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Articles 4231 - 4260 of 9025

Full-Text Articles in Computer Sciences

Distributed Gibbs: A Linear-Space Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Roie Zivan Mar 2019

Distributed Gibbs: A Linear-Space Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Roie Zivan

Research Collection School Of Computing and Information Systems

Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this article, we introduce two new sampling-based DCOP algorithms called Sequential Distributed Gibbs (SD-Gibbs) and Parallel Distributed Gibbs (PD-Gibbs). Both algorithms have memory requirements per agent that is linear in …

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Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li Mar 2019

Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

In practice, a popular and coarse-grained approach for recovering from a problematic commit is to revert it (i.e., undoing the change). However, reverted commits could induce some issues for software development, such as impeding the development progress and increasing the difficulty for maintenance. In order to mitigate these issues, we set out to explore the following central question: can we characterize and identify which commits will be reverted? In this paper, we characterize commits using 27 commit features and build an identification model to identify commits that will be reverted. We first identify reverted commits by analyzing commit messages and …

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Automatic, Highly Accurate App Permission Recommendation, Zhongxin Liu, Xin Xia, David Lo, John Grundy Mar 2019

Automatic, Highly Accurate App Permission Recommendation, Zhongxin Liu, Xin Xia, David Lo, John Grundy

Research Collection School Of Computing and Information Systems

To ensure security and privacy, Android employs a permission mechanism which requires developers to explicitly declare the permissions needed by their applications (apps). Users must grant those permissions before they install apps or during runtime. This mechanism protects users’ private data, but also imposes additional requirements on developers. For permission declaration, developers need knowledge about what permissions are necessary to implement various features of their apps, which is difficult to acquire due to the incompleteness of Android documentation. To address this problem, we present a novel permission recommendation system named PerRec for Android apps. PerRec leverages mining-based techniques and data …

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Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan Mar 2019

Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan

Research Collection School Of Computing and Information Systems

Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is social search. Although there have been extensive studies on social search, existing methods only focus on the relevance of query results but ignore the representativeness. In this paper, we propose a novel Semantic and Influence aware k-Representative (k-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A k-SIR query retrieves a set of …

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See No Evil, Hear No Evil? Dissecting The Impact Of Online Hacker Forums, Wei T. Yue, Qiu-Hong Wang, Kai‐Lung Hui Mar 2019

See No Evil, Hear No Evil? Dissecting The Impact Of Online Hacker Forums, Wei T. Yue, Qiu-Hong Wang, Kai‐Lung Hui

Research Collection School Of Computing and Information Systems

Online hacker forums offer a prominent avenue for sharing hacking knowledge. Using a field dataset culled from multiple sources, we find that online discussion of distributed denial of service (DDOS) attacks in hackforums.net decreases the number of DDOS-attack victims. A 1% increase in discussion decreases DDOS attacks by 0.032% to 0.122%. This means that two DDOS-attack posts per day could reduce the number of victims by 700 to 2,600 per day. We find that discussion topics with similar keywords can variously increase or decrease DDOS attacks, meaning we cannot ascertain the impact of the discussion just by the post nature. …

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Microbtc: Efficient, Flexible And Fair Micropayment For Bitcoin Using Hash Chains, Zhiguo Wan, Robert H. Deng, David Kuo Chuen Lee, Ying Li Mar 2019

Microbtc: Efficient, Flexible And Fair Micropayment For Bitcoin Using Hash Chains, Zhiguo Wan, Robert H. Deng, David Kuo Chuen Lee, Ying Li

Research Collection School Of Computing and Information Systems

While Bitcoin gains increasing popularity in different payment scenarios, the transaction fees make it difficult to be applied to micropayment. Given the wide applicability of micropayment, it is crucial for all cryptocurrencies including Bitcoin to provide effective support therein. In light of this, a number of low-cost micropayment schemes for Bitcoin have been proposed recently to reduce micropayment costs. Existing schemes, however, suffer from drawbacks such as high computation cost, inflexible payment value, and possibly unfair exchanges. The paper proposes two new micropayment schemes, namely the basic MicroBTC and the advanced MicroBTC, for Bitcoin by integrating the hash chain technique …

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Neural Network Based Detection Of Self-Admitted Technical Debt: From Performance To Explainability, Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, John Grundy Mar 2019

Neural Network Based Detection Of Self-Admitted Technical Debt: From Performance To Explainability, Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, John Grundy

Research Collection School Of Computing and Information Systems

Technical debt is a metaphor to reflect the tradeoff software engineers make between short term benefitsand long term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., temporary fixes and workarounds. Previous studies have leveraged human-summarized patterns (which represent n-gram phrases that can be used to identify SATD) or text mining techniques to detect SATD in source code comments. However, several characteristics of SATD features in code comments, such as vocabulary diversity, project uniqueness, length and semantic variations, pose a big challenge to the accuracy …

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Making Wearable Sensing Less Obtrusive, Huy Vu Tran, Archan Misra Mar 2019

