Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (8549)
- Computer Sciences (8532)
- Databases and Information Systems (3451)
- Software Engineering (2164)
- Artificial Intelligence and Robotics (1697)
-
- Information Security (1067)
- Numerical Analysis and Scientific Computing (1024)
- Engineering (956)
- Graphics and Human Computer Interfaces (933)
- Social and Behavioral Sciences (734)
- Business (686)
- Computer Engineering (512)
- Theory and Algorithms (493)
- Operations Research, Systems Engineering and Industrial Engineering (417)
- Programming Languages and Compilers (379)
- OS and Networks (323)
- Communication (310)
- Social Media (248)
- Public Affairs, Public Policy and Public Administration (227)
- Transportation (204)
- Medicine and Health Sciences (186)
- Education (180)
- Management Information Systems (178)
- Data Storage Systems (176)
- E-Commerce (151)
- International and Area Studies (125)
- Asian Studies (124)
- Digital Communications and Networking (123)
- Technology and Innovation (114)
- Health Information Technology (113)
- Keyword
-
- Machine learning (125)
- Deep learning (124)
- Social media (80)
- Artificial intelligence (72)
- Reinforcement learning (69)
-
- Data mining (65)
- Privacy (65)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (55)
- Optimization (55)
- Security (54)
- Singapore (52)
- Visualization (52)
- Task analysis (50)
- Twitter (50)
- Neural networks (49)
- Online learning (49)
- Software engineering (49)
- Training (49)
- Anomaly detection (47)
- Feature extraction (46)
- Collaboration (45)
- Large Language Models (45)
- Blockchain (44)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Natural language processing (39)
- Publication Year
- File Type
Articles 6151 - 6180 of 8722
Full-Text Articles in Entire DC Network
Defending Against Heap Overflow By Using Randomization In Nested Virtual Clusters, Chee Meng Tey, Debin Gao
Defending Against Heap Overflow By Using Randomization In Nested Virtual Clusters, Chee Meng Tey, Debin Gao
Research Collection School Of Computing and Information Systems
Heap based buffer overflows are a dangerous class of vulnerability. One countermeasure is randomizing the location of heap memory blocks. Existing techniques segregate the address space into clusters, each of which is used exclusively for one block size. This approach requires a large amount of address space reservation, and results in lower location randomization for larger blocks.
Adaptive Regret Minimization In Bounded-Memory Games, Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha
Adaptive Regret Minimization In Bounded-Memory Games, Jeremiah Blocki, Nicolas Christin, Anupam Datta, Arunesh Sinha
Research Collection School Of Computing and Information Systems
Organizations that collect and use large volumes of personal information often use security audits to protect data subjects from inappropriate uses of this information by authorized insiders. In face of unknown incentives of employees, a reasonable audit strategy for the organization is one that minimizes its regret. While regret minimization has been extensively studied in repeated games, the standard notion of regret for repeated games cannot capture the complexity of the interaction between the organization (defender) and an adversary, which arises from dependence of rewards and actions on history. To account for this generality, we introduce a richer class of …
Automatically Partition Software Into Least Privilege Components Using Dynamic Data Dependency Analysis, Yongzheng Wu, Jun Sun, Yang Liu, Jin Song Dong
Automatically Partition Software Into Least Privilege Components Using Dynamic Data Dependency Analysis, Yongzheng Wu, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
The principle of least privilege requires that software components should be granted only necessary privileges, so that compromising one component does not lead to compromising others. However, writing privilege separated software is difficult and as a result, a large number of software is monolithic, i.e., it runs as a whole without separation. Manually rewriting monolithic software into privilege separated software requires significant effort and can be error prone. We propose ProgramCutter, a novel approach to automatically partitioning monolithic software using dynamic data dependency analysis. ProgramCutter works by constructing a data dependency graph whose nodes are functions and edges are data …
A Link-Bridged Topic Model For Cross-Domain Document Classification, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong
A Link-Bridged Topic Model For Cross-Domain Document Classification, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the …
From Rssi To Csi: Indoor Localization Via Channel Response, Zheng Yang, Zimu Zhou, Yunhao Liu
From Rssi To Csi: Indoor Localization Via Channel Response, Zheng Yang, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics.
