embedding algorithms, have the desirable property that they do not need to keep the …

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Detecting Automotive Exhaust Gas Based On Fuzzy Inference System, Li. Shujin, Ming Bai, Quan Wang, Bo Chen, Xiaobing Zhao, Ting Yang, Zhaoxia Wang Sep 2009

Detecting Automotive Exhaust Gas Based On Fuzzy Inference System, Li. Shujin, Ming Bai, Quan Wang, Bo Chen, Xiaobing Zhao, Ting Yang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

This paper proposes a method of detecting automotive exhaust gas based on fuzzy logic inference after analyzing the principle of the infrared automobile exhaust gas analyzer and the influence of the environmental temperature on analyzer. This paper analyses the measurement error caused by environmental temperature, and then makes a non-linear error correction of temperature for the infrared sensor using fuzzy inference. The results of simulation have clearly demonstrated that the proposed fuzzy compensation scheme is better than the non-fuzzy method.

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Admission Control For Differentiated Services In Future Generation Cdma Networks, Hwee-Pink Tan, Rudesindo Núñez-Queija, Adriana F. Gabor, Onno J. Boxma Sep 2009

Admission Control For Differentiated Services In Future Generation Cdma Networks, Hwee-Pink Tan, Rudesindo Núñez-Queija, Adriana F. Gabor, Onno J. Boxma

Research Collection School Of Computing and Information Systems

Future Generation CDMA wireless systems, e.g., 3G, can simultaneously accommodate flow transmissions of users with widely heterogeneous applications. As radio resources are limited, we propose an admission control rule that protects users with stringent transmission bit-rate requirements (“streaming traffic”) while offering sufficient capacity over longer time intervals to delay-tolerant users (“elastic traffic”). While our strategy may not satisfy classical notions of fairness, we aim to reduce congestion and increase overall throughput of elastic users. Using time-scale decomposition, we develop approximations to evaluate the performance of our differentiated admission control strategy to support integrated services with transmission bit-rate requirements in a …

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Batch Mode Active Learning With Applications To Text Categorization And Image Retrieval, Steven C. H. Hoi, Rong Jin, Michael R. Lyu Sep 2009

Batch Mode Active Learning With Applications To Text Categorization And Image Retrieval, Steven C. H. Hoi, Rong Jin, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most informative examples for manual labeling in these learning tasks. 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 acquired labeled example. It is also inappropriate for the setup of information retrieval tasks where the user's relevance feedback is often provided for the top K retrieved items. In …

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Communication-Efficient Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi Sep 2009

Communication-Efficient Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Distributed classification aims to learn with accuracy comparable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent environment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asynchronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR—a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust …

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Understanding Early Diffusion Of Digital Wireless Phones, Robert J. Kauffman, Angsana A. Techatassanasoontorn Sep 2009

Understanding Early Diffusion Of Digital Wireless Phones, Robert J. Kauffman, Angsana A. Techatassanasoontorn

Research Collection School Of Computing and Information Systems

There is increasing empirical evidence from academic research and strong recognition among policymakers that wide diffusion and innovative uses of digital wireless phones are important sources of a country's economic growth and social development. Adopters do not necessarily adopt digital wireless phones at the same time though. Although the diffusion of innovation theory suggests five adopter categories according to their degree of innovativeness, this approach lacks theoretical justification and, more importantly, it makes a critical assumption of a normal distribution of adopters that needs empirical validation. This study investigates the basis for defining different adopter categories and factors that affect …

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A Hybrid Firm's Pricing Strategy In Electronic Commerce Under Channel Migration, Robert John Kauffman, Dongwon Lee, Jung Lee, Byungjoon Yoo Sep 2009

A Hybrid Firm's Pricing Strategy In Electronic Commerce Under Channel Migration, Robert John Kauffman, Dongwon Lee, Jung Lee, Byungjoon Yoo

Research Collection School Of Computing and Information Systems

Achieving an effective business design across the Internet and the off-line channel is a critical concern for a hybrid firm's choice of pricing strategy. Two pricing models are proposed to examine how consumer channel migration (one-way channel interaction from the traditional sales channel to the Internet) affects pricing strategy. One model has no interaction between the Internet and off-line channels. The other includes the possibility of one-way migration to the Internet channel and incorporates consumers' channel-switching costs and loyalty to the firm. The two models offer interesting results for understanding traditional and Internet-based selling. A high level of channel migration …

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Hierarchical Self-Healing Key Distribution For Heterogeneous Wireless Sensor Networks, Yanjiang Yang, Jianying Zhou, Robert H. Deng, Feng Bao Sep 2009

Hierarchical Self-Healing Key Distribution For Heterogeneous Wireless Sensor Networks, Yanjiang Yang, Jianying Zhou, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Self-healing group key distribution aims to achieve robust key distribution over lossy channels in wireless sensor networks (WSNs). However, all existing self-healing group key distribution schemes in the literature consider homogenous WSNs which are known to be unscalable. Heterogeneous WSNs have better scalability and performance than homogenous ones. We are thus motivated to study hierarchial self-healing group key distribution, tailored to the heterogeneous WSN architecture. In particular, we revisit and adapt Dutta et al.’s model to the setting of hierarchical self-healing group key distribution, and propose a concrete scheme that achieves computational security and high efficiency.

