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Articles 6121 - 6150 of 8479
Full-Text Articles in Computer Sciences
Collective Diffusion Over Networks: Models And Inference, Akshat Kumar, Daniel Sheldon, Biplav Srivastava
Collective Diffusion Over Networks: Models And Inference, Akshat Kumar, Daniel Sheldon, Biplav Srivastava
Research Collection School Of Computing and Information Systems
Diffusion processes in networks are increasingly used to model the spread of information and social influence. In several applications in computational sustainability such as the spread of wildlife, infectious diseases and traffic mobility pattern, the observed data often consists of only aggregate information. In this work, we present new models that generalize standard diffusion processes to such collective settings. We also present optimization based techniques that can accurately learn the underlying dynamics of the given contagion process, including the hidden network structure, by only observing the time a node becomes active and the associated aggregate information. Empirically, our technique is …
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple kernel learning (MKL) is a promising family of machine learning algorithms using multiple kernel functions for various challenging data mining tasks. Conventional MKL methods often formulate the problem as an optimization task of learning the optimal combinations of both kernels and classifiers, which usually results in some forms of challenging optimization tasks that are often difficult to be solved. Different from the existing MKL methods, in this paper, we investigate a boosting framework of MKL for classification tasks, i.e., we adopt boosting to solve a variant of MKL problem, which avoids solving the complicated optimization tasks. Specifically, we present …
Active Learning With Expert Advice, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang
Active Learning With Expert Advice, Peilin Zhao, Steven C. H. Hoi, Jinfeng Zhuang
Research Collection School Of Computing and Information Systems
Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner. Our goal is to learn an accurate prediction model by asking the oracle the number of questions as small as possible. To address this challenge, we propose …
Gamification Of Education Using Computer Games, Fiona Fui-Hoon Nah, Venkata Telaprolu, Shashank Rallapalli, Pavani R. Venkata
Gamification Of Education Using Computer Games, Fiona Fui-Hoon Nah, Venkata Telaprolu, Shashank Rallapalli, Pavani R. Venkata
Research Collection School Of Computing and Information Systems
We review the literature on gamification and identify principles of gamification and system design elements for gamifying computer educational games. Gamification of education is expected to increase learners’ engagement, which in turn increases learning achievement. We propose a gamification framework that synthesizes findings from the literature. The gamification framework is comprised of principles of gamification, system design elements for gamification, and dimensions of user engagement.
Usability Of Performance Dashboards, Usefulness Of Operational And Tactical Support, And Quality Of Strategic Support: A Research Framework, Bih-Ru Lea, Fiona Fui-Hoon Nah
Usability Of Performance Dashboards, Usefulness Of Operational And Tactical Support, And Quality Of Strategic Support: A Research Framework, Bih-Ru Lea, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Performance dashboards are used as a strategic decision support tool in organizations. In this research, we examine the relationships between the usability of performance dashboards, the usefulness of operational and tactical support, and the quality of strategic support that they provide. We hypothesize that usability of performance dashboards will influence user perceptions of the usefulness of the operational and tactical support provided by the dashboards, which in turn influence the perceived quality of strategic support provided.
Usability Of Performance Dashboards, Usefulness Of Operational And Tactical Support, And Quality Of Strategic Support: A Research Framework, Bih-Ru Lea, Fiona Fui-Hoon Nah
Usability Of Performance Dashboards, Usefulness Of Operational And Tactical Support, And Quality Of Strategic Support: A Research Framework, Bih-Ru Lea, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Performance dashboards are used as a strategic decision support tool in organizations. In this research, we examine the relationships between the usability of performance dashboards, the usefulness of operational and tactical support, and the quality of strategic support that they provide. We hypothesize that usability of performance dashboards will influence user perceptions of the usefulness of the operational and tactical support provided by the dashboards, which in turn influence the perceived quality of strategic support provided.