Tzuyu: Learning Stateful Typestates, Hao Xiao, Jun Sun, Yang Liu, Shang-Wei Lin, Chengnian Sun
Tzuyu: Learning Stateful Typestates, Hao Xiao, Jun Sun, Yang Liu, Shang-Wei Lin, Chengnian Sun
Research Collection School Of Computing and Information Systems
Behavioral models are useful for various software engineering tasks. They are, however, often missing in practice. Thus, specification mining was proposed to tackle this problem. Existing work either focuses on learning simple behavioral models such as finite-state automata, or relies on techniques (e.g., symbolic execution) to infer finite-state machines equipped with data states, referred to as stateful typestates. The former is often inadequate as finite-state automata lack expressiveness in capturing behaviors of data-rich programs, whereas the latter is often not scalable. In this work, we propose a fully automated approach to learn stateful typestates by extending the classic active learning …
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social network analysis has received much attention from corporations recently. Corporations are trying to utilize social media platforms such as Twitter, Facebook and Sina Weibo to expand their own markets. Our system is an online tool to assist these corporations to 1) find potential customers, and 2) track a list of users by specific events from social networks. We employ both textual and network information, and thus produce a keyword-based relevance score for each user in pre-defined dimensions, which indicates the probability of the adoption of a product. Based on the score and its trend, out tool is able to …
Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai
Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai
Research Collection School Of Computing and Information Systems
This book constitutes the proceedings of the 5th International Conference on Social Informatics, SocInfo 2013, held in Kyoto, Japan, in November 2013. The 23 full papers, 15 short papers, and three poster papers included in this volume were carefully reviewed and selected from 103 submissions. The papers present original research work on studying the interplay between socially-centric platforms and social phenomena.
Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Research Collection School Of Computing and Information Systems
Collaborative Filtering (CF) is one of the most successful learning techniques in building real-world recommender systems. Traditional CF algorithms are often based on batch machine learning methods which suffer from several critical drawbacks, e.g., extremely expensive model retraining cost whenever new samples arrive, unable to capture the latest change of user preferences over time, and high cost and slow reaction to new users or products extension. Such limitations make batch learning based CF methods unsuitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. To address these limitations, we investigate online collaborative …
Assessing The Moderating Effect Of Consumer Product Knowledge And Online Shopping Experience On Using Recommedation Agents For Customer Loyalty, Victoria Y. Yoon, R. Eric Hostler, Zhiling Guo, Tor Guimaraes
Assessing The Moderating Effect Of Consumer Product Knowledge And Online Shopping Experience On Using Recommedation Agents For Customer Loyalty, Victoria Y. Yoon, R. Eric Hostler, Zhiling Guo, Tor Guimaraes
Research Collection School Of Computing and Information Systems
Social media technologies have greatly facilitated the creation of many types of user-generated information, e.g., product rating information can be used to generate preference-based recommendation. As a decision support tool, a Recommendation Agent (RA) has been widely adopted by many e-commerce websites. The impact of RAs on online shopping has been extensively examined in the IS literature. However, from Marketing and Social Media perspectives, the widely adopted cognitive–affect–conative–action framework of customer loyalty has not been tested in the presence of RAs. Moreover, there has been little research assessing the impact of increasing consumer knowledge about specific product domains on customer …
Using Micro-Reviews To Select An Efficient Set Of Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas
Using Micro-Reviews To Select An Efficient Set Of Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas
Research Collection School Of Computing and Information Systems
Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose …
Achieving Revocable Fine-Grained Cryptographic Access Control Over Cloud Data, Yanjiang Yang, Xuhua Ding, Haibing Lu, Zhiguo Wan, Jianying Zhou
Achieving Revocable Fine-Grained Cryptographic Access Control Over Cloud Data, Yanjiang Yang, Xuhua Ding, Haibing Lu, Zhiguo Wan, Jianying Zhou
