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Articles 1 - 30 of 106
Full-Text Articles in Physical Sciences and Mathematics
Evaluation Of Protein Backbone Alphabets : Using Predicted Local Structure For Fold Recognition, Kyong Jin Shim
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.
Exploiting Intensity Inhomogeneity To Extract Textured Objects From Natural Scenes, Jundi Ding, Jialie Shen, Hwee Hwa Pang, Songcan Chen, Jingyu Yang
Exploiting Intensity Inhomogeneity To Extract Textured Objects From Natural Scenes, Jundi Ding, Jialie Shen, Hwee Hwa Pang, Songcan Chen, Jingyu Yang
Research Collection School Of Computing and Information Systems
Extracting textured objects from natural scenes is a challenging task in computer vision. The main difficulties arise from the intrinsic randomness of natural textures and the high-semblance between the objects and the background. In this paper, we approach the extraction problem with a seeded region-growing framework that purely exploits the statistical properties of intensity inhomogeneity. The pixels in the interior of potential textured regions are first found as texture seeds in an unsupervised manner. The labels of the texture seeds are then propagated through their respective inhomogeneous neighborhoods, to eventually cover the different texture regions in the image. Extensive experiments …
A Multi-User Steganographic File System On Untrusted Shared Storage, Jin Han, Meng Pan, Debin Gao, Hwee Hwa Pang
A Multi-User Steganographic File System On Untrusted Shared Storage, Jin Han, Meng Pan, Debin Gao, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Existing steganographic file systems enable a user to hide the existence of his secret data by claiming that they are (static) dummy data created during disk initialization. Such a claim is plausible if the adversary only sees the disk content at the point of attack. In a multi-user computing environment that employs untrusted shared storage, however, the adversary could have taken multiple snapshots of the disk content over time. Since the dummy data are static, the differences across snapshots thus disclose the locations of user data, and could even reveal the user passwords. In this paper, we introduce a Dummy-Relocatable …
Sequence Alignment Based Analysis Of Player Behavior In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Sequence Alignment Based Analysis Of Player Behavior In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
This study proposes a sequence alignment-based behavior analysis framework (SABAF) developed for predicting inactive game players that either leave the game permanently or stop playing the game for a long period of time. Sequence similarity scores and derived statistics form profile databases of inactive players and active players from the past. SABAF uses global and local sequence alignment algorithms and a unique scoring scheme to measure similarity between activity sequences. SABAF is tested on the game player activity data of Ever Quest II, a popular massively multiplayer online role-playing game developed by Sony Online Entertainment. SABAF consists of the following …
Smu-Sis At Tac 2010 - Kbp Track Entity Linking, Swapna Gottipati, Jing Jiang
Smu-Sis At Tac 2010 - Kbp Track Entity Linking, Swapna Gottipati, Jing Jiang
Research Collection School Of Computing and Information Systems
Entity linking task is a process of linking the named entity within the unstructured text to the entity in the Knowledge Base. Entity liking to the relevant knowledge is useful in various information extraction and natural language processing applications that improve the user experiences such as search, summarization and so on. We propose the two way entity linking approach to reformulate query, disambiguate the entity and link to the relevant KB repository. This paper describes the details of our participation in TAC 2010 - Knowledge Base Population track. We provided an innovative approach to disambiguate the entity by query reformulation …
Vireo At Trecvid 2010: Semantic Indexing, Known-Item Search, And Content-Based Copy Detection, Chong-Wah Ngo, Shi-Ai Zhu, Hung-Khoon Tan, Wan-Lei Zhao
Vireo At Trecvid 2010: Semantic Indexing, Known-Item Search, And Content-Based Copy Detection, Chong-Wah Ngo, Shi-Ai Zhu, Hung-Khoon Tan, Wan-Lei Zhao
Research Collection School Of Computing and Information Systems
This paper presents our approaches and the comparative analysis of our results for the three TRECVID 2010 tasks that we participated in: semantic indexing, known-item search and content-based copy detection.
Jointly Modeling Aspects And Opinions With A Maxent-Lda Hybrid, Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li
Jointly Modeling Aspects And Opinions With A Maxent-Lda Hybrid, Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and opinions. Recently topic models have been used to identify meaningful review aspects, but existing topic models do not identify aspect-specific opinion words. In this paper, we propose a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words. We show that with a relatively small amount of training data, our model can effectively identify aspect and opinion words simultaneously. We also demonstrate the domain adaptability of our model.
