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Articles 7411 - 7440 of 9003

Full-Text Articles in Computer Sciences

Automatic Generation Of Semantic Fields For Annotating Web Images, Gang Wang, Tat Seng Chua, Chong-Wah Ngo, Yong Cheng Wang Aug 2010

Automatic Generation Of Semantic Fields For Annotating Web Images, Gang Wang, Tat Seng Chua, Chong-Wah Ngo, Yong Cheng Wang

Research Collection School Of Computing and Information Systems

The overwhelming amounts of multimedia contents have triggered the need for automatically detecting the semantic concepts within the media contents. With the development of photo sharing websites such as Flickr, we are able to obtain millions of images with usersupplied tags. However, user tags tend to be noisy, ambiguous and incomplete. In order to improve the quality of tags to annotate web images, we propose an approach to build Semantic Fields for annotating the web images. The main idea is that the images are more likely to be relevant to a given concept, if several tags to the image belong …

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Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau Aug 2010

Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper is concerned with solving decentralized resource allocation and scheduling problems via auctions with negotiable agents by allowing agents to switch their bid generation strategies within the auction process, such that a better system wide performance is achieved on average as compared to the conventional walrasian auction running with agents of fixed bid generation strategy. We propose a negotiation mechanism embedded in auctioneer to solicit bidders’ change of strategies in the process of auction. Finally we benchmark our approach against conventional auctions subject to the real-time large-scale dynamic resource coordination problem to demonstrate the effectiveness of our approach.

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Cloud Services From A Consumer Perspective, Philip Koehler, Arun Anandasivam, Dan Ma Aug 2010

Cloud Services From A Consumer Perspective, Philip Koehler, Arun Anandasivam, Dan Ma

Research Collection School Of Computing and Information Systems

Although there is an increased attention on Cloud Computing in the academic literature in the recent years, most research work focus on technical aspects of cloud computing. Research on consumers’ preferences for cloud services is limited to studies from consulting and industry companies. This paper fills the gap by empirically identifying consumer preferences for cloud service attributes. Using conjoint methods we reveal the relative importance of different attributes of cloud services. The results help both practitioners and academic researchers to better understand the prerequisites of a successful market introduction of cloud services and to design appropriate services. Moreover, the derived …

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Hoare Logic For Graph Programs, Christopher M. Poskitt, Detlef Plump Aug 2010

Hoare Logic For Graph Programs, Christopher M. Poskitt, Detlef Plump

Research Collection School Of Computing and Information Systems

We present a new approach for verifying programs written in GP (for Graph Programs), an experimental programming language for performing computations on graphs at a high level of abstraction. Taking a labelled graph as input, a graph program nondeterministically applies to it a number of graph transformation rules, directed by simple control constructs such as sequential composition and as-long-as-possible iteration. We adapt classical Hoare logic to the domain of graphs, and describe a system of sound proof rules for showing the partial correctness of graph programs.

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A Decision Theoretic Approach To Data Leakage Prevention, Janusz Marecki, Mudhakar Srivastava, Pradeep Reddy Varakantham Aug 2010

A Decision Theoretic Approach To Data Leakage Prevention, Janusz Marecki, Mudhakar Srivastava, Pradeep Reddy Varakantham

Research Collection School Of Computing and Information Systems

In both the commercial and defense sectors a compelling need is emerging for rapid, yet secure, dissemination of information. In this paper we address the threat of information leakage that often accompanies such information flows. We focus on domains with one information source (sender) and many information sinks (recipients) where: (i) sharing is mutually beneficial for the sender and the recipients, (ii) leaking a shared information is beneficial to the recipients but undesirable to the sender, and (iii) information sharing decisions of the sender are determined using imperfect monitoring of the (un)intended information leakage by the recipients.We make two key …

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Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow Aug 2010

Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow

Research Collection School Of Computing and Information Systems

In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive …

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On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood Aug 2010

On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood

Research Collection School Of Computing and Information Systems

This paper introduces the Deliberation Decision Support System (DDSS). The DDSS obtains heuristically (using a genetic algorithm) solutions of interest for constrained optimization models. This is illustrated, without loss of generality, by generalized assignment problems. The DDSS also provides users with graphical tools that support post-solution deliberation for constrained optimization models. The DDSS and this paper, as befits practical concerns, are focused on deliberation with respect to the constraints of the models being used.

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Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang Aug 2010

Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang

Research Collection School Of Computing and Information Systems

Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are …

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Architectural Strategy For Digital Platforms: Technological And Organizational Perspectives, Jason Woodard, Joel West Aug 2010

Architectural Strategy For Digital Platforms: Technological And Organizational Perspectives, Jason Woodard, Joel West

Research Collection School Of Computing and Information Systems

different spatial queries such as kNN query, range query and CNN query. A comprehensive experimental study has been conducted to

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The Bi-Objective Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau Aug 2010

The Bi-Objective Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Physician scheduling is the assignment of physicians to perform different duties in the hospital timetable. In this paper, the goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We present a mathematical programming model to represent the problem as a bi-objective optimization problem. Three different methods based on ε–Constraint Method, Weighted-Sum Method and HillClimbing algorithm are proposed. These methods were tested on a real case from the Surgery Department of a large local government hospital, as well as on randomly generated problem instances. The strengths and weaknesses of the …

