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Full-Text Articles in Entire DC Network
To Trust Or Not To Trust? Predicting Online Trusts Using Trust Antecedent Framework, Viet-An Nguyen, Ee Peng Lim, Jing Jiang, Aixin Sun
To Trust Or Not To Trust? Predicting Online Trusts Using Trust Antecedent Framework, Viet-An Nguyen, Ee Peng Lim, Jing Jiang, Aixin Sun
Research Collection School Of Computing and Information Systems
This paper analyzes the trustor and trustee factors that lead to inter-personal trust using a well studied Trust Antecedent framework in management science. To apply these factors to trust ranking problem in online rating systems, we derive features that correspond to each factor and develop different trust ranking models. The advantage of this approach is that features relevant to trust can be systematically derived so as to achieve good prediction accuracy. Through a series of experiments on real data from Epinions, we show that even a simple model using the derived features yields good accuracy and outperforms MoleTrust, a trust …
Insights Into Malware Detection And Prevention On Mobile Phones, Qiang Yan, Yingjiu Li, Tieyan Li, Robert H. Deng
Insights Into Malware Detection And Prevention On Mobile Phones, Qiang Yan, Yingjiu Li, Tieyan Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
The malware threat for mobile phones is expected to increase with the functionality enhancement of mobile phones. This threat is exacerbated with the surge in population of smart phones instilled with stable Internet access which provides attractive targets for malware developers. Prior research on malware protection has focused on avoiding the negative impact of the functionality limitations of mobile phones to keep the performance cost within the limitations of mobile phones. Being different, this paper investigates the positive impact of these limitations on suppressing the development of mobile malware. We study the state-of-the-art mobile malware, as well as the progress …
A New Approach For Anonymous Password Authentication, Yanjiang Yang, Jianying Zhou, Jian Weng, Feng Bao
A New Approach For Anonymous Password Authentication, Yanjiang Yang, Jianying Zhou, Jian Weng, Feng Bao
Research Collection School Of Computing and Information Systems
Anonymous password authentication reinforces password authentication with the protection of user privacy. Considering the increasing concern of individual privacy nowadays, anonymous password authentication represents a promising privacy-preserving authentication primitive. However, anonymous password authentication in the standard setting has several inherent weaknesses, making its practicality questionable. In this paper, we propose a new and efficient approach for anonymous password authentication. Our approach assumes a different setting where users do not register their passwords to the server; rather, they use passwords to protect their authentication credentials. We present a concrete scheme, and get over a number of challenges in securing password-protected credentials …
Duol: A Double Updating Approach For Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Duol: A Double Updating Approach For Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Research Collection School Of Computing and Information Systems
In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short. Instead of only assigning a fixed weight to the misclassified example received in current trial, the proposed online learning algorithm also tries to update the weight for one …
Learning Bregman Distance Functions And Its Application For Semi-Supervised Clustering, Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu
Learning Bregman Distance Functions And Its Application For Semi-Supervised Clustering, Lei Wu, Rong Jin, Steven C. H. Hoi, Jianke Zhu, Nenghai Yu
Research Collection School Of Computing and Information Systems
Learning distance functions with side information plays a key role in many machine learning and data mining applications. Conventional approaches often assume a Mahalanobis distance function. These approaches are limited in two aspects: (i) they are computationally expensive (even infeasible) for high dimensional data because the size of the metric is in the square of dimensionality; (ii) they assume a fixed metric for the entire input space and therefore are unable to handle heterogeneous data. In this paper, we propose a novel scheme that learns nonlinear Bregman distance functions from side information using a nonparametric approach that is similar to …
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 …
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 …
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 …
Mining Hierarchical Scenario-Based Specifications, David Lo, Shahar Maoz
Mining Hierarchical Scenario-Based Specifications, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Scalability over long traces, as well as comprehensibility and expressivity of results, are major challenges for dynamic analysis approaches to specification mining. In this work we present a novel use of object hierarchies over traces of inter-object method calls, as an abstraction/refinement mechanism that enables user-guided, top-down or bottom-up mining of layered scenario-based specifications, broken down by hierarchies embedded in the system under investigation. We do this using data mining methods that provide statistically significant sound and complete results modulo user-defined thresholds, in the context of Damm and Harel’s live sequence charts (LSC); a visual, modal, scenario-based, inter-object language. Thus, …
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 …
Trust-Oriented Composite Services Selection And Discovery, Lei Li, Yan Wang, Ee Peng Lim
Trust-Oriented Composite Services Selection And Discovery, Lei Li, Yan Wang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In Service-Oriented Computing (SOC) environments, service clients interact with service providers for consuming services. From the viewpoint of service clients, the trust level of a service or a service provider is a critical issue to consider in service selection and discovery, particularly when a client is looking for a service from a large set of services or service providers. However, a service may invoke other services offered by different providers forming composite services. The complex invocations in composite services greatly increase the complexity of trust-oriented service selection and discovery. In this paper, we propose novel approaches for composite service representation, …
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 …
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 …
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.
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.
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 …
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.
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 …
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 …
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 …
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 …
Domain Adaptive Semantic Diffusion For Large Scale Context-Based Video Annotation, Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah Ngo
Domain Adaptive Semantic Diffusion For Large Scale Context-Based Video Annotation, Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Learning to cope with domain change has been known as a challenging problem in many real-world applications. This paper proposes a novel and efficient approach, named domain adaptive semantic diffusion (DASD), to exploit semantic context while considering the domain-shift-of-context for large scale video concept annotation. Starting with a large set of concept detectors, the proposed DASD refines the initial annotation results using graph diffusion technique, which preserves the consistency and smoothness of the annotation over a semantic graph. Different from the existing graph learning methods which capture relations among data samples, the semantic graph treats concepts as nodes and the …
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.
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.
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 …
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 …
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 …
Analysis Of Tradeoffs Between Buffer And Qos Requirements In Wireless Networks, Raphael Rom, Hwee-Pink Tan
Analysis Of Tradeoffs Between Buffer And Qos Requirements In Wireless Networks, Raphael Rom, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In this paper, we consider the scheduling problem where data packets from K input-flows need to be delivered to K corresponding wireless receivers over a heterogeneous wireless channel. Our objective is to design a wireless scheduler that achieves good throughput and fairness performance while minimizing the buffer requirement at each wireless receiver. This is a challenging problem due to the unique characteristics of the wireless channel. We propose a novel idea of exploiting both the long-term and short-term error behavior of the wireless channel in the scheduler design. In addition to typical first-order Quality of Service (QoS) metrics such as …