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Research Collection School Of Computing and Information Systems

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Articles 5371 - 5400 of 6891

Full-Text Articles in Physical Sciences and Mathematics

Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee Apr 2011

Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee

Research Collection School Of Computing and Information Systems

"Identity disclosure" problem on publishing social network data has gained intensive focus from academia. Existing k-anonymization algorithms on social network may result in nontrivial utility loss. The reason is that the number of the edges modified when anonymizing the social network is the only metric to evaluate utility loss, not considering the fact that different edge modifications have different impact on the network structure. To tackle this issue, we propose a novel utility-oriented social network anonymization scheme to achieve privacy protection with relatively low utility loss. First, a proper utility evaluation model is proposed. It focuses on the changes on …

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Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim Apr 2011

Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …

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A High-Throughput Routing Metric For Reliable Multicast In Multi-Rate Wireless Mesh Networks, Xin Zhao, Jun Guo, Chun Tung Chou, Archan Misra, Sanjay Jha Apr 2011

A High-Throughput Routing Metric For Reliable Multicast In Multi-Rate Wireless Mesh Networks, Xin Zhao, Jun Guo, Chun Tung Chou, Archan Misra, Sanjay Jha

Research Collection School Of Computing and Information Systems

We propose a routing metric for enabling highthroughput reliable multicast in multi-rate wireless mesh networks. This new multicast routing metric, called expected multicast transmission time (EMTT), captures the combined effects of 1) MAC-layer retransmission-based reliability, 2) transmission rate diversity, 3) wireless broadcast advantage, and 4) link quality awareness. The EMTT of one-hop transmission of a multicast packet minimizes the amount of expected transmission time (including that required for retransmissions). This is achieved by allowing the sender to adapt its bit-rate for each ongoing transmission/retransmission, optimized exclusively for its nexthop receivers that have not yet received the multicast packet. We model …

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A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi Apr 2011

A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Previous studies of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP solvers. However, for N data examples, the time complexity of NPKL using a standard interior-point SDP solver could be as high as O(N6.5), which prohibits NPKL methods applicable to real applications, even for data sets of moderate size. In this paper, we present a family of efficient NPKL algorithms, termed "SimpleNPKL", which can learn non-parametric kernels from a large set of pairwise constraints efficiently. In particular, we propose two efficient SimpleNPKL algorithms. One …

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Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou Apr 2011

Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou

Research Collection School Of Computing and Information Systems

Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the boosting-based ranking algorithms because it plays a contradictory role against the innate weight of boosting, namely source weight that focuses on adjusting source-domain ranking accuracy. Our experiments show that among three variants, the additive weight-based RankBoost, which dynamically balances the two types …

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Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li Apr 2011

Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li

Research Collection School Of Computing and Information Systems

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …

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Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan Apr 2011

Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan

Research Collection School Of Computing and Information Systems

Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CO Relation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety …

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Peercast: Improving Link Layer Multicast Through Cooperative Relaying, Jie Xiong, Romit Roy Choudhury Apr 2011

Peercast: Improving Link Layer Multicast Through Cooperative Relaying, Jie Xiong, Romit Roy Choudhury

Research Collection School Of Computing and Information Systems

Wireless multicast applications, such as MobiTV, web telecast, and multimedia classrooms, are gaining rapid popularity. The broadcast nature of the wireless channel is amenable to such multicasts because a single packet transmission can be received by all clients. Unfortunately, the rate of this transmission is bottlenecked by data rate of the weakest client, degrading system performance. Attempts to increase the data rate results in lower reliability and higher unfairness. This paper presents PeerCast, a wireless multicast protocol that engages clients in cooperative relaying. The main idea is simple. Instead of multicasting at the bottleneck rate, the access point transmits at …

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Abstracting Events For Data Mining, David Lo, Ganesan Ramalingam, Venkatesh-Prasad Ranganath, Kapil Vaswani Apr 2011

Abstracting Events For Data Mining, David Lo, Ganesan Ramalingam, Venkatesh-Prasad Ranganath, Kapil Vaswani

Research Collection School Of Computing and Information Systems

An event is described herein as being representable by a quantified abstraction of the event. The event includes at least one predicate, and the at least one predicate has at least one constant symbol corresponding thereto. An instance of the constant symbol corresponding to the event is identified, and the instance of the constant symbol is replaced by a free variable to obtain an abstracted predicate. Thus, a quantified abstraction of the event is composed as a pair: the abstracted predicate and a mapping between the free variable and an instance of the constant symbol that corresponds to the predicate. …

