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Articles 2251 - 2280 of 3441
Full-Text Articles in Databases and Information Systems
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social network analysis has received much attention from corporations recently. Corporations are trying to utilize social media platforms such as Twitter, Facebook and Sina Weibo to expand their own markets. Our system is an online tool to assist these corporations to 1) find potential customers, and 2) track a list of users by specific events from social networks. We employ both textual and network information, and thus produce a keyword-based relevance score for each user in pre-defined dimensions, which indicates the probability of the adoption of a product. Based on the score and its trend, out tool is able to …
Social Sensing For Urban Crisis Management: The Case Of Singapore Haze, Philips Kokoh Prasetyo, Ming Gao, Ee Peng Lim, Christie N. Scollon
Social Sensing For Urban Crisis Management: The Case Of Singapore Haze, Philips Kokoh Prasetyo, Ming Gao, Ee Peng Lim, Christie N. Scollon
Research Collection School Of Computing and Information Systems
Sensing social media for trends and events has become possible as increasing number of users rely on social media to share information. In the event of a major disaster or social event, one can therefore study the event quickly by gathering and analyzing social media data. One can also design appropriate responses such as allocating resources to the affected areas, sharing event related information, and managing public anxiety. Past research on social event studies using social media often focused on one type of data analysis (e.g., hashtag clusters, diffusion of events, influential users, etc.) on a single social media data …
Information Vs Interaction: An Alternative User Ranking Model For Social Networks, Wei Xie, Ai Phuong Hoang, Feida Zhu, Ee Peng Lim
Information Vs Interaction: An Alternative User Ranking Model For Social Networks, Wei Xie, Ai Phuong Hoang, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The recent years have seen an unprecedented boom of social network services, such as Twitter, which boasts over 200 million users. In such big social platforms, the influential users are ideal targets for viral marketing to potentially reach an audience of maximal size. Most proposed algorithms rely on the linkage structure of the respective underlying network to determine the information flow and hence indicate a users influence. From social interaction perspective, we built a model based on the dynamic user interactions constantly taking place on top of these linkage structures. In particular, in the Twitter setting we supposed a principle …
Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
Research Collection School Of Computing and Information Systems
Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task …
A Social Network-Empowered Research Analytics Framework For Project Selection, Thushari Silva, Zhiling Guo, Jian Ma, Hongbing Jiang, Huaping Chen
A Social Network-Empowered Research Analytics Framework For Project Selection, Thushari Silva, Zhiling Guo, Jian Ma, Hongbing Jiang, Huaping Chen
Research Collection School Of Computing and Information Systems
Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality, quantity, and citations of published journal articles) of researchers is largely ignored. To overcome these limitations, this paper proposes a social network-empowered research analytics framework (RAF) for research project selections. Scholarmate.com, a professional research social network with easy access to research relevant information, serves as a platform to build researcher profiles from three dimensions, i.e., relevance, productivity and connectivity. Building upon profiles …
Using Micro-Reviews To Select An Efficient Set Of Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas
Using Micro-Reviews To Select An Efficient Set Of Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas
Research Collection School Of Computing and Information Systems
Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose …
Modeling Interaction Features For Debate Side Clustering, Minghui Qiu, Liu Yang, Jing Jiang
Modeling Interaction Features For Debate Side Clustering, Minghui Qiu, Liu Yang, Jing Jiang
Research Collection School Of Computing and Information Systems
Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an …
On Effects Of Visual Query Complexity, Jialie Shen, Cheng Zhiyong
On Effects Of Visual Query Complexity, Jialie Shen, Cheng Zhiyong
Research Collection School Of Computing and Information Systems
As an effective technique to manage large scale image collections, content-based image retrieval (CBIR) has been received great attentions and became a very active research domain in recent years. While assessing system performance is one of the key factors for the related technological advancement, relatively little attention has been paid to model and analyze test queries. This paper documents a study on the problem of determining visual query complexity as a measure for predicting image retrieval performance. We propose a quantitative metric for measuring complexity of image queries for content-based image search engine. A set of experiments are carried out …
