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Articles 5611 - 5640 of 8479
Full-Text Articles in Computer Sciences
Platform Pricing With Endogenous Network Effects, Mei Lin, Ruhai Wu, Wen Zhou
Platform Pricing With Endogenous Network Effects, Mei Lin, Ruhai Wu, Wen Zhou
Research Collection School Of Computing and Information Systems
This paper examines a monopoly platform’s two-sided pricing strategy through modeling the trades between the participating sellers and buyers. In this approach, the network effects emerge endogenously through the equilibrium trading strategies of the two sides. We show that platform pricing depends crucially on the characteristics associated with market liquidity, including both sides’ entry costs, the buyers’ preferences, and the distribution of the sellers’ quality. The platform may subsidize sellers if the market is sufficiently liquid, whereas buyer subsidy can be optimal given an illiquid market. We also illustrate the impact of the sellers’ quality heterogeneity on the platform’s optimal …
Towards Intelligent Caring Agents For Aging-In-Place: Issues And Challenges, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan
Towards Intelligent Caring Agents For Aging-In-Place: Issues And Challenges, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
The aging of the world’s population presents vast societal and individual challenges. The relatively shrinking workforce to support the growing population of the elderly leads to a rapidly increasing amount of technological innovations in the field of elderly care. In this paper, we present an integrated framework consisting of various intelligent agents with their own expertise and responsibilities working in a holistic manner to assist, care, and accompany the elderly around the clock in the home environment. To support the independence of the elderly for Aging-In-Place (AIP), the intelligent agents must well understand the elderly, be fully aware of the …
Modeling The Evolution Of Generativity And The Emergence Of Digital Ecosystems, C. Jason Woodard, Eric K. Clemons
Modeling The Evolution Of Generativity And The Emergence Of Digital Ecosystems, C. Jason Woodard, Eric K. Clemons
Research Collection School Of Computing and Information Systems
Recent literature on sociotechnical systems has employed the concept of generativity to explain the remarkable capacity for digital artifacts to support decentralized innovation and the emergence of rich business ecosystems. In this paper, we propose agent-based computational modeling as a tool for studying the evolution of generativity, and offer a set of building blocks for constructing agent-based models in which generativity evolves. We describe a series of models that we have created using these building blocks, and summarize the results of our computational experiments to date. We find in several different settings that key features of generative systems can themselves …
Measuring Student Performance And Providing Feedback Using Competency Framework, Joelle Elmaleh, Venky Shankararaman
Measuring Student Performance And Providing Feedback Using Competency Framework, Joelle Elmaleh, Venky Shankararaman
Research Collection School Of Computing and Information Systems
A number of Computer Science and Information Systems programs have effectively defined learning outcomes, course level competencies, and conducted assessments at the program level to determine areas for continuous improvement. However, many of these programs do not fully leverage the course competencies during the actual delivery and assessment of the course. This paper presents how course competencies can be used to effectively deliver and assess the course content, and give valuable timely feedback to the students. Using a large first year core course of the BSc (Information Systems Management) program (called Object Oriented Application Development course-OOAD) as an example, this …
Cardioguard: A Brassiere-Based Reliable Ecg Monitoring Sensor System For Supporting Daily Smartphone Healthcare Applications, Sungjun Kwon, Jeehoon Kim, Seungwoo Kang, Youngki Lee, Hyunjae Baek, Kwangsuk Park
Cardioguard: A Brassiere-Based Reliable Ecg Monitoring Sensor System For Supporting Daily Smartphone Healthcare Applications, Sungjun Kwon, Jeehoon Kim, Seungwoo Kang, Youngki Lee, Hyunjae Baek, Kwangsuk Park
Research Collection School Of Computing and Information Systems
