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Research Collection School Of Computing and Information Systems

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Deep Adversarial Subspace Clustering, Pan Zhou, Yunqing Hou, Jiashi Feng Jun 2018

Deep Adversarial Subspace Clustering, Pan Zhou, Yunqing Hou, Jiashi Feng

Research Collection School Of Computing and Information Systems

Most existing subspace clustering methods hinge on self-expression of handcrafted representations and are unaware of potential clustering errors. Thus they perform unsatisfactorily on real data with complex underlying subspaces. To solve this issue, we propose a novel deep adversarial subspace clustering (DASC) model, which learns more favorable sample representations by deep learning for subspace clustering, and more importantly introduces adversarial learning to supervise sample representation learning and subspace clustering. Specifically, DASC consists of a subspace clustering generator and a quality-verifying discriminator, which learn against each other. The generator produces subspace estimation and sample clustering. The discriminator evaluates current clustering performance …

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Covariance Pooling For Facial Expression Recognition, D. Acharya, Zhiwu Huang, D. Paudel, Gool L. Van Jun 2018

Covariance Pooling For Facial Expression Recognition, D. Acharya, Zhiwu Huang, D. Paudel, Gool L. Van

Research Collection School Of Computing and Information Systems

Classifying facial expressions into different categories requires capturing regional distortions of facial landmarks. We believe that second-order statistics such as covariance is better able to capture such distortions in regional facial features. In this work, we explore the benefits of using a manifold network structure for covariance pooling to improve facial expression recognition. In particular, we first employ such kind of manifold networks in conjunction with traditional convolutional networks for spatial pooling within individual image feature maps in an end-to-end deep learning manner. By doing so, we are able to achieve a recognition accuracy of 58.14% on the validation set …

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Towards Model Checking Android Applications, Guangdong Bai, Quanqi Ye, Yongzheng Wu, Heila Botha, Jun Sun, Yang Liu, Jin Song Dong, Willem Visser Jun 2018

Towards Model Checking Android Applications, Guangdong Bai, Quanqi Ye, Yongzheng Wu, Heila Botha, Jun Sun, Yang Liu, Jin Song Dong, Willem Visser

Research Collection School Of Computing and Information Systems

As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often suffers from a high false-positive rate, whereas approaches based on dynamic testing are limited in coverage. In this work, we propose an alternative approach, which is to apply the software model checking technique to verify Android apps. We have built a general framework named DroidPF upon Java PathFinder (JPF), towards model checking Android apps. In the framework, we craft an executable mock-up Android …

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Assessing The Accuracy Of Four Popular Face Recognition Tools For Inferring Gender, Age, And Race, Soon-Gyu Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen Jun 2018

Assessing The Accuracy Of Four Popular Face Recognition Tools For Inferring Gender, Age, And Race, Soon-Gyu Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as …

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Towards Dynamically Monitoring Android Applications On Non-Rooted Devices In The Wild, Xiaoxiao Tang, Daoyuan Wu, Yan Lin, Debin Gao Jun 2018

Towards Dynamically Monitoring Android Applications On Non-Rooted Devices In The Wild, Xiaoxiao Tang, Daoyuan Wu, Yan Lin, Debin Gao

Research Collection School Of Computing and Information Systems

Dynamic analysis is an important technique to reveal sensitive behavior of Android apps. Current works require access to the code-level and system-level events (e.g., API calls and system calls) triggered by the running apps and consequently they can only be conducted on in-lab running environments (e.g., emulators and modified OS). The strict requirement of running environment hinders their deployment in scale and makes them vulnerable to anti-analysis techniques. Furthermore, current dynamic analysis of Android apps exploits input generators to invoke app behavior, which, however, cannot provide sufficient code coverage. We propose to dynamically analyze app behavior on non-rooted devices used …

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Mobiceal: Towards Secure And Practical Plausibly Deniable Encryption On Mobile Devices, Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen Tao Zhu, Yangguang Tian, Zhan Wang, Albert Ching Jun 2018

Mobiceal: Towards Secure And Practical Plausibly Deniable Encryption On Mobile Devices, Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen Tao Zhu, Yangguang Tian, Zhan Wang, Albert Ching

