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Full-Text Articles in Entire DC Network
Gpu-Accelerated Graph Label Propagation For Real-Time Fraud Detection, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Gpu-Accelerated Graph Label Propagation For Real-Time Fraud Detection, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Research Collection School Of Computing and Information Systems
Fraud detection is a pressing challenge for most financial and commercial platforms. In this paper, we study the processing pipeline of fraud detection in a large e-commerce platform of TaoBao. Graph label propagation (LP) is a core component in this pipeline to detect suspicious clusters from the user-interaction graph. Furthermore, the run-time of the LP component occupies 75% overhead of TaoBao’s automated detection pipeline. To enable real-time fraud detection, we propose a GPU-based framework, called GLP, to support large-scale LP workloads in enterprises. We have identified two key challenges when integrating GPU acceleration into TaoBao’s data processing pipeline: (1) programmability …
Learning Contextual Causality Between Daily Events From Time-Consecutive Images, Hongming Zhang, Yintong Huo, Xinran Zhao, Yangqiu Song, Dan Roth
Learning Contextual Causality Between Daily Events From Time-Consecutive Images, Hongming Zhang, Yintong Huo, Xinran Zhao, Yangqiu Song, Dan Roth
Research Collection School Of Computing and Information Systems
Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to …
Projecting Your View Attentively: Monocular Road Scene Layout Estimation Via Cross-View Transformation, Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Projecting Your View Attentively: Monocular Road Scene Layout Estimation Via Cross-View Transformation, Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Research Collection School Of Computing and Information Systems
HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to the deployed expensive sensors and time-consuming computation. Camera-based methods usually need to separately perform road segmentation and view transformation, which often causes distortion and the absence of content. To push the limits of the technology, we present a novel framework that enables reconstructing a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. In particular, we propose a cross-view transformation module, which takes the constraint of cycle consistency between views into account and makes full use …
Sequence-To-Sequence Learning For Automated Software Artifact Generation, Zhongxin Liu, Xin Xia, David Lo
Sequence-To-Sequence Learning For Automated Software Artifact Generation, Zhongxin Liu, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
During the development and maintenance of a software system, developers produce many digital artifacts besides source code, e.g., requirement documents, code comments, change history, bug reports, etc. Such artifacts are valuable for developers to understand and maintain the software system. However, creating software artifacts can be burdensome and developers sometimes neglect to write and maintain important artifacts. This problem can be alleviated by software artifact generation tools, which can assist developers in creating software artifacts and automatically generate artifacts to replace existing empty ones. The focus of this chapter is automated software artifact generation (hereon, SAG) using seq2seq learning. This …
A Large Scale Study Of Long-Time Contributor Prediction For Github Projects, Lingfeng Bao, Xin Xia, David Lo, Gail C. Murphy
A Large Scale Study Of Long-Time Contributor Prediction For Github Projects, Lingfeng Bao, Xin Xia, David Lo, Gail C. Murphy
Research Collection School Of Computing and Information Systems
The continuous contributions made by long time contributors (LTCs) are a key factor enabling open source software (OSS) projects to be successful and survival. We study Github as it has a large number of OSS projects and millions of contributors, which enables the study of the transition from newcomers to LTCs. In this paper, we investigate whether we can effectively predict newcomers in OSS projects to be LTCs based on their activity data that is collected from Github. We collect Github data from GHTorrent, a mirror of Github data. We select the most popular 917 projects, which contain 75,046 contributors. …
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Coordinating Multi-Party Vehicle Routing With Location Congestion Via Iterative Best Response, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This work is motivated by a real-world problem of coordinating B2B pickup-delivery operations to shopping malls involving multiple non-collaborative Logistics Service Providers (LSPs) in a congested city where space is scarce. This problem can be categorized as a Vehicle Routing Problem with Pickup and Delivery, Time Windows and Location Congestion with multiple LSPs (or ML-VRPLC in short), and we propose a scalable, decentralized, coordinated planning approach via iterative best response. We formulate the problem as a strategic game where each LSP is a self-interested agent but is willing to participate in a coordinated planning as long as there are sufficient …
