Databases and Information Systems Commons^™

Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, 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 for annotation. One critical limitation of existing BoW models is the semantic loss during the codebook generation process, in which BoW simply clusters visual words in Euclidian space. However, distance between two visual words in Euclidean space does not necessarily reflect the semantic distance between the two concepts, due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a novel scheme for learning a codebook such that semantically related features will be mapped to the same visual word. In particular, we consider the distance between …

First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King

Research Collection School Of Computing and Information Systems

The ACM SIGMM International Workshop on Social Media(WSM’09) is the first workshop held in conjunction withthe ACM International Multimedia Conference (MM’09) atBejing, P.R. China, 2009. This workshop provides a forumfor researchers and practitioners from all over the world toshare information on their latest investigations on social mediaanalysis, exploration, search, mining, and emerging newsocial media applications.

Mining Globally Distributed Frequent Subgraphs In A Single Labeled Graph, Xing Jiang, Hui Xiong, Chen Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent years have observed increasing efforts on graph mining and many algorithms have been developed for this purpose. However, most of the existing algorithms are designed for discovering frequent subgraphs in a set of labeled graphs only. Also, the few algorithms that find frequent subgraphs in a single labeled graph typically identify subgraphs appearing regionally in the input graph. In contrast, for real-world applications, it is commonly required that the identified frequent subgraphs in a single labeled graph should also be globally distributed. This paper thus fills this crucial void by proposing a new measure, termed G-Measure, to find globally …

Analyzing The Video Popularity Characteristics Of Large-Scale User Generated Content Systems, Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, Sue Moon

Research Collection School Of Computing and Information Systems

User generated content (UGC), now with millions of video producers and consumers, is re-shaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world's largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show …

Distance Metric Learning From Uncertain Side Information With Application To Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu

Research Collection School Of Computing and Information Systems

Automated photo tagging is essential to make massive unlabeled photos searchable by text search engines. Conventional image annotation approaches, though working reasonably well on small testbeds, are either computationally expensive or inaccurate when dealing with large-scale photo tagging. Recently, with the popularity of social networking websites, we observe a massive number of user-tagged images, referred to as "social images", that are available on the web. Unlike traditional web images, social images often contain tags and other user-generated content, which offer a new opportunity to resolve some long-standing challenges in multimedia. In this work, we aim to address the challenge of …

Unsupervised Face Alignment By Robust Nonrigid Mapping, Jianke Zhu, Luc Van Gool, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

We propose a novel approach to unsupervised facial image alignment. Differently from previous approaches, that are confined to affine transformations on either the entire face or separate patches, we extract a nonrigid mapping between facial images. Based on a regularized face model, we frame unsupervised face alignment into the Lucas-Kanade image registration approach. We propose a robust optimization scheme to handle appearance variations. The method is fully automatic and can cope with pose variations and expressions, all in an unsupervised manner. Experiments on a large set of images showed that the approach is effective.

Streaming 3d Meshes Using Spectral Geometry Images, Ying He, Boon Seng Chew, Dayong Wang, Steven C. H. Hoi, Lap Pui Chau

Research Collection School Of Computing and Information Systems

The transmission of 3D models in the form of Geometry Images (GI) is an emerging and appealing concept due to the reduction in complexity from R3 to image space and wide availability of mature image processing tools and standards. However, geometry images often suffer from the artifacts and error during compression and transmission. Thus, there is a need to address the artifact reduction, error resilience and protection of such data information during the transmission across an error prone network. In this paper, we introduce a new concept, called Spectral Geometry Images (SGI), which naturally combines the powerful spectral analysis with …

Parallel Sets In The Real World: Three Case Studies, Robert Kosara, Caroline Ziemkiewicz, F. Joseph Iii Mako, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Parallel Sets are a visualization technique for categorical data. We recently released an implementation to the public in an effort to make our research useful to real users. This paper presents three case studies of Parallel Sets in use with real data.