Research Collection School Of Computing and Information Systems

We introduce MobiCeal, the first practical Plausibly Deniable Encryption (PDE) system for mobile devices that can defend against strong coercive multi-snapshot adversaries, who may examine the storage medium of a user's mobile device at different points of time and force the user to decrypt data. MobiCeal relies on 'dummy write' to obfuscate the differences between multiple snapshots of storage medium due to existence of hidden data. By incorporating PDE in block layer, MobiCeal supports a broad deployment of any block-based file systems on mobile devices. More importantly, MobiCeal is secure against side channel attacks which pose a serious threat to …

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Poster: Understanding The Role Of Reporting In Work Item Tracking Systems For Software Development: An Industrial Case Study, Kochhar Pavneet Singh, Stanislaw Swierc, Trevor Carnahan, Hitesh S. Sajnani, Meiyappan Nagappan Jun 2018

Poster: Understanding The Role Of Reporting In Work Item Tracking Systems For Software Development: An Industrial Case Study, Kochhar Pavneet Singh, Stanislaw Swierc, Trevor Carnahan, Hitesh S. Sajnani, Meiyappan Nagappan

Research Collection School Of Computing and Information Systems

Work item tracking systems such as Visual Studio Team Services, JIRA, and GitHub issue tracker are widely used by software engineers. They help in managing different kinds of deliverables (e.g.features, user stories, bugs), plan sprints, distribute tasks across the team and prioritize the work. While these tools provide reporting capabilities there has been little research into the role these reports play in the overall software development process. In this study, we conduct an empirical investigation on the usage of Analytics Service-A reporting service provided by Visual Studio Team Services (VSTS) to build dashboards and reports out of their work item …

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Inference Of Development Activities From Interaction With Uninstrumented Applications, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Ahmed E. Hassan Jun 2018

Inference Of Development Activities From Interaction With Uninstrumented Applications, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Studying developers’ behavior in software development tasks is crucial for designing effective techniques and tools to support developers’ daily work. In modern software development, developers frequently use different applications including IDEs, Web Browsers, documentation software (such as Office Word, Excel, and PDF applications), and other tools to complete their tasks. This creates significant challenges in collecting and analyzing developers’ behavior data. Researchers usually instrument the software tools to log developers’ behavior for further studies. This is feasible for studies on development activities using specific software tools. However, instrumenting all software tools commonly used in real work settings is difficult and …

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Empath-D: Vr-Based Empathetic App Design For Accessibility, Wonjung Kim, Kenny Tsu Wei Choo, Youngki Lee, Archan Misra, Rajesh Krishna Balan Jun 2018

Empath-D: Vr-Based Empathetic App Design For Accessibility, Wonjung Kim, Kenny Tsu Wei Choo, Youngki Lee, Archan Misra, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

With app-based interaction increasingly permeating all aspects of daily living, it is essential to ensure that apps are designed to be inclusive and are usable by a wider audience such as the elderly, with various impairments (e.g., visual, audio and motor). We propose Empath-D, a system that fosters empathetic design, by allowing app designers, in-situ, to rapidly evaluate the usability of their apps, from the perspective of impaired users. To provide a truly authentic experience, Empath-D carefully orchestrates the interaction between a smartphone and a VR device, allowing the user to experience simulated impairments in a virtual world while interacting …

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Effectiveness Of Bite-Sized Lecture On Student Learning Outcomes, Noi Sian Koh, Swapna Gottipati, Venky Shankararaman Jun 2018

Effectiveness Of Bite-Sized Lecture On Student Learning Outcomes, Noi Sian Koh, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Bite-Sized teaching approach uses relatively small learning units with short term focused activities. The paper presents the effectiveness of Bite-Sized lecture pedagogy on learning outcomes for an analytics course offered by the School of Information Technology at Nanyang Polytechnic. The methodology involves breaking a typical 1 hour lecture into 3 to 4 short lectures followed by related tutorial / practical exercises relevant to each respective short lecture. The results from the exercises shows statistically significant improvements in the assessed learning outcomes for the Bite-Sized lecture over the traditional one hour lecture. 75% of the surveyed respondents agreed that the speed …

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To Detect Stack Buffer Overflow With Polymorphic Canaries, Zhilong Wang, Xuhua Ding, Chengbin Pang, Jian Guo, Jun Zhu, Bing Mao Jun 2018

To Detect Stack Buffer Overflow With Polymorphic Canaries, Zhilong Wang, Xuhua Ding, Chengbin Pang, Jian Guo, Jun Zhu, Bing Mao

