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Articles 6901 - 6930 of 8458
Full-Text Articles in Computer Sciences
Analytics-Modulated Coding Of Surveillance Video, Lai-Tee Cheok, Nikhil Gagvani
Analytics-Modulated Coding Of Surveillance Video, Lai-Tee Cheok, Nikhil Gagvani
Research Collection School Of Computing and Information Systems
Video surveillance systems increasingly use H.264 coding to achieve 24x7 recording and streaming. However, with the proliferation of security cameras, and the need to store several months of video, bandwidth and storage costs can be significant. We propose a new compression technique to significantly improve the coding efficiency of H.264 for surveillance video. Video content is analyzed and video semantics are extracted using video analytics algorithms such as segmentation, classification and tracking. In contrast to existing approaches, our Analytics-Modulated Compression (AMC) scheme does not require coding of object shape information and produces bitstreams that are standards-compliant and not limited to …
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In …
Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu
Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu
Research Collection School Of Computing and Information Systems
Current content-based video copy detection approaches mostly concentrate on the visual cues and neglect the audio information. In this paper, we attempt to tackle the video copy detection task resorting to audio information, which is equivalently important as well as visual information in multimedia processing. Firstly, inspired by bag-of visual words model, a bag-of audio words (BoA) representation is proposed to characterize each audio frame. Different from naive singlebased modeling audio retrieval approaches, BoA is a highlevel model due to its perceptual and semantical property. Within the BoA model, a coherency vocabulary indexing structure is adopted to achieve more efficient …
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Faceted Topic Retrieval Of News Video Using Joint Topic Modeling Of Visual Features And Speech Transcripts, Kong-Wah Wan, Ah-Hwee Tan, Joo-Hwee Lim, Liang-Tien Chia
Research Collection School Of Computing and Information Systems
Because of the inherent ambiguity in user queries, an important task of modern retrieval systems is faceted topic retrieval (FTR), which relates to the goal of returning diverse or novel information elucidating the wide range of topics or facets of the query need. We introduce a generative model for hypothesizing facets in the (news) video domain by combining the complementary information in the visual keyframes and the speech transcripts. We evaluate the efficacy of our multimodal model on the standard TRECVID-2005 video corpus annotated with facets. We find that: (1) the joint modeling of the visual and text (speech transcripts) …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Semantics-Preserving Bag-Of-Words Models And Applications, Lei Wu, Steven C. H. Hoi, Nenghai Yu
Semantics-Preserving Bag-Of-Words Models And Applications, 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 technique for image categorization and annotation tasks. One critical limitation of existing BoW models is that much semantic information is lost during the codebook generation process, an important step of BoW. This is because the codebook generated by BoW is often obtained via building the codebook simply by clustering visual features in Euclidian space. However, visual features related to the same semantics may not distribute in clusters in the Euclidian space, which is primarily due to the semantic gap between low-level features and high-level semantics. In this paper, we propose a …
Non-Parametric Kernel Ranking Approach For Social Image Retrieval, Jinfeng Zhuang, Steven C. H. Hoi
Non-Parametric Kernel Ranking Approach For Social Image Retrieval, Jinfeng Zhuang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Social image retrieval has become an emerging research challenge in web rich media search. In this paper, we address the research problem of text-based social image retrieval, which aims to identify and return a set of relevant social images that are related to a text-based query from a corpus of social images. Regular approaches for social image retrieval simply adopt typical text-based image retrieval techniques to search for the relevant social images based on the associated tags, which may suffer from noisy tags. In this paper, we present a novel framework for social image re-ranking based on a non-parametric kernel …
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.
Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam
Show Me The Numbers: Visual Analytics For Insights, Tin Seong Kam
Research Collection School Of Computing and Information Systems
In this highly volatile and fast-paced financial market, traders and managers working in banking and financial organizations must struggle to cope with large and complex data from multi-sources, that move throughout the market at increasingly high speed. The cost of making poor business and investment decisions is very high. This places great demands on data analysts, who are responsible for providing process information, to support the activities of traders and managers. Static reports and traditional business intelligence tools simply cannot keep up with a market that is changing on a second-to-second basis. By the time the traders and bankers have …
Auditing The Defense Against Cross Site Scripting In Web Applications, Lwin Khin Shar, Hee Beng Kuan Tan
Auditing The Defense Against Cross Site Scripting In Web Applications, Lwin Khin Shar, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
Majority attacks to web applications today are mainly carried out through input manipulation in order to cause unintended actions of these applications. These attacks exploit the weaknesses of web applications in preventing the manipulation of inputs. Among these attacks, cross site scripting attack -- malicious input is submitted to perform unintended actions on a HTML response page -- is a common type of attacks. This paper proposes an approach for thorough auditing of code to defend against cross site scripting attack. Based on the possible methods of implementing defenses against cross site scripting attack, the approach extracts all such defenses …
Effective Music Tagging Through Advanced Statistical Modeling, Jialie Shen, Meng Wang, Shuicheng Yan, Hwee Hwa Pang, Xian-Sheng Hua
Effective Music Tagging Through Advanced Statistical Modeling, Jialie Shen, Meng Wang, Shuicheng Yan, Hwee Hwa Pang, Xian-Sheng Hua
Research Collection School Of Computing and Information Systems
Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this …
A Heuristic Algorithm For Trust-Oriented Service Provider Selection In Complex Social Networks, Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Ee Peng Lim
A Heuristic Algorithm For Trust-Oriented Service Provider Selection In Complex Social Networks, Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In a service-oriented online social network consisting of service providers and consumers, a service consumer can search trustworthy service providers via the social network. This requires the evaluation of the trustworthiness of a service provider along a certain social trust path from the service consumer to the service provider. However, there are usually many social trust paths between participants in social networks. Thus, a challenging problem is which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we first present a novel complex social network structure and a new concept, Quality …
New Constructions For Identity-Based Unidirectional Proxy Re-Encryption, Junzuo Lai, Wen Tao Zhu, Robert H. Deng, Shengli Liu, Weidong Kou
New Constructions For Identity-Based Unidirectional Proxy Re-Encryption, Junzuo Lai, Wen Tao Zhu, Robert H. Deng, Shengli Liu, Weidong Kou
Research Collection School Of Computing and Information Systems
We address the cryptographic topic of proxy re-encryption (PRE), which is a special public-key cryptosystem. A PRE scheme allows a special entity, known as the proxy, to transform a message encrypted with the public key of a delegator (say Alice), into a new ciphertext that is protected under the public key of a delegatee (say Bob), and thus the same message can then be recovered with Bob’s private key. In this paper, in the identity-based setting, we first investigate the relationship between so called mediated encryption and unidirectional PRE. We provide a general framework which converts any secure identity-based unidirectional …
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Most existing reranking approaches to image search focus solely on mining “visual” cues within the initial search results. However, the visual information cannot always provide enough guidance to the reranking process. For example, different images with similar appearance may not always present the same relevant information to the query. Observing that multi-modality cues carry complementary relevant information, we propose the idea of co-reranking for image search, by jointly exploring the visual and textual information. Co-reranking couples two random walks, while reinforcing the mutual exchange and propagation of information relevancy across different modalities. The mutual reinforcement is iteratively updated to constrain …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan