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Font Size: Make Font Size Smaller Make Font Size Default Make Font Size Larger Exploiting Coordination Locales In Distributed Pomdps Via Social Model Shaping, Pradeep Varakantham, Jun Young Kwak, Matthew Taylor, Janusz Marecki, Paul Scerri, Milind Tambe Sep 2009

Font Size: Make Font Size Smaller Make Font Size Default Make Font Size Larger Exploiting Coordination Locales In Distributed Pomdps Via Social Model Shaping, Pradeep Varakantham, Jun Young Kwak, Matthew Taylor, Janusz Marecki, Paul Scerri, Milind Tambe

Research Collection School Of Computing and Information Systems

Distributed POMDPs provide an expressive framework for modeling multiagent collaboration problems, but NEXPComplete complexity hinders their scalability and application in real-world domains. This paper introduces a subclass of distributed POMDPs, and TREMOR, an algorithm to solve such distributed POMDPs. The primary novelty of TREMOR is that agents plan individually with a single agent POMDP solver and use social model shaping to implicitly coordinate with other agents. Experiments demonstrate that TREMOR can provide solutions orders of magnitude faster than existing algorithms while achieving comparable, or even superior, solution quality.

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Exploiting Bilingual Information To Improve Web Search, Wei Gao, John Bitzer, Ming Zhou, Kam-Fai Wong Aug 2009

Exploiting Bilingual Information To Improve Web Search, Wei Gao, John Bitzer, Ming Zhou, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Web search quality can vary widely across languages, even for the same information need. We propose to exploit this variation in quality by learning a ranking function on bilingual queries: queries that appear in query logs for two languages but represent equivalent search interests. For a given bilingual query, along with corresponding monolingual query log and monolingual ranking, we generate a ranking on pairs of documents, one from each language. Then we learn a linear ranking function which exploits bilingual features on pairs of documents, as well as standard monolingual features. Finally, we show how to reconstruct monolingual ranking from …

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Inferring Player Rating From Performance Data In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Muhammad Aurangzeb Ahmad, Nishith Pathak, Jaideep Srivastava Aug 2009

Inferring Player Rating From Performance Data In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Muhammad Aurangzeb Ahmad, Nishith Pathak, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

This paper examines online player performance in EverQuest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. The study uses the game's player performance data to devise performance metrics for online players. We report three major findings. First, we show that the game's point-scaling system overestimates performances of lower level players and underestimates performances of higher level players. We present a novel point-scaling system based on the game's player performance data that addresses the underestimation and overestimation problems. Second, we present a highly accurate predictive model for player performance as a function of past behavior. …

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Multi-Task Transfer Learning For Weakly-Supervised Relation Extraction, Jing Jiang Aug 2009

Multi-Task Transfer Learning For Weakly-Supervised Relation Extraction, Jing Jiang

Research Collection School Of Computing and Information Systems

Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures, we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. The proposed framework models the commonality among different relation types through a shared weight vector, enables knowledge learned from …

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Symphony: Enabling Search-Driven Applications, John C. Shafer, Rakesh Agrawal, Hady W. Lauw Aug 2009

Symphony: Enabling Search-Driven Applications, John C. Shafer, Rakesh Agrawal, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We present the design of Symphony, a platform that enables non-developers to build and deploy a new class of search-driven applications that combine their data and domain expertise with content from search engines and other web services. The Symphony prototype has been built on top of Microsoft’s Live Search infrastructure. While Symphony naturally makes use of the customization capabilities exposed by Live Search, its distinguishing feature is the capability it provides to the application creator to combine their proprietary data and domain expertise with content obtained from Live Search. They can also integrate specialized data obtained from web services to …

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Data Mining For Software Engineering, Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu Aug 2009

Data Mining For Software Engineering, Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu

Research Collection School Of Computing and Information Systems

To improve software productivity and quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks. However, mining SE data poses several challenges. The authors present various algorithms to effectively mine sequences, graphs, and text from such data.

