Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (3555)
- Software Engineering (2197)
- Artificial Intelligence and Robotics (1881)
- Information Security (1102)
- Numerical Analysis and Scientific Computing (1060)
-
- Graphics and Human Computer Interfaces (942)
- Engineering (884)
- Social and Behavioral Sciences (807)
- Business (748)
- Theory and Algorithms (514)
- Computer Engineering (449)
- Programming Languages and Compilers (413)
- Operations Research, Systems Engineering and Industrial Engineering (407)
- OS and Networks (345)
- Communication (326)
- Social Media (264)
- Public Affairs, Public Policy and Public Administration (230)
- Medicine and Health Sciences (196)
- Education (194)
- Transportation (194)
- Management Information Systems (176)
- Data Storage Systems (167)
- E-Commerce (154)
- International and Area Studies (147)
- Technology and Innovation (146)
- Asian Studies (145)
- Health Information Technology (118)
- Higher Education (105)
- Keyword
-
- Machine learning (145)
- Deep learning (129)
- Artificial intelligence (123)
- Social media (82)
- Singapore (73)
-
- Reinforcement learning (72)
- Data mining (70)
- Privacy (67)
- Security (62)
- Cloud computing (60)
- Deep Learning (57)
- Empirical study (55)
- Software engineering (55)
- Optimization (53)
- Online learning (51)
- Visualization (51)
- Neural networks (50)
- Anomaly detection (49)
- Training (49)
- Twitter (49)
- Task analysis (48)
- Blockchain (47)
- Natural language processing (47)
- Collaboration (46)
- Large Language Models (46)
- Feature extraction (45)
- Algorithms (44)
- Access control (43)
- Machine Learning (43)
- Semantics (43)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (8458)
- Dissertations and Theses Collection (Open Access) (189)
- Research Collection Lee Kong Chian School Of Business (59)
- Research Collection Yong Pung How School Of Law (49)
- Research Collection School of Social Sciences (27)
-
- Asian Management Insights (26)
- Research Collection College of Integrative Studies (23)
- Perspectives@SMU (21)
- Research Collection School Of Accountancy (18)
- Dissertations and Theses Collection (15)
- FORCE 2026 (14)
- SMU Press Releases and News (12)
- MITB Thought Leadership Series (11)
- Research Collection Library (10)
- Research Collection School of Computing and Information Systems (10)
- Research@SMU: Connecting the Dots (10)
- PhD Student’s Publications Collection (8)
- LARC Research Publications (7)
- Research Collection School Of Economics (6)
- CCX Research (4)
- SMU Research Data (4)
- Student Publications (4)
- 2024 AI for Research Week (3)
- SCIS Student Publications (3)
- Centre for AI & Data Governance (2019-2025) (2)
- Research Collection Office of Research (2)
- CASTLe: Collection of Articles on Scholarship for Teaching and Learning (1)
- Centre for Computational Law (2022-2025) (1)
- Library Events (1)
- ROSA Journal Articles and Publications (1)
- Publication Type
- File Type
Articles 7591 - 7620 of 9003
Full-Text Articles in Computer Sciences
What Makes Categories Difficult To Classify?, Aixin Sun, Ee Peng Lim, Ying Liu
What Makes Categories Difficult To Classify?, Aixin Sun, Ee Peng Lim, Ying Liu
Research Collection School Of Computing and Information Systems
In this paper, we try to predict which category will be less accurately classified compared with other categories in a classification task that involves multiple categories. The categories with poor predicted performance will be identified before any classifiers are trained and additional steps can be taken to address the predicted poor accuracies of these categories. Inspired by the work on query performance prediction in ad-hoc retrieval, we propose to predict classification performance using two measures, namely, category size and category coherence. Our experiments on 20-Newsgroup and Reuters-21578 datasets show that the Spearman rank correlation coefficient between the predicted rank of …
Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun
Trust Relationship Prediction Using Online Product Review Data, Nan Ma, Ee Peng Lim, Viet-An Nguyen, Aixin Sun
Research Collection School Of Computing and Information Systems
Trust between users is an important piece of knowledge that can be exploited in search and recommendation.Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments …
Vireo/Dvmm At Trecvid 2009: High-Level Feature Extraction, Automatic Video Search, And Content-Based Copy Detection, Chong-Wah Ngo, Yu-Gang Jiang, Xiao-Yong Wei, Wanlei Zhao, Yang Liu, Jun Wang, Shiai Zhu, Shih-Fu Chang
Vireo/Dvmm At Trecvid 2009: High-Level Feature Extraction, Automatic Video Search, And Content-Based Copy Detection, Chong-Wah Ngo, Yu-Gang Jiang, Xiao-Yong Wei, Wanlei Zhao, Yang Liu, Jun Wang, Shiai Zhu, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
This paper presents overview and comparative analysis of our systems designed for 3 TRECVID 2009 tasks: high-level feature extraction, automatic search, and content-based copy detection.
