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- Keyword
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- Microblogging (2)
- Spatial database (2)
- Twitter (2)
- Adaptive Maintenance Advisor (1)
- Algorithm (1)
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- Auto face annotation (1)
- Automated image tagging (1)
- Classification (1)
- Computer-mediated communication (1)
- Containment scope (1)
- Context-aware nearest neighbor search (1)
- Continuous queries (1)
- Direct antagonistic community (1)
- Direction (1)
- Distance metric learning (1)
- Distributed virtual environments (1)
- Document streams (1)
- Emerging trends (1)
- Fast Fourier Transform (FFT) (1)
- Fault detection (1)
- Features of the frequency signatures (FS features) (1)
- General Elections (1)
- Geographically distributed server architecture (1)
- Imbalanced class distribution (1)
- Independent Component Analysis (ICA) (1)
- Induction Motor (1)
- Inductive learning (1)
- Interactivity enhancement (1)
- Location-Dependent Spatial Query (1)
- Location-based recommendation (1)
Articles 1 - 29 of 29
Full-Text Articles in Physical Sciences and Mathematics
Price Points And Price Rigidity, Daniel Levy, Dongwon Lee, Haipeng (Allen) Lee, Robert J. Kauffman, Mark Bergen
Price Points And Price Rigidity, Daniel Levy, Dongwon Lee, Haipeng (Allen) Lee, Robert J. Kauffman, Mark Bergen
Research Collection School Of Computing and Information Systems
We study the link between price points and price rigidity using two data sets: weekly scanner data and Internet data. We find that ‘‘9’’ is the most frequent ending for the penny, dime, dollar, and ten-dollar digits; the most common price changes are those that keep the price endings at ‘‘9’’; 9-ending prices are less likely to change than non-9-ending prices; and the average size of price change is larger for 9-ending than non-9- ending prices. We conclude that 9-ending contributes to price rigidity from penny to dollar digits and across a wide range of product categories, retail formats, and …
Efficient Evaluation Of Continuous Text Seach Queries, Kyriakos Mouratidis, Hwee Hwa Pang
Efficient Evaluation Of Continuous Text Seach Queries, Kyriakos Mouratidis, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Consider a text filtering server that monitors a stream of incoming documents for a set of users, who register their interests in the form of continuous text search queries. The task of the server is to constantly maintain for each query a ranked result list, comprising the recent documents (drawn from a sliding window) with the highest similarity to the query. Such a system underlies many text monitoring applications that need to cope with heavy document traffic, such as news and email monitoring.In this paper, we propose the first solution for processing continuous text queries efficiently. Our objective is to …
Location-Dependent Spatial Query Containment, Ken C. K. Lee, Brandon Unger, Baihua Zheng, Wang-Chien Lee
Location-Dependent Spatial Query Containment, Ken C. K. Lee, Brandon Unger, Baihua Zheng, Wang-Chien Lee
Research Collection School Of Computing and Information Systems
Nowadays, location-related information is highly accessible to mobile users via issuing Location-Dependent Spatial Queries (LDSQs) with respect to their locations wirelessly to Location-Based Service (LBS) servers. Due to the limited mobile device battery energy, scarce wireless bandwidth, and heavy LBS server workload, the number of LDSQs submitted over wireless channels to LBS servers for evaluation should be minimized as appropriate. In this paper, we exploit query containment techniques for LDSQs (called LDSQ containment) to enable mobile clients to determine whether the result of a new LDSQ Q′ is completely covered by that of another LDSQ Q previously answered by a …
Influence Diagrams With Memory States: Representation And Algorithms, Xiaojian Wu, Akshat Kumar, Shlomo Zilberstein
Influence Diagrams With Memory States: Representation And Algorithms, Xiaojian Wu, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Influence diagrams (IDs) offer a powerful framework for decision making under uncertainty, but their applicability has been hindered by the exponential growth of runtime and memory usage--largely due to the no-forgetting assumption. We present a novel way to maintain a limited amount of memory to inform each decision and still obtain near-optimal policies. The approach is based on augmenting the graphical model with memory states that represent key aspects of previous observations--a method that has proved useful in POMDP solvers. We also derive an efficient EM-based message-passing algorithm to compute the policy. Experimental results show that this approach produces highquality …
Mining Direct Antagonistic Communities In Explicit Trust Networks, David Lo, Didi Surian, Zhang Kuan, Ee Peng Lim
Mining Direct Antagonistic Communities In Explicit Trust Networks, David Lo, Didi Surian, Zhang Kuan, Ee Peng Lim
Research Collection School Of Computing and Information Systems
There has been a recent increase of interest in analyzing trust and friendship networks to gain insights about relationship dynamics among users. Many sites such as Epinions, Facebook, and other social networking sites allow users to declare trusts or friendships between different members of the community. In this work, we are interested in extracting direct antagonistic communities (DACs) within a rich trust network involving trusts and distrusts. Each DAC is formed by two subcommunities with trust relationships among members of each sub-community but distrust relationships across the sub-communities. We develop an efficient algorithm that could analyze large trust networks leveraging …
Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain
Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain
Research Collection School Of Computing and Information Systems
We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last …
Direction-Based Surrounder Queries For Mobile Recommendations, Xi Guo, Baihua Zheng, Yoshiharu Ishikawa, Yunjun Gao
Direction-Based Surrounder Queries For Mobile Recommendations, Xi Guo, Baihua Zheng, Yoshiharu Ishikawa, Yunjun Gao
Research Collection School Of Computing and Information Systems
Location-based recommendation services recommend objects to the user based on the user’s preferences. In general, the nearest objects are good choices considering their spatial proximity to the user. However, not only the distance of an object to the user but also their directional relationship are important. Motivated by these, we propose a new spatial query, namely a direction-based surrounder (DBS) query, which retrieves the nearest objects around the user from different directions. We define the DBS query not only in a two-dimensional Euclidean space E">EE but also in a road network R">RR . In the Euclidean space E" …
Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng
Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design ahyper-graph based index structure to support approximated CANN search efficiently.
