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Articles 2731 - 2760 of 4266
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Maximum Rank Query, Kyriakos Mouratidis, Jilian Zhang, Hwee Hwa Pang
Maximum Rank Query, Kyriakos Mouratidis, Jilian Zhang, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
The top-k query is a common means to shortlist a number of options from a set of alternatives, based on the user's preferences. Typically, these preferences are expressed as a vector of query weights, defined over the options' attributes. The query vector implicitly associates each alternative with a numeric score, and thus imposes a ranking among them. The top-k result includes the k options with the highest scores. In this context, we define the maximum rank query (MaxRank). Given a focal option in a set of alternatives, the MaxRank problem is to compute the highest rank this option may achieve …
Using Content-Level Structures For Summarizing Microblog Repost Trees, Jing Li, Wei Gao, Zhongyu Wei, Baolin Peng, Kam-Fai Wong
Using Content-Level Structures For Summarizing Microblog Repost Trees, Jing Li, Wei Gao, Zhongyu Wei, Baolin Peng, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on microblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from contentlevel structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the …
On Indistinguishability In Remote Data Integrity Checking, Xinyu Fan, Guomin Yang, Yi Mu, Yong Yu
On Indistinguishability In Remote Data Integrity Checking, Xinyu Fan, Guomin Yang, Yi Mu, Yong Yu
Research Collection School Of Computing and Information Systems
With a rapid growth of data storage in the cloud, data integrity checking in a remote data storage system has become an important issue. A number of protocols, which allow remote integrity checking by a third party, have been proposed. Although those protocols are provably secure, the data privacy issues in those protocols have not been considered. We believe that these issues are equally important since the communication flows of integrity proofs from the cloud server should not reveal any useful information of the stored data. In this paper, we introduce a new definition of data privacy called ‘INDPrivacy’ by …
Cobweb: A Robust Map Update System Using Gps Trajectories, Zhangqing Shan, Hao Wu, Weiwei Sun, Baihua Zheng
Cobweb: A Robust Map Update System Using Gps Trajectories, Zhangqing Shan, Hao Wu, Weiwei Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
The accuracy and completeness of a digital map plays a critical role in determining the quality of most location-based services. Unfortunately, road networks change frequently. Consequently, we study the issue of automatic map update in this paper. We propose a system called COBWEB which takes all the unmatched trajectories as input and generates the missing road segments with both the geometry properties and topology features well preserved. We conduct a comprehensive experimental study via real trajectory data generated by roughly 15,000 taxis in Singapore within a 5-month period. Compared with existing work, COBWEB demonstrates a better and more stable performance …
Topological Spatial Verification For Instance Search, Wei Zhang, Chong-Wah Ngo
Topological Spatial Verification For Instance Search, Wei Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper proposes an elastic spatial verification method for Instance Search, particularly for dealing with non-planar and non-rigid queries exhibiting complex spatial transformations. Different from existing models that map keypoints between images based on a linear transformation (e.g., affine, homography), our model exploits the topological arrangement of keypoints to address the non-linear spatial transformations that are extremely common in real life situations. In particular, we propose a novel technique to elastically verify the topological spatial consistency with the triangulated graph through a "sketch-and-match" scheme. The spatial topology configuration, emphasizing relative positioning rather than absolute coordinates, is first sketched by a …
Deep Learning For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun
Deep Learning For Just-In-Time Defect Prediction, Xinli Yang, David Lo, Xin Xia, Yun Zhang, Jianling Sun
Research Collection School Of Computing and Information Systems
Defect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time. Nowadays, deep learning is a hot topic in the machine learning literature. Whether deep learning can be used to improve the performance of just-in-time defect prediction is still uninvestigated. In this paper, to bridge this research gap, we propose an approach Deeper which leverages deep learning techniques to predict defect-prone changes. We first build a set of …
Fast Object Retrieval Using Direct Spatial Matching, Zhiyuan Zhong, Jianke Zhu, Steven C. H. Hoi
Fast Object Retrieval Using Direct Spatial Matching, Zhiyuan Zhong, Jianke Zhu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The conventional bag-of-visual-words (BoW) model is popular for the large-scale object retrieval system but suffers from the critical drawback of ignoring spatial information. RANSAC-based methods attempt to remedy this drawback, but often require traversing all the feature matches for each hypothesis, leading to the heavy computational cost which limits the number of gallery images to be verified for each online query. We propose an efficient direct spatial matching (DSM) approach to directly estimate the scale variation using region sizes, in which all feature matches voted for estimating geometric transformation. DSM is much faster than RANSAC-based methods and exhaustive enumeration approaches. …
Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong
Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic …
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Research Collection School Of Computing and Information Systems
In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we …