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Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan Apr 2011

Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan

Research Collection School Of Computing and Information Systems

On-line portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing on-line portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidences show that the stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel on-line portfolio selection strategy named ``Confidence Weighted Mean Reversion'' (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, …

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Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi Apr 2011

Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …

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Strongly Secure Certificateless Key Exchange Without Pairing, Guomin Yang, Chik How Tan Mar 2011

Strongly Secure Certificateless Key Exchange Without Pairing, Guomin Yang, Chik How Tan

Research Collection School Of Computing and Information Systems

In certificateless cryptography, a user secret key is derived from two partial secrets: one is the identity-based secret key (corresponding to the user identity) generated by a Key Generation Center (KGC), and the other is the user selfgenerated secret key (corresponding to a user self-generated and uncertified public key). Two types of adversaries are considered for certificateless cryptography: a Type-I adversary who can replace the user self-generated public key (in transmission or in a public directory), and a Type-II adversary who is an honest-but-curious KGC. In this paper, we present a formal study on certificateless key exchange (CLKE). We show …

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Multi-Objective Zone Mapping In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou, Wentong Cai, Xueyan Tang, Rassul Avani Mar 2011

Multi-Objective Zone Mapping In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Suiping Zhou, Wentong Cai, Xueyan Tang, Rassul Avani

Research Collection School Of Computing and Information Systems

In large-scale distributed virtual environments (DVEs), the NP-hard zone mapping problem concerns how to assign distinct zones of the virtual world to a number of distributed servers to improve overall interactivity. Previously, this problem has been formulated as a single-objective optimization problem, in which the objective is to minimize the total number of clients that are without QoS. This approach may cause considerable network traffic and processing overhead, as a large number of zones may need to be migrated across servers. In this paper, we introduce a multi-objective approach to the zone mapping problem, in which both the total number …

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Chameleon All-But-One Tdfs And Their Application To Chosen-Ciphertext Security, Junzuo Lai, Robert H. Deng, Shengli Liu Mar 2011

Chameleon All-But-One Tdfs And Their Application To Chosen-Ciphertext Security, Junzuo Lai, Robert H. Deng, Shengli Liu

Research Collection School Of Computing and Information Systems

In STOC’08, Peikert and Waters introduced a new powerful primitive called lossy trapdoor functions (LTDFs) and a richer abstraction called all-but-one trapdoor functions (ABO-TDFs). They also presented a black-box construction of CCA-secure PKE from an LTDF and an ABO-TDF. An important component of their construction is the use of a strongly unforgeable one-time signature scheme for CCA-security.In this paper, we introduce the notion of chameleon ABO-TDFs, which is a special kind of ABO-TDFs. We give a generic as well as a concrete construction of chameleon ABO-TDFs. Based on an LTDF and a chameleon ABO-TDF, we presented a black-box construction, free …

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Secure Mobile Subscription Of Sensor-Encrypted Data, Cheng-Kang Chu, Wen-Tao Zhu, Sherman S. M. Chow, Jianying Zhou, Robert H. Deng Mar 2011

Secure Mobile Subscription Of Sensor-Encrypted Data, Cheng-Kang Chu, Wen-Tao Zhu, Sherman S. M. Chow, Jianying Zhou, Robert H. Deng

Research Collection School Of Computing and Information Systems

In an end-to-end encryption model for a wireless sensor network (WSN), the network control center preloads encryption and decryption keys to the sensor nodes and the subscribers respectively, such that a subscriber can use a mobile device in the deployment field to decrypt the sensed data encrypted by the more resource-constrained sensor nodes. This paper proposes SMS-SED, a provably secure yet practically efficient key assignment system featuring a discrete time-based access control, to better support a business model where the sensors deployer rents the WSN to customers who desires a higher flexibility beyond subscribing to strictly consecutive periods. In SMS-SED, …

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Modeling Link Formation Behaviors In Dynamic Social Networks, Viet-An Nguyen, Cane Wing-Ki Leung, Ee Peng Lim Mar 2011

