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Articles 4111 - 4140 of 8481
Full-Text Articles in Computer Sciences
Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng
Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Healthcare Internet-of-things (IoT) has been proposed as a promising means to greatly improve the efficiency and quality of patient care. Medical devices in healthcare IoT measure patients' vital signs and aggregate these data into medical files which are uploaded to the cloud for storage and accessed by healthcare workers. To protect patients' privacy, encryption is normally used to enforce access control of medical files by authorized parties while preventing unauthorized access. In healthcare, it is crucial to enable timely access of patient files in emergency situations. In this paper, we propose a lightweight break-glass access control (LiBAC) system that supports …
Pusc: Privacy-Preserving User-Centric Skyline Computation Over Multiple Encrypted Domains, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Yang Yang
Pusc: Privacy-Preserving User-Centric Skyline Computation Over Multiple Encrypted Domains, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Yang Yang
Research Collection School Of Computing and Information Systems
In this paper, we present a new privacy-preserving user-centric skyline computation framework over different encrypted domains, which we referred to as PUSC. With PUSC, a user can flexibly obtain the skyline set from different service providers without disclosing user preferences to third parties in the system. Specifically, we introduce a secure user-defined vector dominance protocol to compare the vector dominance relationship between two encrypted vectors, according to user's preference. This serves as the core protocol in PUSC. Detailed security analysis shows that the proposed PUSC achieves the goal of selecting skyline set according to authorized users' preferences without leaking their …
Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang
Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call “entailment vectors.” We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that …
Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Online spatio-temporal matching of servers/services to customers is a problem that arises at a large scale in many domains associated with shared transportation (e.g., taxis, ride sharing, super shuttles, etc.) and delivery services (e.g., food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that the matching of servers/services to customers in one stage has a direct impact on the matching in the next stage. For instance, it is efficient for taxis to pick up customers closer to the drop off point of the customer from the first stage of matching. Traditionally, greedy/myopic approaches have been adopted …
Privacy-Preserving Biometric-Based Remote User Authentication With Leakage Resilience, Yangguang Tian, Yingjiu Li, Rongmao Chen, Ximeng Liu, Bing Chang, Xingjie Yu
Privacy-Preserving Biometric-Based Remote User Authentication With Leakage Resilience, Yangguang Tian, Yingjiu Li, Rongmao Chen, Ximeng Liu, Bing Chang, Xingjie Yu
Research Collection School Of Computing and Information Systems
Biometric-based remote user authentication is a useful primitive that allows an authorized user to authenticate to a remote server using his biometrics. Leakage attacks, such as side-channel attacks, allow an attacker to learn partial knowledge of secrets (e.g., biometrics) stored on any physical medium. Leakage attacks can be potentially launched to any existing biometric-based remote user authentication systems. Furthermore, applying plain biometrics is an efficient and straightforward approach when designing remote user authentication schemes. However, this approach jeopardises user’s biometrics privacy. To address these issues, we propose a novel leakage-resilient and privacy-preserving biometric-based remote user authentication framework, such that registered …
An Empirical Study Of Security Issues Posted In Open Source Projects, Mansooreh Zahedi, M. Ali Babar, Christoph Treude
An Empirical Study Of Security Issues Posted In Open Source Projects, Mansooreh Zahedi, M. Ali Babar, Christoph Treude
Research Collection School Of Computing and Information Systems
When developers gain thorough understanding and knowledge of software security, they can produce more secure software. This study aims at empirically identifying and understanding the security issues posted on a random sample of GitHub repositories. We tried to understand the presence of security issues and their key themes and topics. We applied a mixedmethods approach, combining topic modeling techniques and qualitative analysis. Our findings have revealed that a) the rate of security-related issues was rather small (approx. 3% of all issues), b) the majority of the security issues were related to identity management and cryptography topics. We present 7 high-level …
Knowledge As A Bridge: Improving Cross-Domain Answer Selection With External Knowledge, Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Knowledge As A Bridge: Improving Cross-Domain Answer Selection With External Knowledge, Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Research Collection School Of Computing and Information Systems
Answer selection is an important but challenging task. Significant progresses have been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the …
Regular Lossy Functions And Their Applications In Leakage-Resilient Cryptography, Yu Chen, Baodong Qin, Haiyang Xue
Regular Lossy Functions And Their Applications In Leakage-Resilient Cryptography, Yu Chen, Baodong Qin, Haiyang Xue
Research Collection School Of Computing and Information Systems
In STOC 2008, Peikert and Waters introduced a powerful primitive called lossy trapdoor functions (LTFs). In a nutshell, LTFs are functions that behave in one of two modes. In the normal mode, functions are injective and invertible with a trapdoor. In the lossy mode, functions statistically lose information about their inputs. Moreover, the two modes are computationally indistinguishable. In this work, we put forward a relaxation of LTFs, namely, regular lossy functions (RLFs). Compared to LTFs, the functions in the normal mode are not required to be efficiently invertible or even unnecessary to be injective. Instead, they could also be …