Applying Geocaching Principles To Site-Based Citizen Science And Eliciting Reactions Via A Technology Probe, Matthew A. Dunlap, Anthony Tang, Saul Greenberg
Applying Geocaching Principles To Site-Based Citizen Science And Eliciting Reactions Via A Technology Probe, Matthew A. Dunlap, Anthony Tang, Saul Greenberg
Research Collection School Of Computing and Information Systems
Site-based citizen science occurs when volunteers work with scientists to collect data at particular field locations. The benefit is greater data collection at lesser cost. Yet difficulties exist. We developed SCIENCECACHING, a prototype citizen science aid designed to mitigate four specific problems by applying aspects from another thriving location-based activity: geocaching as enabled by mobile devices. Specifically, to ease problems in data collection, SCIENCECACHING treats sites as geocaches: Volunteers find sites opportunistically via geocaching methods and use equipment and other materials pre-stored in cache containers. To ease problems in data validation, SCIENCECACHING flags outlier data as it is entered so …
Identity-Based Lossy Encryption From Learning With Errors, Jingnan He, Bao Li, Xianhui Lu, Dingding Jia, Haiyang Xue, Xiaochao Sun
Identity-Based Lossy Encryption From Learning With Errors, Jingnan He, Bao Li, Xianhui Lu, Dingding Jia, Haiyang Xue, Xiaochao Sun
Research Collection School Of Computing and Information Systems
We extend the notion of lossy encryption to the scenario of identity-based encryption (IBE), and propose a new primitive called identity-based lossy encryption (IBLE). Similar as the case of lossy encryption, we show that IBLE can also achieve selective opening security. Finally, we present a construction of IBLE from the assumption of learning with errors.
The Autoproof Verifier: Usability By Non-Experts And On Standard Code, Carlo A. Furia, Christopher M. Poskitt, Julian Tschannen
The Autoproof Verifier: Usability By Non-Experts And On Standard Code, Carlo A. Furia, Christopher M. Poskitt, Julian Tschannen
Research Collection School Of Computing and Information Systems
Formal verification tools are often developed by experts for experts; as a result, their usability by programmers with little formal methods experience may be severely limited. In this paper, we discuss this general phenomenon with reference to AutoProof: a tool that can verify the full functional correctness of object-oriented software. In particular, we present our experiences of using AutoProof in two contrasting contexts representative of non-expert usage. First, we discuss its usability by students in a graduate course on software verification, who were tasked with verifying implementations of various sorting algorithms. Second, we evaluate its usability in verifying code developed …
Facilitating Image Search With A Scalable And Compact Semantic Mapping, Meng Wang, Weisheng Li, Dong Liu, Bingbing Ni, Jialie Shen, Shuicheng Yan
Facilitating Image Search With A Scalable And Compact Semantic Mapping, Meng Wang, Weisheng Li, Dong Liu, Bingbing Ni, Jialie Shen, Shuicheng Yan
Research Collection School Of Computing and Information Systems
This paper introduces a novel approach to facilitating image search based on a compact semantic embedding. A novel method is developed to explicitly map concepts and image contents into a unified latent semantic space for the representation of semantic concept prototypes. Then, a linear embedding matrix is learned that maps images into the semantic space, such that each image is closer to its relevant concept prototype than other prototypes. In our approach, the semantic concepts equated with query keywords and the images mapped into the vicinity of the prototype are retrieved by our scheme. In addition, a computationally efficient method …
On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim
On Mining Lifestyles From User Trip Data, Meng-Fen Chiang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Large cities today are facing major challenges in planning and policy formulation to keep their growth sustainable. In this paper, we aim to gain useful insights about people living in a city by developing novel models to mine user lifestyles represented by the users' activity centers. Two models, namely ACMM and ACHMM, have been developed to learn the activity centers of each user using a large dataset of bus and subway train trips performed by passengers in Singapore. We show that ACHMM and ACMM yield similar accuracies in location prediction task. We also propose methods to automatically predict "home", "work" …
Detection And Classification Of Malicious Javascript Via Attack Behavior Modelling, Yinxing Xue, Junjie Wang, Yang Liu, Hao Xiao, Jun Sun, Mahinthan Chandramohan
Detection And Classification Of Malicious Javascript Via Attack Behavior Modelling, Yinxing Xue, Junjie Wang, Yang Liu, Hao Xiao, Jun Sun, Mahinthan Chandramohan
Research Collection School Of Computing and Information Systems
Existing malicious JavaScript (JS) detection tools and commercial anti-virus tools mostly use feature-based or signature-based approaches to detect JS malware. These tools are weak in resistance to obfuscation and JS malware variants, not mentioning about providing detailed information of attack behaviors. Such limitations root in the incapability of capturing attack behaviors in these approches. In this paper, we propose to use Deterministic Finite Automaton (DFA) to abstract and summarize common behaviors of malicious JS of the same attack type. We propose an automatic behavior learning framework, named JS∗ , to learn DFA from dynamic execution traces of JS malware, where …
On Multipath Link Characterization And Adaptation For Device-Free Human Detection, Zimu Zhou, Zheng Yang, Chenshu Wu, Yunhao Liu, Lionel M. Ni
On Multipath Link Characterization And Adaptation For Device-Free Human Detection, Zimu Zhou, Zheng Yang, Chenshu Wu, Yunhao Liu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
No abstract provided.