On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu
On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu
Research Collection School Of Computing and Information Systems
Twitter is a popular microblogging site where users can easily use mobile phones or desktop machines to generate short messages to be shared with others in realtime. Twitter has seen heavy usage in many recent international events including Japan earthquake, Iran election, etc. In such events, many tweets may become viral for different reasons. In this paper, we study the virality of socio-political tweet content in the Singapore’s 2011 general election (GE2011). We collected tweet data generated by about 20K Singapore users from 1 April 2011 till 12 May 2011, and the follow relationships among them. We introduce several quantitative …
Using Social Annotations For Trend Discovery In Scientific Publications, Meiqun Hu, Ee Peng Lim, Jing Jiang
Using Social Annotations For Trend Discovery In Scientific Publications, Meiqun Hu, Ee Peng Lim, Jing Jiang
Research Collection School Of Computing and Information Systems
Social tags and citing documents are two forms of social annotations to scientific publications. These social annotations provide useful contextual and temporal information for the annotated work, which encapsulates the attention and interest of the annotators. In this work, we explore the use of social annotations for discovering trends in scientific publications. We propose a trend discovery process that employs trend estimation and trend selection and ranking for analyzing the emerging trends shown in the social annotation profiles. The proposed sigmoid trend estimator allows us to characterize and compare how much, when and how fast the trends emerge. To perform …
Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu
Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu
Research Collection School Of Computing and Information Systems
With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose Spider- Mine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1 − ϵ. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call “spiders”. With the spider structure, our approach adopts a …
Unsupervised Information Extraction With Distributional Prior Knowledge, Cane Wing-Ki Leung, Jing Jiang, Kian Ming A. Chai, Hai Leong Chieu, Loo-Nin Teow
Unsupervised Information Extraction With Distributional Prior Knowledge, Cane Wing-Ki Leung, Jing Jiang, Kian Ming A. Chai, Hai Leong Chieu, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline. Specifically, the proposed prior has shown to be effective when coupled with discriminative features of the candidates.
Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayang Wang, Steven C. H. Hoi, Ying He
Mining Weakly Labeled Web Facial Images For Search-Based Face Annotation, Dayang Wang, Steven C. H. Hoi, Ying He
Research Collection School Of Computing and Information Systems
In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning …
Solution Pluralism And Metaheuristics, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, Frederic H. Murphy, David Harlan Wood
Solution Pluralism And Metaheuristics, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, Frederic H. Murphy, David Harlan Wood
Research Collection School Of Computing and Information Systems
Solution pluralism is an approach to problem solving and deliberation. It employs a plurality of distinct solutions for a decision problem for aiding decision making. The concept is well established in existing practice, although perhaps not recognized as such. This paper: (1) presents the concept as a generalization of established practice, (2) briefly describes successful uses of the concept in practice, and (3) presents several areas that appear would benefit from application of the concept. Throughout, the role of metaheuristics in finding the pluralities of solutions is emphasized.
Trust Network Inference For Online Rating Data Using Generative Models, Freddy Tat Chua Chua, Ee Peng Lim
Trust Network Inference For Online Rating Data Using Generative Models, Freddy Tat Chua Chua, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF)Model, a novel probabilistic model that generate ratings based on a number of rater’s and contributor’s factors. We demonstrate that parameters of the model can be learnt by Collapsed …
Effects Of Mentoring On Player Performance In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Kuo-Wei Hsu, Jaideep Srivastava
Effects Of Mentoring On Player Performance In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Kuo-Wei Hsu, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in Ever Quest II, a popular MMORPG developed by Sony Online Entertainment.