Research Collection School Of Computing and Information Systems

Stack Smashing Protection (SSP) is a simple and highly efficient technique widely used in practice as the front line defense against stack buffer overflow attacks. Unfortunately, SSP is known to be vulnerable to the so-called byte-by-byte attack. Although several remedy schemes are proposed in the recent literature, their security is achieved at the price of practicality, because their complex logics ruin SSP's simplicity and high-efficiency. In this paper, we present an elegant solution named as Polymorphic SSP (P-SSP) that attains the same security without sacrificing SSP's strengths. We also propose three extensions of the basic scheme for better compatibility, stronger …

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Natural And Effective Obfuscation By Head Inpainting, Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, Mario Fritz Jun 2018

Natural And Effective Obfuscation By Head Inpainting, Qianru Sun, Liqian Ma, Seong Joon Oh, Luc Van Gool, Bernt Schiele, Mario Fritz

Research Collection School Of Computing and Information Systems

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. …

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Dimensionality's Blessing: Clustering Images By Underlying Distribution, Wen-Yan Lin, Jian-Huang Lai, Siying Liu, Yasuyuki Matsushita Jun 2018

Dimensionality's Blessing: Clustering Images By Underlying Distribution, Wen-Yan Lin, Jian-Huang Lai, Siying Liu, Yasuyuki Matsushita

Research Collection School Of Computing and Information Systems

Many high dimensional vector distances tend to a constant. This is typically considered a negative “contrastloss” phenomenon that hinders clustering and other machine learning techniques. We reinterpret “contrast-loss” as a blessing. Re-deriving “contrast-loss” using the law of large numbers, we show it results in a distribution’s instances concentrating on a thin “hyper-shell”. The hollow center means apparently chaotically overlapping distributions are actually intrinsically separable. We use this to develop distribution-clustering, an elegant algorithm for grouping of data points by their (unknown) underlying distribution. Distribution-clustering, creates notably clean clusters from raw unlabeled data, estimates the number of clusters for itself and …

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Security And Privacy In Smart Health: Efficient Policy-Hiding Attribute-Based Access Control, Yinghui Zhang, Dong Zheng, Robert H. Deng Jun 2018

Security And Privacy In Smart Health: Efficient Policy-Hiding Attribute-Based Access Control, Yinghui Zhang, Dong Zheng, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the rapid development of the Internet of Things (IoT) and cloud computing technologies, smart health (s-health) is expected to significantly improve the quality of health care. However, data security and user privacy concerns in s-health have not been adequately addressed. As a well-received solution to realize fine-grained access control, ciphertext-policy attribute-based encryption (CP-ABE) has the potential to ensure data security in s-health. Nevertheless, direct adoption of the traditional CP-ABE in s-health suffers two flaws. For one thing, access policies are in cleartext form and reveal sensitive health-related information in the encrypted s-health records (SHRs). For another, it usually supports …

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Fimce: A Fully Isolated Micro-Computing Environment For Multicore Systems, Siqi Zhao, Xuhua Ding Jun 2018

Fimce: A Fully Isolated Micro-Computing Environment For Multicore Systems, Siqi Zhao, Xuhua Ding

Research Collection School Of Computing and Information Systems

Virtualization-based memory isolation has been widely used as a security primitive in various security systems to counter kernel-level attacks. In this article, our in-depth analysis on this primitive shows that its security is significantly undermined in the multicore setting when other hardware resources for computing are not enclosed within the isolation boundary. We thus propose to construct a fully isolated micro-computing environment (FIMCE) as a new primitive. By virtue of its architectural niche, FIMCE not only offers stronger security assurance than its predecessor, but also features a flexible and composable environment with support for peripheral device isolation, thus greatly expanding …

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Region-Aware Reflection Removal With Unified Content And Gradient Priors, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Wen Gao, Alex C. Kot Jun 2018

Region-Aware Reflection Removal With Unified Content And Gradient Priors, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Wen Gao, Alex C. Kot

Research Collection School Of Computing and Information Systems

Removing the undesired reflections in images taken through the glass is of broad application to various image processing and computer vision tasks. Existing single image-based solutions heavily rely on scene priors such as separable sparse gradients caused by different levels of blur, and they are fragile when such priors are not observed. In this paper, we notice that strong reflections usually dominant a limited region in the whole image, and propose a region-aware reflection removal approach by automatically detecting and heterogeneously processing regions with and without reflections. We integrate content and gradient priors to jointly achieve missing contents restoration, as …

