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Articles 4351 - 4380 of 7002

Full-Text Articles in Physical Sciences and Mathematics

A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau Aug 2014

A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper presents a generalization of the Orienteering Problem, the Time-Dependent Orienteering Problem (TDOP) which is based on the real-life application of providing automatic tour guidance to a large leisure facility such as a theme park. In this problem, the travel time between two nodes depends on the time when the trip starts. We formulate the problem as an integer linear programming (ILP) model. We then develop various heuristics in a step by step fashion: greedy construction, local search and variable neighborhood descent, and two versions of iterated local search. The proposed metaheuristics were tested on modified benchmark instances, randomly …

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Diversity-Oriented Bi-Objective Hyper-Heuristics For Patrol Scheduling, Mustafa Misir, Hoong Chuin Lau Aug 2014

Diversity-Oriented Bi-Objective Hyper-Heuristics For Patrol Scheduling, Mustafa Misir, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The patrol scheduling problem is concerned with assigning security teams to different stations for distinct time intervals while respecting a limited number of contractual constraints. The objective is to minimise the total distance travelled while maximising the coverage of the stations with respect to their security requirement levels. This paper introduces a hyper-heuristic strategy focusing on generating diverse solutions for a bi-objective patrol scheduling problem. While a variety of hyper-heuristics have been applied to a large suite of problem domains usually in the form of single-objective optimisation, we suggest an alternative approach for solving the patrol scheduling problem with two …

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Automated Prediction Of Glasgow Outcome Scale For Traumatic Brain Injury, Bolan Su, Thien Anh Dinh, A. K. Ambastha, Tianxia Gong, Tomi Silander, Shijian Lu, C. C. Tchoyoson Lim, Boon Chuan Pang, Cheng Kiang Lee, Tze-Yun Leong, Chew Lim Tan Aug 2014

Automated Prediction Of Glasgow Outcome Scale For Traumatic Brain Injury, Bolan Su, Thien Anh Dinh, A. K. Ambastha, Tianxia Gong, Tomi Silander, Shijian Lu, C. C. Tchoyoson Lim, Boon Chuan Pang, Cheng Kiang Lee, Tze-Yun Leong, Chew Lim Tan

Research Collection School Of Computing and Information Systems

Clinical features found in brain CT scan images are widely used in traumatic brain injury (TBI) as indicators for Glasgow Outcome Scale (GOS) prediction. However, due to the lack of automated methods to measure and quantify the CT scan image features, the computerized prediction of GOS in TBI has not been well studied. This paper introduces an automated GOS prediction system for traumatic brain CT images. Different from most existing systems that perform the prognosis based on pre-processed data, our system directly works on brain CT scan images based on the image features. Our system can also be extended to …

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Jointly Modeling Aspects, Ratings And Sentiments For Movie Recommendation (Jmars), Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, Chong Wang Aug 2014

Jointly Modeling Aspects, Ratings And Sentiments For Movie Recommendation (Jmars), Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, Chong Wang

Research Collection School Of Computing and Information Systems

Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings. The ability to answer questions such as “Does this user care more about the plot or about the special effects?” or ”What is the quality of the movie in terms of acting?” …

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Gta-M: Greedy Trajectory-Aware (M Copies) Routing For Airborne Networks, Xiaoping Ma, Hwee Xian Tan, Alvin C. Valera Aug 2014

Gta-M: Greedy Trajectory-Aware (M Copies) Routing For Airborne Networks, Xiaoping Ma, Hwee Xian Tan, Alvin C. Valera

Research Collection School Of Computing and Information Systems

Airborne networks have potential applications in both civilian and military domains - such as passenger in-flight Internet connectivity, air traffic control and in intelligence, surveillance and reconnaissance (ISR) activities. However, airborne networks suffer from frequent disruptions due to high node mobility, ad hoc connectivity and line-of-sight blockages. These challenges can be alleviated through the use of disruption-tolerant networking (DTN) techniques. In this paper, we propose GTA-m, a multi-copy greedy trajectory-aware routing protocol for airborne networks. GTA-m employs DTN capabilities and exploits the use of flight information to forwarded bundles greedily to intended destination(s). To alleviate the local minima issues that …