Riding Through The Silver Tsunami: A Data Driven Approach To Improve Senior Citizens’ Engagement With Community Senior Activity Centres, Joshua Jie Feng Lam, Hwee-Pink Tan
Riding Through The Silver Tsunami: A Data Driven Approach To Improve Senior Citizens’ Engagement With Community Senior Activity Centres, Joshua Jie Feng Lam, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In Singapore, 1 in 4 persons will be elderly by 2030 In preparation for the Silver Tsunami, the Singapore government and community care providers have collaborations to promote active, independent living amongst elders Current implementation of data driven population health is focused on well being indices using data collected from the general population There is no literature on the use of data analytics in assessing elder
On Predicting Personal Values Of Social Media Users Using Community-Specific Language Features And Personal Value Correlation, Amila Silva, Pei Chi Lo, Ee-Peng Lim
On Predicting Personal Values Of Social Media Users Using Community-Specific Language Features And Personal Value Correlation, Amila Silva, Pei Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Personal values have significant influence on individuals’ behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users’ personal values, and developing effective models to predict their personal …
Counterfactual Zero-Shot And Open-Set Visual Recognition, Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang
Counterfactual Zero-Shot And Open-Set Visual Recognition, Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang
Research Collection School Of Computing and Information Systems
We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea stems from the observation that the generated samples for unseen-classes are often out of the true distribution, which causes severe recognition rate imbalance between the seen-class (high) and unseen-class (low). We show that the key reason is that the generation is not Counterfactual Faithful, and thus we propose a faithful one, whose generation is from the sample-specific counterfactual question: What would the sample look like, if we set its …
Generating Face Images With Attributes For Free, Yaoyao Liu, Qianru Sun, He Xiangnan, Liu An-An, Su Yuting, Chua Tat-Seng
Generating Face Images With Attributes For Free, Yaoyao Liu, Qianru Sun, He Xiangnan, Liu An-An, Su Yuting, Chua Tat-Seng
Research Collection School Of Computing and Information Systems
With superhuman-level performance of face recognition, we are more concerned about the recognition of fine-grained attributes, such as emotion, age, and gender. However, given that the label space is extremely large and follows a long-tail distribution, it is quite expensive to collect sufficient samples for fine-grained attributes. This results in imbalanced training samples and inferior attribute recognition models. To this end, we propose the use of arbitrary attribute combinations, without human effort, to synthesize face images. In particular, to bridge the semantic gap between high-level attribute label space and low-level face image, we propose a novel neural-network-based approach that maps …
Proving Non-Termination By Program Reversal, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic
Proving Non-Termination By Program Reversal, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic
Research Collection School Of Computing and Information Systems
We present a new approach to proving non-termination of non-deterministic integer programs. Our technique is rather simple but efficient. It relies on a purely syntactic reversal of the program's transition system followed by a constraint-based invariant synthesis with constraints coming from both the original and the reversed transition system. The latter task is performed by a simple call to an off-the-shelf SMT-solver, which allows us to leverage the latest advances in SMT-solving. Moreover, our method offers a combination of features not present (as a whole) in previous approaches: it handles programs with non-determinism, provides relative completeness guarantees and supports programs …
Learning From The Master: Distilling Cross-Modal Advanced Knowledge For Lip Reading, Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He
Learning From The Master: Distilling Cross-Modal Advanced Knowledge For Lip Reading, Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal data could lead to a learning ambiguity and thus limits the performance of lip reading. In this paper, we propose a novel collaborative framework for lip reading, and two aspects of issues are considered: 1) the teacher should understand bi-modal knowledge to possibly bridge the inherent cross-modal gap; 2) …
Non-Equivocation In Blockchain: Double-Authentication-Preventing Signatures Gone Contractual, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Tran Viet Xuan Phuong, Dongxi Liu