We propose CardioGuard, a brassiere-based reliable electrocardiogram (ECG) monitoring sensor system, for supporting daily smartphone healthcare applications. It is designed to satisfy two key requirements for user-unobtrusive daily ECG monitoring: reliability of ECG sensing and usability of the sensor. The system is validated through extensive evaluations. The evaluation results showed that the CardioGuard sensor reliably measure the ECG during 12 representative daily activities including diverse movement levels; 89.53% of QRS peaks were detected on average. The questionnaire-based user study with 15 participants showed that the CardioGuard sensor was comfortable and unobtrusive. Additionally, the signal-to-noise ratio test and the washing durability …
A Metrics Suite Of Cloud Computing Adoption Readiness, Robert J. Kauffman, Dan Ma, Martin Yu
A Metrics Suite Of Cloud Computing Adoption Readiness, Robert J. Kauffman, Dan Ma, Martin Yu
Research Collection School Of Computing and Information Systems
Recent research on cloud computing adoption indicates that there has been a lack of deep understanding of its benefits by managers and organizations. This has been an obstacle for adoption. We report on an initial design for a firm-level cloud computing readiness metrics suite. We propose categories and measures to form a set of metrics to measure adoption readiness and assess the required adjustments in strategy and management, technology and operations, and business policies. We reviewed the relevant interdisciplinary literature and interviewed industry professionals to ground our metrics based on theory and practice knowledge. We identified four relevant categories for …
Probabilistic Latent Document Network Embedding, Tuan M. V. Le, Hady W. Lauw
Probabilistic Latent Document Network Embedding, Tuan M. V. Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
A document network refers to a data type that can be represented as a graph of vertices, where each vertex is associated with a text document. Examples of such a data type include hyperlinked Web pages, academic publications with citations, and user profiles in social networks. Such data have very high-dimensional representations, in terms of text as well as network connectivity. In this paper, we study the problem of embedding, or finding a low-dimensional representation of a document network that "preserves" the data as much as possible. These embedded representations are useful for various applications driven by dimensionality reduction, such …
Detecting Camouflaged Applications On Mobile Application Markets, Mon Kywe Su, Yingjiu Li, Huijie Robert Deng, Jason Hong
Detecting Camouflaged Applications On Mobile Application Markets, Mon Kywe Su, Yingjiu Li, Huijie Robert Deng, Jason Hong
Research Collection School Of Computing and Information Systems
Application plagiarism or application cloning is an emerging threat in mobile application markets. It reduces profits of original developers and sometimes even harms the security and privacy of users. In this paper, we introduce a new concept, called camouflaged applications, where external features of mobile applications, such as icons, screenshots, application names or descriptions, are copied. We then propose a scalable detection framework, which can find these suspiciously similar camouflaged applications. To accomplish this, we apply text-based retrieval methods and content-based image retrieval methods in our framework. Our framework is implemented and tested with 30,625 Android applications from the official …
Detecting Flow Anomalies In Distributed Systems, Freddy Chong-Tat Chua, Ee Peng Lim, Bernardo Huberman
Detecting Flow Anomalies In Distributed Systems, Freddy Chong-Tat Chua, Ee Peng Lim, Bernardo Huberman
Research Collection School Of Computing and Information Systems
Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank …
Mydeal: A Mobile Shopping Assistant Matching User Preferences To Promotions, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan
Mydeal: A Mobile Shopping Assistant Matching User Preferences To Promotions, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
A common problem in large urban cities is the huge number of retail options available. In response, a number of shopping assistance applications have been created for mobile phones. However, these applications mostly allow users to know where stores are or find promotions on specific items. What is missing is a system that factors in a user's shopping preferences and automatically tells them which stores are of their interest. The key challenge in this system is twofold; 1) building a matching algorithm that can combine user preferences with fairly unstructured deals and store information to generate a final rank ordered …
Android Or Ios For Better Privacy Protection?, Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, Huijie Robert Deng
Android Or Ios For Better Privacy Protection?, Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, Huijie Robert Deng