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Utilizing Microblogs For Improving Automatic News High-Lights Extraction, Zhongyu Wei, Wei Gao Aug 2014

Utilizing Microblogs For Improving Automatic News High-Lights Extraction, Zhongyu Wei, Wei Gao

Research Collection School Of Computing and Information Systems

Story highlights form a succinct single-document summary consisting of 3-4 highlight sentences that reflect the gist of a news article. Automatically producing news highlights is very challenging. We propose a novel method to improve news highlights extraction by using microblogs. The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently “short and sweet” resulting from the artificial compression effect due to the length limit. Given a news article, we formulate the problem as two rank-then-extract tasks: (1) we find a set of indicative tweets and use …

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Video Event Detection Using Motion Relativity And Feature Selection, Feng Wang, Zhanhu Sun, Yu-Gang Jiang, Chong-Wah Ngo Aug 2014

Video Event Detection Using Motion Relativity And Feature Selection, Feng Wang, Zhanhu Sun, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Event detection plays an essential role in video content analysis. In this paper, we present our approach based on motion relativity and feature selection for video event detection. First, we propose a new motion feature, namely Expanded Relative Motion Histogram of Bag-of-Visual-Words (ERMH-BoW) to employ motion relativity for event detection. In ERMH-BoW, by representing what aspect of an event with Bag-of-Visual-Words (BoW), we construct relative motion histograms between different visual words to depict the objects' activities or how aspect of the event. ERMH-BoW thus integrates both what and how aspects for a complete event description. Meanwhile, we show that by …

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Integrating Motivated Learning And K-Winner-Take-All To Coordinate Multi-Agent Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Janusz Starzyk, Yuan-Sin Tan, Loo-Nin Teow Aug 2014

Integrating Motivated Learning And K-Winner-Take-All To Coordinate Multi-Agent Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Janusz Starzyk, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the tasks for large DOPs, the multi-agent reinforcement learning (MARL) framework is adopted wherein an agent is used to learn the performance of each distinct task using reinforcement learning. To coordinate MARL, we propose a novel coordination strategy integrating Motivated Learning (ML) …

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Guest Editorial: Special Issue On Brain Inspired Models Of Cognitive Memory, Huajin Tang, Kiruthika Ramanathan, Ning Nign Aug 2014

Guest Editorial: Special Issue On Brain Inspired Models Of Cognitive Memory, Huajin Tang, Kiruthika Ramanathan, Ning Nign

Research Collection School Of Computing and Information Systems

Current memory technologies have experienced significant progress in terms of storage capacity, operation speed, integration capability, etc. However, their functions are highly constrained in storing and transferring data in space and time, prompting the need for improvement. In contrast to physical memories, the biological counterpart – cognitive memory – has versatile functions. For instance, human memory stores data associatively such that different modalities of data could be retrieved simultaneously; it can learn different concepts, categorize and store them in an organized manner; it can process and store data concurrently and in a distributed fashion; it can restore content even if …

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Opinion Mining Of Sociopolitical Comments From Social Media, Swapna Gottipati Aug 2014

Opinion Mining Of Sociopolitical Comments From Social Media, Swapna Gottipati

Dissertations and Theses Collection (Open Access)

Opinions are central to almost all human activities by influencing greatly the decision making process. In this thesis, we present the problems of mining issues, extracting entities and suggestive opinions towards the entities, detecting thoughtful comments, and extracting stances and ideological expressions from online comments in the sociopolitical domain. This study is essential for opinion mining applications that are beneficial for policy makers, government sectors and social organizations. Much work has been done to try to uncover consumer sentiments from online comments to help businesses improve their products and services. However, sociopolitical opinion mining poses new challenges due to complex …

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Semantic Visualization For Spherical Representation, Tuan M. V. Le, Hady W. Lauw Aug 2014

Semantic Visualization For Spherical Representation, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Visualization of high-dimensional data such as text documents is widely applicable. The traditional means is to find an appropriate embedding of the high-dimensional representation in a low-dimensional visualizable space. As topic modeling is a useful form of dimensionality reduction that preserves the semantics in documents, recent approaches aim for a visualization that is consistent with both the original word space, as well as the semantic topic space. In this paper, we address the semantic visualization problem. Given a corpus of documents, the objective is to simultaneously learn the topic distributions as well as the visualization coordinates of documents. We propose …