A New Public Remote Integrity Checking Scheme With User Privacy, Yiteng Feng, Yi Mu, Guomin Yang, Joseph Liu
A New Public Remote Integrity Checking Scheme With User Privacy, Yiteng Feng, Yi Mu, Guomin Yang, Joseph Liu
Research Collection School Of Computing and Information Systems
With a cloud storage, users can store their data files on a remote cloud server with a high quality on-demand cloud service and are able to share their data with other users. Since cloud servers are not usually regarded as fully trusted and the cloud data can be shared amongst users, the integrity checking of the remote files has become an important issue. A number of remote data integrity checking protocols have been proposed in the literature to allow public auditing of cloud data by a third party auditor (TPA). However, user privacy is not taken into account in most …
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …
A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan
A Comparative Study Between Motivated Learning And Reinforcement Learning, James T. Graham, Janusz A. Starzyk, Zhen Ni, Haibo He, T.-H. Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with.
Solar: Scalable Online Learning Algorithms For Ranking, Jialei Wang, Ji Wan, Yongdong Zhang, Steven C. H. Hoi
Solar: Scalable Online Learning Algorithms For Ranking, Jialei Wang, Ji Wan, Yongdong Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Traditional learning to rank methods learn ranking models from training data in a batch and offline learning mode, which suffers from some critical limitations, e.g., poor scalability as the model has to be retrained from scratch whenever new training data arrives. This is clearly nonscalable for many real applications in practice where training data often arrives sequentially and frequently. To overcome the limitations, this paper presents SOLAR- a new framework of Scalable Online Learning Algorithms for Ranking, to tackle the challenge of scalable learning to rank. Specifically, we propose two novel SOLAR algorithms and analyze their IR measure bounds theoretically. …
Log-Euclidean Metric Learning On Symmetric Positive Definite Manifold With Application To Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Li, X. Chen
Log-Euclidean Metric Learning On Symmetric Positive Definite Manifold With Application To Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Li, X. Chen
Research Collection School Of Computing and Information Systems
The manifold of Symmetric Positive Definite (SPD) matrices has been successfully used for data representation in image set classification. By endowing the SPD manifold with Log-Euclidean Metric, existing methods typically work on vector-forms of SPD matrix logarithms. This however not only inevitably distorts the geometrical structure of the space of SPD matrix logarithms but also brings low efficiency especially when the dimensionality of SPD matrix is high. To overcome this limitation, we propose a novel metric learning approach to work directly on logarithms of SPD matrices. Specifically, our method aims to learn a tangent map that can directly transform the …
Optimizing Selection Of Competing Features Via Feedback-Directed Evolutionary Algorithms, Tian Huat Tan, Yinxing Xue, Manman Chen, Jun Sun, Yang Liu, Jin Song Dong Dong
Optimizing Selection Of Competing Features Via Feedback-Directed Evolutionary Algorithms, Tian Huat Tan, Yinxing Xue, Manman Chen, Jun Sun, Yang Liu, Jin Song Dong Dong
Research Collection School Of Computing and Information Systems
Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Software Product Line (SPL). A key challenge for deriving a new product is to find a set of features that do not have inconsistencies or conflicts, yet optimize multiple objectives (e.g., minimizing cost and maximizing number of features), which are often competing with each other. Existing works have attempted to make use of evolutionary algorithms (EAs) to address this problem. In …
Reliability Assessment For Distributed Systems Via Communication Abstraction And Refinement, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong
Reliability Assessment For Distributed Systems Via Communication Abstraction And Refinement, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Distributed systems like cloud-based services are ever more popular. Assessing the reliability of distributed systems is highly non-trivial. Particularly, the order of executions among distributed components adds a dimension of non-determinism, which invalidates existing reliability assessment methods based on Markov chains. Probabilistic model checking based on models like Markov decision processes is designed to deal with scenarios involving both probabilistic behavior (e.g., reliabilities of system components) and non-determinism. However, its application is currently limited by state space explosion, which makes reliability assessment of distributed system particularly difficult. In this work, we improve the probabilistic model checking through a method of …
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption, Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) with outsourced decryption not only enables fine-grained sharing of encrypted data, but also overcomes the efficiency drawback (in terms of ciphertext size and decryption cost) of the standard ABE schemes. In particular, an ABE scheme with outsourced decryption allows a third party (e.g., a cloud server) to transform an ABE ciphertext into a (short) El Gamal-type ciphertext using a public transformation key provided by a user so that the latter can be decrypted much more efficiently than the former by the user. However, a shortcoming of the original outsourced ABE scheme is that the correctness of the …