Research Collection School Of Computing and Information Systems
With the rapid growth of the mobile market, security of mobile platforms is receiving increasing attention from both research community as well as the public. In this paper, we make the first attempt to establish a baseline for security comparison between the two most popular mobile platforms. We investigate applications that run on both Android and iOS and examine the difference in the usage of their security sensitive APIs (SS-APIs). Our analysis over 2,600 applications shows that iOS applications consistently access more SS-APIs than their counterparts on Android. The additional privileges gained on iOS are often associated with accessing private …
Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang
Extracting Interest Tags From Twitter User Biographies, Ying Ding, Jing Jiang
Research Collection School Of Computing and Information Systems
Twitter, one of the most popular social media platforms, has been studied from different angles. One of the important sources of information in Twitter is users’ biographies, which are short self-introductions written by users in free form. Biographies often describe users’ background and interests. However, to the best of our knowledge, there has not been much work trying to extract information from Twitter biographies. In this work, we study how to extract information revealing users’ personal interests from Twitter biographies. A sequential labeling model is trained with automatically constructed labeled data. The popular patterns expressing user interests are extracted and …
High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi
High-Dimensional Data Stream Classification Via Sparse Online Learning, Dayong Wang, Pengcheng Wu, Peilin Zhao, Yue Wu, Chunyan Miao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, and high sparsity. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order …
Second Order-Response Surface Model For The Automated Parameter Tuning Problem, Aldy Gunawan, Hoong Chuin Lau
Second Order-Response Surface Model For The Automated Parameter Tuning Problem, Aldy Gunawan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Several automated parameter tuning procedures/configurators have been proposed in order to find the best parameter setting for a target algorithm. These configurators can generally be classified into model-free and model-based approaches. We introduce a recent approach which is based on the hybridization of both approaches. It combines the Design of Experiments (DOE) and Response Surface Methodology (RSM) with prevailing model-free techniques. DOE is mainly used for determining the importance of parameters. A First Order-RSM is initially employed to define the promising region for the important parameters. A Second Order-RSM is then built to approximate the center point as well as …
Midas: Empowering 802.11ac With Multiple-Input Distributed Antenna Systems, Jie Xiong, Karthikeyan Sundaresan, Kyle Jamieson, Mohammad A. Khojastepour, Sampath Rangarajan
Midas: Empowering 802.11ac With Multiple-Input Distributed Antenna Systems, Jie Xiong, Karthikeyan Sundaresan, Kyle Jamieson, Mohammad A. Khojastepour, Sampath Rangarajan
Research Collection School Of Computing and Information Systems
Next generation WLANs (802.11ac) are undergoing a major shift in their communication paradigm with the introduction of multi-user MIMO (MU-MIMO), transitioning from single-user to multi-user communications. We argue that the conventional AP deployment model of co-located antennas as well as their PHY and MAC mechanisms are not designed to realize the complete potential of MUMIMO. We propose to leverage distributed antenna systems (DAS) to empower next generation 802.11ac networks. We highlight the multitude of benefits that DAS brings to MU-MIMO and 802.11ac in general. However, several challenges arise in the process of realizing these benefits in practice, where avoiding client …
Using Consumer Informedness As An Information Strategy, Ting Li, Robert John Kauffman, Eric Van Heck, Peter Vervest, Benedict Dellaert
Using Consumer Informedness As An Information Strategy, Ting Li, Robert John Kauffman, Eric Van Heck, Peter Vervest, Benedict Dellaert
Research Collection School Of Computing and Information Systems
Consumer informedness describes the degree to which consumers are aware of the specific attributes of products or services offered in the marketplace. Understanding how this level of informedness can amplify consumer behaviour provides firms with the opportunity to develop information-based strategies that can encourage their target segment make purchases.