Rich Internet Geoweb For Spatial Data Infrastructure, Tin Seong Kam
Rich Internet Geoweb For Spatial Data Infrastructure, 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 RIGVAT, a rich internet geospatial visual …
Youth Olympic Village Co-Space, Zin-Yan Chua, Yilin Kang, Xing Jiang, Kah-Hoe Pang, Andrew C. Gregory, Chi-Yun Tan, Wai-Lun Wong, Ah-Hwee Tan, Yew-Soon Ong, Chunyan Miao
Youth Olympic Village Co-Space, Zin-Yan Chua, Yilin Kang, Xing Jiang, Kah-Hoe Pang, Andrew C. Gregory, Chi-Yun Tan, Wai-Lun Wong, Ah-Hwee Tan, Yew-Soon Ong, Chunyan Miao
Research Collection School Of Computing and Information Systems
We have designed and implemented a 3D virtual world based on the Co-Space concept encompasses the Youth Olympic Village (YOV) and several sports competition venues. It is a massively multiplayer online (MMO) virtual world built according to the actual, physical locations of the YOV and sports competition venues. On top of that, the Co-Space is being populated with human-like avatars, which are created according to the actual human size and appearance; they perform their activities and interact with the users in realworld context. In addition, autonomous intelligent agents are integrated into the Co-Space to provide context-aware and personalized services to …
Wsm'10: Second Acm Workshop On Social Media, Susanne Boll, Steven C. H. Hoi, Roelof Van Zwol, Jiebo Luo
Wsm'10: Second Acm Workshop On Social Media, Susanne Boll, Steven C. H. Hoi, Roelof Van Zwol, Jiebo Luo
Research Collection School Of Computing and Information Systems
The ACM SIGMM International Workshop on Social Media (WSM'10) is the second workshop held in conjunction with the ACM International Multimedia Conference (MM'10) at Firenze, Italy, 2010. This workshop provides a forum for researchers and practitioners from all over the world to share information on their latest investigations on social media analysis, exploration, search, mining, and emerging new social media applications.
Context Modeling For Ranking And Tagging Bursty Features In Text Streams, Xin Zhao, Jing Jiang, Jing He, Xiaoming Li, Hongfei Yan, Dongdong Shan
Context Modeling For Ranking And Tagging Bursty Features In Text Streams, Xin Zhao, Jing Jiang, Jing He, Xiaoming Li, Hongfei Yan, Dongdong Shan
Research Collection School Of Computing and Information Systems
Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty …
Business Network-Based Value Creation In Electronic Commerce, Robert John Kauffman, Ting Li, Eric Van Heck
Business Network-Based Value Creation In Electronic Commerce, Robert John Kauffman, Ting Li, Eric Van Heck
Research Collection School Of Computing and Information Systems
Information technologies (IT) have affected economic activities within and beyond the boundaries of the firm, changing the face of e-commerce. This article explores the circumstances under which value is created in business networks made possible by IT. Business networks combine the capabilities of multiple firms to produce and deliver products and services that none of them could more economically produce on its own and for which there is demand in the market. We call this business network-based value creation. We apply economic theory to explain the conditions under which business networks will exist and are able to sustain their value-producing …
Detecting Product Review Spammers Using Rating Behaviors, Ee Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, Hady Wirawan Lauw
Detecting Product Review Spammers Using Rating Behaviors, Ee Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic be- haviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their im- pact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a sub- set of highly suspicious …
Finding Unusual Review Patterns Using Unexpected Rules, Nitin Jindal, Bing Liu, Ee Peng Lim
Finding Unusual Review Patterns Using Unexpected Rules, Nitin Jindal, Bing Liu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com …
Mining Collaboration Patterns From A Large Developer Network, Didi Surian, David Lo, Ee Peng Lim
Mining Collaboration Patterns From A Large Developer Network, Didi Surian, David Lo, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In this study, we extract patterns from a large developer collaborations network extracted from Source Forge. Net at high and low level of details. At the high level of details, we extract various network-level statistics from the network. At the low level of details, we extract topological sub-graph patterns that are frequently seen among collaborating developers. Extracting sub graph patterns from large graphs is a hard NP-complete problem. To address this challenge, we employ a novel combination of graph mining and graph matching by leveraging network-level properties of a developer network. With the approach, we successfully analyze a snapshot of …
Modeling 3d Facial Expressions Using Geometry Videos, Jiazhi Xia, Ying He, Dao T. P. Quynh, Xiaoming Chen, Steven C. H. Hoi