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Movespace: On-Body Athletic Interaction For Running And Cycling, Velko Vechev, Alexandru Dancu, Simon T. Perrault, Quentin Xavier Louis Roy, Morten Fjeld, Shengdong Zhao Jun 2018

Movespace: On-Body Athletic Interaction For Running And Cycling, Velko Vechev, Alexandru Dancu, Simon T. Perrault, Quentin Xavier Louis Roy, Morten Fjeld, Shengdong Zhao

Research Collection School Of Computing and Information Systems

Wearables are increasingly used during training to quantify performance and provide valuable real-time information. However, interacting with these devices in motion may disrupt the movements of the activity. We propose a method of interaction involving tapping specific locations on the body, identify candidate locations for running and cycling, and compare them in a series of controlled experiments with athletes. A purpose-built prototype measures speed of interaction and gives feedback cues for athletes to report the physical effects on the activity itself. Our results suggest that specific locations are faster and have minimal disruption to movement, even under induced fatigue conditions. …

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Effect Of Temporality, Physical Activity, And Cognitive Load On Spatiotemporal Vibrotactile Pattern Recognition, Qin Chen, Simon T. Perrault, Quentin Xavier Louis Roy, Lonce Wyse Jun 2018

Effect Of Temporality, Physical Activity, And Cognitive Load On Spatiotemporal Vibrotactile Pattern Recognition, Qin Chen, Simon T. Perrault, Quentin Xavier Louis Roy, Lonce Wyse

Research Collection School Of Computing and Information Systems

Previous research demonstrated the ability for users to accurately recognize Spatiotemporal Vibrotactile Patterns (SVP): sequences of vibrations on different motors occurring either sequentially or simultaneously. However, the experiments were only run in a lab setting and the ability for users to recognize SVP in a real-world environment remains unclear. In this paper, we investigate how several factors may affect recognition: (1) physical activity (running), (2) cognitive task (i.e. primary task, typing), (3) distribution of the vibration motors across body parts and (4) temporality of the patterns. Our results suggest that physical activity has very little impact, specifically compared to cognitive …

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D-Pruner: Filter-Based Pruning Method For Deep Convolutional Neural Network, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan Jun 2018

D-Pruner: Filter-Based Pruning Method For Deep Convolutional Neural Network, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

The emergence of augmented reality devices such as Google Glass and Microsoft Hololens has opened up a new class of vision sensing applications. Those applications often require the ability to continuously capture and analyze contextual information from video streams. They often adopt various deep learning algorithms such as convolutional neural networks (CNN) to achieve high recognition accuracy while facing severe challenges to run computationally intensive deep learning algorithms on resource-constrained mobile devices. In this paper, we propose and explore a new class of compression technique called D-Pruner to efficiently prune redundant parameters within a CNN model to run the model …

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D-Pruner: Filter-Based Pruning Method For Deep Convolutional Neural Network, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan Jun 2018

D-Pruner: Filter-Based Pruning Method For Deep Convolutional Neural Network, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

The emergence of augmented reality devices such as Google Glass and Microsoft Hololens has opened up a new class of vision sensing applications. Those applications often require the ability to continuously capture and analyze contextual information from video streams. They often adopt various deep learning algorithms such as convolutional neural networks (CNN) to achieve high recognition accuracy while facing severe challenges to run computationally intensive deep learning algorithms on resource-constrained mobile devices. In this paper, we propose and explore a new class of compression technique called D-Pruner to efficiently prune redundant parameters within a CNN model to run the model …

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Position Manipulation Attacks To Balise-Based Train Automatic Stop Control, Yongdong Wu, Zhuo Wei, Jian Weng, Robert H. Deng Jun 2018

Position Manipulation Attacks To Balise-Based Train Automatic Stop Control, Yongdong Wu, Zhuo Wei, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

Balise is a popular wayside device to provide accurate location information for subway station parking by sending telegrams to passing trains. By craftily disturbing wireless signals of balise telegrams, this paper proposes three attacks that may make passengers fall and even cause injury. Concretely, the first attack is to jam telegrams such that balises cannot be detected by a passing train; the second attack changes the location of transmitting telegrams by jamming and replaying; and the third attack is to change the total time of transmitting telegrams. All the attacks exploit the train localization mechanism such that a passing train …

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Annapurna: Building A Real-World Smartwatch-Based Automated Food Journal, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee Jun 2018