Mental Development And Representation Building Through Motivated Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Motivated learning is a new machine learning approach that extends reinforcement learning idea to dynamically changing, and highly structured environments. In this approach a machine is capable of defining its own objectives and learns to satisfy them though an internal reward system. The machine is forced to explore the environment in response to externally applied negative (pain) signals that it must minimize. In doing so, it discovers relationships between objects observed through its sensory inputs and actions it performs on the observed objects. Observed concepts are not predefined but are emerging as a result of successful operations. For the optimum …
Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
Self-Organizing Agents For Reinforcement Learning In Virtual Worlds, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
We present a self-organizing neural model for creating intelligent learning agents in virtual worlds. As agents in a virtual world roam, interact and socialize with users and other agents as in real world without explicit goals and teachers, learning in virtual world presents many challenges not found in typical machine learning benchmarks. In this paper, we highlight the unique issues and challenges of building learning agents in virtual world using reinforcement learning. Specifically, a self-organizing neural model, named TD-FALCON (Temporal Difference - Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function in …
Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan
Self-Organizing Neural Networks For Behavior Modeling In Games, Shu Feng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCON) are used to learn non-player characters' behavior rules according to recorded patterns. Behavior learning abilities of these two models are investigated by learning specific sample Bots in the Unreal Tournament game in a supervised manner. Our empirical experiments demonstrate that both SGNN and FALCON are able to recognize important behavior patterns and learn the necessary knowledge to operate in the Unreal environment. …
Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan
Towards Probabilistic Memetic Algorithm: An Initial Study On Capacitated Arc Routing Problem, Liang Feng, Yew-Soon Ong, Quang Huy Nguyen, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Capacitated arc routing problem (CARP) has attracted much attention due to its generality to many real world problems. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving CARP ranging from small to medium size. In this paper we propose a formal probabilistic memetic algorithm for CARP that is equipped with an adaptation mechanism to control the degree of global exploration against local exploitation while the search progresses. Experimental study on benchmark instances of CARP showed that the proposed probabilistic scheme led to improved search performances when introduced into a recently proposed state-of-the-art …
Impact Of Flow And Brand Equity In 3d Virtual Worlds, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, David Dewester, So Ra Park
Impact Of Flow And Brand Equity In 3d Virtual Worlds, Fiona Fui-Hoon Nah, Brenda Eschenbrenner, David Dewester, So Ra Park
Research Collection School Of Computing and Information Systems
This research is a partial test of Park et al.’s (2008) model to assess the impact of flow and brand equity in 3D virtual worlds. It draws on flow theory as its main theoretical foundation to understand and empirically assess the impact of flow on brand equity and behavioral intention in 3D virtual worlds. The findings suggest that the balance of skills and challenges in 3D virtual worlds influences users’ flow experience, which in turn influences brand equity. Brand equity then increases behavioral intention. The authors also found that the impact of flow on behavioral intention in 3D virtual worlds …
Semantic Context Modeling With Maximal Margin Conditional Random Fields For Automatic Image Annotation, Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-Seng Chua, Chong-Wah Ngo
Semantic Context Modeling With Maximal Margin Conditional Random Fields For Automatic Image Annotation, Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-Seng Chua, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources, semantic context has been exploited in AIA and brings promising results. However, previous works either casted the problem into structural classification or adopted multi-layer modeling, which suffer from the problems of scalability or model efficiency. In this paper, we propose a novel discriminative Conditional Random Field (CRF) model for semantic context modeling in AIA, which is built over semantic concepts and treats an image as a whole observation without segmentation. Our model captures the interactions between semantic …
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Prediction Of Protein Subcellular Localization: A Machine Learning Approach, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus.
Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong Koh, Xuan Khoa Nguyen, C. Jason Woodard
Using Hadoop And Cassandra For Taxi Data Analytics: A Feasibility Study, Alvin Jun Yong Koh, Xuan Khoa Nguyen, C. Jason Woodard
Research Collection School Of Computing and Information Systems
This paper reports on a preliminary study to assess the feasibility of using the Open Cirrus Cloud Computing Research testbed to provide offline and online analytical support for taxi fleet operations. In the study, we benchmarked the performance gains from distributing the offline analysis of GPS location traces over multiple virtual machines using the Apache Hadoop implementation of the MapReduce paradigm. We also explored the use of the Apache Cassandra distributed database system for online retrieval of vehicle trace data. While configuring the testbed infrastructure was straightforward, we encountered severe I/O bottlenecks in running the benchmarks due to the lack …
A Hybrid Method To Detect Deflation Fraud In Cost-Per-Action Online Advertising, Xuhua Ding
A Hybrid Method To Detect Deflation Fraud In Cost-Per-Action Online Advertising, Xuhua Ding
Research Collection School Of Computing and Information Systems
Web advertisers prefer the cost-per-action (CPA) advertisement model whereby an advertiser pays a web publisher according to the actual amount of transactions, rather than the volume of advertisement clicks. The main obstacle for a wide deployment of this model is the deflation fraud. Namely, a dishonest advertiser under-reports the transaction count in order to discharge less. In this paper, we present a mechanism to detect such a fraud using a hybrid of cryptography and probability tools. With the assistance from a small number of users, the publisher can detect deflation fraud with a success probability growing exponentially with the fraud …
Income, Endogenous Market Structure And Innovation, Mei Lin, Shaojin Li, Andrew B. Whinston
Income, Endogenous Market Structure And Innovation, Mei Lin, Shaojin Li, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
We investigate the effect of income distribution on R&D in a dynamic framework. Our model captures both the infinite R&D race among heterogeneous innovators and a market where successful innovators generate revenues. The market structure of successful innovations is endogenous – firms produce vertically differentiated substitute goods and compete in price. Based on firms' equilibrium market revenues, we derive numerical solutions of the Markov perfect equilibrium innovation rate of the dynamic problem. A key insight in our results is that explicitly modeling price competition and the market structure plays an important role in evaluating the impact of rising income inequality …
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While finite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infinite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infinite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) …
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Efficient Mutual Nearest Neighbor Query Processing For Moving Object Trajectories, Yunjun Gao, Baihua Zheng, Gencai Chen, Qing Li, Chun Chen, Gang Chen
Research Collection School Of Computing and Information Systems
Given a set D of trajectories, a query object q, and a query time extent Γ, a mutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D, the set of trajectories that are among the k1 nearest neighbors (NNs) of q within Γ, and meanwhile, have q as one of their k2 NNs. This type of queries is useful in many applications such as decision making, data mining, and pattern recognition, as it considers both the proximity of the trajectories to q and the proximity of q to the trajectories. In this paper, we first formalize MNN search …
Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi Zhang, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Ee Peng Lim
Do Wikipedians Follow Domain Experts? A Domain-Specific Study On Wikipedia Contribution, Yi Zhang, Aixin Sun, Anwitaman Datta, Kuiyu Chang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Wikipedia is one of the most successful online knowledge bases, attracting millions of visits daily. Not surprisingly, its huge success has in turn led to immense research interest for a better understanding of the collaborative knowledge building process. In this paper, we performed a (terrorism) domain-specific case study, comparing and contrasting the knowledge evolution in Wikipedia with a knowledge base created by domain experts. Specifically, we used the Terrorism Knowledge Base (TKB) developed by experts at MIPT. We identified 409 Wikipedia articles matching TKB records, and went ahead to study them from three aspects: creation, revision, and link evolution. We …
Designing The Dynamics Of Service Innovations, Arcot Desai Narasimhalu
Designing The Dynamics Of Service Innovations, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
There is no serious tool available to design service innovations even as it is gaining in important attention from the academic and industrial worlds. This paper presents a method that is specifically developed to help service innovators plan and design their innovations. The method recognizes the dependencies that exist across a service provider, customers and suppliers and help identify potential inconsistencies in the design of service innovations.
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Stevent: Spatio-Temporal Event Model For Social Network Discovery, Hady W. Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Research Collection School Of Computing and Information Systems
Spatio-temporal data concerning the movement of individuals over space and time contains latent information on the associations among these individuals. Sources of spatio-temporal data include usage logs of mobile and Internet technologies. This article defines a spatio-temporal event by the co-occurrences among individuals that indicate potential associations among them. Each spatio-temporal event is assigned a weight based on the precision and uniqueness of the event. By aggregating the weights of events relating two individuals, we can determine the strength of association between them. We conduct extensive experimentation to investigate both the efficacy of the proposed model as well as the …