Static Validation Of C Preprocessor Macros, Andreas Saebjornsen, Lingxiao Jiang, Daniel Quinlan, Zhendong Su
Static Validation Of C Preprocessor Macros, Andreas Saebjornsen, Lingxiao Jiang, Daniel Quinlan, Zhendong Su
Research Collection School Of Computing and Information Systems
The widely used C preprocessor (CPP) is generally considered a source of difficulty for understanding and maintaining C/C++ programs. The main reason for this difficulty is CPP’s purely lexical semantics, i.e., its treatment of both input and output as token streams. This can easily lead to errors that are difficult to diagnose, and it has been estimated that up to 20% of all macros are erroneous. To reduce such errors, more restrictive, replacement languages for CPP have been proposed to limit expanded macros to be valid C syntactic units. However, there is no practical tool that can effectively validate CPP …
Udel/Smu At Trec 2009 Entity Track, Wei Zheng, Swapna Gottipati, Jing Jiang, Hui Fang
Udel/Smu At Trec 2009 Entity Track, Wei Zheng, Swapna Gottipati, Jing Jiang, Hui Fang
Research Collection School Of Computing and Information Systems
We report our methods and experiment results from the collaborative participation of the InfoLab group from University of Delaware and the school of Information Systems from Singapore Management University in the TREC 2009 Entity track. Our general goal is to study how we may apply language modeling approaches and natural language processing techniques to the task. Specically, we proposed to find supporting information based on segment retrieval, to extract entities using Stanford NER tagger, and to rank entities based on a previously proposed probabilistic framework for expert finding.
Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim
Minimum Latency Broadcasting In Multiradio, Multichannel, Multirate Wireless Meshes, Junaid Qadir, Chuntung Chou, Archan Misra, Joo Ghee Lim
Research Collection School Of Computing and Information Systems
We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study …
Mining Communities In Networks: A Solution For Consistency And Its Evaluation, Haewoon Kwak, Yoonchan Choi, Young-Ho Eom, Hawoong Jeong, Sue Moon
Mining Communities In Networks: A Solution For Consistency And Its Evaluation, Haewoon Kwak, Yoonchan Choi, Young-Ho Eom, Hawoong Jeong, Sue Moon
Research Collection School Of Computing and Information Systems
Online social networks pose significant challenges to computer scientists, physicists, and sociologists alike, for their massive size, fast evolution, and uncharted potential for social computing. One particular problem that has interested us is community identification. Many algorithms based on various metrics have been proposed for communities in networks [18, 24], but a few algorithms scale to very large networks. Three recent community identification algorithms, namely CNM [16], Wakita [59], and Louvain [10], stand out for their scalability to a few millions of nodes. All of them use modularity as the metric of optimization. However, all three algorithms produce inconsistent communities …
Ensemble And Individual Noise Reduction Method For Induction-Motor Signature Analysis, Zhaoxia Wang, C.S. Chang, Tw Chua, W.W Tan
Ensemble And Individual Noise Reduction Method For Induction-Motor Signature Analysis, Zhaoxia Wang, C.S. Chang, Tw Chua, W.W Tan
Research Collection School Of Computing and Information Systems
Unlike a fixed-frequency power supply, the voltagesupplying an inverter-fed motor is heavily corrupted by noises,which are produced from high-frequency switching leading tonoisy stator currents. To extract useful information from statorcurrentmeasurements, a theoretically sound and robust denoisingmethod is required. The effective filtering of these noisesis difficult with certain frequency-domain techniques, such asFourier transform or Wavelet analysis, because some noises havefrequencies overlapping with those of the actual signals, andsome have high noise-to-frequency ratios. In order to analyze thestatistical signatures of different types of signals, a certainnumber is required of the individual signals to be de-noisedwithout sacrificing the individual characteristic and quantity ofthe …
Online Fault Detection Of Induction Motors Using Independent Component Analysis And Fuzzy Neural Network, Zhaoxia Wang, C. S. Chang, X. German, W.W. Tan
Online Fault Detection Of Induction Motors Using Independent Component Analysis And Fuzzy Neural Network, Zhaoxia Wang, C. S. Chang, X. German, W.W. Tan
Research Collection School Of Computing and Information Systems
This paper proposes the use of independent component analysis and fuzzy neural network for online fault detection of induction motors. The most dominating components of the stator currents measured from laboratory motors are directly identified by an improved method of independent component analysis, which are then used to obtain signatures of the stator current with different faults. The signatures are used to train a fuzzy neural network for detecting induction-motor problems such as broken rotor bars and bearing fault. Using signals collected from laboratory motors, the robustness of the proposed method for online fault detection is demonstrated for various motor …
User Interfaces For Visual Analysis And Monitoring In Business Intelligence, Lars Grammel, Margaret-Anne Storey, Christoph Treude
User Interfaces For Visual Analysis And Monitoring In Business Intelligence, Lars Grammel, Margaret-Anne Storey, Christoph Treude
Research Collection School Of Computing and Information Systems