Non-Equivocation In Blockchain: Double-Authentication-Preventing Signatures Gone Contractual, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Tran Viet Xuan Phuong, Dongxi Liu
Research Collection School Of Computing and Information Systems
Equivocation is one of the most fundamental problems that need to be solved when designing distributed protocols. Traditional methods to defeat equivocation rely on trusted hardware or particular assumptions, which may hinder their adoption in practice. The advent of blockchain and decentralized cryptocurrencies provides an auspicious breakthrough paradigm to resolve the problem above. In this paper, we propose a blockchain-based solution to address contractual equivocation, which supports user-defined fine-grained policybased equivocation. Specifically, users will be de-incentive if the statements they made breach the predefined access rules. The core of our solution is a newly introduced primitive named Policy-Authentication-Preventing Signature (PoAPS), …
Multi-View Collaborative Network Embedding, Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiaoli Li
Multi-View Collaborative Network Embedding, Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiaoli Li
Research Collection School Of Computing and Information Systems
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose Multi-view collAborative Network Embedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables …
Engaging Drivers Via Competition: A Case Study With Arena, Hao Cheng, Shuyu Wei, Lingyu Zhang, Zimu Zhou, Yongxin. Tong
Engaging Drivers Via Competition: A Case Study With Arena, Hao Cheng, Shuyu Wei, Lingyu Zhang, Zimu Zhou, Yongxin. Tong
Research Collection School Of Computing and Information Systems
Sustained work enthusiasms of drivers are crucial for the success of large-scale ride-hailing platforms. In this paper, we conduct the first-of-its-kind exploration to encourage active participation of drivers via competition. We design Arena, a competition where drivers compete for prizes via completing more trips. Through a pilot study covering over 2,600 participants, we uncover the easy-win problem, an overlooked and serious issue in competition design for real-world drivers. It refers to situations where one competitor does not show up during competition whereas the other easily wins. To solve the easy-win problem without impairing motivation of drivers, we devise a novel …
Efficient Attribute-Based Encryption With Repeated Attributes Optimization, Fawad Khan, Hui Li, Yinghui Zhang, Haider Abbas, Tahreem Yaqoob
Efficient Attribute-Based Encryption With Repeated Attributes Optimization, Fawad Khan, Hui Li, Yinghui Zhang, Haider Abbas, Tahreem Yaqoob
Research Collection School Of Computing and Information Systems
Internet of Things (IoT) is an integration of various technologies to provide technological enhancements. To enforce access control on low power operated battery constrained devices is a challenging issue in IoT scenarios. Attribute-based encryption (ABE) has emerged as an access control mechanism to allow users to encrypt and decrypt data based on an attributes policy. However, to accommodate the expressiveness of policy for practical application scenarios, attributes may be repeated in a policy. For certain policies, the attributes repetition cannot be avoided even after applying the boolean optimization techniques to attain an equivalent smaller length boolean formula. For such policies, …
Ganmut: Learning Interpretable Conditional Space For A Gamut Of Emotions, S. D'Apolito, D.P. Paundel, Zhiwu Huang, A.R. Vergara, Gool L. Van
Ganmut: Learning Interpretable Conditional Space For A Gamut Of Emotions, S. D'Apolito, D.P. Paundel, Zhiwu Huang, A.R. Vergara, Gool L. Van
Research Collection School Of Computing and Information Systems
Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labeling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability. In this paper, we propose a novel GAN-based framework which learns an expressive and interpretable conditional space (usable as a label space) of emotions, instead of conditioning on …
Assessing Real-Time Flow Experience In Human-Computer Interaction: An Electroencephalogram (Eeg) Study, Fiona Fui-Hoon Nah, Tejaswini Yelamanchili, Keng Siau, Langtao Chen
Assessing Real-Time Flow Experience In Human-Computer Interaction: An Electroencephalogram (Eeg) Study, Fiona Fui-Hoon Nah, Tejaswini Yelamanchili, Keng Siau, Langtao Chen
Research Collection School Of Computing and Information Systems
An optimal experience termed flow can be experienced by users who are deeply involved in human-computer interaction (HCI). The electroencephalogram (EEG) provides an innovative way to assess in real-time if users are approaching or experiencing the flow state in various applications of HCI. However, the current literature remains unclear and inconsistent with regard to the relationship between EEG activity and flow experience. The objective of this research is to assess EEG activity corresponding to the state of flow. A laboratory experiment was conducted to collect EEG data of the flow and other states (including baseline) in the context of HCI.
