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Full-Text Articles in Entire DC Network
Optimization Approaches For Solving Chance Constrained Stochastic Orienteering Problems, Pradeep Varakantham, Akshat Kumar
Optimization Approaches For Solving Chance Constrained Stochastic Orienteering Problems, Pradeep Varakantham, Akshat Kumar
Research Collection School Of Computing and Information Systems
Orienteering problems (OPs) are typically used to model routing and trip planning problems. OP is a variant of the well known traveling salesman problem where the goal is to compute the highest reward path that includes a subset of nodes and has an overall travel time less than the specified deadline. Stochastic orienteering problems (SOPs) extend OPs to account for uncertain travel times and are significantly harder to solve than deterministic OPs. In this paper, we contribute a scalable mixed integer LP formulation for solving risk aware SOPs, which is a principled approximation of the underlying stochastic optimization problem. Empirically, …
Adaptable Ciphertext-Policy Attribute-Based Encryption, Junzuo Lai, Robert H. Deng, Yanjiang Yang, Jian Weng
Adaptable Ciphertext-Policy Attribute-Based Encryption, Junzuo Lai, Robert H. Deng, Yanjiang Yang, Jian Weng
Research Collection School Of Computing and Information Systems
In this paper, we introduce a new cryptographic primitive, called adaptable ciphertext-policy attribute-based encryption (CP-ABE). Adaptable CP-ABE extends the traditional CP-ABE by allowing a semi-trusted proxy to modify a ciphertext under one access policy into ciphertexts of the same plaintext under any other access policies; the proxy, however, learns nothing about the underlying plaintext. With such “adaptability” possessed by the proxy, adaptable CP-ABE has many real world applications, such as handling policy changes in CP-ABE encryption of cloud data and outsourcing of CP-ABE encryption. Specifically, we first specify a formal model of adaptable CP-ABE; then, based on the CP-ABE scheme …
Self-Blindable Credential: Towards Anonymous Entity Authentication Upon Resource-Constrained Devices, Yanjiang Yang, Xuhua Ding, Haibing Lu, Jian Weng, Jianying Zhou
Self-Blindable Credential: Towards Anonymous Entity Authentication Upon Resource-Constrained Devices, Yanjiang Yang, Xuhua Ding, Haibing Lu, Jian Weng, Jianying Zhou
Research Collection School Of Computing and Information Systems
We are witnessing the rapid expansion of smart devices in our daily life. The need for individual privacy protection calls for anonymous entity authentication techniques with affordable efficiency upon the resource-constrained smart devices. Towards this objective, in this paper we propose self-blindable credential, a lightweight anonymous entity authentication primitive.We provide a formulation of the primitive and present two concrete instantiations. The first scheme implements verifier-local revocation and the second scheme enhances the former with forward security. Our analytical performance results show that our schemes outperform relevant existing schemes.
Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim
Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Advances in web technologies have rendered onlineadvertising as an effective means for small and large businesses to target different market segments on the fly. Online advertising is a huge industry. According to Gartner Inc., worldwide online advertising revenue is projected tohit $11.4 billion in 2013, up from $9.6 billion in 2012. Global revenue will also reach $24.5 billion in 2016, with online advertising creating opportunities for app developers, advertising networks, and service providersin various regions. An online advertising ecosystem is typically coordinated by an advertising commissioner, acting as a broker between advertisers and content publishers. An advertiser plans a budget, …
Constraint-Based Automatic Symmetry Detection, Shao Jie Zhang, Jun Sun, Chengnian Sun, Yang Liu, Junwei Ma, Jin Song Dong
Constraint-Based Automatic Symmetry Detection, Shao Jie Zhang, Jun Sun, Chengnian Sun, Yang Liu, Junwei Ma, Jin Song Dong
Research Collection School Of Computing and Information Systems
We present an automatic approach to detecting symmetry relations for general concurrent models. Despite the success of symmetry reduction in mitigating state explosion problem, one essential step towards its soundness and effectiveness, i.e., how to discover sufficient symmetries with least human efforts, is often either overlooked or oversimplified. In this work, we show how a concurrent model can be viewed as a constraint satisfaction problem (CSP), and present an algorithm capable of detecting symmetries arising from the CSP which induce automorphisms of the model. To the best of our knowledge, our method is the first approach that can automatically detect …
Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
Research Collection School Of Computing and Information Systems
Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task …
Mining Branching-Time Scenarios, Dirk Fahland, David Lo, Shahar Maoz
Mining Branching-Time Scenarios, Dirk Fahland, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows …
Automatic Recommendation Of Api Methods From Feature Requests, Ferdian Thung, Shaowei Wang, David Lo, Julia Lawall
Automatic Recommendation Of Api Methods From Feature Requests, Ferdian Thung, Shaowei Wang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, …
Symmetry Robust Descriptor For Non-Rigid Surface Matching, Zhiyuan Zhang, Kangkang Yin, Kelvin W. C. Foong
Symmetry Robust Descriptor For Non-Rigid Surface Matching, Zhiyuan Zhang, Kangkang Yin, Kelvin W. C. Foong
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel shape descriptor that is robust in differentiating intrinsic symmetric points on geometric surfaces. Our motivation is that even the state-of-theart shape descriptors and non-rigid surface matching algorithms suffer from symmetry flips. They cannot differentiate surface points that are symmetric or near symmetric. Hence a left hand of one human model may be matched to a right hand of another. Our Symmetry Robust Descriptor (SRD) is based on a signed angle field, which can be calculated from the gradient fields of the harmonic fields of two point pairs. Experiments show that the proposed shape …
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Attempts have been made to understand driver development in terms of code clones. In this paper, we propose an alternate view, based on the metaphor of a gene. Guided by this metaphor, we study the structure of Linux 3.10 ethernet platform driver probe functions.