Unified Modeling Language: The Teen Years And Growing Pains, J. Erickson, Keng Siau
Unified Modeling Language: The Teen Years And Growing Pains, J. Erickson, Keng Siau
Research Collection School Of Computing and Information Systems
Unified Modeling Language (UML) is adopted by the Object Management Group as a standardized general-purpose modeling language for object-oriented software engineering. Despite its status as a standard, UML is still in a development stage and many studies have highlighted its weaknesses and challenges - including those related to human factor issues. Further, UML has grown considerably more complex since its inception. This paper traces the history of Unified Modeling Language (UML) from its formation to its current state and discusses the current state of the UML language. The paper first introduces UML and its various diagrams, and discusses its characteristics …
Mining Direct Antagonistic Communities In Signed Social Networks, David Lo, Didi Surian, Philips Kokoh Prasetyo, Zhang Kuan, Ee Peng Lim
Mining Direct Antagonistic Communities In Signed Social Networks, David Lo, Didi Surian, Philips Kokoh Prasetyo, Zhang Kuan, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In this work, we mine Direct Antagonistic Communities (DACs) within such signed networks. Each DAC consists of two sub-communities with positive relationships among members of each sub-community, and negative relationships among members of the other sub-community. Identifying direct antagonistic communities …
Keystroke Timing Analysis Of On-The-Fly Web Apps, Chee Meng Tey, Payas Gupta, Debin Gao, Yan Zhang
Keystroke Timing Analysis Of On-The-Fly Web Apps, Chee Meng Tey, Payas Gupta, Debin Gao, Yan Zhang
Research Collection School Of Computing and Information Systems
The Google Suggestions service used in Google Search is one example of an interactivity rich Javascript application. In this paper, we analyse the timing side channel of Google Suggestions by reverse engineering the communication model from obfuscated Javascript code. We consider an attacker who attempts to infer the typing pattern of a victim. From our experiments involving 11 participants, we found that for each keypair with at least 20 samples, the mean of the inter-keystroke timing can be determined with an error of less than 20%.
Improved Reachability Analysis In Dtmc Via Divide And Conquer, Songzheng Song, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong
Improved Reachability Analysis In Dtmc Via Divide And Conquer, Songzheng Song, Lin Gui, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Discrete Time Markov Chains (DTMCs) are widely used to model probabilistic systems in many domains, such as biology, network and communication protocols. There are two main approaches for probability reachability analysis of DTMCs, i.e., solving linear equations or using value iteration. However, both approaches have drawbacks. On one hand, solving linear equations can generate accurate results, but it can be only applied to relatively small models. On the other hand, value iteration is more scalable, but often suffers from slow convergence. Furthermore, it is unclear how to parallelize (i.e., taking advantage of multi-cores or distributed computers) these two approaches. In …
A Formal Semantics For Complete Uml State Machines With Communications, Shuang Liu, Yang Liu, Étienne André, Christine Choppy, Jun Sun, Bimlesh Wadhwa, Jin Song Dong
A Formal Semantics For Complete Uml State Machines With Communications, Shuang Liu, Yang Liu, Étienne André, Christine Choppy, Jun Sun, Bimlesh Wadhwa, Jin Song Dong
Research Collection School Of Computing and Information Systems
UML is a widely used notation, and formalizing its semantics is an important issue. Here, we concentrate on formalizing UML state machines, used to express the dynamic behaviour of software systems. We propose a formal operational semantics covering all features of the latest version (2.4.1) of UML state machines specification. We use labelled transition systems as the semantic model, so as to use automatic verification techniques like model checking. Furthermore, our proposed semantics includes synchronous and asynchronous communications between state machines. We implement our approach in USM2C, a model checker supporting editing, simulation and automatic verification of UML state machines. …
Introducing Programmers To Pair Programming: A Controlled Experiment, A. S. M. Sajeev, Subhajit Datta
Introducing Programmers To Pair Programming: A Controlled Experiment, A. S. M. Sajeev, Subhajit Datta
Research Collection School Of Computing and Information Systems
Pair programming is a key characteristic of the Extreme Programming (XP) method. Through a controlled experiment we investigate pair programming behaviour of programmers without prior experience in XP. The factors investigated are: (a) characteristics of pair programming that are less favored (b) perceptions of team effectiveness and how they relate to product quality, and (c) whether it is better to train a pair by giving routine tasks first or by giving complex tasks first. Our results show that: (a) the least liked aspects of pair programming were having to share the screen, keyboard and mouse, and having to switch between …
