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Articles 6451 - 6480 of 9024
Full-Text Articles in Computer Sciences
The Influence Of Online Word-Of-Mouth On Long Tail Formation, Bin Gu, Qian Tang, Andrew B. Whinston
The Influence Of Online Word-Of-Mouth On Long Tail Formation, Bin Gu, Qian Tang, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
The long tail phenomenon has been attributed to both supply side and demand side economies. While the cause on the supply side is well-known, research on the demand side has largely focused on the awareness effect of online information that helps consumers discover new and often niche products. This study expands the demand side factors by showing that online information also influences the long tail phenomenon through the informative effect, which affects consumers' evaluation of product quality. We examine the informative effect in the context of online WOM. Two sets of theories suggest opposite directions for the implication of the …
Firm Strategy And The Internet In U.S. Commercial Banking, Kim Huat Goh, Robert J. Kauffman
Firm Strategy And The Internet In U.S. Commercial Banking, Kim Huat Goh, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
As information technology (IT) becomes more accessible, sustaining any competitive advantage from it becomes challenging. This has caused some critics to dismiss IT as a less valuable resource. We argue that, in addition to being able to generate strategic advantage, IT should also be viewed as a strategic necessity that prevents competitive disadvantage in rapidly changing business environments. We test a set of hypotheses on strategic advantage and strategic necessity in the context of Internet banking investments among the entire population of the United States Federal Deposit Insurance Corporation (FDIC) banks from 2003 to 2005. We seek to understand whether …
Adaptive Computer‐Generated Forces For Simulator‐Based Training, Expert Systems With Applications, Teck-Hou Teng, Ah-Hwee Tan, Loo-Nin Teow
Adaptive Computer‐Generated Forces For Simulator‐Based Training, Expert Systems With Applications, Teck-Hou Teng, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Simulator-based training is in constant pursuit of increasing level of realism. The transition from doctrine-driven computer-generated forces (CGF) to adaptive CGF represents one such effort. The use of doctrine-driven CGF is fraught with challenges such as modeling of complex expert knowledge and adapting to the trainees’ progress in real time. Therefore, this paper reports on how the use of adaptive CGF can overcome these challenges. Using a self-organizing neural network to implement the adaptive CGF, air combat maneuvering strategies are learned incrementally and generalized in real time. The state space and action space are extracted from the same hierarchical doctrine …
Towards A Hybrid Framework For Detecting Input Manipulation Vulnerabilities, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar, Bindu Madhavi Padmanabhuni
Towards A Hybrid Framework For Detecting Input Manipulation Vulnerabilities, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar, Bindu Madhavi Padmanabhuni
Research Collection School Of Computing and Information Systems
Input manipulation vulnerabilities such as SQL Injection, Cross-site scripting, Buffer Overflow vulnerabilities are highly prevalent and pose critical security risks. As a result, many methods have been proposed to apply static analysis, dynamic analysis or a combination of them, to detect such security vulnerabilities. Most of the existing methods classify vulnerabilities into safe and unsafe. They have both false-positive and false-negative cases. In general, security vulnerability can be classified into three cases: (1) provable safe, (2) provable unsafe, (3) unsure. In this paper, we propose a hybrid framework-Detecting Input Manipulation Vulnerabilities (DIMV), to verify the adequacy of security vulnerability defenses …
Exposing And Mitigating Privacy Loss In Crowdsourced Survey Platforms, Thivya Kandappu, Vijay Sivaraman, Arik Friedman, Roksana Borell
Exposing And Mitigating Privacy Loss In Crowdsourced Survey Platforms, Thivya Kandappu, Vijay Sivaraman, Arik Friedman, Roksana Borell
Research Collection School Of Computing and Information Systems
Crowdsourcing platforms such as Amazon Mechanical Turk and Google Consumer Surveys can profile users based on their inputs to online surveys. In this work we first demonstrate how easily user privacy can be compromised by collating information from multiple surveys. We then propose, develop, and evaluate a crowdsourcing survey platform called Loki that allows users to control their privacy loss via atsource obfuscation.