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
No abstract provided.
Search Of Small Objects By Topology Matching, Context Modeling, And Pattern Mining, Wei Zhang, Chong-Wah Ngo
Search Of Small Objects By Topology Matching, Context Modeling, And Pattern Mining, Wei Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
No abstract provided.
Vireo/Ecnu @ Trecvid 2013: A Video Dance Of Detection, Recounting And Search With Motion Relativity And Concept Learning From Wild, Chong-Wah Ngo, Feng Wang, Wei Zhang, Chun-Chet Tan, Zhanhu Sun, Shi-Ai Zhu, Ting Yao
Vireo/Ecnu @ Trecvid 2013: A Video Dance Of Detection, Recounting And Search With Motion Relativity And Concept Learning From Wild, Chong-Wah Ngo, Feng Wang, Wei Zhang, Chun-Chet Tan, Zhanhu Sun, Shi-Ai Zhu, Ting Yao
Research Collection School Of Computing and Information Systems
The VIREO group participated in four tasks: instance search, multimedia event recounting, multimedia event detection, and semantic indexing. In this paper, we will present our approaches and discuss the evaluation results
Multimedia Modeling, Chong-Wah Ngo, Klaus Schoeffmann, Yiannis Andreopoulos, Christian Breiteneder
Multimedia Modeling, Chong-Wah Ngo, Klaus Schoeffmann, Yiannis Andreopoulos, Christian Breiteneder
Research Collection School Of Computing and Information Systems
Multimedia modeling aims to study computational models for addressing real-world multimedia problems from various perspectives, including information fusion, perceptual understanding, performance evaluation and social media. The topic becomes increasingly important with the massive amount of data available over the Internet, representing different pieces of information in heterogeneous forms that need to be consolidated before being used for multimedia problems. On the other hand, the advancement in technologies such as mobile and sensing devices drive the needs for revisiting the existing models for not only dealing with audio-visual cues but also incorporating various sensory modalities that have potential in providing cheaper …
Why Do I Retweet It? An Information Propagation Model For Microblogs, Fabio Pezzoni, Jisun An, Andrea Passarella, Jon Crowcroft, Marco Conti
Why Do I Retweet It? An Information Propagation Model For Microblogs, Fabio Pezzoni, Jisun An, Andrea Passarella, Jon Crowcroft, Marco Conti
Research Collection School Of Computing and Information Systems
Microblogging platforms are Web 2.0 services that represent a suitable environment for studying how information is propagated in social networks and how users can become influential. In this work we analyse the impact of the network features and of the users' behaviour on the information diffusion. Our analysis highlights a strong relation between the level of visibility of a message in the flow of information seen by a user and the probability that the user further disseminates the message. In addition, we also highlight the existence of other latent factors that impact on the dissemination probability, correlated with the properties …
Electroweak Measurements In Electron-Positron Collisions At W-Boson-Pair Energies At Lep, S. Schael, Manoj Thulasidas
Electroweak Measurements In Electron-Positron Collisions At W-Boson-Pair Energies At Lep, S. Schael, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
Electroweak measurements performed with data taken at the electron–positron collider LEP at CERN from 1995 to 2000 are reported. The combined data set considered in this report corresponds to a total luminosity of about 3 fb −1 collected by the four LEP experiments ALEPH, DELPHI, L3 and OPAL, at centre-of-mass energies ranging from 130 GeV to 209 GeV. Combining the published results of the four LEP experiments, the measurements include total and differential cross-sections in photon-pair, fermion-pair and four-fermion production, the latter resulting from both double-resonant WW and ZZ production as well as singly resonant production. Total and differential cross-sections …
A Scalable Approach For Malware Detection Through Bounded Feature Space Behavior Modeling, Mahinthan Chandramohan, Hee Beng Kuan Tan, Lionel C Briand, Lwin Khin Shar, Bindu Madhavi Padmanabhuni
A Scalable Approach For Malware Detection Through Bounded Feature Space Behavior Modeling, Mahinthan Chandramohan, Hee Beng Kuan Tan, Lionel C Briand, Lwin Khin Shar, Bindu Madhavi Padmanabhuni
Research Collection School Of Computing and Information Systems
