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Full-Text Articles in Entire DC Network
Qos-Aware Data Transmission And Wireless Energy Transfer: Performance Modeling And Optimization, D. Niyato, P. Wang, W. L. Yeow, Hwee-Pink Tan
Qos-Aware Data Transmission And Wireless Energy Transfer: Performance Modeling And Optimization, D. Niyato, P. Wang, W. L. Yeow, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
With wireless energy transfer, a mobile node can operate perpetually without having a wired connection to charge its battery. In this paper, we present a quality of service (QoS) aware data transmission and wireless energy transfer for the mobile node. The node can request for wireless energy transfer or transmit a packet when the node is in a coverage area of an access point. The node supports service differentiation for different type of traffic (i.e., low and high priority data). To meet the QoS requirement of each traffic type, we present the performance modeling and optimization framework. The objective is …
On Modeling Community Behaviors And Sentiments In Microblogging, Tuan Anh Hoang, William Cohen, Ee Peng Lim
On Modeling Community Behaviors And Sentiments In Microblogging, Tuan Anh Hoang, William Cohen, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In this paper, we propose the CBS topic model, a probabilistic graphical model, to derive the user communities in microblogging networks based on the sentiments they express on their generated content and behaviors they adopt. As a topic model, CBS can uncover hidden topics and derive user topic distribution. In addition, our model associates topic-specific sentiments and behaviors with each user community. Notably, CBS has a general framework that accommodates multiple types of behaviors simultaneously. Our experiments on two Twitter datasets show that the CBS model can effectively mine the representative behaviors and emotional topics for each community. We also …
Automated Mentor Assignment In Blended Learning Environments, Chris Boesch, Kevin Steppe
Automated Mentor Assignment In Blended Learning Environments, Chris Boesch, Kevin Steppe
Research Collection School Of Computing and Information Systems
In this paper we discuss the addition of automatic assignment of mentors during inclass lab work to an existing online platform for programing practice. SingPath is an web based tool for users to practice programming in several software languages. The platform started as a tool to provide students with online feedback on solutions to programming problems and expanded over time to support different of blended learning needs for a variety of classes and classroom settings. The SingPath platform supports traditional self-directed learning mechanisms such as badges and completion metrics as well as features for use in classrooms, such as tournaments. …
Predictive Analytics For Outpatient Appointments, Nang Laik Ma, Khataniar Seemanta, Dan Wu, Serene Seng Ying Ng
Predictive Analytics For Outpatient Appointments, Nang Laik Ma, Khataniar Seemanta, Dan Wu, Serene Seng Ying Ng
Research Collection School Of Computing and Information Systems
Healthcare is a very important industry where analytics has been applied successfully to generate insights about patients, identify bottleneck and to improve the business efficiency. In this paper, we aim to look at the patient appointment process as the hospital is experiencing high volume of ?no shows. ?No shows have a high impact on longer appointment lead time for patients, poor patient satisfaction and loss of revenue for hospital. We use data analytics to identify pattern of ?no shows, develop a statistical model to predict the probability of ?no shows and finally operationalizing the model to embed the analytics solution …
Latent Factor Transition For Dynamic Collaborative Filtering, Chengyi Zhang, Ke Wang, Hongkun Yu, Jianling Sun, Ee Peng Lim
Latent Factor Transition For Dynamic Collaborative Filtering, Chengyi Zhang, Ke Wang, Hongkun Yu, Jianling Sun, Ee Peng Lim
Research Collection School Of Computing and Information Systems
No abstract provided.