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Effect Of Human Biases On Human-Agent Teams, Praveen Paruchuri, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri Aug 2010

Effect Of Human Biases On Human-Agent Teams, Praveen Paruchuri, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri

Research Collection School Of Computing and Information Systems

As human-agent teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these …

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A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang Aug 2010

A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang

Research Collection School Of Computing and Information Systems

In this work, we study the task of personalized tag recommendation in social tagging systems. To reach out to tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for incorporating translations by similar users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such similarity measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with personomy translation methods based …

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Messaging Behavior Modeling In Mobile Social Networks, Byung-Won On, Ee Peng Lim, Jing Jiang, Freddy Tat Chua Chua, Viet-An Nguyen, Loo Nin Teow Aug 2010

Messaging Behavior Modeling In Mobile Social Networks, Byung-Won On, Ee Peng Lim, Jing Jiang, Freddy Tat Chua Chua, Viet-An Nguyen, Loo Nin Teow

Research Collection School Of Computing and Information Systems

Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message and responding to an incoming message. We propose various models for the two behaviors also known as engagingness and responsiveness. Our experiments show that …

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A New Hardware-Assisted Pir With O(N) Shuffle Cost, Xuhua Ding, Yanjiang Yang, Robert H. Deng, Shuhong Wang Aug 2010

A New Hardware-Assisted Pir With O(N) Shuffle Cost, Xuhua Ding, Yanjiang Yang, Robert H. Deng, Shuhong Wang

Research Collection School Of Computing and Information Systems

Since the concept of private information retrieval (PIR) was first formalized by Chor et al., various constructions have been proposed with a common goal of reducing communication complexity. Unfortunately, none of them is suitable for practical settings mainly due to the prohibitively high cost for either communications or computations. The booming of the Internet and its applications, especially, the recent trend in outsourcing databases, fuels the research on practical PIR schemes. In this paper, we propose a hardware-assisted PIR scheme with a novel shuffle algorithm. Our PIR construction entails O(n) offline computation cost, and constant online operations and O(log n) …

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An Efficient Signcryption Scheme With Key Privacy And Its Extension To Ring Signcryption, Chung Ki Li, Guomin Yang, Duncan S. Wong, Xiaotie Deng, Sherman S. M. Chow Aug 2010

An Efficient Signcryption Scheme With Key Privacy And Its Extension To Ring Signcryption, Chung Ki Li, Guomin Yang, Duncan S. Wong, Xiaotie Deng, Sherman S. M. Chow

Research Collection School Of Computing and Information Systems

In Information Processing Letters (2006), Tan pointed out that the anonymous signcryption scheme proposed by Yang, Wong and Deng (YWD) in ISC 2005 provides neither confidentiality nor anonymity. However, no discussion has been made on how a secure scheme can be made and there is no secure scheme available to date. In this paper, we propose a modification of YWD scheme which resolves the security issues of the original scheme without sacrificing its high efficiency and simple design. Indeed, we show that our scheme achieves confidentiality, existential unforgeability and anonymity with more precise reduction bounds. We also give a variation …

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Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou Jul 2010

Learning To Rank Only Using Training Data From Related Domain, Wei Gao, Peng Cai, Kam-Fai Wong, Aoying Zhou

Research Collection School Of Computing and Information Systems

Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related domain to learn to rank retrieved documents in the target domain, in which no labeled data is available. We present a simple yet effective approach based on instance-weighting scheme. Our method first estimates the importance of each related-domain document relative to the target domain. Then heuristics are studied to transform the importance of individual documents to the pairwise …

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A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan Jul 2010

A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents a neural model that learns episodic traces in response to a continual stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatiotemporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Comparing with prior systems, the proposed episodic memory model presents a robust approach to encoding key events and episodes and recalling them using partial and erroneous cues. We present experimental studies, …

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Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau Jul 2010

Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper introduces the SoI problem, that of finding nonoptimal solutions of interest for constrained optimization models. SoI problems subsume finding FoIs (feasible solutions of interest), and IoIs (infeasible solutions of interest). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making (discussed in the paper) come into play and for this purpose the SoIs can be of …

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Design And Implementation Of A Middleware For Easy Development And Provision Of Stream-Based Services, Seungwoo Kang, Youngki Lee, Sunghwan Ihm, Souneil Park, Su-Myeon Kim, Junehwa Song Jul 2010

Design And Implementation Of A Middleware For Easy Development And Provision Of Stream-Based Services, Seungwoo Kang, Youngki Lee, Sunghwan Ihm, Souneil Park, Su-Myeon Kim, Junehwa Song

Research Collection School Of Computing and Information Systems

This paper proposes MISSA, a novel middleware to facilitate the development and provision of stream-based services in emerging pervasive environments. The stream-based services utilize voluminous and continuously updated data streams as their input. The characteristics of data streams bring new requirements on the development and provision of the services. To satisfy the requirements, a unique service model and a runtime system are designed in MISSA. The key concept of our service model is to separate service logic from handling data streams. This significantly mitigates the burden on service developers by allowing them to only concentrate on the service logic. Job …

