Physical Sciences and Mathematics Commons^™

Towards Mining Comprehensive Android Sandboxes, Tien-Duy B. Le, Lingfeng Bao, David Lo, Debin Gao, Li Li

Research Collection School Of Computing and Information Systems

Android is the most widely used mobile operating system with billions of users and devices. The popularity of Android apps have enticed malware writers to target them. Recently, Jamrozik et al. proposed an approach, named Boxmate, to mine sandboxes to protect Android users from malicious behaviors. In a nutshell, Boxmate analyzes the execution of an app, and collects a list of sensitive APIs that are invoked by that app in a monitoring phase. Then, it constructs a sandbox that can restrict accesses to sensitive APIs not called by the app. In such a way, malicious behaviors that are not observed …

Perflearner: Learning From Bug Reports To Understand And Generate Performance Test Frames, Xue Han, Tingting Yu, David Lo

Research Collection School Of Computing and Information Systems

Software performance is important for ensuring the quality of software products. Performance bugs, defined as programming errors that cause significant performance degradation, can lead to slow systems and poor user experience. While there has been some research on automated performance testing such as test case generation, the main idea is to select workload values to increase the program execution times. These techniques often assume the initial test cases have the right combination of input parameters and focus on evolving values of certain input parameters. However, such an assumption may not hold for highly configurable real-word applications, in which the combinations …

Better Inpatient Health Quality At Lower Cost: Should I Participate In The Online Healthcare Community First?, Kai Luo, Qiu-Hong Wang, Hock Hai Teo, Xi Chen

Research Collection School Of Computing and Information Systems

As policy makers across the globe look to health information technology (HIT) as a meansof improving the efficiency of the healthcare systems, it has sparked significant interestin understanding how HIT might help achieve that. While researchers have examined anddocumented the efficiency-improving effect of various institution HITs (e.g., electronicclinic pathways and telemedicine), the impacts of consumer HITs such as onlinehealthcare communities have been generally overlooked. Utilizing two unique datasetsfrom both an online healthcare community and a general hospital, we study the impactof online healthcare community on offline inpatient care efficiency. Through rigorousanalysis, we find that communications between physicians and patients on …

Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro.

Privacy-Preserving Remote User Authentication With K-Times Untraceability, Yangguang Tian, Yingjiu Li, Binanda Sengupta, Robert H. Deng, Albert Ching, Weiwei Liu

Research Collection School Of Computing and Information Systems

Remote user authentication has found numerous real-world applications, especially in a user-server model. In this work, we introduce the notion of anonymous remote user authentication with k-times untraceability (k-RUA) for a given parameter k, where authorized users authenticate themselves to an authority (typically a server) in an anonymous and k-times untraceable manner. We define the formal security models for a generic k-RUA construction that guarantees user authenticity, anonymity and user privacy. We provide a concrete instantiation of k-RUA having the following properties: (1) a third party cannot impersonate an authorized user by producing valid transcripts for the user while conversing …

Integrated Reward Scheme And Surge Pricing In A Ride-Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye

Research Collection School Of Computing and Information Systems

Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. To address the limitation of current …

Pribioauth: Privacy-Preserving Biometric-Based Remote User Authentication, Yangguang Tian, Yingjiu Li, Ximeng Liu, Robert H. Deng, Binanda Sengupta

Research Collection School Of Computing and Information Systems

Biometric-based remote user authentication (BRUA) is a useful primitive that allows an authorized user to remotely authenticate to a cloud server using biometrics. However, the existing BRUA solutions in the client-server setting lack certain privacy considerations. For example, authorized user's multiple sessions should not be linked while his identity remains anonymous to cloud server. In this work, we introduce an identity-concealed and unlinkable biometric-based remote user authentication framework, such that authorized users authenticate to an honest-but-curious server in an anonymous and unlinkable manner. In particular, we employ two non-colluding cloud servers to perform the complex biometrics matching. We formalize two …

Attention-Based Lstm-Cnns For Uncertainty Identification On Chinese Social Media Texts, Binyang Li, Kaiming Zhou, Wei Gao, Xu Han Han, Linna Zhou

Research Collection School Of Computing and Information Systems

Uncertainty identification is an important semantic processing task, which is crucial to the quality of information in terms of factuality in many techniques, e.g. topic detection, question answering. Especially in social media, the texts are written informally which are widely used in many applications, so the factuality has become a premier concern. However, existing approaches that still rely on lexical cues suffer greatly from the casual or word-of-mouth peculiarity of social media, in which the cue phrases are often expressed in sub-standard form or even omitted from sentences. To tackle these problems, this paper proposes the attention-based LSTM-CNNs for the …

Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang

Research Collection School Of Computing and Information Systems

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep …

Data Center Holistic Demand Response Algorithm To Smooth Microgrid Tie-Line Power Fluctuation, Ting Yang, Yingjie Zhao, Haibo Pen, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal …

Effectiveness Of Physical Robot Versus Robot Simulator In Teaching Introductory Programming, Oka Kurniawan, Norman Tiong Seng Lee, Subhajit Datta, Nachamma Sockalingam, Pey Lin Leong

Research Collection School Of Computing and Information Systems

This study reports the use of a physical robot and robot simulator in an introductory programming course in a university and measures students' programming background conceptual learning gain and learning experience. One group used physical robots in their lessons to complete programming assignments, while the other group used robot simulators. We are interested in finding out if there is any difference in the learning gain and experiences between those that use physical robots as compared to robot simulators. Our results suggest that there is no significant difference in terms of students' learning between the two approaches. However, the control group …

Automatically `Verifying’ Discrete-Time Complex Systems Through Learning, Abstraction And Refinement, Jingyi Wang, Jun Sun, Shengchao Qin, Cyrille Jegourel

Research Collection School Of Computing and Information Systems

Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded …

Co-Location Resistant Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Due to increasing number of avenues for conducting cross-virtual machine (VM) side-channel attacks, the security of public IaaS cloud data centers is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. To reduce the probability of malicious co-location, we propose a novel VM placement algorithm called “Previously Co-Located Users First”. We perform a theoretical and empirical analysis of our proposed algorithm to evaluate its resource efficiency and security. Our results, obtained using real-world cloud traces containing millions of VM requests and thousands of …

The Living Wall Display: Physical Augmentation Of Interactive Content Using An Autonomous Mobile Display, Yuki Onishi, Yoshiki Kudo, Kazuki Takashima, Anthony Tang, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

The Living Wall Display displays interactive content on a mobile wall screen that moves in concert with content animation. To augment the interaction experience, the display dynamically changes its position and orientation, responding to the content animation triggered by user interactions. We implement three proof of concept prototypes that represent pseudo force impact of the interactive content using physical screen movement. Pilot studies show that the Living Wall augments content expressiveness, and increases the sense of presence of the screen content.

Vr Safari Park: A Concept-Based World Building Interface Using Blocks And World Tree, Shotaro Ichikawa, Anthony Tang, Kazuki Takashima, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

We present a concept-based world building approach, realized in a system called VR Safari Park, which allows users to rapidly create and manipulate a world simulation. Conventional world building tools focus on the manipulation and arrangement of entities to set up the simulation, which is time consuming as it requires frequent view and entity manipulations. Our approach focuses on a far simpler mechanic, where users add virtual blocks which represent world entities (e.g. animals, terrain, weather, etc.) to a World Tree, which represents the simulation. In so doing, the World Tree provides a quick overview of the simulation, and users …

Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong

Research Collection School Of Computing and Information Systems

In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelization, and model these two tasks in the same network in a bi-directional manner with the input itself as training supervision. These two tasks are modeled as a cascaded network which consists of three stages for different purposes. GridNet transfers the input image into multi-scale grid-structured images with different aliasing …

Active Matting, Xin Yang, Ke Xu, Shaozhe Chen, Shengfeng He, Baocai Yin, Rynson Lau

Research Collection School Of Computing and Information Systems

Image matting is an ill-posed problem. It requires a user input trimap or some strokes to obtain an alpha matte of the foreground object. A fine user input is essential to obtain a good result, which is either time consuming or suitable for experienced users who know where to place the strokes. In this paper, we explore the intrinsic relationship between the user input and the matting algorithm to address the problem of where and when the user should provide the input. Our aim is to discover the most informative sequence of regions for user input in order to produce …

Cross Euclidean-To-Riemannian Metric Learning With Application To Face Recognition From Video, Zhiwu Huang, R. Wang, S. Shan, Gool L Van

Research Collection School Of Computing and Information Systems

Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geometry of the underlying Riemannian manifolds. In this paper, we propose a novel metric learning framework to learn a distance metric across a Euclidean space and a Riemannian manifold to fuse average appearance and pattern variation of faces within one video. The proposed metric learning framework can handle three typical tasks of video-based face recognition: Video-to-Still, Still-to-Video and Video-to-Video settings. To accomplish this new framework, by exploiting typical Riemannian …

Authorized Function Homomorphic Signature, Qingwen Guo, Qiong Huang, Guomin Yang

Research Collection School Of Computing and Information Systems

Homomorphic signature (HS) is a novel primitive that allows an agency to carry out arbitrary (polynomial time) computation f on the signed data (m) over right arrow and accordingly gain a signature sigma(h) for the computation result f ((m) over right arrow) with respect to f on behalf of the data owner (DO). However, since DO lacks control of the agency's behavior, receivers would believe that DO did authenticate the computation result even if the agency misbehaves and applies a function that the DO does not want. To address the problem above, in this paper we introduce a new primitive …

Delta Debugging Microservice Systems, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Wenhai Li, Chao Ji, Dan Ding

Research Collection School Of Computing and Information Systems

Debugging microservice systems involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, in this paper, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failureinducing deltas of circumstances (e.g., deployment, environmental configurations) for effective debugging. Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas following the idea of delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines a set of …

Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …

Analyzing And Modeling Users In Multiple Online Social Platforms, Roy Lee Ka Wei

Dissertations and Theses Collection (Open Access)

This dissertation addresses the empirical analysis on user-generated data from multiple online social platforms (OSPs) and modeling of latent user factors in multiple OSPs setting.