Online Fault Detection Of Induction Motors Using Frequency Domain Independent Components Analysis, Zhaoxia Wang, C. S. Chang
Online Fault Detection Of Induction Motors Using Frequency Domain Independent Components Analysis, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
This paper proposes an online fault detection method for induction motors using frequency-domain independent component analysis. Frequency-domain results, which are obtained by applying Fast Fourier Transform (FFT) to measured stator current time-domain waveforms, are analyzed with the aim of extracting frequency signatures of healthy and faulty motors with broken rotor-bar or bearing problem. Independent components analysis (ICA) is applied for such an aim to the FFT results. The obtained independent components as well as the FFT results are then used to obtain the combined fault signatures. The proposed method overcomes problems occurring in many existing FFT-based methods. Results using laboratory-collected …
Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li
Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li
Research Collection School Of Computing and Information Systems
Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
Continuous Visible Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Xiaofa Guo
Continuous Visible Nearest Neighbor Query Processing In Spatial Databases, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Xiaofa Guo
Research Collection School Of Computing and Information Systems
In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CVNN query returns a set of $${\langle p, R\rangle}$$ tuples such that $${p \in P}$$ is the nearest neighbor to every point r along the interval $${R \subseteq q}$$ as well as pis visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R due to the obstruction of …
Link Type Based Pre-Cluster Pair Model For Coreference Resolution, Yang Song, Houfeng Wang, Jing Jiang
Link Type Based Pre-Cluster Pair Model For Coreference Resolution, Yang Song, Houfeng Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
This paper presents our participation in the CoNLL-2011 shared task, Modeling Unrestricted Coreference in OntoNotes. Coreference resolution, as a difficult and challenging problem in NLP, has attracted a lot of attention in the research community for a long time. Its objective is to determine whether two mentions in a piece of text refer to the same entity. In our system, we implement mention detection and coreference resolution seperately. For mention detection, a simple classification based method combined with several effective features is developed. For coreference resolution, we propose a link type based pre-cluster pair model. In this model, pre-clustering of …
Supervisory Evolutionary Optimization Strategy For Adaptive Maintenance Schedules, Zhaoxia Wang, C. S. Chang
Supervisory Evolutionary Optimization Strategy For Adaptive Maintenance Schedules, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
No abstract provided.
Continuous Nearest Neighbor Search In The Presence Of Obstacles, Yunjun Gao, Baihua Zheng, Gang Chen, Chun Chen, Qing Li
Continuous Nearest Neighbor Search In The Presence Of Obstacles, Yunjun Gao, Baihua Zheng, Gang Chen, Chun Chen, Qing Li
Research Collection School Of Computing and Information Systems
Despite the ubiquity of physical obstacles (e.g., buildings, hills, and blindages, etc.) in the real world, most of spatial queries ignore the obstacles. In this article, we study a novel form of continuous nearest-neighbor queries in the presence of obstacles, namely continuous obstructed nearest-neighbor (CONN) search, which considers the impact of obstacles on the distance between objects. Given a data setP, an obstacle set O, and a query line segment q, in a two-dimensional space, a CONN query retrieves the nearest neighbor p ∈ P of each point p′ on q according to the obstructed distance, the shortest path between …
Fragile Online Relationship: A First Look At Unfollow Dynamics In Twitter, Haewoon Kwak, Hyunwoo Chun, Sue. Moon
Fragile Online Relationship: A First Look At Unfollow Dynamics In Twitter, Haewoon Kwak, Hyunwoo Chun, Sue. Moon
Research Collection School Of Computing and Information Systems
We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results.They unfollowed those who left many tweets within a short time, created tweets about uninteresting …
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Research Collection School Of Computing and Information Systems
We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …
Multi-Objective Zone Mapping In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou, Wentong Cai, Xueyan Tang, Rassul Avani
Multi-Objective Zone Mapping In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou, Wentong Cai, Xueyan Tang, Rassul Avani
Research Collection School Of Computing and Information Systems
In large-scale distributed virtual environments (DVEs), the NP-hard zone mapping problem concerns how to assign distinct zones of the virtual world to a number of distributed servers to improve overall interactivity. Previously, this problem has been formulated as a single-objective optimization problem, in which the objective is to minimize the total number of clients that are without QoS. This approach may cause considerable network traffic and processing overhead, as a large number of zones may need to be migrated across servers. In this paper, we introduce a multi-objective approach to the zone mapping problem, in which both the total number …
Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He
Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He
Research Collection School Of Computing and Information Systems
With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only …