Consistent Stereo Image Editing, Tao Yan, Shengfeng He, Rynson W.H. Lau, Yun Xu
Consistent Stereo Image Editing, Tao Yan, Shengfeng He, Rynson W.H. Lau, Yun Xu
Research Collection School Of Computing and Information Systems
Stereo images and videos are very popular in recent years, and techniques for processing this media are attracting a lot of attention. In this paper, we extend the shift-map method for stereo image editing. Our method simultaneously processes the left and right images on pixel level using a global optimization algorithm. It enforces photo consistence between the two images and preserves 3D scene structures. It also addresses the occlusion and disocclusion problem, which may enable many stereo image editing functions, such as depth mapping, object depth adjustment and non-homogeneous image resizing. Our experiments show that the proposed method produces high …
Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei
Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei
Research Collection School Of Computing and Information Systems
Our objective is to estimate the relevance of an image to a query for image search purposes. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of bridging the gap between semantic textual queries as well as users’ search intents and image visual content. Image search engines therefore primarily rely on static and textual features. Visual features are mainly used to identify potentially useful recurrent patterns or relevant training examples for complementing search by image reranking. Second, image rankers are trained on query-image pairs labeled by human experts, making the …
Merged Aggregate Nearest Neighbor Query Processing In Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu
Merged Aggregate Nearest Neighbor Query Processing In Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu
Research Collection School Of Computing and Information Systems
Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted …
Learning Topics And Positions From Debatepedia, Swapna Gottopati, Minghui Qiu, Yanchuan Sim, Jing Jiang, Noah Smith
Learning Topics And Positions From Debatepedia, Swapna Gottopati, Minghui Qiu, Yanchuan Sim, Jing Jiang, Noah Smith
Research Collection School Of Computing and Information Systems
We explore Debatepedia, a communityauthored encyclopedia of sociopolitical debates, as evidence for inferring a lowdimensional, human-interpretable representation in the domain of issues and positions. We introduce a generative model positing latent topics and cross-cutting positions that gives special treatment to person mentions and opinion words. We evaluate the resulting representation’s usefulness in attaching opinionated documents to arguments and its consistency with human judgments about positions.
The Vireo Team At Mediaeval 2013: Violent Scenes Detection By Mid-Level Concepts Learnt From Youtube, Chun Chet Tan, Chong-Wah Ngo
The Vireo Team At Mediaeval 2013: Violent Scenes Detection By Mid-Level Concepts Learnt From Youtube, Chun Chet Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
The Violent Scenes Detection task continues to pose challenge in detecting violent scenes in Hollywood movies. In this working notes paper, we present the framework of our system and briefly discuss the performance results obtained in both objective and subjective subtasks. Besides using the low-level features for training the SVM classifiers for violent scenes detection, we show the feasibility in using the concept detectors to infer the occurrence of violent scenes. External Youtube data is exploited in our implementation to provide more diverse definition to violent scene concepts. Furthermore, we explore the feasibility of using Conditional Random Fields (CRF) to …
Predictive Handling Of Asynchronous Concept Drifts In Distributed Environments, Hock Hee Ang, Vivek Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi
Predictive Handling Of Asynchronous Concept Drifts In Distributed Environments, Hock Hee Ang, Vivek Gopalkrishnan, Indre Zliobaite, Mykola Pechenizkiy, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In a distributed computing environment, peers collaboratively learn to classify concepts of interest from each other. When external changes happen and their concepts drift, the peers should adapt to avoid increase in misclassification errors. The problem of adaptation becomes more difficult when the changes are asynchronous, i.e., when peers experience drifts at different times. We address this problem by developing an ensemble approach, PINE, that combines reactive adaptation via drift detection, and proactive handling of upcoming changes via early warning and adaptation across the peers. With empirical study on simulated and real-world data sets, we show that PINE handles asynchronous …
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Research Collection School Of Computing and Information Systems
Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we …
Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu
Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