Practical Attribute-Based Multi-Keyword Search Scheme In Mobile Crowdsourcing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Xinghua Li, Zhiquan Liu, Hui Li
Practical Attribute-Based Multi-Keyword Search Scheme In Mobile Crowdsourcing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Xinghua Li, Zhiquan Liu, Hui Li
Research Collection School Of Computing and Information Systems
Cloud-based mobile crowd-sourcing has been an attractive solution to provide data storage and share services for resource-limited mobile devices in a privacy-preserving manner, but how to enable mobile users to issue search queries and achieve fine-grained access control over ciphertexts simultaneously is still a big challenge for various circumstances. Although the ciphertext-policy attribute-based keyword search technology combining attribute-based encryption with searchable encryption has become a hot research topic, it just deals with equivalent attributes rather than more practical attribute comparisons, like “greater than” or “less than.” In this paper, we devise a practical cryptographic primitive called attribute-based multi-keyword search scheme …
Secure And Efficient Outsourcing Of Large-Scale Overdetermined Systems Of Linear Equations, Shiran Pan, Wen-Tao Zhu, Qiongxiao Wang, Bing Chang
Secure And Efficient Outsourcing Of Large-Scale Overdetermined Systems Of Linear Equations, Shiran Pan, Wen-Tao Zhu, Qiongxiao Wang, Bing Chang
Research Collection School Of Computing and Information Systems
We address overdetermined systems of linear equations, where the number of unknowns is smaller than the number of equations so that only approximate solutions exist instead of exact solutions. Such systems are prevalent in many areas of science and engineering, and finding the optimal solutions is mathematically known as the linear least squares (LLS) problem. Real-world overdetermined systems are often large-scale and computationally expensive to solve. Consequently, we are interested in connecting the LLS problem with cloud computing, where a resource-constrained client outsources the problem to a powerful but untrusted cloud. Among several security considerations is that the input of …
Trusting Artificial Intelligence In Healthcare, W. Wang, Keng Siau
Trusting Artificial Intelligence In Healthcare, W. Wang, Keng Siau
Research Collection School Of Computing and Information Systems
Artificial Intelligence (AI) is able to perform at humans and even surpass human’s performances in some tasks. Recent cases about self-driving cars, cashier-free supermarket Amazon Go, and virtual assistants such as Apple’s Siri and Google Assistant have illustrated the current and future potential of AI. AI and its applications have infiltrated human’s work and daily life. It is inevitable that humans need to build a working relationship with AI and its applications. On one hand, humans can benefit from this new technology, for instance, a home robot can release housewife from mundane and monotonous tasks (Siau 2017, Siau 2018). On …
Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau
Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau
Research Collection School Of Computing and Information Systems
AI-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data security, data privacy, and ethical problems of AI-based technology also pose significant risks for users, developers, and governments. As the AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. This study will focus on the ethics and morality issues that may be caused by AI, andmay arise because of AI. This research uses …
Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah
Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Mining is an important industrial and economic sector that plays a major role in the economic development of a country and provides many employment opportunities. Implementation of Artificial Intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with the first application to autonomous trucks. The autonomous technologies provide many economic benefits to the mining industry through cost reduction, productivity improvement, reduction in exposure of workers to hazardous conditions, continuous production, and improved safety. However, implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This paper discusses the current status …
Code Smells For Model-View-Controller Architectures, Maurício Aniche, Gabriele Bavota, Christoph Treude, Marco Aurélio Gerosa, Arie Van Deursen
Code Smells For Model-View-Controller Architectures, Maurício Aniche, Gabriele Bavota, Christoph Treude, Marco Aurélio Gerosa, Arie Van Deursen
Research Collection School Of Computing and Information Systems
Previous studies have shown the negative effects that low-quality code can have on maintainability proxies, such as code change- and defect-proneness. One of the symptoms of low-quality code are code smells, defined as sub-optimal implementation choices. While this definition is quite general and seems to suggest a wide spectrum of smells that can affect software systems, the research literature mostly focuses on the set of smells defined in the catalog by Fowler and Beck, reporting design issues that can potentially affect any kind of system, regardless of their architecture (e.g., Complex Class). However, systems adopting a specific architecture (e.g., the …
Unusual Events In Github Repositories, Christoph Treude, Larissa Leite, Maurício Aniche
Unusual Events In Github Repositories, Christoph Treude, Larissa Leite, Maurício Aniche
Research Collection School Of Computing and Information Systems
In large and active software projects, it becomes impractical for a developer to stay aware of all project activity. While it might not be necessary to know about each commit or issue, it is arguably important to know about the ones that are unusual. To investigate this hypothesis, we identified unusual events in 200 GitHub projects using a comprehensive list of ways in which an artifact can be unusual and asked 140 developers responsible for or affected by these events to comment on the usefulness of the corresponding information. Based on 2,096 answers, we identify the subset of unusual events …
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Research Collection School Of Computing and Information Systems
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random …
Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen
Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer …
Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan
Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results …
The Price Of Usability: Designing Operationalizable Strategies For Security Games, Sara Marie Mccarthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe
The Price Of Usability: Designing Operationalizable Strategies For Security Games, Sara Marie Mccarthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe
Research Collection School Of Computing and Information Systems
We consider the problem of allocating scarce security resources among heterogeneous targets to thwart a possible attack. It is well known that deterministic solutions to this problem being highly predictable are severely suboptimal. To mitigate this predictability, the game-theoretic security game model was proposed which randomizes over pure (deterministic) strategies, causing confusion in the adversary. Unfortunately, such mixed strategies typically involve randomizing over a large number of strategies, requiring security personnel to be familiar with numerous protocols, making them hard to operationalize. Motivated by these practical considerations, we propose an easy to use approach for computing strategies that are easy …
Privacy-Preserving Mining Of Association Rule On Outsourced Cloud Data From Multiple Parties, Lin Liu, Jinshu Su, Rongmao Chen, Ximeng Liu, Xiaofeng Wang, Shuhui Chen, Ho-Fung Fung Leung
Privacy-Preserving Mining Of Association Rule On Outsourced Cloud Data From Multiple Parties, Lin Liu, Jinshu Su, Rongmao Chen, Ximeng Liu, Xiaofeng Wang, Shuhui Chen, Ho-Fung Fung Leung
Research Collection School Of Computing and Information Systems
It has been widely recognized as a challenge to carry out data analysis and meanwhile preserve its privacy in the cloud. In this work, we mainly focus on a well-known data analysis approach namely association rule mining. We found that the data privacy in this mining approach have not been well considered so far. To address this problem, we propose a scheme for privacy-preserving association rule mining on outsourced cloud data which are uploaded from multiple parties in a twin-cloud architecture. In particular, we mainly consider the scenario where the data owners and miners have different encryption keys that are …
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Research Collection School Of Computing and Information Systems
Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …
Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao
Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
This chapter is based on exploiting the network-based representations of proteins, metagraphs, in protein-protein interaction network to identify candidate disease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To enhance PPI networks, we utilize biological properties of individual proteins as well. More specifically, we integrate keywords from UniProt database describing protein properties into the PPI network and construct a novel heterogeneous PPI-Keyword (PPIK) network consisting …
A Bayesian Latent Variable Model Of User Preferences With Item Context, Aghiles Salah, Hady W. Lauw
A Bayesian Latent Variable Model Of User Preferences With Item Context, Aghiles Salah, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse. We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users’ choices. Intuitively, items that occur within the same “context” (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect. Therefore, accounting for the item’s context would complement the sparse user-item interactions by extending a user’s preference to other items …
Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong
Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong
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 …
Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou
Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou
Research Collection School Of Computing and Information Systems
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we …
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence …
Face Detection Using Deep Learning: An Improved Faster Rcnn Approach, Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
Face Detection Using Deep Learning: An Improved Faster Rcnn Approach, Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark
Analysis Of Public Transportation Patterns In A Densely Populated City With Station-Based Shared Bikes, Di Wang, Evan Wu, Ah-Hwee Tan
Analysis Of Public Transportation Patterns In A Densely Populated City With Station-Based Shared Bikes, Di Wang, Evan Wu, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public …
Towards 'Verifying' A Water Treatment System, Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu
Towards 'Verifying' A Water Treatment System, Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu
Research Collection School Of Computing and Information Systems
Modeling and verifying real-world cyber-physical systems is challenging, which is especially so for complex systems where manually modeling is infeasible. In this work, we report our experience on combining model learning and abstraction refinement to analyze a challenging system, i.e., a real-world Secure Water Treatment system (SWaT). Given a set of safety requirements, the objective is to either show that the system is safe with a high probability (so that a system shutdown is rarely triggered due to safety violation) or not. As the system is too complicated to be manually modeled, we apply latest automatic model learning techniques to …
Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun
Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun
Research Collection School Of Computing and Information Systems
Scalable and automatic formal verification for concurrent systems is always demanding. In this paper, we propose a verification framework to support automated compositional reasoning for concurrent programs with shared variables. Our framework models concurrent programs as succinct automata and supports the verification of multiple important properties. Safety verification and simulations of succinct automata are parallel compositional, and safety properties of succinct automata are preserved under refinements. We generate succinct automata from infinite state concurrent programs in an automated manner. Furthermore, we propose the first automated approach to checking rely-guarantee based simulations between infinite state concurrent programs. We have prototyped our …