Modeling Link Formation Behaviors In Dynamic Social Networks, Viet-An Nguyen, Cane Wing-Ki Leung, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Online social networks are dynamic in nature. While links between users are seemingly formed and removed randomly, there exists some interested link formation behaviors demonstrated by users performing link creation and removal activities. Uncovering these behaviors not only allows us to gain deep insights of the users, but also pave the way to decipher how social links are formed. In this paper, we propose a general framework to define user link formation behaviors using well studied local link structures (i.e., triads and dyads) in a dynamic social network where links are formed at different timestamps. Depending on the role a …

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Authenticated Key Exchange Under Bad Randomness, Guomin Yang, Shanshan Duan, Duncan S. Wong, Chik How Tan, Huaxiong Wang Mar 2011

Authenticated Key Exchange Under Bad Randomness, Guomin Yang, Shanshan Duan, Duncan S. Wong, Chik How Tan, Huaxiong Wang

Research Collection School Of Computing and Information Systems

We initiate the formal study on authenticated key exchange (AKE) under bad randomness. This could happen when (1) an adversary compromises the randomness source and hence directly controls the randomness of each AKE session; and (2) the randomness repeats in different AKE sessions due to reset attacks. We construct two formal security models, Reset-1 and Reset-2, to capture these two bad randomness situations respectively, and investigate the security of some widely used AKE protocols in these models by showing that they become insecure when the adversary is able to manipulate the randomness. On the positive side, we propose simple but …

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Certificateless Public Key Encryption: A New Generic Construction And Two Pairing-Free Schemes, Guomin Yang, Chik How Tan Mar 2011

Certificateless Public Key Encryption: A New Generic Construction And Two Pairing-Free Schemes, Guomin Yang, Chik How Tan

Research Collection School Of Computing and Information Systems

The certificateless encryption (CLE) scheme proposed by Baek, Safavi-Naini and Susilo is computation-friendly since it does not require any pairing operation. Unfortunately, an error was later discovered in their security proof and so far the provable security of the scheme remains unknown. Recently, Fiore, Gennaro and Smart showed a generic way (referred to as the FGS transformation) to transform identity-based key agreement protocols to certificateless key encapsulation mechanisms (CL-KEMs). As a typical example, they showed that the pairing-free CL-KEM underlying Baek et al.’s CLE can be “generated” by applying their transformation to the Fiore–Gennaro (FG) identity-based key agreement (IB-KA) protocol.In …

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An Energy Efficient Quality Adaptive Multi-Modal Sensor Framework For Context Recognition, Nirmalya Roy, Archan Misra, Christine Julien, Sajal K. Das, Jit Biswas Mar 2011

An Energy Efficient Quality Adaptive Multi-Modal Sensor Framework For Context Recognition, Nirmalya Roy, Archan Misra, Christine Julien, Sajal K. Das, Jit Biswas

Research Collection School Of Computing and Information Systems

Proliferation of mobile applications in unpredictable and changing environments requires applications to sense and act on changing operational contexts. In such environments, understanding the context of an entity is essential for adaptability of the application behavior to changing situations. In our view, context is a high-level representation of a user or entity’s state and can capture activities, relationships, capabilities, etc. Inherently, however, these high-level context measures are difficult to sense directly and instead must be inferred through the combination of many data sources. In pervasive computing environments where this context is of significant importance, a multitude of sensors is already …

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A Virtualization-Based Approach For Zone Migration In Distributed Virtual Environments, Nguyen Binh Duong Ta, Thang Nguyen, Tran Nguyen, Do Nguyen, Xueyan Tang, Wentong Cai, Suiping Zhou Mar 2011

A Virtualization-Based Approach For Zone Migration In Distributed Virtual Environments, Nguyen Binh Duong Ta, Thang Nguyen, Tran Nguyen, Do Nguyen, Xueyan Tang, Wentong Cai, Suiping Zhou

Research Collection School Of Computing and Information Systems

This paper deals with the zone migration problem in large-scale distributed virtual environments (DVEs), e.g., massively multi-player online games, distributed military simulations, etc. To support real-time interactions among thousands of concurrent, geographically separated clients, a distributed server architecture is generally needed. In such architecture, the large virtual world can be partitioned into multiple smaller zones, enabling load distributions or zone-to-server mappings to improve interactivity. For example, a zone might be mapped (assigned) to a server location near most of its clients to reduce network latency. In this paper, we consider the problem of live zone migration over wide area networks …

