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Full-Text Articles in Entire DC Network
Adaptive Duty Cycling In Sensor Networks With Energy Harvesting Using Continuous-Time Markov Chain And Fluid Models, Wai Hong Ronald Chan, Pengfei Zhang, Ido Nevat, Sai Ganesh Nagarajan, Alvin C. Valera, Hwee Xian Tan, Natarajan Gautam
Adaptive Duty Cycling In Sensor Networks With Energy Harvesting Using Continuous-Time Markov Chain And Fluid Models, Wai Hong Ronald Chan, Pengfei Zhang, Ido Nevat, Sai Ganesh Nagarajan, Alvin C. Valera, Hwee Xian Tan, Natarajan Gautam
Research Collection School Of Computing and Information Systems
Abstract:The dynamic and unpredictable nature of energy harvesting sources available for wireless sensor networks, and the time variation in network statistics like packet transmission rates and link qualities, necessitate the use of adaptive duty cycling techniques. Such adaptive control allows sensor nodes to achieve long-run energy neutrality, where energy supply and demand are balanced in a dynamic environment such that the nodes function continuously. In this paper, we develop a new framework enabling an adaptive duty cycling scheme for sensor networks that takes into account the node battery level, ambient energy that can be harvested, and application-level QoS requirements. We …
Low Effort Crowdsourcing: Leveraging Peripheral Attention For Crowd Work, Vaish Rajan, Peter Organisciak, Kotaro Hara, Jeffrey P. Bigham, Haoqi Zhang
Low Effort Crowdsourcing: Leveraging Peripheral Attention For Crowd Work, Vaish Rajan, Peter Organisciak, Kotaro Hara, Jeffrey P. Bigham, Haoqi Zhang
Research Collection School Of Computing and Information Systems
Crowdsourcing systems leverage short bursts of focusedattention from many contributors to achieve a goal. Byrequiring people’s full attention, existing crowdsourcingsystems fail to leverage people’s cognitive surplus in themany settings for which they may be distracted, performingor waiting to perform another task, or barely payingattention. In this paper, we study opportunities for loweffortcrowdsourcing that enable people to contribute toproblem solving in such settings. We discuss the designspace for low-effort crowdsourcing, and through a seriesof prototypes, demonstrate interaction techniques, mechanisms,and emerging principles for enabling low-effortcrowdsourcing.
Recovering Household Preferences For Digital Entertainment, Jin Li, Zhiling Guo, Robert J. Kauffman
Recovering Household Preferences For Digital Entertainment, Jin Li, Zhiling Guo, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
Cable TV return path data made possible by current generation set-top boxes present a new opportunity to analyze household viewing behavior and recover household viewing preferences from it. This research develops a model of household viewing preference that supports quantifying a household's valuation for different categories of digital content within the constraints of the programs to which it subscribes. This study uses a data set of more than 1 million observations on households from a digital entertainment firm that offers basic and premium services. Our estimation is via a Bayesian hierarchical model that employs the Gibbs sampler. The results show …
Improving Internet Security Through Mandatory Information Disclosure, Qian Tang, Andrew B. Whinston
Improving Internet Security Through Mandatory Information Disclosure, Qian Tang, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
Although disclosure has long been considered as a solution to internalize externalities, mandatory security information disclosure is still in debate. We propose a mandatory disclosure mechanism based on existing data. The information is disclosed as straightforward rankings of organizations for users to understand, interpret, and make comparisons. As a result, the disclosure can influence organizations through reputational effects. We created a public website to disclose information regularly and conducted a quasi-experiment on outgoing spam to test the effectiveness of our mechanism on four matched country groups. For each treated country, we released the ranking list of top 10 most spamming …
Designing A Portfolio Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir
Designing A Portfolio Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir
Research Collection School Of Computing and Information Systems
Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …
Algorithm Selection Via Ranking, Jayadi Oentaryo Richard, Handoko Stephanus Daniel, Hoong Chuin Lau
Algorithm Selection Via Ranking, Jayadi Oentaryo Richard, Handoko Stephanus Daniel, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