Machine Learning In Wireless Sensor Networks: Algorithms, Strategies, And Applications, Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee-Pink Tan
Machine Learning In Wireless Sensor Networks: Algorithms, Strategies, And Applications, Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are …
Ubiquitous Data-Centric Sensor Networks, Ting Yang, Peng Yung Woo, Zhaoxia Wang, Javid Taheri, Hoong Chor Chin, Guaqiang Hu
Ubiquitous Data-Centric Sensor Networks, Ting Yang, Peng Yung Woo, Zhaoxia Wang, Javid Taheri, Hoong Chor Chin, Guaqiang Hu
Research Collection School Of Computing and Information Systems
Ubiquitous data-centric sensor networks (U-DCSN) are a new integrated science and technology, which focus on data instead of individual sensor nodes. The network, as a dynamic database system, can accurately acquire data, perform high-performance processing of big data, and effectively access data from different users/actuators. Due this characteristic, U-DCSN hold huge potentials on service improvement in a wide range of applications and have attracted significant attention in recent years, for example, mobile cloud and consumer electronics. The modern mobile cloud, comprised of mobile devices (smart phones, tablets, and embedded sensor nodes), provides unlimited information resources, putting “cloud into a pocket.” …
Adoption Of Mobile Information Services: An Empirical Study, S. Gao, J. Krogstie, Keng Siau
Adoption Of Mobile Information Services: An Empirical Study, S. Gao, J. Krogstie, Keng Siau
Research Collection School Of Computing and Information Systems
This study investigates the adoption of mobile information services at a Norwegian university. By expanding the Technology Acceptance Model (TAM), a new research model, known as the mobile services acceptance model (MSAM), is proposed. Based on the research model, seven research hypotheses are presented. The proposed research model and research hypotheses were empirically tested using data collected from a survey of users of a mobile service, extended Mobile Student Information Systems (eMSIS), at a Norwegian university. The findings indicate that the fitness of the research model is good. Support was also found for the seven research hypotheses. Among the factors, …
Laser: A Living Analytics Experimentation System For Large-Scale Online Controlled Experiments, Kwan-Hui Lim, Ee Peng Lim, Achananuparp Palakorn, Adrian Vu, Agus Trisnajaya Kwee, Feida Zhu
Laser: A Living Analytics Experimentation System For Large-Scale Online Controlled Experiments, Kwan-Hui Lim, Ee Peng Lim, Achananuparp Palakorn, Adrian Vu, Agus Trisnajaya Kwee, Feida Zhu
Research Collection School Of Computing and Information Systems
Tracking user browsing data and measuring the effectiveness of website design and web services are important to businesses that want to attract the consumers today who spend much more time online than before. Instead of using randomized controlled experiments, the existing approach simply tracks user browsing behaviors before and after a change is made to website design or web services, and evaluate the differences. To address the effects caused by hidden factors (e.g. promotion activities on the website) and to give fair comparison of different website designs, we propose the LASER system, a unified experimentation platform that enables randomized online …
Do You Know The Speaker?: An Online Experiment With Authority Messages On Event Websites, Kwan-Hui Lim, Binyan Jiang, Ee Peng Lim, Achananuparp Palakorn
Do You Know The Speaker?: An Online Experiment With Authority Messages On Event Websites, Kwan-Hui Lim, Binyan Jiang, Ee Peng Lim, Achananuparp Palakorn
Research Collection School Of Computing and Information Systems
With the widespread adoption of the Web, many companies and organizations have established websites that provide information and support online transactions (e.g., buying products or viewing content). Unfortunately, users have limited attention to spare for interacting with online sites. Hence, it is of utmost importance to design sites that attract user attention and effectively guide users to the product or content items they like. Thus, we propose a novel and scalable experimentation approach to evaluate the effectiveness of online site designs. Our case study focuses on the effects of an authority message on visitors' browsing behavior on workshop and seminar …
Modeling Contextual Agreement In Preferences, Ha Loc Do, Hady Wirawan Lauw
Modeling Contextual Agreement In Preferences, Ha Loc Do, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Personalization, or customizing the experience of each individual user, is seen as a useful way to navigate the huge variety of choices on the Web today. A key tenet of personalization is the capacity to model user preferences. The paradigm has shifted from that of individual preferences, whereby we look at a user's past activities alone, to that of shared preferences, whereby we model the similarities in preferences between pairs of users (e.g., friends, people with similar interests). However, shared preferences are still too granular, because it assumes that a pair of users would share preferences across all items. We …
On Finding The Point Where There Is No Return: Turning Point Mining On Game Data, Wei Gong, Ee Peng Lim, Feida Zhu, Achananuparp Palakorn, David Lo
On Finding The Point Where There Is No Return: Turning Point Mining On Game Data, Wei Gong, Ee Peng Lim, Feida Zhu, Achananuparp Palakorn, David Lo
Research Collection School Of Computing and Information Systems