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How Information Management Capability Influences Firm Performance, Sunil Mithas, Narayan Ramasubbu, V. Sambamurthy Mar 2011

How Information Management Capability Influences Firm Performance, Sunil Mithas, Narayan Ramasubbu, V. Sambamurthy

Research Collection School Of Computing and Information Systems

How do information technology capabilities contribute to firm performance? This study develops a conceptual model linking IT-enabled information management capability with three important organizational capabilities (customer management capability, process management capability, and performance management capability). We argue that these three capabilities mediate the relationship between information management capability and firm performance. To test our conceptual model, we use a rare archival data set that contains actual scores from multidimensional and high-quality assessments of firms and intraorganizational units of a conglomerate business group that had adopted a model of performance excellence for organizational transformation based on the Baldrige criteria. This research …

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Fraud Detection In Online Consumer Reviews, Nan Hu, Ling Liu, Vallbh Sambamurthy Feb 2011

Fraud Detection In Online Consumer Reviews, Nan Hu, Ling Liu, Vallbh Sambamurthy

Research Collection School Of Computing and Information Systems

Increasingly, consumers depend on social information channels, such as user-posted online reviews, to make purchase decisions. These reviews are assumed to be unbiased reflections of other consumers' experiences with the products or services. While extensively assumed, the literature has not tested the existence or non-existence of review manipulation. By using data from Amazon and Barnes & Noble, our study investigates if vendors, publishers, and writers consistently manipulate online consumer reviews. We document the existence of online review manipulation and show that the manipulation strategy of firms seems to be a monotonically decreasing function of the product's true quality or the …

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Manipulation In Digital Word-Of-Mouth: A Reality Check For Book Reviews, Nan Hu, Indranil Bose, Yunjun Gao, Ling Liu Feb 2011

Manipulation In Digital Word-Of-Mouth: A Reality Check For Book Reviews, Nan Hu, Indranil Bose, Yunjun Gao, Ling Liu

Research Collection School Of Computing and Information Systems

Built upon the discretionary accrual-based earnings management framework, our paper develops a discretionary manipulation proxy to study the management of online reviews. We reveal that fraudulent review manipulation is a serious problem for 1) non-bestseller books; 2) books whose reviews are classified as not very helpful; 3) books that experience greater variability in the helpfulness of their online reviews; and 4) popular books as well as high-priced books. We also show that review management decreases with the passage of time. Just like fraudulent earnings management, manipulated online reviews reflect inauthentic information from which consumers might derive wrong valuation especially for …

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Searching Patterns For Relation Extraction Over The Web: Rediscovering The Pattern-Relation Duality, Yuan Fang, Kevin Chen-Chuan Chang Feb 2011

Searching Patterns For Relation Extraction Over The Web: Rediscovering The Pattern-Relation Duality, Yuan Fang, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

While tuple extraction for a given relation has been an active research area, its dual problem of pattern search- to find and rank patterns in a principled way- has not been studied explicitly. In this paper, we propose and address the problem of pattern search, in addition to tuple extraction. As our objectives, we stress reusability for pattern search and scalability of tuple extraction, such that our approach can be applied to very large corpora like the Web. As the key foundation, we propose a conceptual model PRDualRank to capture the notion of precision and recall for both tuples and …

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Evolution Of Developer Collaboration On The Jazz Platform: A Study Of A Large Scale Agile Project, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta Feb 2011

Evolution Of Developer Collaboration On The Jazz Platform: A Study Of A Large Scale Agile Project, Subhajit Datta, Renuka Sindhgatta, Bikram Sengupta

Research Collection School Of Computing and Information Systems

Collaboration is a key aspect of the agile philosophy of software development. As a software system matures over iterations, trends of developer collaboration can offer valuable insights into project dynamics. In this paper, we study evolution of developer collaboration for a large scale agile project on the Jazz platform. We construct networks of collaboration based on developer affiliations across comments on work items and file changes; and then compare parameters of such networks with established results from networks of scientific collaborations. The comparisons illuminate interesting facets of developer collaboration on the Jazz platform. Such perception helps deeper understanding of the …

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Distance Metric Learning From Uncertain Side Information For Automated Photo Tagging, Lei Wu, Steven C. H. Hoi, Rong Jin, Jianke Zhu, Nenghai Yu Feb 2011

