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Full-Text Articles in Entire DC Network
Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He
Mining Social Images With Distance Metric Learning For Automated Image Tagging, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Ying He
Research Collection School Of Computing and Information Systems
With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only …
Near-Duplicate Keyframe Retrieval By Semi-Supervised Learning And Nonrigid Image Matching, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan
Near-Duplicate Keyframe Retrieval By Semi-Supervised Learning And Nonrigid Image Matching, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Near-duplicate keyframe (NDK) retrieval techniques are critical to many real-world multimedia applications. Over the last few years, we have witnessed a surge of attention on studying near-duplicate image/keyframe retrieval in multimedia community. To facilitate an effective approach to NDK retrieval on large-scale data, we suggest an effective Multi-Level Ranking (MLR) scheme that effectively retrieves NDKs in a coarse-to-fine manner. One key stage of the MLR ranking scheme is how to learn an effective ranking function with extremely small training examples in a near-duplicate detection task. To attack this challenge, we employ a semi-supervised learning method, semi-supervised support vector machines, which …
Enhancing Bag-Of-Words Models By Efficient Semantics-Preserving Metric Learning, Lei Wu, Steven C. H. Hoi
Enhancing Bag-Of-Words Models By Efficient Semantics-Preserving Metric Learning, Lei Wu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The authors present an online semantics preserving, metric learning technique for improving the bag-of-words model and addressing the semantic-gap issue. This article investigates the challenge of reducing the semantic gap for building BoW models for image representation; propose a novel OSPML algorithm for enhancing BoW by minimizing the semantic loss, which is efficient and scalable for enhancing BoW models for large-scale applications; apply the proposed technique for large-scale image annotation and object recognition; and compare it to the state of the art.
An Effective Approach To Pose Invariant 3d Face Recognition, Dayong Wang, Steven C. H. Hoi, Ying He
An Effective Approach To Pose Invariant 3d Face Recognition, Dayong Wang, Steven C. H. Hoi, Ying He
Research Collection School Of Computing and Information Systems
One critical challenge encountered by existing face recognition techniques lies in the difficulties of handling varying poses. In this paper, we propose a novel pose invariant 3D face recognition scheme to improve regular face recognition from two aspects. Firstly, we propose an effective geometry based alignment approach, which transforms a 3D face mesh model to a well-aligned 2D image. Secondly, we propose to represent the facial images by a Locality Preserving Sparse Coding (LPSC) algorithm, which is more effective than the regular sparse coding algorithm for face representation. We conducted a set of extensive experiments on both 2D and 3D …
Workshop Report From Web2se 2011: 2nd International Workshop On Web 2.0 For Software Engineering, Christoph Treude, Margaret-Anne Storey, Arie Van Deursen, Andrew Begel, Sue Black
Workshop Report From Web2se 2011: 2nd International Workshop On Web 2.0 For Software Engineering, Christoph Treude, Margaret-Anne Storey, Arie Van Deursen, Andrew Begel, Sue Black
Research Collection School Of Computing and Information Systems
Web 2.0 technologies, such as wikis, blogs, tags and feeds, have been adopted and adapted by software engineers. With the annual Web2SE workshop, we provide a venue for research on Web 2.0 for software engineering by highlighting state-of-the-art work, identifying current research areas, discussing implications of Web 2.0 on software engineering, and outlining the risks and challenges for researchers. This report highlights the paper and tool presentations, and the discussions among participants at Web2SE 2011 in Honolulu, as well as future directions of the Web2SE workshop community.