An Efficient Partial Shape Matching Algorithm For 3d Tooth Recognition, Zhiyuan Zhang, Xin Zhong, Sim Heng Ong, Kelvin W. C. Foong
An Efficient Partial Shape Matching Algorithm For 3d Tooth Recognition, Zhiyuan Zhang, Xin Zhong, Sim Heng Ong, Kelvin W. C. Foong
Research Collection School Of Computing and Information Systems
As a new biometric strategy, tooth recognition has drawn much attention in recent years. However, most existing work focus mainly on 2D dental radiographs which are less informative and vulnerable to noise and pose variance. Although there are already several attempts on 3D tooth recognition, the results are still inaccurate and performance is inefficient. Moreover, existing methods cannot recognize precisely when the post-mortem data contains incomplete teeth. In this work, we propose an efficient and accurate partial shape matching algorithm to recognize 3D teeth for human identification. Given the ante-mortem and post-mortem teeth models which were taken from patients using …
Modeling Temporal Adoptions Using Dynamic Matrix Factorization, Freddy Chong-Tat Chua, Richard Jayadi Oentaryo, Ee Peng Lim
Modeling Temporal Adoptions Using Dynamic Matrix Factorization, Freddy Chong-Tat Chua, Richard Jayadi Oentaryo, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The problem of recommending items to users is relevant to many applications and the problem has often been solved using methods developed from Collaborative Filtering (CF). Collaborative Filtering model-based methods such as Matrix Factorization have been shown to produce good results for static rating-type data, but have not been applied to time-stamped item adoption data. In this paper, we adopted a Dynamic Matrix Factorization (DMF) technique to derive different temporal factorization models that can predict missing adoptions at different time steps in the users' adoption history. This DMF technique is an extension of the Non-negative Matrix Factorization (NMF) based on …
Fundamental Limits On End-To-End Throughput Of Network Coding In Multi-Rate And Multicast Wireless Networks, Luiz Felipe Viera, Mario Gerla, Archan Misra
Fundamental Limits On End-To-End Throughput Of Network Coding In Multi-Rate And Multicast Wireless Networks, Luiz Felipe Viera, Mario Gerla, Archan Misra
Research Collection School Of Computing and Information Systems
This paper investigates the interaction between network coding and link-layer transmission rate diversity in multi-hop wireless networks. By appropriately mixing data packets at intermediate nodes, network coding allows a single multicast flow to achieve higher throughput to a set of receivers. Broadcast applications can also exploit link-layer rate diversity, whereby individual nodes can transmit at faster rates at the expense of corresponding smaller coverage area. We first demonstrate how combining rate-diversity with network coding can provide a larger capacity for data dissemination of a single multicast flow, and how consideration of rate diversity is critical for maximizing system throughput. Next …
Two Formulas For Success In Social Media: Social Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston
Two Formulas For Success In Social Media: Social Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
This paper examines social learning and network effects that are particularly important for online videos, considering the limited marketing campaigns of user-generated content. Rather than combining both social learning and network effects under the umbrella of social contagion or peer influence, we develop a theoretical model and empirically identify social learning and network effects separately. Using a unique data set from YouTube, we find that both mechanisms have statistically and economically significant effects on video views, and which mechanism dominates depends on the specific video type.