Modeling 3d Facial Expressions Using Geometry Videos, Jiazhi Xia, Ying He, Dao T. P. Quynh, Xiaoming Chen, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The significant advances in developing high-speed shape acquisition devices make it possible to capture the moving and deforming objects at video speeds. However, due to its complicated nature, it is technically challenging to effectively model and store the captured motion data. In this paper, we present a set of algorithms to construct geometry videos for 3D facial expressions, including hole filling, geodesic-based face segmentation, and expression-invariant parametrization. Our algorithms are efficient and robust, and can guarantee the exact correspondence of the salient features (eyes, mouth and nose). Geometry video naturally bridges the 3D motion data and 2D video, and provides …
Online Multiple Kernel Learning: Algorithms And Mistake Bounds, Rong Jin, Steven C. H. Hoi, Tianbao Yang
Online Multiple Kernel Learning: Algorithms And Mistake Bounds, Rong Jin, Steven C. H. Hoi, Tianbao Yang
Research Collection School Of Computing and Information Systems
Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing the intersecting research. In this paper, we introduce a new research problem, termed Online Multiple Kernel Learning (OMKL), that aims to learn a kernel based prediction function from a pool of predefined kernels in an online learning fashion. OMKL is generally more challenging than typical online learning because both the kernel classifiers and their linear combination weights must be learned simultaneously. In this work, we consider two setups for OMKL, i.e. combining …
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Research Collection School Of Computing and Information Systems
Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework …
Cast2face: Character Identification In Movie With Actor-Character Correspondence, Mengdi Xu, Xiaotong Yuan, Jialie Shen, Shuicheng Yan
Cast2face: Character Identification In Movie With Actor-Character Correspondence, Mengdi Xu, Xiaotong Yuan, Jialie Shen, Shuicheng Yan
Research Collection School Of Computing and Information Systems
We investigate the problem of automatically identifying characters in a movie with the supervision of actor-character name correspondence provided by the movie cast. Our proposed framework, namely Cast2Face, is featured by: (i) we restrict the names to assign within the set of character names in the cast; (ii) for each character, by using the corresponding actor's name as a key word, we retrieve from Google image search a group of face images to form the gallery set; and (iii) the probe face tracks in the movie are then identified as one of the actors by robust multi-task joint sparse representation …
Trajectory-Based Visualization Of Web Video Topics, Juan Cao, Chong-Wah Ngo, Yong-Dong Zhang, Dong-Ming Zhang, Liang Ma
Trajectory-Based Visualization Of Web Video Topics, Juan Cao, Chong-Wah Ngo, Yong-Dong Zhang, Dong-Ming Zhang, Liang Ma
Research Collection School Of Computing and Information Systems
While there have been research efforts in organizing largescale web videos into topics, efficient browsing of web video topics remains a challenging problem not yet addressed. The related issues include how to efficiently browse and track the evolution of topics and eventually locate the videos of interest. In this paper, we introduce a novel interface for visualizing video topics as evolution trajectories. The trajectory visualization is capable of highlighting milestone events and depicting the topical hotness over time. The interface also allows multi-level browsing from topics to events and to videos, resulting in search exploration could be more efficiently conducted …
Context-Aware Query Recommendations, Alexandros Ntoulas, Heasoo Hwang, Lise Getoor, Stelios Paparizos, Hady Wirawan Lauw
Context-Aware Query Recommendations, Alexandros Ntoulas, Heasoo Hwang, Lise Getoor, Stelios Paparizos, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Described is a search-related technology in which context information regarding a user's prior search actions is used in making query recommendations for a current user action, such as a query or click. To determine whether each set or subset of context information is relevant to the user action, data obtained from a query log is evaluated. More particularly, a query transition (query-query) graph and a query click (query-URL) graph are extracted from the query log; vectors are computed for the current action and each context/sub-context and evaluated against vectors in the graphs to determine current action-to-context similarity. Also described is …
Embellishing Text Search Queries To Protect User Privacy, Hwee Hwa Pang, Xuhua Ding, Xiaokui Xiao
Embellishing Text Search Queries To Protect User Privacy, Hwee Hwa Pang, Xuhua Ding, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
Users of text search engines are increasingly wary that their activities may disclose confidential information about their business or personal profiles. It would be desirable for a search engine to perform document retrieval for users while protecting their intent. In this paper, we identify the privacy risks arising from semantically related search terms within a query, and from recurring highspecificity query terms in a search session. To counter the risks, we propose a solution for a similarity text retrieval system to offer anonymity and plausible deniability for the query terms, and hence the user intent, without degrading the system’s precision-recall …
P2pdoctagger: Content Management Through Automated P2p Collaborative Tagging, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi
P2pdoctagger: Content Management Through Automated P2p Collaborative Tagging, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
As the amount of user generated content grows, personal information management has become a challenging problem. Several information management approaches, such as desktop search, document organization and (collaborative) document tagging have been proposed to address this, however they are either inappropriate or inefficient. Automated collaborative document tagging approaches mitigate the problems of manual tagging, but they are usually based on centralized settings which are plagued by problems such as scalability, privacy, etc. To resolve these issues, we present P2PDocTagger, an automated and distributed document tagging system based on classification in P2P networks. P2P-DocTagger minimizes the efforts of individual peers and …
Shortest Path Computation On Air Indexes, Georgios Kellaris, Kyriakos Mouratidis
Shortest Path Computation On Air Indexes, Georgios Kellaris, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Shortest path computation is one of the most common queries in location-based services that involve transportation net- works. Motivated by scalability challenges faced in the mo- bile network industry, we propose adopting the wireless broad- cast model for such location-dependent applications. In this model the data are continuously transmitted on the air, while clients listen to the broadcast and process their queries locally. Although spatial problems have been considered in this environment, there exists no study on shortest path queries in road networks. We develop the rst framework to compute shortest paths on the air, and demonstrate the practicality and …
Diract: Agent-Based Interactive Storytelling, Yundong Cai, Zhiqi Shen, Chunyan Miao, Ah-Hwee Tan
Diract: Agent-Based Interactive Storytelling, Yundong Cai, Zhiqi Shen, Chunyan Miao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
A lot of researches haven been done on the interactive storytelling authoring , e.g. by a director agent, or by interactions among a number of character agents. However, it is still difficult to construct the interactive storytelling for novice users, due to a need of various agents development and complex communication among the agents. We propose an agent-based interactive storytelling architecture, namely DIRACT (short of “Direct and Act”). It is composed of numerous atomic DIRACT agents, which are goal oriented and use an unified communication protocol. By removing the difference between the director and character, each DIRACT agent can either …
Learning Personal Agents With Adaptive Player Modeling In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
Learning Personal Agents With Adaptive Player Modeling In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
There has been growing interest in creating intelligent agents in virtual worlds that do not follow fixed scripts predefined by the developers, but react accordingly based on actions performed by human players during their interaction. In order to achieve this objective, previous approaches have attempted to model the environment and the user’s context directly. However, a critical component for enabling personalized virtual world experience is missing, namely the capability to adapt over time to the habits and eccentricity of a particular player. To address the above issue, this paper presents a cognitive agent with learning player model capability for personalized …
A Probabilistic Approach To Personalized Tag Recommendation, Meiqun Hu, Ee Peng Lim, Jing Jiang
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 …
Team Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Team Performance Prediction In Massively Multiplayer Online Role-Playing Games (Mmorpgs), Kyong Jin Shim, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of teams. This study uses performance data of game players and teams in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for task performing teams. The prediction models provide a projection of task performing team's future performance based on the past performance patterns of participating players on the team as well as team characteristics. While the existing game system lacks the ability to predict team-level performance, the prediction models proposed in this study are expected to be …
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
We introduce MobiTOP, a Web-based system for organizing and retrieving hierarchical location-based annotations. Each annotation contains multimedia content (such as text, images, video) associated with a location, and users are able to annotate existing annotations to an arbitrary depth, in effect creating a hierarchy. An evaluation was conducted on a group of potential users to ascertain their perceptions of the usability of the application. The results were generally positive and the majority of the participants saw MobiTOP as a useful platform to share location-based information. We conclude with implications of our work and opportunities for future research.
Mining Interaction Behaviors For Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Amruta Purandare, Loo Nin Teow
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 …