The abundance of algorithms developed to solve different problems has given rise to an important research question: How do we choose the best algorithm for a given problem? Known as algorithm selection, this issue has been prevailing in many domains, as no single algorithm can perform best on all problem instances. Traditional algorithm selection and portfolio construction methods typically treat the problem as a classification or regression task. In this paper, we present a new approach that provides a more natural treatment of algorithm selection and portfolio construction as a ranking task. Accordingly, we develop a Ranking-Based Algorithm Selection (RAS) …
Semi-Universal Portfolios With Transaction Costs, Dingjiang Huang, Yan Zhu, Bin Li, Shuigeng Zhou, Steven C. H. Hoi
Semi-Universal Portfolios With Transaction Costs, Dingjiang Huang, Yan Zhu, Bin Li, Shuigeng Zhou, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Online portfolio selection (PS) has been extensively studied in artificial intelligence and machine learning communities in recent years. An important practical issue of online PS is transaction cost, which is unavoidable and nontrivial in real financial trading markets. Most existing strategies, such as universal portfolio (UP) based strategies, often rebalance their target portfolio vectors at every investment period, and thus the total transaction cost increases rapidly and the final cumulative wealth degrades severely. To overcome the limitation, in this paper we investigate new investment strategies that rebalances its portfolio only at some selected instants. Specifically, we design a novel on-line …
Automatic, High Accuracy Prediction Of Reopened Bugs, Xin Xia, David Lo, Emad Shihab, Xinyu Wang, Bo Zhou
Automatic, High Accuracy Prediction Of Reopened Bugs, Xin Xia, David Lo, Emad Shihab, Xinyu Wang, Bo Zhou
Research Collection School Of Computing and Information Systems
Bug fixing is one of the most time-consuming and costly activities of the software development life cycle. In general, bugs are reported in a bug tracking system, validated by a triage team, assigned for someone to fix, and finally verified and closed. However, in some cases bugs have to be reopened. Reopened bugs increase software maintenance cost, cause rework for already busy developers and in some cases even delay the future delivery of a software release. Therefore, a few recent studies focused on studying reopened bugs. However, these prior studies did not achieve high performance (in terms of precision and …
Human Action Classification Based On Sequential Bag-Of-Words Model, Hong Liu, Qiaoduo Zhang, Qianru Sun
Human Action Classification Based On Sequential Bag-Of-Words Model, Hong Liu, Qiaoduo Zhang, Qianru Sun
Research Collection School Of Computing and Information Systems
Recently, approaches utilizing spatial-temporal features have achieved great success in human action classification. However, they typically rely on bag-of-words (BoWs) model, and ignore the spatial and temporal structure information of visual words, bringing ambiguities among similar actions. In this paper, we present a novel approach called sequential BoWs for efficient human action classification. It captures temporal sequential structure by segmenting the entire action into sub-actions. Each sub-action has a tiny movement within a narrow range of action. Then the sequential BoWs are created, in which each sub-action is assigned with a certain weight and salience to highlight the distinguishing sections. …
Online Learning On Incremental Distance Metric For Person Re-Identification, Yuke Sun, Hong Liu, Qianru Sun
Online Learning On Incremental Distance Metric For Person Re-Identification, Yuke Sun, Hong Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Person re-identification is to match persons appearing across non-overlapping cameras. The matching is challenging due to visual ambiguities and disparities of human bodies. Most previous distance metrics are learned by off-line and supervised approaches. However, they are not practical in real-world applications in which online data comes in without any label. In this paper, a novel online learning approach on incremental distance metric, OL-IDM, is proposed. The approach firstly modifies Self-Organizing Incremental Neural Network (SOINN) using Mahalanobis distance metric to cluster incoming data into neural nodes. Such metric maximizes the likelihood of a true image pair matches with a smaller …
Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong
Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong
Research Collection School Of Computing and Information Systems
The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. …
From Cells To Streets: Estimating Mobile Paths With Cellular-Side Data, Qatar Computing Research Institute, University Of Birmingham, Seattle University Of Washington, Haewoon Kwak