An Empirical Study On The Adequacy Of Testing In Open Source Projects, Pavneet Singh Kochhar, Ferdian Thung, David Lo, Julia Lawall
An Empirical Study On The Adequacy Of Testing In Open Source Projects, Pavneet Singh Kochhar, Ferdian Thung, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
During software maintenance, testing is crucial to ensure the quality of code as it evolves. With the increasing size and complexity of software, adequate software testing has become increasingly important. Code coverage is an important metric to gauge the effectiveness of test cases and the adequacy of testing. However, what is the coverage level exhibited by large-scale open-source projects? What is the correlation between software metrics and the code coverage of the software?In this study, we investigate the state-of-the-practice of testing by measuring code coverage in open-source software projects. We examine over300 large open-source projects written in Java, coming from …
Automated Runtime Recovery For Qos-Based Service Composition, Tian Huat Tan, Manman Chen, Étienne André, Jun Sun, Yang Liu, Jin Song Dong
Automated Runtime Recovery For Qos-Based Service Composition, Tian Huat Tan, Manman Chen, Étienne André, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Service composition uses existing service-based applications as components to achieve a business goal. The composite service operates in a highly dynamic environment; hence, it can fail at any time due to the failure of component services. Service composition languages such as BPEL provide a compensation mechanism to rollback the error. But such a compensation mechanism has several issues. For instance, it cannot guarantee the functional properties of the composite service after compensation. In this work, we propose an automated approach based on a genetic algorithm to calculate the recovery plan that could guarantee the satisfaction of functional properties of the …
Perspectives On Task Ownership In Mobile Operating System Development [Invited Talk], Subhajit Datta
Perspectives On Task Ownership In Mobile Operating System Development [Invited Talk], Subhajit Datta
Research Collection School Of Computing and Information Systems
There can be little contention about Stroustrup's epigrammatic remark: our civilization runs on software. However a caveat is increasingly due, much of the software that runs our civilization, runs on mobile devices today. Mobile operating systems have come to play a preeminent role in the ubiquity and utility of such devices. The development ecosystem of Android - one of the most popular mobile operating systems - presents an interesting context for studying whether and how collaboration dynamics in mobile development differ from conventional software development. In this paper, we examine factors that influence task ownership in Android development. Our results …
Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder
Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder
Research Collection School Of Computing and Information Systems
In large scale software development ecosystems, there is a common perception that higher developer involvement leads to faster resolution of bugs. This is based on conjectures around more ``eyeballs" making bugs "shallow" -- whose validity and applicability are not without dispute. In this paper, we posit that the level of developer attention as well as its extent of diversity influence how quickly bugs get resolved. We report results from a study of 1,000+ Android bugs. We find statistically significant evidence that attention and diversity have contrasting relationships with the resolution time of bugs, even after controlling for factors such as …
Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica
Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica
Research Collection School Of Computing and Information Systems
In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and …
An Ecological Model For Digital Platforms Maintenance And Evolution, Paolo Rocchi, Paolo Spagnoletti, Subhajit Datta
An Ecological Model For Digital Platforms Maintenance And Evolution, Paolo Rocchi, Paolo Spagnoletti, Subhajit Datta
Research Collection School Of Computing and Information Systems
The maintenance of software products has been studied extensively in both software engineering and management information systems. Such studies are mainly focused on the activities that take place prior to starting the maintenance phase. Their contribution is either related to the improvement of software quality or to validating contingency models for reducing maintenance efforts. The continuous maintenance philosophy suggests to shift the attention within the maintenance phase for better coping with the evolutionary trajectories of digital platforms. In this paper, we examine the maintenance process of a digital platform from the perspective of the software vendor. Based on our empirical …
A First Look At Global News Coverage Of Disasters By Using The Gdelt Dataset, Haewoon Kwak, Jisun. An
A First Look At Global News Coverage Of Disasters By Using The Gdelt Dataset, Haewoon Kwak, Jisun. An
Research Collection School Of Computing and Information Systems
In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant variables in our hierarchical (mixed-effect) regression model, such as population, political stability, damage, and more, are well aligned with a series of previous research. However, we find strong regionalism in news geography, highlighting the necessity of comprehensive datasets for the study of global news coverage.