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) is well suited for finegrained access control for data residing on a cloud server. However, existing approaches for user revocation are not satisfactory. In this work, we propose a new approach which works by splitting an authorized user’s decryption capability between the cloud and the user herself. User revocation is attained by simply nullifying the decryption ability at the cloud, requiring neither key update nor re-generation of cloud data. We propose a concrete scheme instantiating the approach, which features lightweight computation at the user side. This makes it possible for users to use resource-constrained devices such as …
Efficient Index-Based Approaches For Skyline Queries In Location-Based Applications, Ken C. K. Lee, Baihua Zheng, Cindy Chen, Chi-Yin Chow
Efficient Index-Based Approaches For Skyline Queries In Location-Based Applications, Ken C. K. Lee, Baihua Zheng, Cindy Chen, Chi-Yin Chow
Research Collection School Of Computing and Information Systems
Enriching many location-based applications, various new skyline queries are proposed and formulated based on the notion of locational dominance, which extends conventional one by taking objects' nearness to query positions into account additional to objects' nonspatial attributes. To answer a representative class of skyline queries for location-based applications efficiently, this paper presents two index-based approaches, namely, augmented R-tree and dominance diagram. Augmented R-tree extends R-tree by including aggregated nonspatial attributes in index nodes to enable dominance checks during index traversal. Dominance diagram is a solution-based approach, by which each object is associated with a precomputed nondominance scope wherein query points …
Predicting Best Answerers For New Questions: An Approach Leveraging Topic Modeling And Collaborative Voting, Yuan Tian, Pavneet Singh Kochhar, Ee Peng Lim, Feida Zhu, David Lo
Predicting Best Answerers For New Questions: An Approach Leveraging Topic Modeling And Collaborative Voting, Yuan Tian, Pavneet Singh Kochhar, Ee Peng Lim, Feida Zhu, David Lo
Research Collection School Of Computing and Information Systems
Community Question Answering (CQA) sites are becoming increasingly important source of information where users can share knowledge on various topics. Although these platforms bring new opportunities for users to seek help or provide solutions, they also pose many challenges with the ever growing size of the community. The sheer number of questions posted everyday motivates the problem of routing questions to the appropriate users who can answer them. In this paper, we propose an approach to predict the best answerer for a new question on CQA site. Our approach considers both user interest and user expertise relevant to the topics …
Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin
Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin
Research Collection School Of Computing and Information Systems
The TPC benchmarks have helped users evaluate database system performance at different scales. Although each benchmark is domain-specific, it is not equally relevant to different applications in the same domain. The present proliferation of applications also leaves many of them uncovered by the very limited number of current TPC benchmarks. There is therefore a need to develop tools for application-specific database benchmarking. This paper presents UpSizeR, a software that addresses the Dataset Scaling Problem: Given an empirical set of relational tables D and a scale factor s, generate a database state e D that is similar to D but s …
Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang
Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang
Research Collection School Of Computing and Information Systems
This article shows that when the nonzero coefficients of the population correlation matrix are all greater in absolute value than (C1logp/n)1/2 for some constant C1, we can obtain covariance selection consistency by thresholding the sample correlation matrix. Furthermore, the rate (logp/n)1/2 is shown to be optimal.
Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng
Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng
Research Collection School Of Computing and Information Systems
Classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the …
Budgeted Personalized Incentive Approaches For Smoothing Congestion In Resource Networks, Pradeep Varakantham, Na Fu, William Yeoh, Shih-Fen Cheng, Hoong Chuin Lau
Budgeted Personalized Incentive Approaches For Smoothing Congestion In Resource Networks, Pradeep Varakantham, Na Fu, William Yeoh, Shih-Fen Cheng, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Congestion occurs when there is competition for resources by sel sh agents. In this paper, we are concerned with smoothing out congestion in a network of resources by using personalized well-timed in- centives that are subject to budget constraints. To that end, we provide: (i) a mathematical formulation that computes equilibrium for the re- source sharing congestion game with incentives and budget constraints; (ii) an integrated approach that scales to larger problems by exploiting the factored network structure and approximating the attained equilib- rium; (iii) an iterative best response algorithm for solving the uncon- strained version (no budget) of the …
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.