Gaming expertise is usually accumulated through playing or watching many game instances, and identifying critical moments in these game instances called turning points. Turning point rules (shorten as TPRs) are game patterns that almost always lead to some irreversible outcomes. In this paper, we formulate the notion of irreversible outcome property which can be combined with pattern mining so as to automatically extract TPRs from any given game datasets. We specifically extend the well-known PrefixSpan sequence mining algorithm by incorporating the irreversible outcome property. To show the usefulness of TPRs, we apply them to Tetris, a popular game. We mine …
Robust Tracking With Discriminative Ranking Middle-Level Patches, Hong Liu, Zilin Liang, Qianru Sun
Robust Tracking With Discriminative Ranking Middle-Level Patches, Hong Liu, Zilin Liang, Qianru Sun
Research Collection School Of Computing and Information Systems
The appearance model has been shown to be essential for robust visual tracking since it is the basic criterion to locating targets in video sequences. Though existing tracking-by-detection algorithms have shown to be greatly promising, they still suffer from the drift problem, which is caused by updating appearance models. In this paper, we propose a new appearance model composed of ranking middle-level patches to capture more object distinctiveness than traditional tracking-by-detection models. Targets and backgrounds are represented by both low-level bottom-up features and high-level top-down patches, which can compensate each other. Bottom-up features are defined at the pixel level, and …
Profit-Maximizing Incentive For Participatory Sensing, Tie Luo, Hwee-Pink Tan, Lirong Xia
Profit-Maximizing Incentive For Participatory Sensing, Tie Luo, Hwee-Pink Tan, Lirong Xia
Research Collection School Of Computing and Information Systems
We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we formulate as a profit-maximization problem. We use a contribution-dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and …
Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu
Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
See https://ink.library.smu.edu.sg/sis_research/2924/. Distance metric learning (DML) is an important technique to improve similarity search in content-based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single-modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single-modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high-dimensional feature space can be extremely …
Cimloc: A Crowdsourcing Indoor Digital Map Construction System For Localization, Xiuming Zhang, Yunye Jin, Hwee Xian Tan, Wee-Seng Soh
Cimloc: A Crowdsourcing Indoor Digital Map Construction System For Localization, Xiuming Zhang, Yunye Jin, Hwee Xian Tan, Wee-Seng Soh
Research Collection School Of Computing and Information Systems
Indoor maps, as crucial prerequisites for many indoor localization and navigation systems, are sometimes inaccessible. The absence of an indoor map database and the high cost of manually constructing an indoor map produce a need for an inexpensive and efficient way to dynamically construct indoor maps. The ubiquity of sensor-equipped mobile devices enables us to crowdsource user trajectories, out of which indoor digital maps can be automatically constructed at low costs. Similar to other crowdsourced data, the collected user trajectories are often noisy and of low fidelity, which poses a challenge to the accurate map construction. To alleviate this problem, …
A Hamming Embedding Kernel With Informative Bag-Of-Visual Words For Video Semantic Indexing, Feng Wang, Wen-Lei Zhao, Chong-Wah Ngo, Bernard Merialdo
A Hamming Embedding Kernel With Informative Bag-Of-Visual Words For Video Semantic Indexing, Feng Wang, Wen-Lei Zhao, Chong-Wah Ngo, Bernard Merialdo
Research Collection School Of Computing and Information Systems
In this article, we propose a novel Hamming embedding kernel with informative bag-of-visual words to address two main problems existing in traditional BoW approaches for video semantic indexing. First, Hamming embedding is employed to alleviate the information loss caused by SIFT quantization. The Hamming distances between keypoints in the same cell are calculated and integrated into the SVM kernel to better discriminate different image samples. Second, to highlight the concept-specific visual information, we propose to weight the visual words according to their informativeness for detecting specific concepts. We show that our proposed kernels can significantly improve the performance of concept …
Celebrowser: An Example Of Browsing Big Data On Small Device, Song Tan, Chong-Wah Ngo, Jun Xu, Yong Rui
Celebrowser: An Example Of Browsing Big Data On Small Device, Song Tan, Chong-Wah Ngo, Jun Xu, Yong Rui
Research Collection School Of Computing and Information Systems
In this demonstration, we demonstrate a mobile-based celebrity video browsing system called CeleBrowser. Using this system, users can interactively switch among four views: people-centric, timeline-centric, month-centric and topic-centric, for browsing celebrity-related hot videos. A peculiarity of the demonstration is to highlight the advantage of multiperspective information organization and presentation in engaging users for exploratory browsing of large number of Web videos on a device with small screen. Technology-wise the demonstration shows how query logs collected for six months from two vertical search engines are leveraged for mining hot events and videos of celebrities.