Hierarchical Reinforcement Learning: A Comprehensive Survey, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Hierarchical Reinforcement Learning: A Comprehensive Survey, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Research Collection School Of Computing and Information Systems
Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future …
Prediction Of Sleep Quality In Live-Alone Diabetic Seniors Using Unobtrusive In-Home Sensors, Barry Nuqoba, Hwee-Pink Tan
Prediction Of Sleep Quality In Live-Alone Diabetic Seniors Using Unobtrusive In-Home Sensors, Barry Nuqoba, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Diabetes, a chronic disease that occurs when the pancreas does not produce enough insulin or when the body cannot effectively utilize its insulin, is increasingly recognized as a significant health burden and affects many older adults. Poor sleep quality in diabetic seniors worsens the diabetes condition, but most seniors are tend to regard poor sleep quality as a usual event and do not seek treatment. This study aims to detect poor sleep quality in diabetic seniors through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental health of diabetic seniors. We derive …
Virtual Reality For Hazard Mitigation And Community Resilience: An Interdisciplinary Collaboration With Community Engagement To Enhance Risk Awareness, N. J. Stone, G. Yan, Fiona Fui-Hoon Nah, C. Sabharwal, K. Angle, F. E. Hatch, S. Runnels, V. Brown, G. M. Schoor, C. Engelbrecht
Virtual Reality For Hazard Mitigation And Community Resilience: An Interdisciplinary Collaboration With Community Engagement To Enhance Risk Awareness, N. J. Stone, G. Yan, Fiona Fui-Hoon Nah, C. Sabharwal, K. Angle, F. E. Hatch, S. Runnels, V. Brown, G. M. Schoor, C. Engelbrecht
Research Collection School Of Computing and Information Systems
To achieve community resilience and mitigate the consequences of natural hazards, community officials must balance competing priorities for local resources and funding. Besides the challenge of dealing with multiple competing priorities, community officials face another challenge: low risk awareness of natural hazards by the public and other stakeholders. Considering that virtual reality (VR) has been used to enhance learning and to change attitudes and behaviors, animating natural hazards in VR has the potential to enhance stakeholders’ (e.g., the public, local/state/federal governments, insurance agencies, and property owners) risk awareness. Informed stakeholders make better decisions related to protective action. Therefore, we propose …
Discovering Interpretable Latent Space Directions Of Gans Beyond Binary Attributes, Huiting Yang, Liangyu Chai, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He
Discovering Interpretable Latent Space Directions Of Gans Beyond Binary Attributes, Huiting Yang, Liangyu Chai, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He
Research Collection School Of Computing and Information Systems
Generative adversarial networks (GANs) learn to map noise latent vectors to high- fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free- form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions …
Spatially-Invariant Style-Codes Controlled Makeup Transfer, Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, Shengfeng He
Spatially-Invariant Style-Codes Controlled Makeup Transfer, Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Transferring makeup from the misaligned reference image is challenging. Previous methods overcome this barrier by computing pixel-wise correspondences between two images, which is inaccurate and computational-expensive. In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process. To this end, we propose a Style-based Controllable GAN model that consists of three components, each of which corresponds to target style-code encoding, face identity features extraction, and makeup fusion, respectively. In particular, a Part-specific Style Encoder encodes the component-wise makeup style of the reference image into a style-code in an intermediate latent space …
Delving Deep Into Many-To-Many Attention For Few-Shot Video Object Segmentation, Haoxin Chen, Hanjie Wu, Nanxuan Zhao, Sucheng Ren, Shengfeng He
Delving Deep Into Many-To-Many Attention For Few-Shot Video Object Segmentation, Haoxin Chen, Hanjie Wu, Nanxuan Zhao, Sucheng Ren, Shengfeng He
Research Collection School Of Computing and Information Systems
This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object information. This is a many-to-many problem and often relies on full-rank attention, which is computationally intensive. In this paper, we propose a novel Domain Agent Network (DAN), breaking down the full-rank attention into two smaller ones. We consider one single frame of the query video as the domain agent, bridging between the support …
Ultrapin: Inferring Pin Entries Via Ultrasound, Liu, Ximing, Robert H. Deng, Robert H. Deng
Ultrapin: Inferring Pin Entries Via Ultrasound, Liu, Ximing, Robert H. Deng, Robert H. Deng
Research Collection School Of Computing and Information Systems