Business intelligence is concerned with understanding and leveraging the vast amounts of information stored in the databases of modern enterprises. Visualization techniques have been used to make sense of this data for a long time, first in the form of simple charts, and nowadays in the form of interactive visualizations. By leveraging the strengths of the human perceptual system and incorporating user interaction, they support the flexible analysis of data as well as data monitoring by users. The recent progress in the fields of information and data visualization as well as new hardware developments and trends in business intelligence have …
Semantic Context Transfer Across Heterogeneous Sources For Domain Adaptive Video Search, Yu-Gang Jiang, Chong-Wah Ngo, Shih-Fu Chang
Semantic Context Transfer Across Heterogeneous Sources For Domain Adaptive Video Search, Yu-Gang Jiang, Chong-Wah Ngo, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propose a novel approach that leverages heterogeneous knowledge sources for domain adaptive video search. First, instead of utilizing WordNet as most existing works, we exploit the context information associated with Flickr images to estimate query-detector similarity. The resulting measurement, named Flickr context similarity (FCS), reflects the co-occurrence statistics of words in image context rather than textual …
Semantics-Preserving Bag-Of-Words Models For Efficient Image Annotation, Lei Wu, Steven C. H. Hoi, Nenghai Yu
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 …
A Service Choice Model For Optimizing Taxi Service Delivery, Shih-Fen Cheng, Xin Qu
A Service Choice Model For Optimizing Taxi Service Delivery, Shih-Fen Cheng, Xin Qu
Research Collection School Of Computing and Information Systems
Taxi service has undergone radical revamp in recent years. In particular, significant investments in communication system and GPS devices have improved quality of taxi services through better dispatches. In this paper, we propose to leverage on such infrastructure and build a service choice model that helps individual drivers in deciding whether to serve a specific taxi stand or not. We demonstrate the value of our model by applying it to a real-world scenario. We also highlight interesting new potential approaches that could significantly improve the quality of taxi services.
Mining Quantified Temporal Rules: Formalism, Algorithms, And Evaluation, David Lo, Ganesan Ramalingam, Venkatesh-Prasad Ranganath, Kapil Vaswani
Mining Quantified Temporal Rules: Formalism, Algorithms, And Evaluation, David Lo, Ganesan Ramalingam, Venkatesh-Prasad Ranganath, Kapil Vaswani
Research Collection School Of Computing and Information Systems
Libraries usually impose constraints on how clients should use them. Often these constraints are not well-documented. In this paper, we address the problem of recovering such constraints automatically, a problem referred to as specification mining. Given some client programs that use a given library, we identify constraints on the library usage that are (almost) satisfied by the given set of clients.The class of rules we target for mining combines simple binary temporal operators with state predicates (involving equality constraints) and quantification. This is a simple yet expressive subclass of temporal properties that allows us to capture many common API usage …
Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua
Scalable Detection Of Partial Near-Duplicate Videos By Visual-Temporal Consistency, Hung-Khoon Tan, Chong-Wah Ngo, Richang Hong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Following the exponential growth of social media, there now exist huge repositories of videos online. Among the huge volumes of videos, there exist large numbers of near-duplicate videos. Most existing techniques either focus on the fast retrieval of full copies or near-duplicates, or consider localization in a heuristic manner. This paper considers the scalable detection and localization of partial near-duplicate videos by jointly considering visual similarity and temporal consistency. Temporal constraints are embedded into a network structure as directed edges. Through the structure, partial alignment is novelly converted into a network flow problem where highly efficient solutions exist. To precisely …
Localizing Volumetric Motion For Action Recognition In Realistic Videos, Xiao Wu, Chong-Wah Ngo, Jintao Li, Yongdong Zhang
Localizing Volumetric Motion For Action Recognition In Realistic Videos, Xiao Wu, Chong-Wah Ngo, Jintao Li, Yongdong Zhang
Research Collection School Of Computing and Information Systems
This paper presents a novel motion localization approach for recognizing actions and events in real videos. Examples include StandUp and Kiss in Hollywood movies. The challenge can be attributed to the large visual and motion variations imposed by realistic action poses. Previous works mainly focus on learning from descriptors of cuboids around space time interest points (STIP) to characterize actions. The size, shape and space-time position of cuboids are fixed without considering the underlying motion dynamics. This often results in large set of fragmentized cuboids which fail to capture long-term dynamic properties of realistic actions. This paper proposes the detection …
Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Towards Google Challenge: Combining Contextual And Social Information For Web Video Categorization, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Web video categorization is a fundamental task for web video search. In this paper, we explore the Google challenge from a new perspective by combing contextual and social information under the scenario of social web. The semantic meaning of text (title and tags), video relevance from related videos, and user interest induced from user videos, are integrated to robustly determine the video category. Experiments on YouTube videos demonstrate the effectiveness of the proposed solution. The performance reaches 60% improvement compared to the traditional text based classifiers.