A New Unpredictability-Based Rfid Privacy Model, Anjia Yang, Yunhui Zhuang, Duncan S. Wong, Guomin Yang
A New Unpredictability-Based Rfid Privacy Model, Anjia Yang, Yunhui Zhuang, Duncan S. Wong, Guomin Yang
Research Collection School Of Computing and Information Systems
Ind-privacy and unp-privacy, later refined to unp∗-privacy, are two different classes of privacy models for RFID authentication protocols. These models have captured the major anonymity and untraceability related attacks regarding RFID authentication protocols with privacy, and existing work indicates that unp∗-privacy seems to be a stronger notion when compared with ind-privacy. In this paper, we continue studying the RFID privacy models, and there are two folds regarding our results. First of all, we describe a new traceability attack and show that schemes proven secure in unp∗-privacy may not be secure against this new and practical type of traceability attacks. We …
Visual Tracking Via Locality Sensitive Histograms, Shengfeng He, Qingxiong Yang, Rynson W.H. Lau, Jian Wang, Ming-Hsuan Yang
Visual Tracking Via Locality Sensitive Histograms, Shengfeng He, Qingxiong Yang, Rynson W.H. Lau, Jian Wang, Ming-Hsuan Yang
Research Collection School Of Computing and Information Systems
This paper presents a novel locality sensitive histogram algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrences of each intensity value by adding ones to the corresponding bin, a locality sensitive histogram is computed at each pixel location and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value declines exponentially with respect to the distance to the pixel location where the histogram is computed, thus every pixel is considered but those that are far away can be neglected due to the very small weights …
A Direct Mining Approach To Efficient Constrained Graph Pattern Discovery, Feida Zhu, Zequn Zhang, Qiang Qu
A Direct Mining Approach To Efficient Constrained Graph Pattern Discovery, Feida Zhu, Zequn Zhang, Qiang Qu
Research Collection School Of Computing and Information Systems
Despite the wealth of research on frequent graph pattern mining, how to efficiently mine the complete set of those with constraints still poses a huge challenge to the existing algorithms mainly due to the inherent bottleneck in the mining paradigm. In essence, mining requests with explicitly-specified constraints cannot be handled in a way that is direct and precise. In this paper, we propose a direct mining framework to solve the problem and illustrate our ideas in the context of a particular type of constrained frequent patterns — the “skinny” patterns, which are graph patterns with a long backbone from which …
Real Time Event Detection In Twitter, Xun Wang, Feida Zhu, Jing Jiang, Sujian Li
Real Time Event Detection In Twitter, Xun Wang, Feida Zhu, Jing Jiang, Sujian Li
Research Collection School Of Computing and Information Systems
Event detection has been an important task for a long time. When it comes to Twitter, new problems are presented. Twitter data is a huge temporal data flow with much noise and various kinds of topics. Traditional sophisticated methods with a high computational complexity aren’t designed to handle such data flow efficiently. In this paper, we propose a mixture Gaussian model for bursty word extraction in Twitter and then employ a novel time-dependent HDP model for new topic detection. Our model can grasp new events, the location and the time an event becomes bursty promptly and accurately. Experiments show the …
Cameo: A Middleware For Mobile Advertisement Delivery, Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Krishna Balan, Srinivasan Seshan
Cameo: A Middleware For Mobile Advertisement Delivery, Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Krishna Balan, Srinivasan Seshan
Research Collection School Of Computing and Information Systems
Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and far from ideal. In particular, as we show, applications which use mobile advertising are capable of using significant amounts of a mobile users' critical resources without being controlled or held accountable. This paper seeks to redress this situation by enabling advertisement supported applications to become significantly more "user-friendly". To this end, we present the design and implementation of CAMEO, a …
Experiences With Performance Tradeoffs In Practical, Continuous Indoor Localization, Azeem J. Khan, Vikash Ranjan, Trung-Tuan Luong, Rajesh Krishna Balan, Archan Misra
Experiences With Performance Tradeoffs In Practical, Continuous Indoor Localization, Azeem J. Khan, Vikash Ranjan, Trung-Tuan Luong, Rajesh Krishna Balan, Archan Misra
Research Collection School Of Computing and Information Systems