A New General Framework For Secure Public Key Encryption With Keyword Search, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang
A New General Framework For Secure Public Key Encryption With Keyword Search, Rongmao Chen, Yi Mu, Guomin Yang, Fuchun Guo, Xiaofen Wang
Research Collection School Of Computing and Information Systems
Public Key Encryption with Keyword Search (PEKS), introduced by Boneh et al. in Eurocrypt’04, allows users to search encrypted documents on an untrusted server without revealing any information. This notion is very useful in many applications and has attracted a lot of attention by the cryptographic research community. However, one limitation of all the existing PEKS schemes is that they cannot resist the Keyword Guessing Attack (KGA) launched by a malicious server. In this paper, we propose a new PEKS framework named Dual-Server Public Key Encryption with Keyword Search (DS-PEKS). This new framework can withstand all the attacks, including the …
Privacy-Preserving Offloading Of Mobile App To The Public Cloud, Yue Duan, Mu Zhang, Heng Yin, Yuzhe Tang
Privacy-Preserving Offloading Of Mobile App To The Public Cloud, Yue Duan, Mu Zhang, Heng Yin, Yuzhe Tang
Research Collection School Of Computing and Information Systems
To support intensive computations on resource-restricting mobile devices, studies have been made to enable the offloading of a part of a mobile program to the cloud. However, none of the existing approaches considers user privacy when transmitting code and data off the device, resulting in potential privacy breach. In this paper, we present the design and implementation of a system that automatically performs fine-grained privacy-preserving Android app offloading. It utilizes static analysis and bytecode instrumentation techniques to ensure transparent and efficient Android app offloading while preserving user privacy. We evaluate the effectiveness and performance of our system using two Android …
Online Learning To Rank For Content-Based Image Retrieval, Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong. Zhang, Jintao Li
Online Learning To Rank For Content-Based Image Retrieval, Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong. Zhang, Jintao Li
Research Collection School Of Computing and Information Systems
A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highly desired to combine multiple types of different feature representations and diverse distance measures in order to close the semantic gap. In this paper, we investigate a new framework of learning to rank for CBIR, which aims to seek the optimal combination of …
An Empirical Study Of Classifier Combination On Cross-Project Defect Prediction, Yun Zhang, David Lo, Xin Xia, Jianling Sun
An Empirical Study Of Classifier Combination On Cross-Project Defect Prediction, Yun Zhang, David Lo, Xin Xia, Jianling Sun
Research Collection School Of Computing and Information Systems
To help developers better allocate testing and debugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of buggy classes. These techniques work well as long as a sufficient amount of data is available to train a prediction model. However, there is rarely enough training data for new software projects. To deal with this problem, cross-project defect prediction, which transfers a prediction model trained using data from one project to another, has been proposed and is regarded as …
Structured Learning From Heterogeneous Behavior For Social Identity Linkage, Siyuan Liu, Shuhui Wang, Feida Zhu
Structured Learning From Heterogeneous Behavior For Social Identity Linkage, Siyuan Liu, Shuhui Wang, Feida Zhu
Research Collection School Of Computing and Information Systems
Social identity linkage across different social media platforms is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. In this paper, we propose a solution framework, HYDRA, which consists of three key steps: (I) we model heterogeneous behavior by long-term topical distribution analysis and multi-resolution temporal behavior matching against high noise and information missing, and the behavior similarity are described by multi-dimensional similarity vector for each user pair; (II) we build structure consistency models to maximize the structure and behavior consistency on users' core social structure across different platforms, …
Active Semi-Supervised Approach For Checking App Behavior Against Its Description, Ma Siqi, Shaowei Wang, David Lo, Deng, Robert H., Cong Sun
Active Semi-Supervised Approach For Checking App Behavior Against Its Description, Ma Siqi, Shaowei Wang, David Lo, Deng, Robert H., Cong Sun
Research Collection School Of Computing and Information Systems
Mobile applications are popular in recent years. They are often allowed to access and modify users' sensitive data. However, many mobile applications are malwares that inappropriately use these sensitive data. To detect these malwares, Gorla et al. Propose CHABADA which compares app behaviors against its descriptions. Data about known malwares are not used in their work, which limits its effectiveness. In this work, we extend the work by Gorla et al. By proposing an active and semi-supervised approach for detecting malwares. Different from CHABADA, our approach will make use of both known benign and malicious apps to predict other malicious …