Making Wearable Sensing Less Obtrusive, Huy Vu Tran, Archan Misra

Research Collection School Of Computing and Information Systems

Sensing is a crucial part of any cyber-physical system. Wearable device has its huge potential for sensing applications because it is worn on the user body. However, wearable sensing can cause obtrusiveness to the user. Obtrusiveness can be seen as a perception of a lack of usefulness [1] such as a lag in user interaction channel. In addition, being worn by a user, it is not connected to a power supply, and thus needs to be removed to be charged regularly. This can cause a nuisance to elderly or disabled people. However, there are also opportunities for wearable devices to …

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Design And Assessment Of Myoelectric Games For Prosthesis Training Of Upper Limb Amputees, Meeralakshmi Radhakrishnan, Asim Smailagic, Brian French, Daniel P. Siewiorek, Rajesh Krishna Balan Mar 2019

Design And Assessment Of Myoelectric Games For Prosthesis Training Of Upper Limb Amputees, Meeralakshmi Radhakrishnan, Asim Smailagic, Brian French, Daniel P. Siewiorek, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we present the design and evaluation of our system, which provides an engaging game-based pre-prosthesis training environment for upper limb transradial amputees. We believe that patients who train using such a training tool will demonstrate significantly higher improvement in functional performance tests using a myoelectric prosthesis than when conventional pre-prosthesis training protocols are used. We re-designed two simple games to be playable using three muscle contractions which are appropriate to pre-prosthesis exercises and are detected by an EMG-based arm sleeve. Through user studies conducted with 16 non-amputee subjects, we show that the proposed games are enjoyable, fun …

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Welcome Message From The General Chairs, Nabil I. Alshurafa, Archan Misra, Abhishek Mukherji Mar 2019

Welcome Message From The General Chairs, Nabil I. Alshurafa, Archan Misra, Abhishek Mukherji

Research Collection School Of Computing and Information Systems

No abstract provided.

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Ict: In-Field Calibration Transfer For Air Quality Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele Mar 2019

Ict: In-Field Calibration Transfer For Air Quality Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele

Research Collection School Of Computing and Information Systems

Recent years have witnessed a growing interest in urban air pollution monitoring, where hundreds of low-cost air quality sensors are deployed city-wide. To guarantee data accuracy and consistency, these sensors need periodic calibration after deployment. Since access to ground truth references is often limited in large-scale deployments, it is difficult to conduct city-wide post-deployment sensor calibration. In this work we propose In-field Calibration Transfer (ICT), a calibration scheme that transfers the calibration parameters of source sensors (with access to references) to target sensors (without access to references). On observing that (i) the distributions of ground truth in both source and …

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Automation Tax Vs Robot-Tax, Vincent Ooi Mar 2019

Automation Tax Vs Robot-Tax, Vincent Ooi

Research Collection Yong Pung How School Of Law

The positive impact of developments in technology on the economy has historically outweighed the disruptive impact on employment. Society has benefited from the efficiency gains derived from the application of technology in production, while workers displaced by these technologies have largely been successfully retrained and employed in other jobs. However, the pace of development of the “Fourth Industrial Revolution” now presents a risk of mass displacement of human labour, particularly in tasks that are repetitive and menial. The “Fourth Industrial Revolution” is characterised by significant progress in a closely-linked cluster of areas such as robot dexterity, machine learning, processing power, …

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Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen Mar 2019

Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority …

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Fc2: Cloud-Based Cluster Provisioning For Distributed Machine Learning, Nguyen Binh Duong Ta Feb 2019

Fc2: Cloud-Based Cluster Provisioning For Distributed Machine Learning, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Training large, complex machine learning models such as deep neural networks with big data requires powerful computing clusters, which are costly to acquire, use and maintain. As a result, many machine learning researchers turn to cloud computing services for on-demand and elastic resource provisioning capabilities. Two issues have arisen from this trend: (1) if not configured properly, training models on cloud-based clusters could incur significant cost and time, and (2) many researchers in machine learning tend to focus more on model and algorithm development, so they may not have the time or skills to deal with system setup, resource selection …

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Bots In Libraries: They're Coming For Your Jobs (Or Is It?), Salihin Mohammed Ali Feb 2019

Bots In Libraries: They're Coming For Your Jobs (Or Is It?), Salihin Mohammed Ali

Research Collection Library

With advancements in Artificial Intelligence (AI) and Machine Learning (ML), we have seen a rise in the use of bots, specifically chatbots, to deliver information services. Motivated by the Smart Nation programme, these chatbots have sprung up in sectors as transport, healthcare, banking and education in Singapore. What are these chatbots? How do they work? Will they take our jobs? SMU Libraries tries to answer these questions by delving into the mechanics of creating chatbots. The proof-of-concept aims to find out and understand use cases where these bots can be useful to delivering library information services to its campus community. …

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Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar Feb 2019

Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar

Research Collection School Of Computing and Information Systems

In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how …

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Send Hardest Problems My Way: Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Yue Duan, Jifeng Xuan Feb 2019

Send Hardest Problems My Way: Probabilistic Path Prioritization For Hybrid Fuzzing, Lei Zhao, Yue Duan, Jifeng Xuan