This paper describes our experiences and observations with the first version of a localization system that continuous tracks the indoor location of a large number of consumer mobile devices. Unlike past work that focuses principally on the accuracy of the location tracking algorithm, we study the performance of the localization system in terms of key additional metrics: scalability and energy-efficiency, which can sometimes conflict with the desire for high accuracy. To ensure that our solution can handle both Android and iOS-based mobile devices (& other closed mobile platforms), we adapt the conventional client-side fingerprinting-based localization approaches to develop a novel …
A Latent Variable Model For Viewpoint Discovery From Threaded Forum Posts, Minghui Qiu, Jing Jiang
A Latent Variable Model For Viewpoint Discovery From Threaded Forum Posts, Minghui Qiu, Jing Jiang
Research Collection School Of Computing and Information Systems
Threaded discussion forums provide an important social media platform. Its rich user generated content has served as an important source of public feedback. To automatically discover the viewpoints or stances on hot issues from forum threads is an important and useful task. In this paper, we propose a novel latent variable model for viewpoint discovery from threaded forum posts. Our model is a principled generative latent variable model which captures three important factors: viewpoint specific topic preference, user identity and user interactions. Evaluation results show that our model clearly outperforms a number of baseline models in terms of both clustering …
Mining User Relations From Online Discussions Using Sentiment Analysis And Probabilistic Matrix Factorization, Minghui Qiu, Liu Yang, Jing Jiang
Mining User Relations From Online Discussions Using Sentiment Analysis And Probabilistic Matrix Factorization, Minghui Qiu, Liu Yang, Jing Jiang
Research Collection School Of Computing and Information Systems
Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However, recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions, we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. Experiments with two tasks show that the learned latent factor representation can give good performance on a relation polarity prediction task and improve the performance of a subgroup detection task.
Architectural Control And Value Migration In Layered Ecosystems: The Case Of Open-Source Cloud Management Platforms, Richard Tee, C. Jason Woodard
Architectural Control And Value Migration In Layered Ecosystems: The Case Of Open-Source Cloud Management Platforms, Richard Tee, C. Jason Woodard
Research Collection School Of Computing and Information Systems
Our paper focuses on strategic decision making in layered business ecosystems, highlighting the role of cross-layer interactions in shaping choices about product design and platform governance. Based on evidence from the cloud computing ecosystem, we analyze how concerns about architectural control and expectations regarding future value migration influence the design of product interfaces and the degree of openness to external contributions. We draw on qualitative longitudinal data to trace the development of two open-source platforms for managing cloudbased computing resources. We focus in particular on the emergence of a layered "stack" in which these platforms must compete with both vertically …
Think Twice Before You Share: Analyzing Privacy Leakage Under Privacy Control In Online Social Networks, Yan Li, Yingjiu Li, Qiang Yan, Robert H. Deng
Think Twice Before You Share: Analyzing Privacy Leakage Under Privacy Control In Online Social Networks, Yan Li, Yingjiu Li, Qiang Yan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Online Social Networks (OSNs) have become one of the major platforms for social interactions. Privacy control is deployed in popular OSNs to protect user’s data. However, user’s sensitive information could still be leaked even when privacy rules are properly configured. We investigate the effectiveness of privacy control against privacy leakage from the perspective of information flow. Our analysis reveals that the existing privacy control mechanisms do not protect the flow of personal information effectively. By examining typical OSNs including Facebook, Google+, and Twitter, we discover a series of privacy exploits which are caused by the conflicts between privacy control and …
Launching Generic Attacks On Ios With Approved Third-Party Applications, Jin Han, Mon Kywe Su, Qiang Yan, Feng Bao, Robert H. Deng, Debin Gao, Yingjiu Li, Jianying Zhou
Launching Generic Attacks On Ios With Approved Third-Party Applications, Jin Han, Mon Kywe Su, Qiang Yan, Feng Bao, Robert H. Deng, Debin Gao, Yingjiu Li, Jianying Zhou
Research Collection School Of Computing and Information Systems