Parallel Sets In The Real World: Three Case Studies, Robert Kosara, Caroline Ziemkiewicz, F. Joseph Iii Mako, Tin Seong Kam
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.
Sharing Mobile Multimedia Annotations To Support Inquiry-Based Learning Using Mobitop, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin-Leng Theng, Quang Minh Nguyen, Thi Nhu Quynh Kim, Ee Peng Lim, Chew-Hung Chang, Kalyani Chatterjea, Aixin Sun
Sharing Mobile Multimedia Annotations To Support Inquiry-Based Learning Using Mobitop, Khasfariyati Razikin, Dion Hoe-Lian Goh, Yin-Leng Theng, Quang Minh Nguyen, Thi Nhu Quynh Kim, Ee Peng Lim, Chew-Hung Chang, Kalyani Chatterjea, Aixin Sun
Research Collection School Of Computing and Information Systems
Mobile devices used in educational settings are usually employed within a collaborative learning activity in which learning takes place in the form of social interactions between team members while performing a shared task. We introduce MobiTOP (Mobile Tagging of Objects and People), a geospatial digital library system which allows users to contribute and share multimedia annotations via mobile devices. A key feature of MobiTOP that is well suited for collaborative learning is that annotations are hierarchical, allowing annotations to be annotated by other users to an arbitrary depth. A group of student-teachers involved in an inquiry-based learning activity in geography …
Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang
Distribution-Based Concept Selection For Concept-Based Video Retrieval, Juan Cao, Hongfang Jing, Chong-Wah Ngo, Yongdong Zhang
Research Collection School Of Computing and Information Systems
Query-to-concept mapping plays one of the keys to concept-based video retrieval. Conventional approaches try to find concepts that are likely to co-occur in the relevant shots from the lexical or statistical aspects. However, the high probability of co-occurrence alone cannot ensure its effectiveness to distinguish the relevant shots from the irrelevant ones. In this paper, we propose distribution-based concept selection (DBCS) for query-to-concept mapping by analyzing concept score distributions of within and between relevant and irrelevant sets. In view of the imbalance between relevant and irrelevant examples, two variants of DBCS are proposed respectively by considering the two-sided and onesided …
Continuous Monitoring Of Spatial Queries In Wireless Broadcast Environments, Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias
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 …
A Study Of Content Authentication In Proxy-Enabled Multimedia Delivery Systems: Model, Techniques, And Applications, Robert H. Deng, Yanjiang Yang
A Study Of Content Authentication In Proxy-Enabled Multimedia Delivery Systems: Model, Techniques, And Applications, Robert H. Deng, Yanjiang Yang
Research Collection School Of Computing and Information Systems
Compared with the direct server-user approach, the server-proxy-user architecture for multimedia delivery promises significantly improved system scalability. The introduction of the intermediary transcoding proxies between content servers and end users in this architecture, however, brings unprecedented challenges to content security. In this article, we present a systematic study on the end-to-end content authentication problem in the server-proxy-user context, where intermediary proxies transcode multimedia content dynamically. We present a formal model for the authentication problem, propose a concrete construction for authenticating generic data modality and formally prove its security. We then apply the generic construction to authenticating specific multimedia formats, for …
A Surprise Triggered Adaptive And Reactive (Star) Framework For Online Adaptation In Non-Stationary Environments, Truong-Huy Dinh Nguyen, Tze-Yun Leong
A Surprise Triggered Adaptive And Reactive (Star) Framework For Online Adaptation In Non-Stationary Environments, Truong-Huy Dinh Nguyen, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We consider the task of developing an adaptive autonomous agent that can interact with non-stationary environments. Traditional learning approaches such as Reinforcement Learning assume stationary characteristics over the course of the problem, and are therefore unable to learn the dynamically changing settings correctly. We introduce a novel adaptive framework that can detect dynamic changes due to non-stationary elements. The Surprise Triggered Adaptive and Reactive (STAR) framework is inspired by human adaptability in dealing with daily life changes. An agent adopting the STAR framework consists primarily of two components, Adapter and Reactor. The Reactor chooses suitable actions based on predictions made …
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
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
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
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 …
First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King
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
No abstract provided.
First Acm Sigmm International Workshop On Social Media (Wsm'09), Suzanne Boll, Steven C. H. Hoi, Jiebo Luo, Rong Jin, Dong Xu, Irwin King
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
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
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 …