Distance Metric Learning From Uncertain Side Information For 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 an important technique for many intelligent multimedia information systems, for example, smart photo management system and intelligent digital media library. To attack the challenge, several machine learning techniques have been developed and applied for automated photo tagging. For example, supervised learning techniques have been applied to automated photo tagging by training statistical classifiers from a collection of manually labeled examples. Although the existing approaches work well for small testbeds with relatively small number of annotation words, due to the long-standing challenge of object recognition, they often perform poorly in large-scale problems. Another limitation of the existing …

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A Two-View Learning Approach For Image Tag Ranking, Jinfeng Zhuang, Steven C. H. Hoi Feb 2011

A Two-View Learning Approach For Image Tag Ranking, Jinfeng Zhuang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Tags of social images play a central role for text-based social image retrieval and browsing tasks. However, the original tags annotated by web users could be noisy, irrelevant, and often incomplete for describing the image contents, which may severely deteriorate the performance of text-based image retrieval models. In this paper, we aim to overcome the challenge of social tag ranking for a corpus of social images with rich user-generated tags by proposing a novel two-view learning approach. It can effectively exploit both textual and visual contents of social images to discover the complicated relationship between tags and images. Unlike the …

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Pgtp: Power Aware Game Transport Protocol For Multi-Player Mobile Games, Bhojan Anand, Jeena Sebastian, Soh Yu Ming, Akhihebbal L. Ananda, Mun Choon Chan, Rajesh Krishna Balan Feb 2011

Pgtp: Power Aware Game Transport Protocol For Multi-Player Mobile Games, Bhojan Anand, Jeena Sebastian, Soh Yu Ming, Akhihebbal L. Ananda, Mun Choon Chan, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Applications on the smartphones are able to capitalize on the increasingly advanced hardware to provide a user experience reasonably impressive. However, the advancement of these applications are hindered battery lifetime of the smartphones. The battery technologies have a relatively low growth rate. Applications like mobile multiplayer games are especially power hungry as they maximize the use of the network, display and CPU resources. The PGTP, presented in this paper is aware of both the transport requirement of these multiplayer mobile games and the limitation posed by battery resource. PGTP dynamically controls the transport based on the criticality of game state …

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Adnext: A Visit-Pattern-Aware Mobile Advertising System For Urban Commercial Complexes, Byoungjip Kim, Jin-Young Ha, Sangjeong Lee, Seungwoo Kang, Youngki Lee, Yunseok Rhee, Lama Nachman, Junehwa Song Feb 2011

Adnext: A Visit-Pattern-Aware Mobile Advertising System For Urban Commercial Complexes, Byoungjip Kim, Jin-Young Ha, Sangjeong Lee, Seungwoo Kang, Youngki Lee, Yunseok Rhee, Lama Nachman, Junehwa Song

Research Collection School Of Computing and Information Systems

As smartphones have become prevalent, mobile advertising is getting significant attention as being not only a killer application in future mobile commerce, but also as an important business model of emerging mobile applications to monetize them. In this paper, we present AdNext, a visit-pattern-aware mobile advertising system for urban commercial complexes. AdNext can provide highly relevant ads to users by predicting places that the users will next visit. AdNext predicts the next visit place by learning the sequential visit patterns of commercial complex users in a collective manner. As one of the key enabling techniques for AdNext, we develop a …

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Cryptanalysis Of A Certificateless Signcryption Scheme In The Standard Model, Jian Weng, Guoxiang Yao, Robert H. Deng, Min-Rong Chen, Xianxue Li Feb 2011

Cryptanalysis Of A Certificateless Signcryption Scheme In The Standard Model, Jian Weng, Guoxiang Yao, Robert H. Deng, Min-Rong Chen, Xianxue Li

Research Collection School Of Computing and Information Systems

Certificateless signcryption is a useful primitive which simultaneously provides the functionalities of certificateless encryption and certificateless signature. Recently, Liu et al. [15] proposed a new certificateless signcryption scheme, and claimed that their scheme is provably secure without random oracles in a strengthened security model, where the malicious-but-passive KGC attack is considered. Unfortunately, by giving concrete attacks, we indicate that Liu et al. certificateless signcryption scheme is not secure in this strengthened security model.

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