In recent years, malware (malicious software) has greatly evolved and has become very sophisticated. The evolution of malware makes it difficult to detect using traditional signature-based malware detectors. Thus, researchers have proposed various behavior-based malware detection techniques to mitigate this problem. However, there are still serious shortcomings, related to scalability and computational complexity, in existing malware behavior modeling techniques. This raises questions about the practical applicability of these techniques. This paper proposes and evaluates a bounded feature space behavior modeling (BOFM) framework for scalable malware detection. BOFM models the interactions between software (which can be malware or benign) and security-critical …
Social-Loc: Improving Indoor Localization With Social Sensing, Jung-Hyun Jun, Yu Gu, Long Cheng, Banghui Lu, Jun Sun, Ting Zhu, Jianwei Niu
Social-Loc: Improving Indoor Localization With Social Sensing, Jung-Hyun Jun, Yu Gu, Long Cheng, Banghui Lu, Jun Sun, Ting Zhu, Jianwei Niu
Research Collection School Of Computing and Information Systems
Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the …
A Social Network-Empowered Research Analytics Framework For Project Selection, Thushari Silva, Zhiling Guo, Jian Ma, Hongbing Jiang, Huaping Chen
A Social Network-Empowered Research Analytics Framework For Project Selection, Thushari Silva, Zhiling Guo, Jian Ma, Hongbing Jiang, Huaping Chen
Research Collection School Of Computing and Information Systems
Traditional approaches for research project selection by government funding agencies mainly focus on the matching of research relevance by keywords or disciplines. Other research relevant information such as social connections (e.g., collaboration and co-authorship) and productivity (e.g., quality, quantity, and citations of published journal articles) of researchers is largely ignored. To overcome these limitations, this paper proposes a social network-empowered research analytics framework (RAF) for research project selections. Scholarmate.com, a professional research social network with easy access to research relevant information, serves as a platform to build researcher profiles from three dimensions, i.e., relevance, productivity and connectivity. Building upon profiles …
Social Sensing For Urban Crisis Management: The Case Of Singapore Haze, Philips Kokoh Prasetyo, Ming Gao, Ee Peng Lim, Christie N. Scollon
Social Sensing For Urban Crisis Management: The Case Of Singapore Haze, Philips Kokoh Prasetyo, Ming Gao, Ee Peng Lim, Christie N. Scollon
Research Collection School Of Computing and Information Systems
Sensing social media for trends and events has become possible as increasing number of users rely on social media to share information. In the event of a major disaster or social event, one can therefore study the event quickly by gathering and analyzing social media data. One can also design appropriate responses such as allocating resources to the affected areas, sharing event related information, and managing public anxiety. Past research on social event studies using social media often focused on one type of data analysis (e.g., hashtag clusters, diffusion of events, influential users, etc.) on a single social media data …
Information Vs Interaction: An Alternative User Ranking Model For Social Networks, Wei Xie, Ai Phuong Hoang, Feida Zhu, Ee Peng Lim
Information Vs Interaction: An Alternative User Ranking Model For Social Networks, Wei Xie, Ai Phuong Hoang, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The recent years have seen an unprecedented boom of social network services, such as Twitter, which boasts over 200 million users. In such big social platforms, the influential users are ideal targets for viral marketing to potentially reach an audience of maximal size. Most proposed algorithms rely on the linkage structure of the respective underlying network to determine the information flow and hence indicate a users influence. From social interaction perspective, we built a model based on the dynamic user interactions constantly taking place on top of these linkage structures. In particular, in the Twitter setting we supposed a principle …
Got Issues? Who Cares About It? A Large Scale Investigation Of Issue Trackers From Github, Tegawende F. Bissyande, David Lo, Lingxiao Jiang, Laurent Reveillere, Jacques Klein, Yves Le Traon
Got Issues? Who Cares About It? A Large Scale Investigation Of Issue Trackers From Github, Tegawende F. Bissyande, David Lo, Lingxiao Jiang, Laurent Reveillere, Jacques Klein, Yves Le Traon
Research Collection School Of Computing and Information Systems
Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering …
Challenges And Opportunities In Taxi Fleet Anomaly Detection, Rijurekha Sen, Rajesh Krishna Balan
Challenges And Opportunities In Taxi Fleet Anomaly Detection, Rijurekha Sen, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