Community Discovery In Social Networks Via Heterogeneous Link Association And Fusion, Lei Meng, Ah-Hwee Tan
Community Discovery In Social Networks Via Heterogeneous Link Association And Fusion, Lei Meng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a prior, do not address the weighting problem for fusing heterogeneous types of links and have a heavy computational cost. In this paper, we explore the feasibility of a newly proposed heterogeneous data clustering algorithm, called Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART), for discovering communities in heterogeneous social networks. Different from existing algorithms, GHF-ART performs real-time matching of patterns and one-pass learning which guarantee its low computational cost. With a vigilance parameter to …
A Hybrid Scheme For Authenticating Scalable Video Codestreams, Zhuo Wei, Yongdong Wu, Robert H. Deng, Xuhua Ding
A Hybrid Scheme For Authenticating Scalable Video Codestreams, Zhuo Wei, Yongdong Wu, Robert H. Deng, Xuhua Ding
Research Collection School Of Computing and Information Systems
A scalable video coding (SVC) codestream consists of one base layer and possibly several enhancement layers. The base layer, which contains the lowest quality and resolution images, is the foundation of the SVC codestream and must be delivered to recipients, whereas enhancement layers contain richer contour/texture of images in order to supplement the base layer in resolution, quality, and temporal scalabilities. This paper presents a novel hybrid authentication (HAU) scheme. The HAU employs both cryptographic authentication and content-based authentication techniques to ensure integrity and authenticity of the SVC codestreams. Our analysis and experimental results indicate that the HAU is able …
Recurrent Chinese Restaurant Process With A Duration-Based Discount For Event Identification From Twitter, Qiming Diao, Jing Jiang
Recurrent Chinese Restaurant Process With A Duration-Based Discount For Event Identification From Twitter, Qiming Diao, Jing Jiang
Research Collection School Of Computing and Information Systems
Due to the fast development of social media on the Web, Twitter has become one of the major platforms for people to express themselves. Because of the wide adoption of Twitter, events like breaking news and release of popular videos can easily catch people’s attention and spread rapidly on Twitter, and the number of relevant tweets approximately reflects the impact of an event. Event identification and analysis on Twitter has thus become an important task. Recently the Recurrent Chinese Restaurant Process (RCRP) has been successfully used for event identification from news streams and news-centric social media streams. However, these models …
Where Am I? : Studying Users’ Indoor Navigation Location Needs, Kartik Muralidharan, Archan Misra, Rajesh Krishna Balan
Where Am I? : Studying Users’ Indoor Navigation Location Needs, Kartik Muralidharan, Archan Misra, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Location has emerged as the single-most important context whilst building pervasive mobile applications. Several mobile applications have appeared that use location to provide a host of services such as location-specific advertising as well as navigation. As a result, the key challenge of positioning techniques has been to provide the most precise location of the user (device) and much effort has been put in computing this fine grained location in indoor environments. This is under the assumption that highly accurate location is crucial for all indoor services. To understand the location accuracy, that should prove sufficient, for users to navigate to …
Clonepedia: Summarizing Code Clones By Common Syntactic Context For Software Maintenance, Yun Lin, Zhenchang Xing, Xin Peng, Yang Liu, Jun Sun, Wenyun Zhao, Jin Song Dong
Clonepedia: Summarizing Code Clones By Common Syntactic Context For Software Maintenance, Yun Lin, Zhenchang Xing, Xin Peng, Yang Liu, Jun Sun, Wenyun Zhao, Jin Song Dong
Research Collection School Of Computing and Information Systems
Code clones have to be made explicit and be managed in software maintenance. Researchers have developed many clone detection tools to detect and analyze code clones in software systems. These tools report code clones as similar code fragments in source files. However, clone-related maintenance tasks (e.g., refactorings) often involve a group of code clones appearing in larger syntactic context (e.g., code clones in sibling classes or code clones calling similar methods). Given a list of low-level code-fragment clones, developers have to manually summarize from bottom up low-level code clones that are relevant to the syntactic context of a maintenance task. …
A Comparative Study On The Effectiveness Of Part-Of-Speech Tagging Techniques On Bug Reports, Yuan Tian, David Lo
A Comparative Study On The Effectiveness Of Part-Of-Speech Tagging Techniques On Bug Reports, Yuan Tian, David Lo
Research Collection School Of Computing and Information Systems
Many software artifacts are written in natural language or contain substantial amount of natural language contents. Thus these artifacts could be analyzed using text analysis techniques from the natural language processing (NLP) community, e.g., the part-of-speech (POS) tagging technique that assigns POS tags (e.g., verb, noun, etc.) to words in a sentence. In the literature, several studies have already applied POS tagging technique on software artifacts to recover important words in them, which are then used for automating various tasks, e.g., locating buggy files for a given bug report, etc. There are many POS tagging techniques proposed and they are …