Efficient Strong Designated Verifier Signature Schemes Without Random Oracle Or With Non-Delegatability, Qiong Huang, Guomin Yang, Duncan S. Wong, Willy Susilo
Efficient Strong Designated Verifier Signature Schemes Without Random Oracle Or With Non-Delegatability, Qiong Huang, Guomin Yang, Duncan S. Wong, Willy Susilo
Research Collection School Of Computing and Information Systems
Designated verifier signature (DVS) allows a signer to convince a designated verifier that a signature is generated by the signer without letting the verifier transfer the conviction to others, while the public can still tell that the signature must be generated by one of them. Strong DVS (SDVS) strengthens the latter part by restricting the public from telling whether the signature is generated by one of them or by someone else. In this paper, we propose two new SDVS schemes. Compared with existing SDVS schemes, the first new scheme has almost the same signature size and meanwhile, is proven secure …
Identity-Based Strong Designated Verifier Signature Revisited, Qiong Huang, Guomin Yang, Duncan S. Wong, Willy Susilo
Identity-Based Strong Designated Verifier Signature Revisited, Qiong Huang, Guomin Yang, Duncan S. Wong, Willy Susilo
Research Collection School Of Computing and Information Systems
Designated verifier signature (DVS) allows the signer to persuade a verifier the validity of a statement but prevent the verifier from transferring the conviction. Strong designated verifier signature (SDVS) is a variant of DVS, which only allows the verifier to privately check the validity of the signer’s signature. In this work we observe that the unforgeability model considered in the existing identity-based SDVS schemes is not strong enough to capture practical attacks, and propose to consider another model which is shown to be strictly stronger than the old one. We then propose a new efficient construction of identity-based SDVS scheme, …
Trends And Controversies: Ai, Virtual Worlds, And Massively Multiplayer Online Games, Hsinchun Chen, Yulei Zhang, W. S. Bainbridge, Kyong Jin Shim, N. Pathak, M. A. Ahmad, C. Delong, Z. Borbora, A. Mahapatra, J. Srivastava
Trends And Controversies: Ai, Virtual Worlds, And Massively Multiplayer Online Games, Hsinchun Chen, Yulei Zhang, W. S. Bainbridge, Kyong Jin Shim, N. Pathak, M. A. Ahmad, C. Delong, Z. Borbora, A. Mahapatra, J. Srivastava
Research Collection School Of Computing and Information Systems
The rich social media data generated in virtual worlds has important implications for business, education, social science, and society at large. Similarly, massively multiplayer online games (MMOGs) have become increasingly popular and have online communities comprising tens of millions of players. They serve as unprecedented tools for theorizing about and empirically modeling the social and behavioral dynamics of individuals, groups, and networks within large communities. Some technologists consider virtual worlds and MMOGs to be likely candidates to become the Web 3.0. AI can play a significant role, from multiagent avatar research and immersive virtual interface design to virtual world and …
Solving The Teacher Assignment Problem By Two Metaheuristics, Aldy Gunawan, Kien Ming Ng
Solving The Teacher Assignment Problem By Two Metaheuristics, Aldy Gunawan, Kien Ming Ng
Research Collection School Of Computing and Information Systems
The timetabling problem arising from a university in Indonesia is addressed in this paper.It involves the assignment of teachers to the courses and course sections. We formulate theproblem as a mathematical programming model. Two different algorithms, mainly basedon simulated annealing (SA) and tabu search (TS) algorithms, are proposed for solving theproblem. The proposed algorithms consist of two phases. The first phase involves allocatingthe teachers to the courses and determining the number of courses to be assigned to eachteacher. The second phase involves assigning the teachers to the course sections in order tobalance the teachers’ load. The performance of the proposed …
Solving The Quadratic Assignment Problem By A Hybrid Algorithm, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh
Solving The Quadratic Assignment Problem By A Hybrid Algorithm, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh
Research Collection School Of Computing and Information Systems
This paper presents a hybrid algorithm to solve the Quadratic Assignment Problem (QAP). The proposed algorithminvolves using the Greedy Randomized Adaptive Search Procedure (GRASP) to obtain an initial solution, and then using a combinedSimulated Annealing (SA) and Tabu Search (TS) algorithm to improve the solution. Experimental results indicate that the hybridalgorithm is able to obtain good quality solutions for QAPLIB test problems within reasonable computation time.