Semantics-Aware Android Malware Classification Using Weighted Contextual Api Dependency Graphs, Mu Zhang, Yue Duan, Heng Yin, Zhiruo Zhao
Semantics-Aware Android Malware Classification Using Weighted Contextual Api Dependency Graphs, Mu Zhang, Yue Duan, Heng Yin, Zhiruo Zhao
Research Collection School Of Computing and Information Systems
The drastic increase of Android malware has led to a strong interest in developing methods to automate the malware analysis process. Existing automated Android malware detection and classification methods fall into two general categories: 1) signature-based and 2) machine learning-based. Signature-based approaches can be easily evaded by bytecode-level transformation attacks. Prior learning-based works extract features from application syntax, rather than program semantics, and are also subject to evasion. In this paper, we propose a novel semantic-based approach that classifies Android malware via dependency graphs. To battle transformation attacks, we extract a weighted contextual API dependency graph as program semantics to …
Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen
Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen
Research Collection School Of Computing and Information Systems
In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, …
Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen
Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen
Research Collection School Of Computing and Information Systems
We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. Specifically, we represent each set with multiple statistics – mean, covariance matrix and Gaussian distribution, which generally complement each other for set modeling. However, it is not trivial to fuse them since the mean vector with dd-dimension often lies in Euclidean space RdRd, whereas the covariance matrix typically resides on Riemannian manifold Sym+dSymd+. Besides, according to information geometry, the space of Gaussian distribution can be embedded into another Riemannian manifold Sym+d+1Symd+1+. To fuse these statistics from heterogeneous spaces, we propose a Hybrid …
Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu
Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu
Research Collection School Of Computing and Information Systems
Manual annotation of celebrities in Web videos is an essential task in many people-related Web services. The task, however, poses a significant challenge even to skillful annotators, mainly due to the large quantity of unfamiliar and greatly varied celebrities, and the lack of a customized system for it. This work develops CeleLabel, an interactive system for manually annotating celebrities in the Web video domain. The peculiarity of CeleLabel is to exploit and display multiple types of information that could assist the annotation, including video content, context surrounding and within a video, celebrity images on the Web, and human factors. Using …
Scalable Visual Instance Mining With Threads Of Features, Wei Zhang, Hongzhi Li, Chong-Wah Ngo, Shih-Fu Chang
Scalable Visual Instance Mining With Threads Of Features, Wei Zhang, Hongzhi Li, Chong-Wah Ngo, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
We address the problem of visual instance mining, which is to extract frequently appearing visual instances automatically from a multimedia collection. We propose a scalable mining method by exploiting Thread of Features (ToF). Specifically, ToF, a compact representation that links consistent features across images, is extracted to reduce noises, discover patterns, and speed up processing. Various instances, especially small ones, can be discovered by exploiting correlated ToFs. Our approach is significantly more effective than other methods in mining small instances. At the same time, it is also more efficient by requiring much fewer hash tables. We compared with several state-of-the-art …
Organizing Video Search Results To Adapted Semantic Hierarchies For Topic-Based Browsing, Jiajun Wang, Yu-Gang Jiang, Qiang Wang, Kuiyuan Yang, Chong-Wah Ngo
Organizing Video Search Results To Adapted Semantic Hierarchies For Topic-Based Browsing, Jiajun Wang, Yu-Gang Jiang, Qiang Wang, Kuiyuan Yang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Organizing video search results into semantically structured hierarchies can greatly improve the efficiency of browsing complex query topics. Traditional hierarchical clustering techniques are inadequate since they lack the ability to generate semantically interpretable structures. In this paper, we introduce an approach to organize video search results to an adapted semantic hierarchy. As many hot search topics such as celebrities and famous cities have Wikipedia pages where hierarchical topic structures are available, we start from the Wikipedia hierarchies and adjust the structures according to the characteristics of the returned videos from a search engine. Ordinary clustering based on textual information of …
Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo
Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual keywords with visual image content. Image search engines therefore highly depend on the surrounding texts, which are often noisy or too few to accurately describe the image content. Second, ranking functions are trained on query-image pairs labeled by human labelers, making the annotation intellectually expensive and thus cannot be scaled up. We demonstrate that the above two fundamental challenges …