Information-Theoretic Multi-View Domain Adaptation: A Theoretical And Empirical Study, Pei Yang, Wei Gao
Information-Theoretic Multi-View Domain Adaptation: A Theoretical And Empirical Study, Pei Yang, Wei Gao
Research Collection School Of Computing and Information Systems
Multi-view learning aims to improve classification performance by leveraging the consistency among different views of data. The incorporation of multiple views was paid little attention in the studies of domain adaptation, where the view consistency based on source data is largely violated in the target domain due to the distribution gap between different domain data. In this paper, we leverage multiple views for cross-domain document classification. The central idea is to strengthen the views' consistency on target data by identifying the associations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) using a multi-way clustering …
An Active Resource Orchestration Framework For Pan-Scale Sensor-Rich Environments, Youngki Lee, Chulhong Min, Younghyun Ju, Seungwoo Kang, Yunseok Rhee, Junehwa Song
An Active Resource Orchestration Framework For Pan-Scale Sensor-Rich Environments, Youngki Lee, Chulhong Min, Younghyun Ju, Seungwoo Kang, Yunseok Rhee, Junehwa Song
Research Collection School Of Computing and Information Systems
In this paper, we present Orchestrator, an active resource orchestration framework for a PAN-scale sensor-rich mobile computing platform. Incorporating diverse sensing devices connected to a mobile phone, the platform will serve as a common base to accommodate personal context-aware applications. A major challenge for the platform is to simultaneously support concurrent applications requiring continuous and complex context processing, with highly scarce and dynamic resources. To address the challenge, we build Orchestrator, which actively coordinates applications' resource uses over the distributed mobile and sensor devices. As a key approach, it adopts an active resource use orchestration, which prepares multiple alternative plans …
Online Feature Selection And Its Applications, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Online Feature Selection And Its Applications, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Research Collection School Of Computing and Information Systems
Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of online feature selection (OFS) in …
Retrieval-Based Face Annotation By Weak Label Regularized Local Coordinate Coding, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu, Mei Tao, Jiebo Luo
Retrieval-Based Face Annotation By Weak Label Regularized Local Coordinate Coding, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu, Mei Tao, Jiebo Luo
Research Collection School Of Computing and Information Systems
Auto face annotation, which aims to detect human faces from a facial image and assign them proper human names, is a fundamental research problem and beneficial to many real-world applications. In this work, we address this problem by investigating a retrieval-based annotation scheme of mining massive web facial images that are freely available over the Internet. In particular, given a facial image, we first retrieve the top n similar instances from a large-scale web facial image database using content-based image retrieval techniques, and then use their labels for auto annotation. Such a scheme has two major challenges: 1) how to …
Teaching Tip: The Flipped Classroom, Heng Ngee Mok
Teaching Tip: The Flipped Classroom, Heng Ngee Mok
Research Collection School Of Computing and Information Systems
The flipped classroom has been gaining popularity in recent years. In theory, flipping the classroom appears sound: passive learning activities such as unidirectional lectures are pushed to outside class hours in the form of videos, and precious class time is spent on active learning activities. Yet the courses for information systems (IS) undergraduates at the university that the author is teaching at are still conducted in the traditional lecture-in-class, homework-after-class style. In order to increase students’ engagement with the course content and to improve their experience with the course, the author implemented a trial of the flipped classroom model for …
Keystroke Biometrics: The User Perspective, Chee Meng Tey, Payas Gupta, Kartik Muralidaran, Debin Gao
Keystroke Biometrics: The User Perspective, Chee Meng Tey, Payas Gupta, Kartik Muralidaran, Debin Gao
Research Collection School Of Computing and Information Systems
Usability is an important aspect of security, because poor usability motivates users to find shortcuts that bypass the system. Existing studies on keystroke biometrics evaluate the usability issue in terms of the average false rejection rate (FRR). We show in this paper that such an approach underestimates the user impact in two ways. First, the FRR of keystroke biometrics changes for the worse under a range of common conditions such as background music, exercise and even game playing. In a user study involving 111 participants, the average penalties (increases) in FRR are 0.0360 and 0.0498, respectively, for two different classifiers. …