Development Of An Instrument To Measure The Adoption Of Mobile Services, Shang Gao, John Krogstie, Keng Siau
Development Of An Instrument To Measure The Adoption Of Mobile Services, Shang Gao, John Krogstie, Keng Siau
Research Collection School Of Computing and Information Systems
Currently, there is no standard instrument for measuring user adoption of mobile services. Based on the mobile service acceptance model, this paper reports on the development of a survey instrument designed to measure user perception on mobile services acceptance. A survey instrument was developed by using some existing scales from prior instruments and by creating additional items which might appear to fit the construct definitions. In addition, a pilot study was conducted by distributing the survey to 25 users of a mobile service called Mobile Student Information Systems. As a result, a survey instrument containing 22 items were retained. Furthermore, …
Real-Time Traffic Estimation Using Data Expansion, Roger Lederman, Laura Wynter
Real-Time Traffic Estimation Using Data Expansion, Roger Lederman, Laura Wynter
Research Collection School Of Computing and Information Systems
This paper presents a method for estimating missing real-time traffic volumes on a road network using both historical and real-time traffic data. The method was developed to address urban transportation networks where a non-negligible subset of the network links do not have real-time link volumes, and where that data is needed to populate other real-time traffic analytics. Computation is split between an offline calibration and a real-time estimation phase. The offline phase determines link-to-link splitting probabilities for traffic flow propagation that are subsequently used in real-time estimation. The real-time procedure uses current traffic data and is efficient enough to scale …
Editorial: Special Issue On Ubiquitous Electronic Commerce Systems, Robert H. Deng, Jari Veijalainen, Shiguo Lian, Dimitris Kanellopoulos
Editorial: Special Issue On Ubiquitous Electronic Commerce Systems, Robert H. Deng, Jari Veijalainen, Shiguo Lian, Dimitris Kanellopoulos
Research Collection School Of Computing and Information Systems
Ubiquitous computing is a post-desktop model of human-computer interaction in which information processing has been thoroughly integrated into everyday objects and activities. Emerging ubiquitous electronic commerce systems (UECS) are expected to be available anytime, anywhere, and using different official or personal computing devices. Systems and services such as digital libraries, on-line business transactions, mobile office and mobile TV are widely deployed. Users will be able to access these services anytime, anywhere, while using any computing device in a pervasive way.
Instance-Based Parameter Tuning Via Search Trajectory Similarity Clustering, Linda Lindawati, Hoong Chuin Lau, David Lo
Instance-Based Parameter Tuning Via Search Trajectory Similarity Clustering, Linda Lindawati, Hoong Chuin Lau, David Lo
Research Collection School Of Computing and Information Systems
This paper is concerned with automated tuning of parameters in local-search based meta-heuristics. Several generic approaches have been introduced in the literature that returns a ”one-size-fits-all” parameter configuration for all instances. This is unsatisfactory since different instances may require the algorithm to use very different parameter configurations in order to find good solutions. There have been approaches that perform instance-based automated tuning, but they are usually problem-specific. In this paper, we propose CluPaTra, a generic (problem-independent) approach to perform parameter tuning, based on CLUstering instances with similar PAtterns according to their search TRAjectories. We propose representing a search trajectory as …
Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati
Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati
Research Collection School Of Computing and Information Systems
Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter …
Mining Event Structures From Web Videos, Xiao Wu, Yi-Jie Lu, Qiang Peng, Chong-Wah Ngo
Mining Event Structures From Web Videos, Xiao Wu, Yi-Jie Lu, Qiang Peng, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
The article is discussing the issues of mining event structures from Web video search results using text analysis, burst detection, and clustering as with the proliferation of social media, the volume of Web videos have grown exponentially.