Traditional batch model-based Collaborative Filtering (CF) approaches typically assume a collection of users' rating data is given a priori for training the model. They suffer from a common yet critical drawback, i.e., the model has to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommender systems where users' rating data often arrives sequentially and frequently. In this paper, we investigate a novel efficient and scalable online collaborative filtering technique for on-the-fly recommender systems, which is able to effectively online update the recommendation model from a sequence of rating observations. Specifically, we …
Skyhunter: A Multi-Surface Environment For Supporting Oil And Gas Exploration, Teddy Seyed, Mario Costa Sousa, Frank Maurer, Anthony Tang
Skyhunter: A Multi-Surface Environment For Supporting Oil And Gas Exploration, Teddy Seyed, Mario Costa Sousa, Frank Maurer, Anthony Tang
Research Collection School Of Computing and Information Systems
The process of oil and gas exploration and its result, the decision to drill for oil in a specific location, relies on a number of distinct but related domains. These domains require effective collaboration to come to a decision that is both cost effective and maintains the integrity of the environment. As we show in this paper, many of the existing technologies and practices that support the oil and gas exploration process overlook fundamental user issues such as collaboration, interaction and visualization. The work presented in this paper is based upon a design process that involved expert users from an …
A Unified Model For Topics, Events And Users On Twitter, Qiming Diao, Jing Jiang
A Unified Model For Topics, Events And Users On Twitter, Qiming Diao, Jing Jiang
Research Collection School Of Computing and Information Systems
With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture …
Riga: A Rich Internet Geospatial Analytics Application For Area-Based Data, Tin Seong Kam
Riga: A Rich Internet Geospatial Analytics Application For Area-Based Data, Tin Seong Kam
Research Collection School Of Computing and Information Systems
In this information age, more and more public statistical data such as population census, household living, local economy and business establishment are distributed over the internet within the framework of spatial data infrastructure. By and large, these data are organized geographically such as region, province as well as district. Usually, they are published in the form of digital maps over the internet as simple points, lines and polygons markers limited or no analytical function available to transform these data into useful information. To meet the analytical needs of casual public data users, we contribute RIGA, a rich internet geospatial analytics …
The Myths Of G-Tech For Business Decision Making, Tin Seong Kam
The Myths Of G-Tech For Business Decision Making, Tin Seong Kam
Research Collection School Of Computing and Information Systems
More than 80% of organisation data are location related - the locations where transactions are done, where retailers are found, and of customers who buy their products. Since the early 2005, there has been an increasing interest among the business community to use geospatial technology to enhance decision making process at both strategic and operational levels. Millions of dollars and man-hours have been invested into driving their geo-technology development and implementation. The use of geospatial technology in business, however, tends to confine to simple mapping. Many of these failures are the victims of misperception. Some of the perpetrators are practitioners. …
The Impact Of Ineffective Internal Control On The Value Relevance Of Accounting Information, Nan Hu, Baolei Qi, Gaoliang Tian, Lee Yao, Zhen Zeng
The Impact Of Ineffective Internal Control On The Value Relevance Of Accounting Information, Nan Hu, Baolei Qi, Gaoliang Tian, Lee Yao, Zhen Zeng
Research Collection School Of Computing and Information Systems
This paper investigates the value relevance of accounting information in the presence of ineffective internal control (IIC). Based on Ohlson's valuation model, this paper first documents that IIC can directly affect a firm's market value after control cost of capital, corporate governance, and other, value-relevant variables. Second, this paper finds that the value relevance of earnings and book value in determining a firm's market value are significantly reduced. Collectively, the results of this paper indicate that the effectiveness of internal controls can directly affect a firm's market value and the value relevance of accounting information.
Web-Scale Near-Duplicate Search: Techniques And Applications, Chong-Wah Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik
Web-Scale Near-Duplicate Search: Techniques And Applications, Chong-Wah Ngo, Changsheng Xu, Wessel Kraaij, Abdulmotaleb El Saddik
Research Collection School Of Computing and Information Systems
This paper presents some of the most recent advances in the research on Web-scale near-duplicate search and explores the potential for bringing this research a substantial step further. It contains high-quality contributions addressing various aspects of the Web-scale near-duplicate search problem in a number of relevant domains. The topics range from feature representation, matching, and indexing from different novel aspects to the adaptation of current technologies for mobile media search and photo archaeology mining.