Research Collection School Of Computing and Information Systems

Hybrid fuzzing which combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, the state-of-the-art hybrid fuzzing systems deploy ``demand launch'' and ``optimal switch'' strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to oversimplified assumptions. We then propose a novel ``discriminative dispatch'' strategy to better utilize the capability of concolic execution. We design a novel Monte Carlo based probabilistic path prioritization model to quantify each path's difficulty and prioritize them for concolic execution. This model treats …

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A Sampling Approach For Proactive Project Scheduling Under Generalized Time-Dependent Workability Uncertainty, Wen Song, Donghun Kang, Jie Zhang, Zhiguang Cao, Hui Xi Feb 2019

A Sampling Approach For Proactive Project Scheduling Under Generalized Time-Dependent Workability Uncertainty, Wen Song, Donghun Kang, Jie Zhang, Zhiguang Cao, Hui Xi

Research Collection School Of Computing and Information Systems

In real-world project scheduling applications, activity durations are often uncertain. Proactive scheduling can effectively cope with the duration uncertainties, by generating robust baseline solutions according to a priori stochastic knowledge. However, most of the existing proactive approaches assume that the duration uncertainty of an activity is not related to its scheduled start time, which may not hold in many real-world scenarios. In this paper, we relax this assumption by allowing the duration uncertainty to be time-dependent, which is caused by the uncertainty of whether the activity can be executed on each time slot. We propose a stochastic optimization model to …

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Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van Feb 2019

Manifold-Valued Image Generation With Wasserstein Generative Adversarial Nets, Zhiwu Huang, Wu J., G. L. Van

Research Collection School Of Computing and Information Systems

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to …

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An Attribute-Based Framework For Secure Communications In Vehicular Ad Hoc Networks, Hui Cui, Robert H. Deng, Guilin Wang Feb 2019

An Attribute-Based Framework For Secure Communications In Vehicular Ad Hoc Networks, Hui Cui, Robert H. Deng, Guilin Wang

Research Collection School Of Computing and Information Systems

In this paper, we introduce an attribute-based framework to achieve secure communications in vehicular ad hoc networks (VANETs), which enjoys several advantageous features. The proposed framework employs attribute-based signature (ABS) to achieve message authentication and integrity and protect vehicle privacy, which greatly mitigates the overhead caused by pseudonym/private key change or update in the existing solutions for VANETs based on symmetric key, asymmetric key, and identity-based cryptography and group signature. In addition, we extend a standard ABS scheme with traceability and revocation mechanisms and seamlessly integrate them into the proposed framework to support vehicle traceability and revocation by a trusted …

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Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2019

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …

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Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw Feb 2019

Comparelda: A Topic Model For Document Comparison, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation …

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Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw Feb 2019

Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only cameras on hand, documents on the Web (e.g., reviews, blog posts, tweets) are increasingly multimodal in nature, with photos in addition to textual content. A question arises whether the visual component could be useful for sentiment analysis as well. In this work, we propose Visual Aspect Attention Network or VistaNet, leveraging both textual and …

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Preference-Aware Task Assignment In On-Demand Taxi Dispatching: An Online Stable Matching Approach, Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng Feb 2019

Preference-Aware Task Assignment In On-Demand Taxi Dispatching: An Online Stable Matching Approach, Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng

Research Collection School Of Computing and Information Systems

No abstract provided.

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Deception In Finitely Repeated Security Games, Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman Feb 2019

Deception In Finitely Repeated Security Games, Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman

Research Collection School Of Computing and Information Systems

Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities, objectives, or other underlying characteristics. In a repeated interaction setting, the defender can collect attack data over time to reduce this uncertainty and learn an effective defense. However, a clever attacker can manipulate the attack data to mislead the defender, influencing the learning process toward its own benefit. We investigate strategic deception on the part of an attacker with private type information, who interacts repeatedly with a defender. We present a detailed computation and analysis of both players’ optimal strategies given the attacker may …

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Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2019

Multiagent Decision Making For Maritime Traffic Management, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale …

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Topical Co-Attention Networks For Hashtag Recommendation On Microblogs, Yang Li, Ting Liu, Jingwen Hu, Jing Jiang Feb 2019

Topical Co-Attention Networks For Hashtag Recommendation On Microblogs, Yang Li, Ting Liu, Jingwen Hu, Jing Jiang

Research Collection School Of Computing and Information Systems

Hashtags provide a simple and natural way of organizing content in microblog services. Along with the fast growing of microblog services, the task of recommending hashtags for microblogs has been given increasing attention in recent years. However, much of the research depends on hand-crafted features. Motivated by the successful use of neural models for many natural language processing tasks, in this paper, we adopt an attention based neural network to learn the representation of a microblog post. Unlike previous works, which only focus on content attention of microblogs, we propose a novel Topical CoAttention Network (TCAN) that jointly models content …

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Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi Feb 2019

Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order …

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Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu Feb 2019

Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu

Research Collection School Of Computing and Information Systems

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale …

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Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi Feb 2019

Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, …

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