While PIN-based user authentication systems such as ATM have long been considered to be secure enough, they are facing new attacks, named UltraPIN, which can be launched from commodity smartphones. As a target user enters a PIN on a PIN-based user authentication system, an attacker may use UltraPIN to infer the PIN from a short distance (50 cm to 100 cm). In this process, UltraPIN leverages smartphone speakers to issue human-inaudible ultrasound signals and uses smartphone microphones to keep recording acoustic signals. It applies a series of signal processing techniques to extract high-quality feature vectors from low-energy and high-noise signals …
When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang
When Program Analysis Meets Bytecode Search: Targeted And Efficient Inter-Procedural Analysis Of Modern Android Apps In Backdroid, Daoyuan Wu, Debin Gao, Robert H. Deng, Rocky Chang
Research Collection School Of Computing and Information Systems
Widely-used Android static program analysis tools,e.g., Amandroid and FlowDroid, perform the whole-app interprocedural analysis that is comprehensive but fundamentallydifficult to handle modern (large) apps. The average app size hasincreased three to four times over five years. In this paper, weexplore a new paradigm of targeted inter-procedural analysis thatcan skip irrelevant code and focus only on the flows of securitysensitive sink APIs. To this end, we propose a technique calledon-the-fly bytecode search, which searches the disassembled appbytecode text just in time when a caller needs to be located. In thisway, it guides targeted (and backward) inter-procedural analysisstep by step until reaching …
Minimizing The Regret Of An Influence Provider, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Baihua Zheng
Minimizing The Regret Of An Influence Provider, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Baihua Zheng
Research Collection School Of Computing and Information Systems
Influence maximization has been studied extensively from the perspective of the influencer. However, the influencer typically purchases influence from a provider, for example in the form of purchased advertising. In this paper, we study the problem from the perspective of the influence provider. Specifically, we focus on influence providers who sell Out-of-Home (OOH) advertising on billboards. Given a set of requests from influencers, how should an influence provider allocate resources to minimize regret, whether due to forgone revenue from influencers whose needs were not met or due to over-provisioning of resources to meet the needs of influencers? We formalize this …
Incorrectness Logic For Graph Programs, Christopher M. Poskitt
Incorrectness Logic For Graph Programs, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Program logics typically reason about an over-approximation of program behaviour to prove the absence of bugs. Recently, program logics have been proposed that instead prove the presence of bugs by means of under-approximate reasoning, which has the promise of better scalability. In this paper, we present an under-approximate program logic for a nondeterministic graph programming language, and show how it can be used to reason deductively about program incorrectness, whether defined by the presence of forbidden graph structure or by finitely failing executions. We prove this 'incorrectness logic' to be sound and complete, and speculate on some possible future applications …
Catch You With Cache: Out-Of-Vm Introspection To Trace Malicious Executions, Chao Su, Xuhua Ding, Qinghai Zeng
Catch You With Cache: Out-Of-Vm Introspection To Trace Malicious Executions, Chao Su, Xuhua Ding, Qinghai Zeng
Research Collection School Of Computing and Information Systems
Out-of-VM introspection is an imperative part of security analysis. The legacy methods either modify the system, introducing enormous overhead, or rely heavily on hardware features, which are neither available nor practical in most cloud environments. In this paper, we propose a novel analysis method, named as Catcher, that utilizes CPU cache to perform out-of-VM introspection. Catcher does not make any modifications to the target program and its running environment, nor demands special hardware support. Implemented upon Linux KVM, it natively introspects the target's virtual memory. More importantly, it uses the cache-based side channel to infer the target control flow. To …
Expressive Bilateral Access Control For Internet-Of-Things In Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Jinhua Ma, Xinyi Huang, Hwee Hwa Pang, Robert H. Deng
Expressive Bilateral Access Control For Internet-Of-Things In Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Jinhua Ma, Xinyi Huang, Hwee Hwa Pang, Robert H. Deng
Research Collection School Of Computing and Information Systems
As a versatile system architecture, cloud-fog Internet-of-Things (IoT) enables multiple resource-constrained devices to communicate and collaborate with each other. By outsourcing local data and immigrating expensive workloads to cloud service providers and fog nodes (FNs), resource-constrained devices can enjoy data services with low latency and minimal cost. To protect data security and privacy in the untrusted cloud-fog environment, many cryptographic mechanisms have been invented. Unfortunately, most of them are impractical when directly applied to cloud-fog IoT computing, mainly due to the large number of resource-constrained end-devices (EDs). In this paper, we present a secure cloud-fog IoT data sharing system with …