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Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang Jul 2010

Generating Templates Of Entity Summaries With An Entity-Aspect Model And Pattern Mining, Peng Li, Jing Jiang, Yinglin Wang

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. We …

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On The Sampling Of Web Images For Learning Visual Concept Classifiers, Shiai Zhu, Gang Wang, Chong-Wah Ngo, Yu-Gang Jiang Jul 2010

On The Sampling Of Web Images For Learning Visual Concept Classifiers, Shiai Zhu, Gang Wang, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Visual concept learning often requires a large set of training images. In practice, nevertheless, acquiring noise-free training labels with sufficient positive examples is always expensive. A plausible solution for training data collection is by sampling the largely available user-tagged images from social media websites. With the general belief that the probability of correct tagging is higher than that of incorrect tagging, such a solution often sounds feasible, though is not without challenges. First, user-tags can be subjective and, to certain extent, are ambiguous. For instance, an image tagged with “whales” may be simply a picture about ocean museum. Learning concept …

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Analytics-Modulated Coding Of Surveillance Video, Lai-Tee Cheok, Nikhil Gagvani Jul 2010

Analytics-Modulated Coding Of Surveillance Video, Lai-Tee Cheok, Nikhil Gagvani

Research Collection School Of Computing and Information Systems

Video surveillance systems increasingly use H.264 coding to achieve 24x7 recording and streaming. However, with the proliferation of security cameras, and the need to store several months of video, bandwidth and storage costs can be significant. We propose a new compression technique to significantly improve the coding efficiency of H.264 for surveillance video. Video content is analyzed and video semantics are extracted using video analytics algorithms such as segmentation, classification and tracking. In contrast to existing approaches, our Analytics-Modulated Compression (AMC) scheme does not require coding of object shape information and produces bitstreams that are standards-compliant and not limited to …

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Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong Jul 2010

Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In …

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Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu Jul 2010

Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu

Research Collection School Of Computing and Information Systems

Current content-based video copy detection approaches mostly concentrate on the visual cues and neglect the audio information. In this paper, we attempt to tackle the video copy detection task resorting to audio information, which is equivalently important as well as visual information in multimedia processing. Firstly, inspired by bag-of visual words model, a bag-of audio words (BoA) representation is proposed to characterize each audio frame. Different from naive singlebased modeling audio retrieval approaches, BoA is a highlevel model due to its perceptual and semantical property. Within the BoA model, a coherency vocabulary indexing structure is adopted to achieve more efficient …

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Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia Jul 2010

Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia

Research Collection School Of Computing and Information Systems

Because of the inherent ambiguity in user queries, an important task of modern retrieval systems is faceted topic retrieval (FTR), which relates to the goal of returning diverse or novel information elucidating the wide range of topics or facets of the query need. We introduce a generative model for hypothesizing facets in the (news) video domain by combining the complementary information in the visual keyframes and the speech transcripts. We evaluate the efficacy of our multimodal model on the standard TRECVID-2005 video corpus annotated with facets. We find that: (1) the joint modeling of the visual and text (speech transcripts) …

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Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang Jul 2010

Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang

Research Collection School Of Computing and Information Systems

The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …

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Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu Jul 2010

Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu

Research Collection School Of Computing and Information Systems

The Bag-of-Words (BoW) model is a promising image representation technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that much semantic information is lost during the codebook generation process, an important step of BoW. This is because the codebook generated by BoW is often obtained via building the codebook simply by clustering visual features in Euclidian space. However, visual features related to the same semantics may not distribute in clusters in the Euclidian space, which is primarily due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a …

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Non-Parametric Kernel Ranking Approach For Social Image Retrieval, Jinfeng Zhuang, Steven C. H. Hoi Jul 2010

Non-Parametric Kernel Ranking Approach For Social Image Retrieval, Jinfeng Zhuang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Social image retrieval has become an emerging research challenge in web rich media search. In this paper, we address the research problem of text-based social image retrieval, which aims to identify and return a set of relevant social images that are related to a text-based query from a corpus of social images. Regular approaches for social image retrieval simply adopt typical text-based image retrieval techniques to search for the relevant social images based on the associated tags, which may suffer from noisy tags. In this paper, we present a novel framework for social image re-ranking based on a non-parametric kernel …

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Evaluation Of Protein Backbone Alphabets: Using Predicted Local Structure For Fold Recognition, Kyong Jin Shim Jul 2010

Evaluation Of Protein Backbone Alphabets: Using Predicted Local Structure For Fold Recognition, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone.

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Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam Jul 2010

Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam

Research Collection School Of Computing and Information Systems

In this highly volatile and fast-paced financial market, traders and managers working in banking and financial organizations must struggle to cope with large and complex data from multi-sources, that move throughout the market at increasingly high speed. The cost of making poor business and investment decisions is very high. This places great demands on data analysts, who are responsible for providing process information, to support the activities of traders and managers. Static reports and traditional business intelligence tools simply cannot keep up with a market that is changing on a second-to-second basis. By the time the traders and bankers have …

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