In the first part of this dissertation, we conducted cross-platform empirical studies to better understand user's social and work activities in multiple OSPs. In particular, we proposed new methodologies to analyze users' friendship maintenance and collaborative activities in multiple OSPs. We also apply the proposed methodologies on real-world OSP datasets, and the findings from our empirical studies have provided us with a better understanding on users' social and work activities which are previously not uncovered …

Cross-Border Interbank Payments And Settlements: Emerging Opportunities For Digital Transformation, Yi Meng Lau, Et Al

Research Collection School Of Computing and Information Systems

The report “Cross-Border Interbank Payments and Settlements” is a cross-jurisdictional industry collaboration between Canada, Singapore and the United Kingdom to examine the existing challenges and frictions that arise when undertaking crossborder payments. This report explores proposals for new and more efficient models for processing cross-border transactions.

Recommending Who To Follow In The Software Engineering Twitter Space, Abhabhisheksh Sharma, Yuan Tian, Agus Sulistya, Dinusha Wijedasa, David Lo

Research Collection School Of Computing and Information Systems

With the advent of social media, developers are increasingly using it in their software development activities. Twitter is one of the popular social mediums used by developers. A recent study by Singer et al. found that software developers use Twitter to “keep up with the fast-paced development landscape.” Unfortunately, due to the general-purpose nature of Twitter, it’s challenging for developers to use Twitter for their development activities. Our survey with 36 developers who use Twitter in their development activities highlights that developers are interested in following specialized software gurus who share relevant technical tweets.To help developers perform this task, in …

Improving Knowledge Tracing Model By Integrating Problem Difficulty, Sein Minn, Feida Zhu, Michel C. Desmarais

Research Collection School Of Computing and Information Systems

Intelligent Tutoring Systems (ITS) are designed for providing personalized instructions to students with the needs of their skills. Assessment of student knowledge acquisition dynamically is nontrivial during her learning process with ITS. Knowledge tracing, a popular student modeling technique for student knowledge assessment in adaptive tutoring, which is used for tracing student's knowledge state and detecting student's knowledge acquisition by using decomposed individual skill or problems with a single skill per problem. Unfortunately, recent KT models fail to deal with practices of complex skill composition and variety of concepts included in a problem simultaneously. Our goal is to investigate a …

Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi

Research Collection School Of Computing and Information Systems

With recent increases in incidences of political violence globally, the world has now become more uncertain and less predictable. Of particular concern is the case of violence against civilians, who are often caught in the crossfire between armed state or non-state actors. Classical methods of studying political violence and international relations need to be updated. Adopting the use of data analytic tools and techniques of studying big data would enable academics and policy makers to make sense of a rapidly changing world.

Learning Probabilistic Models For Model Checking: An Evolutionary Approach And An Empirical Study, Jingyi Wang, Jun Sun, Qixia Yuan, Jun Pang

Research Collection School Of Computing and Information Systems

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on …

Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang

Research Collection School Of Computing and Information Systems

Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …

Double Learning Or Double Blinding: An Investigation Of Vendor Private Information Acquisition And Consumer Learning Via Online Reviews, Nan Hu, Kevin E. Dow, Alain Yee Loong Chong, Ling Liu

Research Collection School Of Computing and Information Systems

In this paper, building upon information acquisition theory and using portfolio methods and system equations, we made an empirical investigation into how online vendors and consumers are learning from each other, and how online reviews, prices, and sales interact among each other. First, this study shows that vendors acquire information from both private and public channels to learn the quality of their products to make price adjustment. Second, for the more popular products and newly released products, vendors are more motivated to acquire private information that is more precise than the average precision to adjust their price. Third, we document …

Imaginary People Representing Real Numbers: Generating Personas From Online Social Media Data, Jisun An, Haewoon Kwak, Soongyo Jung, Joni Salminen, M. Admad, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology to automate creating imaginary people, referred to as personas, by processing complex behavioral and demographic data of social media audiences. From a popular social media account containing more than 30 million interactions by viewers from 198 countries engaging with more than 4,200 online videos produced by a global media corporation, we demonstrate that our methodology has several novel accomplishments, including: (a) identifying distinct user behavioral segments based on the user content consumption patterns; (b) identifying impactful demographics groupings; and (c) creating rich persona descriptions by automatically adding pertinent attributes, such as names, photos, and personal characteristics. …