A Usability Study Of A Mobile Content Sharing System, Alton Yeow-Kuan Chua, Dion Hoe-Lian Goh, Khasfariyati Razikin, Ee Peng Lim
A Usability Study Of A Mobile Content Sharing System, Alton Yeow-Kuan Chua, Dion Hoe-Lian Goh, Khasfariyati Razikin, Ee Peng Lim
Research Collection School Of Computing and Information Systems
We investigate the usability of MobiTOP (Mobile Tagging of Objects and People), a mobile location-based content sharing system. MobiTOP allows users to annotate real world locations with both multimedia and textual content and concurrently, share the annotations among its users. In addition, MobiTOP provides additional functionality such as clustering of annotations and advanced search and filtering options. A usability evaluation of the system was conducted in the context of a travel companion for tourists. The results suggested the potential of the system in terms of functionality for mobile content sharing. Participants agreed that the features in MobiTOP were generally usable …
Innovation And Price Competition In A Two-Sided Market, Mei Lin, Shaojin Li, Andrew B. Whinston
Innovation And Price Competition In A Two-Sided Market, Mei Lin, Shaojin Li, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
We examine a platform's optimal two-sided pricing strategy while considering seller-side innovation decisions and price competition. We model the innovation race among sellers in both finite and infinite horizons. In the finite case, we analytically show that the platform's optimal seller-side access fee fully extracts the sellers' surplus, and that the optimal buyer-side access fee mitigates price competition among sellers. The platform's optimal strategy may be to charge or subsidize buyers depending on the degree of variation in the buyers' willingness to pay for quality; this optimal strategy induces full participation on both sides. Furthermore, a wider quality gap among …
Concept-Driven Multi-Modality Fusion For Video Search, Xiao-Yong Wei, Yu-Gang Jiang, Chong-Wah Ngo
Concept-Driven Multi-Modality Fusion For Video Search, Xiao-Yong Wei, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
As it is true for human perception that we gather information from different sources in natural and multi-modality forms, learning from multi-modalities has become an effective scheme for various information retrieval problems. In this paper, we propose a novel multi-modality fusion approach for video search, where the search modalities are derived from a diverse set of knowledge sources, such as text transcript from speech recognition, low-level visual features from video frames, and high-level semantic visual concepts from supervised learning. Since the effectiveness of each search modality greatly depends on specific user queries, prompt determination of the importance of a modality …
Real-Time Road Traffic Prediction With Spatio-Temporal Correlations, Wanli Min, Laura Wynter
Real-Time Road Traffic Prediction With Spatio-Temporal Correlations, Wanli Min, Laura Wynter
Research Collection School Of Computing and Information Systems
Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed …
Improving Service Through Just-In-Time Concept In A Dynamic Operational Environment, Kar Way Tan, Hoong Chuin Lau, Na Fu
Improving Service Through Just-In-Time Concept In A Dynamic Operational Environment, Kar Way Tan, Hoong Chuin Lau, Na Fu
Research Collection School Of Computing and Information Systems
This paper is concerned with the problem of Just-In-Time (JIT) job scheduling in a dynamic environment under uncertainty to attain timely service. We provide an approach, based on robust scheduling concepts, to analytically evaluate the expected cost of earliness and tardiness for each job and also the project. In addition, we search for a schedule execution policy with the minimum robust cost such that for a given risk level (epsilon), the actual realized schedule has (1 - epsilon) probability of completing with less than or equal to this robust cost. Our method is quite generic, and can be applied to …
Lightweight Delegated Subset Test With Privacy Protection, Xuhua Zhou, Xuhua Ding, Kefei Chen
Lightweight Delegated Subset Test With Privacy Protection, Xuhua Zhou, Xuhua Ding, Kefei Chen
Research Collection School Of Computing and Information Systems
Delegated subset tests are mandatory in many applications, such as content-based networks and outsourced text retrieval, where an untrusted server evaluates the degree of matching between two data sets. We design a novel scheme to protect the privacy of the data sets in comparison against the untrusted server, with half of the computation cost and half of the ciphertext size of existing solutions based on predicate only encryption supporting inner product.