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Social Tipping Points And Earth Systems Dynamics, R.A. Bentley, E. Maddison, P. Ranner, J. Bissell, C. Caiado, P. Bhatanacharoen, Timothy Adrian Robert Clark, Botha M., Akinbami F., Hollow M., Michie R., Huntley B., Curtis S., Garnett P. Aug 2014

Social Tipping Points And Earth Systems Dynamics, R.A. Bentley, E. Maddison, P. Ranner, J. Bissell, C. Caiado, P. Bhatanacharoen, Timothy Adrian Robert Clark, Botha M., Akinbami F., Hollow M., Michie R., Huntley B., Curtis S., Garnett P.

Research Collection Lee Kong Chian School Of Business

Recently, Early Warning Signals (EWS) have been developed to predict tipping points in Earth Systems. This discussion highlights the potential to apply EWS to human social and economic systems, which may also undergo similar critical transitions. Social tipping points are particularly difficult to predict, however, and the current formulation of EWS, based on a physical system analogy, may be insufficient. As an alternative set of EWS for social systems, we join with other authors encouraging a focus on heterogeneity, connectivity through social networks and individual thresholds to change.

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On Macro And Micro Exploration Of Hashtag Diffusion In Twitter, Yazhe Wang, Baihua Zheng Aug 2014

On Macro And Micro Exploration Of Hashtag Diffusion In Twitter, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

This exploratory work studies hashtag diffusion in Twitter. The analysis is conducted from two aspects. From the macro perspective, we study general properties of hashtag diffusion, and classify hashtags into three main classes based on their temporal dynamics referred as 'single spike', 'multi-spikes', and 'fluctuation', and find that each of these classes has some unique characteristics. From the micro perspective, we investigate individual diffusion.We adopt Edelman's 'topology of influence' theory to identify four type of users with different influence levels in diffusion based on their dynamic retweet behaviors. The results of our study are useful for gaining more insights of …

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Automatic Fine-Grained Issue Report Reclassification, Pavneet Singh Kochhar, Ferdian Thung, David Lo Aug 2014

Automatic Fine-Grained Issue Report Reclassification, Pavneet Singh Kochhar, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Issue tracking systems are valuable resources during software maintenance activities. These systems contain different categories of issue reports such as bug, request for improvement (RFE), documentation, refactoring, task etc. While logging issue reports into a tracking system, reporters can indicate the category of the reports. Herzig et al. Recently reported that more than 40% of issue reports are given wrong categories in issue tracking systems. Among issue reports that are marked as bugs, more than 30% of them are not bug reports. The misclassification of issue reports can adversely affects developers as they then need to manually identify the categories …

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Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan Aug 2014

Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan

Research Collection School Of Computing and Information Systems

Human’s impact on earth through global warming is more or less an accepted fact. Ocean freight is estimated to contribute 4-5% of global carbon emissions and manufacturing companies can aid in reducing this amount. Many companies that ship goods through full container loads do not have the capabilities to ensure the containers they are using minimizes their carbon footprint. One of the reasons is the choice of non-ideal container sizes for their shipments. This paper provides a mathematical model to minimize companies’ shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes. Using data from a …

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Urban Planning Process: Can Technology Enhance Participatory Communication?, Rojin Vishkaie, Richard Levy, Anthony Tang Aug 2014

Urban Planning Process: Can Technology Enhance Participatory Communication?, Rojin Vishkaie, Richard Levy, Anthony Tang

Research Collection School Of Computing and Information Systems

Oftentimes, within the urban planning process, urban planners and GIS experts must work together using desktop Computer-Aided Design (CAD) and Geographic Information System (GIS). However, participatory communication and visualization which are important in the urban planning process, are not a central focus in the design of current computer-aided planning technologies. This study tends to provide an understanding of technological challenges and complexities urban planners and GIS experts encounter while engaging in a participatory environment during the urban planning process. This study also explores the perceptions of urban planners and GIS experts about the potential impact and usefulness of interactive surfaces …

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A Palm Vein Identification System Based On Gabor Wavelet Features, Ran Wang, Guoyou Wang, Zhong Chen, Zhigang Zeng, Yong Wang Aug 2014