iOS is Apple’s mobile operating system, which is used on iPhone, iPad and iPod touch. Any third-party applications developed for iOS devices are required to go through Apple’s application vetting process and appear on the official iTunes App Store upon approval.When an application is downloaded from the store and installed on an iOS device, it is given a limited set of privileges, which are enforced by iOS application sandbox. Although details of the vetting process and the sandbox are kept as black box by Apple, it was generally believed that these iOS security mechanisms are effective in defending against malwares. …
Energy-Efficient Collaborative Query Processing Framework For Mobile Sensing Services, Jin Yang, Tianli Mo, Lipyeow Lim, Kai Uwe Sattler, Archan Misra
Energy-Efficient Collaborative Query Processing Framework For Mobile Sensing Services, Jin Yang, Tianli Mo, Lipyeow Lim, Kai Uwe Sattler, Archan Misra
Research Collection School Of Computing and Information Systems
Many emerging context-aware mobile applications involve the execution of continuous queries over sensor data streams generated by a variety of on-board sensors on multiple personal mobile devices (aka smartphones). To reduce the energyoverheads of such large-scale, continuous mobile sensing and query processing, this paper introduces CQP, a collaborative query processing framework that exploits the overlap (in both the sensor sources and the query predicates) across multiple smartphones. The framework automatically identifies the shareable parts of multiple executing queries, and then reduces the overheads of repetitive execution and data transmissions, by having a set of 'leader' mobile nodes execute and disseminate …
Cugar: A Model For Open Innovation In Science And Technology Parks, Arcot Desai Narasimhalu
Cugar: A Model For Open Innovation In Science And Technology Parks, Arcot Desai Narasimhalu
Research Collection School Of Computing and Information Systems
This paper reviews key elements of a Science or Technology Park in the context of open innovation. Insights into and recommendations on key issues related to intellectual property, licensing and venture capital that would be of interest to any Science Park are presented later.
When Do Consumers Purchase Online?: Based On Inter-Purchase Time, Youngsoo Kim
When Do Consumers Purchase Online?: Based On Inter-Purchase Time, Youngsoo Kim
Research Collection School Of Computing and Information Systems
This study is motivated by the premise that online consumers can make a purchase at any time of day if they have even a tiny time slot along with Internet access. To identify the increased shopping time flexibility, we first characterize the patterns of online purchase timing in comparison to those in the offline market. The results show (1) the breakdown of purchase timing regularity and (2) the change of weekly spike purchase occurrence. Second, we build online inter-purchase time model and estimate it with the data collected from one of the premier online vendors in Korea. We verify new …
Mitigating Access-Driven Timing Channels In Clouds Using Stopwatch, Peng Li, Debin Gao, Michael K. Reiter
Mitigating Access-Driven Timing Channels In Clouds Using Stopwatch, Peng Li, Debin Gao, Michael K. Reiter
Research Collection School Of Computing and Information Systems
This paper presents StopWatch , a system that defends against timing-based side-channel attacks that arise from coresidency of victims and attackers in infrastructure-as-a-service clouds. StopWatchtriplicates each cloud-resident guest virtual machine (VM) and places replicas so that the three replicas of a guest VM are coresident with nonoverlapping sets of (replicas of) other VMs. StopWatch uses thetiming of I/O events at a VM's replicas collectively to determine the timings observed by each one or by an external observer, so that observable timing behaviors are similarly likely in the absence of any other individual, coresident VM. We detail the design and implementation …
A Latent Variable Model For Viewpoint Discovery From Threaded Forum Posts, Minghui Qiu, Jing Jiang
A Latent Variable Model For Viewpoint Discovery From Threaded Forum Posts, Minghui Qiu, Jing Jiang
Research Collection School Of Computing and Information Systems
No abstract provided.
Mining User Relations From Online Discussions Using Sentiment Analysis And Probabilistic Matrix Factorization, Minghui Qiu, Liu Yang, Jing Jiang
Mining User Relations From Online Discussions Using Sentiment Analysis And Probabilistic Matrix Factorization, Minghui Qiu, Liu Yang, Jing Jiang
Research Collection School Of Computing and Information Systems
No abstract provided.
Sociophone: Everyday Face-To-Face Interaction Monitoring Platform Using Multi-Phone Sensor Fusion, Youngki Lee, Chulhong Min, Chanyou Hwang, Jaeung Lee, Inseok Hwang, Younghyun Ju, Chungkuk Yoo, Miri Moon, Uichin Lee, Junehwa Song
Sociophone: Everyday Face-To-Face Interaction Monitoring Platform Using Multi-Phone Sensor Fusion, Youngki Lee, Chulhong Min, Chanyou Hwang, Jaeung Lee, Inseok Hwang, Younghyun Ju, Chungkuk Yoo, Miri Moon, Uichin Lee, Junehwa Song
Research Collection School Of Computing and Information Systems
In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. …