Would Price Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh
Would Price Limits Have Made Any Difference To The 'Flash Crash' On May 6, 2010, Wing Bernard Lee, Shih-Fen Cheng, Annie Koh
Research Collection School Of Computing and Information Systems
On May 6, 2010, the U.S. equity markets experienced a brief but highly unusual drop in prices across a number of stocks and indices. The Dow Jones Industrial Average (see Figure 1) fell by approximately 9% in a matter of minutes, and several stocks were traded down sharply before recovering a short time later. The authors contend that the events of May 6, 2010 exhibit patterns consistent with the type of "flash crash" observed in their earlier study (2010). This paper describes the results of nine different simulations created by using a large-scale computer model to reconstruct the critical elements …
Randomly Projected Kd-Trees With Distance Metric Learning For Image Retrieval, Pengcheng Wu, Steven Hoi, Duc Dung Nguyen, Ying He
Randomly Projected Kd-Trees With Distance Metric Learning For Image Retrieval, Pengcheng Wu, Steven Hoi, Duc Dung Nguyen, Ying He
Research Collection School Of Computing and Information Systems
Efficient nearest neighbor (NN) search techniques for highdimensional data are crucial to content-based image retrieval (CBIR). Traditional data structures (e.g., kd-tree) usually are only efficient for low dimensional data, but often perform no better than a simple exhaustive linear search when the number of dimensions is large enough. Recently, approximate NN search techniques have been proposed for high-dimensional search, such as Locality-Sensitive Hashing (LSH), which adopts some random projection approach. Motivated by similar idea, in this paper, we propose a new high dimensional NN search method, called Randomly Projected kd-Trees (RP-kd-Trees), which is to project data points into a lower-dimensional …
Ensemble-Based Method For Task 2: Predicting Traffic Jam, Jingrui He, Qing He, Grzegorz Swirszcz, Yiannis Kamarianakis, Rick Lawrence, Wei Shen, Laura Wynter
Ensemble-Based Method For Task 2: Predicting Traffic Jam, Jingrui He, Qing He, Grzegorz Swirszcz, Yiannis Kamarianakis, Rick Lawrence, Wei Shen, Laura Wynter
Research Collection School Of Computing and Information Systems
In this paper, we describe our solution for ICDM 2010 Contest Task 2 (Jams), where the task is to predict future where the next traffic jams will occur in morning rush hour, given data gathered during the initial phase of this peak period. Our solution, which is based on an ensemble approach, finished Second in the final evaluation.
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search engines are used to return a long list of hundreds or even thousands of videos in response to a query topic. Efficient navigation of videos becomes difficult and users often need to painstakingly explore the search list for a gist of the search result. This paper addresses the challenge of topical summarization by providing a timeline-based visualization of videos through matching of heterogeneous sources. To overcome the so called sparse-text problem of web videos, auxiliary information from Google context is exploited. Google Trends is used to predict the milestone events of a topic. Meanwhile, the typical scenes of web …
Optimized Algorithms For Predictive Range And Knn Queries On Moving Objects, Rui Zhang, H.V. Jagadish, Bing Tian Dai, Kotagiri Ramamohanarao
Optimized Algorithms For Predictive Range And Knn Queries On Moving Objects, Rui Zhang, H.V. Jagadish, Bing Tian Dai, Kotagiri Ramamohanarao
Research Collection School Of Computing and Information Systems
There have been many studies on management of moving objects recently. Most of them try to optimize the performance of predictive window queries. However, not much attention is paid to two other important query types: the predictive range query and the predictive k nearest neighbor query. In this article, we focus on these two types of queries. The novelty of our work mainly lies in the introduction of the Transformed Minkowski Sum, which can be used to determine whether a moving bounding rectangle intersects a moving circular query region. This enables us to use the traditional tree traversal algorithms to …
Revisiting Address Space Randomization, Zhi Wang, Renquan Cheng, Debin Gao
Revisiting Address Space Randomization, Zhi Wang, Renquan Cheng, Debin Gao
Research Collection School Of Computing and Information Systems
Address space randomization is believed to be a strong defense against memory error exploits. Many code and data objects in a potentially vulnerable program and the system could be randomized, including those on the stack and heap, base address of code, order of functions, PLT, GOT, etc. Randomizing these code and data objects is believed to be effective in obfuscating the addresses in memory to obscure locations of code and data objects. However, attacking techniques have advanced since the introduction of address space randomization. In particular, return-oriented programming has made attacks without injected code much more powerful than what they …
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.
Automobile Exhaust Gas Detection Based On Fuzzy Temperature Compensation System, Zhiyong Wang, Hao Ding, Fufei Hao, Zhaoxia Wang, Zhen Sun, Shujin Li
Automobile Exhaust Gas Detection Based On Fuzzy Temperature Compensation System, Zhiyong Wang, Hao Ding, Fufei Hao, Zhaoxia Wang, Zhen Sun, Shujin Li
Research Collection School Of Computing and Information Systems
A temperature compensation scheme of detecting automobile exhaust gas based on fuzzy logic inference is presented in this paper. The principles of the infrared automobile exhaust gas analyzer and the influence of the environmental temperature on analyzer are discussed. A fuzzy inference system is designed to improve the measurement accuracy of the measurement equipment by reducing the measurement errors caused by environmental temperature. The case studies demonstrate the effectiveness of the proposed method. The fuzzy compensation scheme is promising as demonstrated by the simulation results in this paper.