A Palm Vein Identification System Based On Gabor Wavelet Features, Ran Wang, Guoyou Wang, Zhong Chen, Zhigang Zeng, Yong Wang

Research Collection School Of Computing and Information Systems

As a new and promising biometric feature, thermal palm vein pattern has drawn lots of attention in research and application areas. Many algorithms have been proposed for authentication since palm vein has special characteristics, such as liveness detection and hard to forgery. However, the detection accuracy of palm vein quite depends on the preprocessing and feature representation, which is supposed to be translation and rotation invariant to some extent. In this paper, we proposed an effective method for palm vein identification based on Gabor wavelet features which contains five steps: image acquisition, ROI detection, image preprocessing, features extraction, and matching. …

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Collaborative Online Multitask Learning, Guangxia Li, Steven C. H. Hoi, Kuiyu Chang, Wenting Liu, Ramesh Jain Aug 2014

Collaborative Online Multitask Learning, Guangxia Li, Steven C. H. Hoi, Kuiyu Chang, Wenting Liu, Ramesh Jain

Research Collection School Of Computing and Information Systems

We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional categories. This particular online learning task is challenging for a number of reasons. First of all, to meet the critical requirements of online applications, a highly efficient and scalable classification solution that can make immediate predictions with low learning cost is …

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Online Multiple Kernel Regression, Doyen Sahoo, Steven C. H. Hoi, Bin Li Aug 2014

Online Multiple Kernel Regression, Doyen Sahoo, Steven C. H. Hoi, Bin Li

Research Collection School Of Computing and Information Systems

Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this paper presents a novel …

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Managing Complexity Through Selective Decoupling, C. Jason Woodard, Eric K. Clemons Aug 2014

Managing Complexity Through Selective Decoupling, C. Jason Woodard, Eric K. Clemons

Research Collection School Of Computing and Information Systems

Designers of complex systems are often confounded by the tendency for design changes to increase performance on some dimensions while decreasing it on others. While adopting a more modular architecture may temper these opposing effects, modularization can also deprive designers of opportunities to harness complementarities among system elements. This paper explores this tension using an NK model in which product designers can modify the structure of their fitness landscapes by suppressing or restoring interactions between components. We find that these changes can lead to improved performance by flattening harmful interactions that would otherwise cause search efforts to become trapped on …

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Unleashing Dec-Mdps In Security Games: Enabling Effective Defender Teamwork, Eric Shieh, Albert Jiang, Amulya Yadav, Pradeep Reddy Varakantham, Milind Tambe Aug 2014

Unleashing Dec-Mdps In Security Games: Enabling Effective Defender Teamwork, Eric Shieh, Albert Jiang, Amulya Yadav, Pradeep Reddy Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

Multiagent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multiagent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. This paper is the first to remedy this situation by integrating the powerful teamwork mechanisms offered by Dec-MDPs into security games. We offer the following novel contributions in this paper: (i) New models of security games where a defender team’s pure strategy is defined as a DecMDP policy for addressing coordination under uncertainty; (ii) New algorithms based on column …

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The Learning Curves In Open-Source Software (Oss) Development Network, Youngsoo Kim, Lingxiao Jiang Aug 2014

The Learning Curves In Open-Source Software (Oss) Development Network, Youngsoo Kim, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

We examine the learning curves of individual software developers in Open-Source Software (OSS) Development. We collected the dataset of multi-year code change histories from the repositories for five open source software projects involving more than 100 developers. We build and estimate regression models to assess individual developers' learning progress (in reducing the likelihood they may make a bug). Our estimation results show that developer's coding experience does not decrease bug ratios while cumulative bug-fixing experience leads to learning progress. The results may have implications and provoke future research on project management about allocating resources on tasks that add new code …

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Generating Supplementary Travel Guides From Social Media, Liu Yang, Jing Jiang, Lifu Huang, Minghui Qiu, Lizi Liao Aug 2014

Generating Supplementary Travel Guides From Social Media, Liu Yang, Jing Jiang, Lifu Huang, Minghui Qiu, Lizi Liao