Topicsketch: Real-Time Bursty Topic Detection From Twitter, Wei Xie, Feida Zhu, Jing Jiang, Ee Peng Lim, Ke Wang
Topicsketch: Real-Time Bursty Topic Detection From Twitter, Wei Xie, Feida Zhu, Jing Jiang, Ee Peng Lim, Ke Wang
Research Collection School Of Computing and Information Systems
Twitter has become one of the largest platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modeling and analysis in Twitter, it remains a huge challenge to detect bursty topics in real-time. As existing methods can hardly scale …
Query-Document-Dependent Fusion: A Case Study Of Multimodal Music Retrieval, Zhonghua Li, Bingjun Zhang, Yi Yu, Jialie Shen, Ye Wang
Query-Document-Dependent Fusion: A Case Study Of Multimodal Music Retrieval, Zhonghua Li, Bingjun Zhang, Yi Yu, Jialie Shen, Ye Wang
Research Collection School Of Computing and Information Systems
In recent years, multimodal fusion has emerged as a promising technology for effective multimedia retrieval. Developing the optimal fusion strategy for different modality (e.g. content, metadata) has been the subject of intensive research. Given a query, existing methods derive a unified fusion strategy for all documents with the underlying assumption that the relative significance of a modality remains the same across all documents. However, this assumption is often invalid. We thus propose a general multimodal fusion framework, query-document-dependent fusion (QDDF), which derives the optimal fusion strategy for each query-document pair via intelligent content analysis of both queries and documents. By …
An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham
An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham
Research Collection School Of Computing and Information Systems
In this paper, we illustrate how massive agent-based simulation can be used to investigate an exciting new application domain of experience management in theme parks, which covers topics like congestion control, incentive design, and revenue management. Since all visitors are heterogeneous and self-interested, we argue that a high-quality agent-based simulation is necessary for studying various problems related to experience management. As in most agent-base simulations, a sound understanding of micro-level behaviors is essential to construct high-quality models. To achieve this, we designed and conducted a first-of-its-kind real-world experiment that helps us understand how typical visitors behave in a theme-park environment. …
Modeling Preferences With Availability Constraints, Bingtian Dai, Hady W. Lauw
Modeling Preferences With Availability Constraints, Bingtian Dai, Hady W. Lauw
Research Collection School Of Computing and Information Systems
User preferences are commonly learned from historical data whereby users express preferences for items, e.g., through consumption of products or services. Most work assumes that a user is not constrained in their selection of items. This assumption does not take into account the availability constraint, whereby users could only access some items, but not others. For example, in subscription-based systems, we can observe only those historical preferences on subscribed (available) items. However, the objective is to predict preferences on unsubscribed (unavailable) items, which do not appear in the historical observations due to their (lack of) availability. To model preferences in …
A Local Social Network Approach For Research Management, Xiaoyan Liu, Zhiling Guo, Zhenjiang Lin, Jian Ma
A Local Social Network Approach For Research Management, Xiaoyan Liu, Zhiling Guo, Zhenjiang Lin, Jian Ma
Research Collection School Of Computing and Information Systems
Traditional methods to evaluate research performance focus on citation count, quality and quantity of research output by individual researchers. These measures overlook the roles an individual plays in research collaboration, which is critical in an institutional research management environment due to the inherent interdependency among research entities. In order to address the organizational research management needs, we propose a research social network approach to better analyze local collaboration networks. For this purpose, we develop a new “collaboration supportiveness” measure to quantify an individual researcher's collaboration ability. Insights derived from this research are very helpful for managers to effectively allocate resources, …
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 …
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
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.
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 …
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 …
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.
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, …
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.
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 …
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 …
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 …
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).
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.
What You Want Is Not What You Get: Predicting Sharing Policies For Text-Based Content On Facebook, Arunesh Sinha, Li Yan, Lujo Bauer
What You Want Is Not What You Get: Predicting Sharing Policies For Text-Based Content On Facebook, Arunesh Sinha, Li Yan, Lujo Bauer
Research Collection Lee Kong Chian School Of Business
As the amount of content users publish on social networking sites rises, so do the danger and costs of inadvertently sharing content with an unintended audience. Studies repeatedly show that users frequently misconfigure their policies or misunderstand the privacy features offered by social networks. A way to mitigate these problems is to develop automated tools to assist users in correctly setting their policy. This paper explores the viability of one such approach: we examine the extent to which machine learning can be used to deduce users' sharing preferences for content posted on Facebook. To generate data on which to evaluate …
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 …