Continuous Monitoring Of Spatial Queries In Wireless Broadcast Environments, Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias

Research Collection School Of Computing and Information Systems

Wireless data broadcast is a promising technique for information dissemination that leverages the computational capabilities of the mobile devices in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may then tune in the broadcast channel and process their queries locally without contacting the server. Previous work on spatial query processing for wireless broadcast systems has only considered snapshot queries over static data. In this paper, we propose an air indexing framework that 1) outperforms the existing (i.e., snapshot) techniques in …

Context Is Highly Contextual!, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

Temporal Data Classification Using Linear Classifiers, Peter Revesz, Thomas Triplet

School of Computing: Conference and Workshop Papers

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality.

Robust Lifetime Measurement In Large-Scale P2p Systems With Non-Stationary Arrivals, Xiaoming Wang, Zhongmei Yao, Yueping Zhang, Dmitri Loguinov

Computer Science Faculty Publications

Characterizing user churn has become an important topic in studying P2P networks, both in theoretical analysis and system design. Recent work has shown that direct sampling of user lifetimes may lead to certain bias (arising from missed peers and round-off inconsistencies) and proposed a technique that estimates lifetimes based on sampled residuals. In this paper, however, we show that under non-stationary arrivals, which are often present in real systems, residual-based sampling does not correctly reconstruct user lifetimes and suffers a varying degree of bias, which in some cases makes estimation completely impossible. We overcome this problem using two contributions: a …

Self-Authentication Of Audio Signals By Chirp Coding, Jonathan Blackledge, Eugene Coyle

Conference papers

This paper discusses a new approach to ‘watermarking’ digital signals using linear frequency modulated or ‘chirp’ coding. The principles underlying this approach are based on the use of a matched filter to provide a reconstruction of a chirped code that is uniquely robust in the case of signals with very low signal-to-noise ratios. Chirp coding for authenticating data is generic in the sense that it can be used for a range of data types and applications (the authentication of speech and audio signals, for example). The theoretical and computational aspects of the matched filter and the properties of a chirp …

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 …

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.

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 …

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 …

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 …

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 …

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 …

In-Group / Out-Group Dynamics And Effectiveness In Partially Distributed Teams, Faina Privman

Dissertations

When organizations collaborate they often do so using partially distributed teams (PDTs). In a Partially Distributed Team there exist at least two distinct sub-groups. In addition, at least one of the sub-groups has two or more members that are geographically co-located. Co-located members can meet face to face; chat in the hallway; have lunch together; and otherwise socialize with one another. On the other hand, remote members must rely on technology to communicate and work together. This distinct characteristic of partially distributed teams makes them especially susceptible to the In-Group / Out Group dynamic (Huang and Ocker, 2006). This dynamic …

Design Development And Evaluation Of Collario, A Group Support System For Collaborative Scenario Creation, Xiang Yao

Dissertations

In the fields of Emergency Management and Business Continuity Planning, scenarios are a widely used tool for planning, training and knowledge sharing purposes. The ability to create and discuss emergency scenarios in virtual teams can lead to many potential applications, such as discussing emergency scenarios by world-wide experts, conducting on-line exercises, and creating Communities of Practices. Existing scenario creation systems, like NxMsel provided by FEMA, allow distributed groups to create scenarios together. However, collaborative support in these systems is generally limited.