Annapurna: Building A Real-World Smartwatch-Based Automated Food Journal, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

We describe the design and implementation of a smartwatch-based, completely unobtrusive, food journaling system, where the smartwatch helps to intelligently capture useful images of food that an individual consumes throughout the day. The overall system, called Annapurna, is based on three key components: (a) a smartwatch-based gesture recognizer to identify eating gestures, (b) a smartwatch-based image capturer that obtains a small set of relevant and useful images with a low energy overhead, and (c) a server-based image filtering engine that removes irrelevant uploaded images, and then catalogs them through a portal. Our primary challenge is to make the system robust …

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Fimce: A Fully Isolated Micro-Computing Environment For Multicore Systems, Siqi Zhao, Xuhua Ding Jun 2018

Fimce: A Fully Isolated Micro-Computing Environment For Multicore Systems, Siqi Zhao, Xuhua Ding

Research Collection School Of Computing and Information Systems

Virtualization-based memory isolation has been widely used as a security primitive in various security systems to counter kernel-level attacks. In this article, our in-depth analysis on this primitive shows that its security is significantly undermined in the multicore setting when other hardware resources for computing are not enclosed within the isolation boundary. We thus propose to construct a fully isolated micro-computing environment (FIMCE) as a new primitive. By virtue of its architectural niche, FIMCE not only offers stronger security assurance than its predecessor, but also features a flexible and composable environment with support for peripheral device isolation, thus greatly expanding …

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Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan Jun 2018

Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Solving combinatorial optimization problems using a fixed set of operators has been known to produce poor quality solutions. Thus, adaptive operator selection (AOS) methods have been proposed. But, despite such effort, challenges such as the choice of suitable AOS method and configuring it correctly for given specific problem instances remain. To overcome these challenges, this work proposes a novel approach known as I-AOS-DOE to perform Instance-specific selection of AOS methods prior to evolutionary search. Furthermore, to configure the AOS methods for the respective problem instances, we apply a Design of Experiment (DOE) technique to determine promising regions of parameter values …

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Sotorrent: Reconstructing And Analyzing The Evolution Of Stack Overflow Posts, Sebastian Baltes, Lorik Dumani, Christoph Treude, Stephan Diehl May 2018

Sotorrent: Reconstructing And Analyzing The Evolution Of Stack Overflow Posts, Sebastian Baltes, Lorik Dumani, Christoph Treude, Stephan Diehl

Research Collection School Of Computing and Information Systems

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built SOTorrent, an open dataset based on the official SO data dump. SOTorrent provides access to the version history …

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Learning From Mutants: Using Code Mutation To Learn And Monitor Invariants Of A Cyber-Physical System, Yuqi Chen, Christopher M. Poskitt, Jun Sun May 2018

Learning From Mutants: Using Code Mutation To Learn And Monitor Invariants Of A Cyber-Physical System, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the …

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Expressive Query Over Outsourced Encrypted Data, Yang Yang, Ximeng Liu, Robert H. Deng May 2018

Expressive Query Over Outsourced Encrypted Data, Yang Yang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Data security and privacy concerns in cloud storage services compel data owners to encrypt their sensitive data before outsourcing. Standard encryption systems, however, hinder users from issuing search queries on encrypted data. Though various systems for search over encrypted data have been proposed in the literature, existing systems use different encrypted index structures to conduct search on different search query patterns and hence are not compatible with each other. In this paper, we propose a query over encrypted data system which supports expressive search query patterns, such as single/conjunctive keyword query, range query, boolean query and mixed boolean query, all …

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Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee May 2018

Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee

Research Collection School Of Computing and Information Systems

Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.

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Evidence Aggregation For Answer Re-Ranking In Open-Domain Question Answering, Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell May 2018

Evidence Aggregation For Answer Re-Ranking In Open-Domain Question Answering, Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell

Research Collection School Of Computing and Information Systems

A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from …

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Efficient And Expressive Keyword Search Over Encrypted Data In The Cloud, Hui Cui, Zhiguo Wan, Deng, Robert H., Guilin Wang, Yingjiu Li May 2018

Efficient And Expressive Keyword Search Over Encrypted Data In The Cloud, Hui Cui, Zhiguo Wan, Deng, Robert H., Guilin Wang, Yingjiu Li

Research Collection School Of Computing and Information Systems

Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient since they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and …

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