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Multilayer Image Inpainting Approach Based On Neural Networks, Quan Wang, Zhaoxia Wang, Che Sau Chang, Ting Yang Aug 2009

Multilayer Image Inpainting Approach Based On Neural Networks, Quan Wang, Zhaoxia Wang, Che Sau Chang, Ting Yang

Research Collection School Of Computing and Information Systems

This paper describes an image inpainting approach based on the self-organizing map for dividing an image into several layers, assigning each damaged pixel to one layer, and then restoring these damaged pixels by the information of their respective layer. These inpainted layers are then fused together to provide the final inpainting results. This approach takes advantage of the neural network's ability of imitating human's brain to separate objects of an image into different layers for inpainting. The approach is promising as clearly demonstrated by the results in this paper.

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Essential Spreadsheet Modeling Course For Business Students, Thin Yin Leong, Michelle L. F. Cheong Aug 2009

Essential Spreadsheet Modeling Course For Business Students, Thin Yin Leong, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Ask any student at the Singapore Management University (SMU) to name one of the most practical and useful courses offered by the university. The answer would inevitably include CAT. CAT stands for the "Computer as an Analysis Tool" course. Originally based on a course of the same title offered by the Wharton Business School, the focus of CAT was shifted to provide business students the essential practical skills and necessary "real-world" exposure to better use personal computers for resolving business problems. The course is basically centered on using the Excel spreadsheet to work on ambiguous ill-defined problems.

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Game Action Based Power Management For Multiplayer Online Game, Bhojan Anand, A. L. Ananda, Mun Choon Chan, Long Thanh Le, Rajesh Krishna Balan Aug 2009

Game Action Based Power Management For Multiplayer Online Game, Bhojan Anand, A. L. Ananda, Mun Choon Chan, Long Thanh Le, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Current mobile devices embrace a wide range of functionalities including high speed network support, hardware accelerated 3D graphics, and multimedia capabilities. These capabilities have boosted the interest for enabling multiplayer online games (MOG) support on such devices. However, the lack of similar growth in battery technology limits the usability of these devices for MOGs. In this paper, we present energy conservation techniques for highly interactive MOGs. These are games, such as first-person shooters, where crisp user interaction is paramount to the overall game experience. Hence, conserving energy while preserving crisp user interaction becomes a critical consideration in this domain. We …

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Ssnetviz: A Visualization Engine For Heterogeneous Semantic Social Networks, Ee Peng Lim, Maureen Maureen, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang Aug 2009

Ssnetviz: A Visualization Engine For Heterogeneous Semantic Social Networks, Ee Peng Lim, Maureen Maureen, Nelman Lubis Ibrahim, Aixin Sun, Anwitaman Datta, Kuiyu Chang

Research Collection School Of Computing and Information Systems

SSnetViz is an ongoing research to design and implement a visualization engine for heterogeneous semantic social networks. A semantic social network is a multi-modal network that contains nodes representing di®erent types of people or object entities, and edges representing relationships among them. When multiple heterogeneous semantic social networks are to be visualized together, SSnetViz provides a suite of functions to store heterogeneous semantic social networks, to integrate them for searching and analysis. We will illustrate these functions using social networks related to terrorism research, one crafted by domain experts and another from Wikipedia.

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Extracting Paraphrases Of Technical Terms From Noisy Parallel Software Corpus, Xiaoyin Wang, David Lo, Jing Jiang, Lu Zhang, Hong Mei Aug 2009

Extracting Paraphrases Of Technical Terms From Noisy Parallel Software Corpus, Xiaoyin Wang, David Lo, Jing Jiang, Lu Zhang, Hong Mei

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of extracting technical paraphrases from a parallel software corpus, namely, a collection of duplicate bug reports. Paraphrase acquisition is a fundamental task in the emerging area of text mining for software engineering. Existing paraphrase extraction methods are not entirely suitable here due to the noisy nature of bug reports. We propose a number of techniques to address the noisy data problem. The empirical evaluation shows that our method significantly improves an existing method by upto 58%

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Setting Discrete Bid Levels Adaptively In Repeated Auctions, Jilian Zhang, Hoong Chuin Lau, Jialie Shen Aug 2009

Setting Discrete Bid Levels Adaptively In Repeated Auctions, Jilian Zhang, Hoong Chuin Lau, Jialie Shen

Research Collection School Of Computing and Information Systems

The success of an auction design often hinges on its ability to set parameters such as reserve price and bid levels that will maximize an objective function such as the auctioneer revenue. Works on designing adaptive auction mechanisms have emerged recently, and the challenge is in learning different auction parameters by observing the bidding in previous auctions. In this paper, we propose a non-parametric method for determining discrete bid levels dynamically so as to maximize the auctioneer revenue. First, we propose a non-parametric kernel method for estimating the probabilities of closing price with past auction data. Then a greedy strategy …

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