To enhance fleet operation and management, logistics companies instrument their vehicles with GPS receivers and network connectivity to servers. Mobility traces from such large fleets provide significant information on commuter travel patterns, traffic congestion and road anomalies, and hence several researchers have mined such datasets to gain useful urban insights. These logistics companies, however, incur significant cost in deploying and maintaining their vast network of instrumented vehicles. Thus research problems, that are not only of interest to urban planners, but to the logistics companies themselves are important to attract and engage these companies for collaborative data analysis. In this paper, …
Efficient Lossy Trapdoor Functions Based On Subgroup Membership Assumptions, Haiyang Xue, Bao Li, Xianhui Lu, Dingding Jia, Yamin Liu
Efficient Lossy Trapdoor Functions Based On Subgroup Membership Assumptions, Haiyang Xue, Bao Li, Xianhui Lu, Dingding Jia, Yamin Liu
Research Collection School Of Computing and Information Systems
We propose a generic construction of lossy trapdoor function from the subgroup membership assumption. We present three concrete constructions based on the k-DCR assumption over Z∗ N2 , the extended psubgroup assumption over Z∗ N2 , and the decisional RSA subgroup membership assumption over Z∗ N . Our constructions are more efficient than the previous construction from the DCR assumption over Z∗ Ns (s ≥ 3).
Cell: A Compositional Verification Framework, Kun Ji, Yang Liu, Jun Sun, Jun Sun, Jin Song Dong, Truong Khanh Nguyen
Cell: A Compositional Verification Framework, Kun Ji, Yang Liu, Jun Sun, Jun Sun, Jin Song Dong, Truong Khanh Nguyen
Research Collection School Of Computing and Information Systems
This paper presents CELL, a comprehensive and extensible framework for compositional verification of concurrent and real-time systems based on commonly used semantic models. For each semantic model, CELL offers three libraries, i.e., compositional verification paradigms, learning algorithms and model checking methods to support various state-of-the-art compositional verification approaches. With well-defined APIs, the framework could be applied to build customized model checkers. In addition, each library could be used independently for verification and program analysis purposes. We have built three model checkers with CELL. The experimental results show that the performance of these model checkers can offer similar or often better …
Clustering Algorithms For Maximizing The Lifetime Of Wireless Sensor Networks With Energy-Harvesting Sensors, Pengfei Zhang, Gaoxi Xiao, Hwee-Pink Tan
Clustering Algorithms For Maximizing The Lifetime Of Wireless Sensor Networks With Energy-Harvesting Sensors, Pengfei Zhang, Gaoxi Xiao, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed …
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Research Collection School Of Computing and Information Systems
Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we …
Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu
Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
Traditional batch model-based Collaborative Filtering (CF) approaches typically assume a collection of users' rating data is given a priori for training the model. They suffer from a common yet critical drawback, i.e., the model has to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommender systems where users' rating data often arrives sequentially and frequently. In this paper, we investigate a novel efficient and scalable online collaborative filtering technique for on-the-fly recommender systems, which is able to effectively online update the recommendation model from a sequence of rating observations. Specifically, we …
Automatic Recovery Of Root Causes From Bug-Fixing Changes, Ferdian Thung, David Lo, Lingxiao Jiang
Automatic Recovery Of Root Causes From Bug-Fixing Changes, Ferdian Thung, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
What is the root cause of this failure? This question is often among the first few asked by software debuggers when they try to address issues raised by a bug report. Root cause is the erroneous lines of code that cause a chain of erroneous program states eventually leading to the failure. Bug tracking and source control systems only record the symptoms (e.g., bug reports) and treatments of a bug (e.g., committed changes that fix the bug), but not its root cause. Many treatments contain non-essential changes, which are intermingled with root causes. Reverse engineering the root cause of a …
Automated Library Recommendation, Ferdian Thung, David Lo, Julia Lawall
Automated Library Recommendation, Ferdian Thung, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association …