From Cells To Streets: Estimating Mobile Paths With Cellular-Side Data, Qatar Computing Research Institute, University Of Birmingham, Seattle University Of Washington, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Through their normal operation, cellular networks are a repository of continuous location information from their subscribed devices. Such information, however, comes at a coarse granularity both in terms of space, as well as time. For otherwise inactive devices, location information can be obtained at the granularity of the associated cellular sector, and at infrequent points in time, that are sensitive to the structure of the network itself, and the level of mobility of the device. In this paper, we are asking the question of whether such sparse information can help to identify the paths followed by mobile connected devices throughout …
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 …
Anomaly Detection Through Enhanced Sentiment Analysis On Social Media Data, Zhaoxia Wang, Victor Joo, Chuan Tong, Xin Xin, Hoong Chor Chin
Anomaly Detection Through Enhanced Sentiment Analysis On Social Media Data, Zhaoxia Wang, Victor Joo, Chuan Tong, Xin Xin, Hoong Chor Chin
Research Collection School Of Computing and Information Systems
Anomaly detection in sentiment analysis refers to detecting abnormal opinions, sentiment patterns or special temporal aspects of such patterns in a collection of data. The anomalies detected may be due to sudden sentiment changes hidden in large amounts of text. If these anomalies are undetected or poorly managed, the consequences may be severe, e.g. A business whose customers reveal negative sentiments and will no longer support the establishment. Social media platforms, such as Twitter, provide a vast source of information, which includes user feedback, opinion and information on most issues. Many organizations also leverage social media platforms to publish information …
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 …
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 …
Orchestrating Service Innovation Using Design Moves: The Dynamics Of Fit Between Service And Enterprise It Architectures, Narayan Ramasubbu, Charles Jason Woodard, Sunil Mithas
Orchestrating Service Innovation Using Design Moves: The Dynamics Of Fit Between Service And Enterprise It Architectures, Narayan Ramasubbu, Charles Jason Woodard, Sunil Mithas
Research Collection School Of Computing and Information Systems
Service science perspectives highlight the central role of information technology (IT) in transforming the design and delivery of services. To discern the mechanisms through which IT impacts service innovation, we explore the dynamics of the relationship between enterprise IT and service architectures, and how these dynamics influence the performance of service innovation projects. We conducted six case studies to investigate how firms orchestrated service innovation, focusing on the design of the service architecture and its relationship to enterprise systems. We synthesize the case findings to develop a set of propositions on the antecedents and consequences of fit (or misfit) between …
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 …
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 …
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 …
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 …
Sensor-Free Corner Shape Detection By Wireless Networks, Yuxi Wang, Zimu Zhou, Kaishun Wu
Sensor-Free Corner Shape Detection By Wireless Networks, Yuxi Wang, Zimu Zhou, Kaishun Wu
Research Collection School Of Computing and Information Systems
Due to the rapid growth of the smartphone applications and the fast development of the Wireless Local Area Networks (WLANs), numerous indoor location-based techniques have been proposed during the past several decades. Floorplan, which defines the structure and functionality of a specific indoor environment, becomes a hot topic nowadays. Conventional floorplan techniques leverage smartphone sensors combined with WiFi signals to construct the floorplan of a building. However, existing approaches with sensors cannot detect the shape of a corner, and the sensors cost huge amount of energy during the whole floorplan constructing process. In this paper, we propose a sensor-free approach …
Pads: Passive Detection Of Moving Targets With Dynamic Speed Using Phy Layer Information, Kun Qian, Chenshu Wu, Zheng Yang, Yunhao Liu, Zimu Zhou
Pads: Passive Detection Of Moving Targets With Dynamic Speed Using Phy Layer Information, Kun Qian, Chenshu Wu, Zheng Yang, Yunhao Liu, Zimu Zhou
Research Collection School Of Computing and Information Systems
Device-free passive detection is an emerging technology to detect whether there exists any moving entities in the area of interests without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, etc. Despite of the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to finer-grained channel descriptor at physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored …
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 …
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 …