Research Collection School Of Computing and Information Systems

In this paper we study how to summarize travel-related information in forum threads to generate supplementary travel guides. Such summaries presumably can provide additional and more up-to-date information to tourists. Existing multi-document summarization methods have limitations for this task because (1) they do not generate structured summaries but travel guides usually follow a certain template, and (2) they do not put emphasis on named entities but travel guides often recommend points of interest to travelers. To overcome these limitations, we propose to use a latent variable model to align forum threads with the section structure of well-written travel guides. The …

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Direct Neighbor Search, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang Aug 2014

Direct Neighbor Search, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc.) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an …

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Integrating Motivated Learning And K-Winner-Take-All To Coordinate Multi-Agent Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Janusz Starzyk, Yuan-Sin Tan, Loo-Nin Teow Aug 2014

Integrating Motivated Learning And K-Winner-Take-All To Coordinate Multi-Agent Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Janusz Starzyk, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the tasks for large DOPs, the multi-agent reinforcement learning (MARL) framework is adopted wherein an agent is used to learn the performance of each distinct task using reinforcement learning. To coordinate MARL, we propose a novel coordination strategy integrating Motivated Learning (ML) …

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Ranking Model Selection And Fusion For Effective Microblog Search, Zhongyu Wei, Wei Gao, Tarek El-Ganainy, Walid Magdy, Kam-Fai Wong Jul 2014

Ranking Model Selection And Fusion For Effective Microblog Search, Zhongyu Wei, Wei Gao, Tarek El-Ganainy, Walid Magdy, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced …

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Symbolic Analysis Of An Electric Vehicle Charging Protocol, Li Li, Jun Pang, Yang Liu, Jun Sun, Jin Song Dong Jul 2014

Symbolic Analysis Of An Electric Vehicle Charging Protocol, Li Li, Jun Pang, Yang Liu, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

In this paper, we describe our analysis of a recently proposed electric vehicle charing protocol. The protocol builds on complex cryptographic primitives such as commitment, zeroknowledge proofs, BBS+ signature and etc. Moreover, interesting properties such as secrecy, authentication, anonymity, and location privacy are claimed on this protocol. It thus presents a challenge for formal verification, as no single existing tool for security protocol analysis support for all the required features. In our analysis, we employ and combine the strength of two stateof-the-art symbolic verifiers, Tamarin and ProVerif, to check all important properties of the protocol.

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Detecting Differences Across Multiple Instances Of Code Clones, Yun Lin, Zhenchang Xing, Yinxing Xue, Yang Liu, Xin Peng, Jun Sun, Wenyun Zhao Jul 2014

Detecting Differences Across Multiple Instances Of Code Clones, Yun Lin, Zhenchang Xing, Yinxing Xue, Yang Liu, Xin Peng, Jun Sun, Wenyun Zhao

Research Collection School Of Computing and Information Systems

Clone detectors find similar code fragments (i.e., instances of code clones) and report large numbers of them for industrial systems. To maintain or manage code clones, developers often have to investigate differences of multiple cloned code fragments. However,existing program differencing techniques compare only two code fragments at a time. Developers then have to manually combine several pairwise differencing results. In this paper, we present an approach to automatically detecting differences across multiple clone instances. We have implemented our approach as an Eclipse plugin and evaluated its accuracy with three Java software systems. Our evaluation shows that our algorithm has precision …

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A Simple Polynomial-Time Randomized Distributed Algorithm For Connected Row Convex Constraints, T. K. Satish Kumar, Nguyen Duc Thien, William Yeoh, Sven Koenig Jul 2014

A Simple Polynomial-Time Randomized Distributed Algorithm For Connected Row Convex Constraints, T. K. Satish Kumar, Nguyen Duc Thien, William Yeoh, Sven Koenig

Research Collection School Of Computing and Information Systems

In this paper, we describe a simple randomized algorithm that runs in polynomial time and solves connected row convex (CRC) constraints in distributed settings. CRC constraints generalize many known tractable classes of constraints like 2-SAT and implicational constraints. They can model problems in many domains including temporal reasoning and geometric reasoning, and generally speaking, play the role of "Gaussians" in the logical world. Our simple randomized algorithm for solving them in distributed settings, therefore, has a number of important applications. We support our claims through a theoretical analysis and empirical results.

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On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim Jul 2014

On Predicting Religion Labels In Microblogging Networks, Minh Thap Nguyen, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to …

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