This dissertation explores an innovative solution to provide various types of collaboration support around a knowledge structure and uses …

Wireless Networks: Spert: A Stateless Protocol For Energy-Sensitive Real-Time Routing For Wireless Sensor Network, Sohail Jabbar, Abid Ali Minhas, Raja Adeel Akhtar

International Conference on Information and Communication Technologies

Putting constraints on performance of a system in the temporal domain, some times turns right into wrong and update into outdate. These are the scenarios where apposite value of time inveterate in the reality. But such timing precision not only requires tightly scheduled performance constraints but also requires optimal design and operation of all system components. Any malfunctioning at any relevant aspect may causes a serious disaster and even loss of human lives. Managing and interacting with such real-time system becomes much intricate when the resources are limited as in wireless sensor nodes. A wireless sensor node is typically comprises …

Application Of Ict Iii: Use Of Information And Mobile Computing Technologies In Healthcare Facilities Of Saudi Arabia, Abdul Ahad Siddiqi, Munir Ahmed, Yasser M. Alginahi, Abdulrahman Alharby

International Conference on Information and Communication Technologies

Information technology forms an important part of the healthcare solution. Accurate and up-to-date information is essential to continuous quality improvement in any organization, and particularly so in an area as complex as healthcare. Therefore, diverse information systems must be integrated across the healthcare enterprise. The knowledge base in the medical field is large, complex, and growing rapidly. It includes scientific knowledge, as well as familiarity with the day-to-day business of providing healthcare. It is crucial to identify the processes in the healthcare sector that would most benefit from the support of information technology. This study is focused on the analysis …

Networks - Ii: Overhead Analysis Of Security Implementation Using Ipsec, Muhammad Awais Azam, Zaka -Ul- Mustafa, Usman Tahir, S. M. Ahsan, Muhammad Adnan Naseem, Imran Rashid, Muhammad Adeel

International Conference on Information and Communication Technologies

Authentication, access control, encryption and auditing make up the essential elements of network security. Researchers have dedicated a large amount of efforts to implement security features that fully incorporate the use of all these elements. Currently, data networks mainly provide authentication and confidentiality services. Confidentiality alone is not able to protect the system, thus, suitable security measures must be taken. However, this security is itself an overhead which must be accounted for. A trade-off must exist between performance and security. This trade-off must be carefully managed so as not to deteriorate the systems being secured. This calls for the true …

Networks - I: Investigating Access To Heterogeneous Storage Systems Using Linked Data In Unicore Grid Middleware, Roger Menday, M. Shahbaz Memon, A. Shiraz Memon, Achim Streit

International Conference on Information and Communication Technologies

The Grid provides access to execution and storage resources - through middleware - by normalizing the access to resources. UNICORE calls this quality dasiaseamlessnesspsila. One result of this is that a request for some action at one resource can, with relative ease, be re-targeted to another, making possible the coordinated usage of multiple resources. In this paper we describe a seamless interface to heterogeneous storage systems, using the (Semantic) Web as middleware for the Grid. Data is accumulated by experiments and simulations running on execution systems, instruments and sensors, and by importing data from other locations. Storage systems are regular …

Networks - I: Collaborative 3d Digital Content Creation Exploiting A Grid Network, M. Gkion, M. Z. Patoli, A. Al-Barakati, W. Zhang, P. Newbury, M. White

International Conference on Information and Communication Technologies

The increase in ease of the production of computer simulated graphics has opened new opportunities in the 3D industry. There are unlimited applications for the delivery of 3D Graphics especially concerning 3D multimedia presentation of digital content. Apart from aesthetic and entertaining reasons, experts apply computer simulations to visualize environments and to identify early errors or costs in order to limit the need of making real prototypes. Thus, 3D Graphics also minimize the time required for developing the final product. Existing 3D applications give partial support to users to engage in collaborative contribution for the production of a 3D model. …

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 …

An Empirical Investigation Of Filter Attribute Selection Techniques For Software Quality Classification, Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang

Computer Science Faculty Publications

Attribute selection is an important activity in data preprocessing for software quality modeling and other data mining problems. The software quality models have been used to improve the fault detection process. Finding faulty components in a software system during early stages of software development process can lead to a more reliable final product and can reduce development and maintenance costs. It has been shown in some studies that prediction accuracy of the models improves when irrelevant and redundant features are removed from the original data set. In this study, we investigated four filter attribute selection techniques, Automatic Hybrid Search (AHS), …

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 …