Generative Models For Item Adoptions Using Social Correlation, Freddy Chong Tat Chua, Hady Wirawan Lauw, Ee Peng Lim
Generative Models For Item Adoptions Using Social Correlation, Freddy Chong Tat Chua, Hady Wirawan Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users’ item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and …
An Empirical Study On Uncertainty Identification In Social Media Context, Zhongyu Wei, Junwen Chen, Wei Gao, Binyang Li, Lanjun Zhou, Yulan He, Kam-Fai Wong
An Empirical Study On Uncertainty Identification In Social Media Context, Zhongyu Wei, Junwen Chen, Wei Gao, Binyang Li, Lanjun Zhou, Yulan He, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.
Multi-View Discriminant Transfer Learning, Pei Yang Yang, Wei Gao
Multi-View Discriminant Transfer Learning, Pei Yang Yang, Wei Gao
Research Collection School Of Computing and Information Systems
We study to incorporate multiple views of data in a perceptive transfer learning framework and propose a Multi-view Discriminant Transfer (MDT) learning approach for domain adaptation. The main idea is to find the optimal discriminant weight vectors for each view such that the correlation between the two-view projected data is maximized, while both the domain discrepancy and the view disagreement are minimized simultaneously. Furthermore, we analyze MDT theoretically from discriminant analysis perspective to explain the condition and reason, under which the proposed method is not applicable. The analytical results allow us to investigate whether there exist within-view and/or betweenview conflicts, …
Open Source Software Development Process Model – A Grounded Theory Approach, Keng Siau, Y. Tian
Open Source Software Development Process Model – A Grounded Theory Approach, Keng Siau, Y. Tian
Research Collection School Of Computing and Information Systems
The open source movement has provided software users with more choices, lower software acquisition cost, more flexible software customization, and possibly higher quality software product. Although the development of open source software is dynamic and it encourages innovations, the process can be chaotic. An Open Source Software Development (OSSD) process model to enhance the survivability of OSSD projects is needed. This research uses the grounded theory approach to derive a Phase-Role-Skill-Responsibility (PRSR) OSSD process model. The three OSSD process phases -- Launch Stage, Before the First Release, and Between Releases -- address the characteristics of the OSSD process as well …
Incremental And Accuracy-Aware Personalized Pagerank Through Scheduled Approximation, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying
Incremental And Accuracy-Aware Personalized Pagerank Through Scheduled Approximation, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying
Research Collection School Of Computing and Information Systems
As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the …
Self-Organizing Cognitive Models For Virtual Agents, Yilin Kang, Ah-Hwee Tan
Self-Organizing Cognitive Models For Virtual Agents, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Three key requirements of realistic characters or agents in virtual world can be identified as autonomy, interactivity, and personification. Working towards these challenges, this paper proposes a brain inspired agent architecture that integrates goal-directed autonomy, natural language interaction and human-like personification. Based on self-organizing neural models, the agent architecture maintains explicit mental representation of desires, intention, personalities, self-awareness, situation awareness and user awareness. Autonomous behaviors are generated via evaluating the current situation with active goals and learning the most appropriate social or goal-directed rule from the available knowledge, in accordance with the personality of each individual agent. We have built …
Large Scale Online Kernel Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang, Zhi-Yong Liu
Large Scale Online Kernel Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
In this work, we present a new framework for large scale online kernel classification, making kernel methods efficient and scalable for large-scale online learning tasks. Unlike the regular budget kernel online learning scheme that usually uses different strategies to bound the number of support vectors, our framework explores a functional approximation approach to approximating a kernel function/matrix in order to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) the Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) the …
An Empirical Analysis Of A Network Of Expertise, Le Truc Viet, Minh Thap Nguyen
An Empirical Analysis Of A Network Of Expertise, Le Truc Viet, Minh Thap Nguyen
Research Collection School Of Computing and Information Systems
In this paper, we analyze the network of expertise constructed from the interactions of users on the online questionanswering (QA) community of Stack Overflow. This community was built with the intention of helping users with their programming tasks and, thus, questions are expected to be highly factual. This also indicates that the answers one provides may be highly indicative of one's level of expertise on the subject matter. Therefore, our main concern is how to model and characterize the user's expertise based on the constructed network and its centrality measures. We used the user's reputation established on Stack Overflow as …