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Jaehoon Yu - IEEE Xplore Author Profile

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Author details

Jaehoon Yu

Also published under: Jaehoon yu

Publications

36

Citations

143

Publications by Year

20132024

Co-Authors:

Eric AliwargaKota AndoYusuke ArakiXu BaiNaoki Banno

Show All Co-Authors (66)

Jaehoon Yu

Also published under: Jaehoon yu

Affiliation

Tokyo Institute of Technology

Midori-ku

Yokohama, Japan

Publication Topics

Deep Neural Network,
Neural Network,
Power Consumption,
Datapath,
Active Memory,
Bit-width,
Computational Complexity,
Control,
Convolutional Neural Network,
Cooperative Control,
Inference Time,
Ising Model

Biography

Jaehoon Yu (S’11–M’13) received the B.E. degree in electrical and electronic engineering and the M.S. degree in communications and computer engineering from Kyoto University, Kyoto, Japan, in 2005 and 2007, respectively, and the Ph.D. degree in information systems engineering from Osaka University, Osaka, Japan, in 2013.,He is currently an Assistant Professor at the Department of Information Systems Engineering, Osaka University.,Dr. Yu is a member of the Institute of Electronics, Information and Communication Engineers (IEICE).(Based on document published on 26 March 2018).

Publications

36

Citations

143

Publications by Year

20132024

Co-Authors:

Eric Aliwarga
Kota Ando
Yusuke Araki
Xu Bai
Naoki Banno

Show All Co-Authors (66)

Author's Published Works

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Showing 1-25 of 79 results

Conferences (62)

Journals (16)

Magazines (1)

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Jin Song Dong(58)
Jun Sun(44)
Yang Liu(31)
Yun Lin(14)
Guangdong Bai(12)
School of Computing, National University of Singapore, Singapore(29)
National University of Singapore, Singapore(20)
Singapore University of Technology and Design, Singapore(16)
National University of Singapore(13)
School of Computer Engineering, Nanyang Technological University, Singapore(10)
IEEE Transactions on Software Engineering(10)
2016 21st International Conference on Engineering of Complex Computer Systems (ICECCS)(4)
2012 IEEE 17th International Conference on Engineering of Complex Computer Systems(3)
2015 20th International Conference on Engineering of Complex Computer Systems (ICECCS)(3)
2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)(3)
IEEE(79)
Media(1)
Dubai, United Arab Emirates(4)
Shanghai, China(4)
Gold Coast, QLD, Australia(3)
Melbourne, VIC, Australia(3)
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Model Checking(34)
State Space(14)
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Revisiting the Conflict-Resolving Problem from a Semantic Perspective

Jinhao Dong;Jun Sun;Yun Lin;Yedi Zhang;Murong Ma;Jin Song Dong;Dan Hao

2024 39th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2024 | Conference Paper |

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Collaborative software development significantly enhances development productivity by enabling multiple contributors to work concurrently on different branches. Despite these advantages, such collaboration often increases the likelihood of causing conflicts. Resolving these conflicts brings huge challenges, primarily due to the necessity of comprehending the differences between conflicting version...Show More

Revisiting the Conflict-Resolving Problem from a Semantic Perspective

Jinhao Dong;Jun Sun;Yun Lin;Yedi Zhang;Murong Ma;Jin Song Dong;Dan Hao

2024 39th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2024 | Conference Paper |

Detecting and Explaining Anomalies Caused by Web Tamper Attacks via Building Consistency-based Normality

Yifan Liao;Ming Xu;Yun Lin;Xiwen Teoh;Xiaofei Xie;Ruitao Feng;Frank Liauw;Hongyu Zhang;Jin Song Dong

2024 39th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2024 | Conference Paper |

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Web applications are crucial infrastructures in the modern society, which have high demand of reliability and security. However, their frontend can be manipulable by the clients (e.g., the frontend code can be modified to bypass some validation steps), which incurs the runtime anomaly when operating the web service. Existing state-of-the-art anomaly detectors largely learn a deep learning model fr...Show More

Detecting and Explaining Anomalies Caused by Web Tamper Attacks via Building Consistency-based Normality

Yifan Liao;Ming Xu;Yun Lin;Xiwen Teoh;Xiaofei Xie;Ruitao Feng;Frank Liauw;Hongyu Zhang;Jin Song Dong

2024 39th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2024 | Conference Paper |

Continuous-Time Distributed Generalized Nash Equilibrium Seeking in Nonsmooth Fuzzy Aggregative Games

Jingxin Liu;Xiaofeng Liao;Jin-Song Dong;Amin Mansoori

IEEE Transactions on Control of Network Systems

Year: 2024 | Volume: 11, Issue: 3 | Journal Article |

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This article explores the variational generalized Nash equilibrium seeking for a class of fuzzy aggregative games, where each player's cost function is fuzzy and nonsmooth, and the strategy profile is constrained by a coupling nonlinear inequality, a affine coupling equality with $\alpha$-cuts and heterogeneous local convex sets. Under the differential inclusion framework, a continuous-time distri...Show More

Continuous-Time Distributed Generalized Nash Equilibrium Seeking in Nonsmooth Fuzzy Aggregative Games

Jingxin Liu;Xiaofeng Liao;Jin-Song Dong;Amin Mansoori

IEEE Transactions on Control of Network Systems

Year: 2024 | Volume: 11, Issue: 3 | Journal Article |

A Quaternion-Valued Neural Network Approach to Nonsmooth Nonconvex Constrained Optimization in Quaternion Domain

Jingxin Liu;Xiaofeng Liao;Jin-Song Dong

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

Cited by: Papers (1)

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Based on the framework of differentialinclusion, we design a quaternion-valued neural network (QVNN) to solve a class of nonsmooth nonconvex optimization problems with equality, bounded and inequality constraints in quaternion domain. The modeling of the network avoids the calculation of penalty factors and guarantees the convergence in finite time to the feasible region and the sets of critical p...Show More

A Quaternion-Valued Neural Network Approach to Nonsmooth Nonconvex Constrained Optimization in Quaternion Domain

Jingxin Liu;Xiaofeng Liao;Jin-Song Dong

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization

Jingxin Liu;Xiaofeng Liao;Jin-Song Dong

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 7 | Journal Article |

Cited by: Papers (2)

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This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the ...Show More

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization

Jingxin Liu;Xiaofeng Liao;Jin-Song Dong

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 7 | Journal Article |

Insight Analysis for Tennis Strategy and Tactics

Zhaoyu Liu;Kan Jiang;Zhe Hou;Yun Lin;Jin Song Dong

2023 IEEE International Conference on Data Mining (ICDM)

Year: 2023 | Conference Paper |

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Nowadays there are a wealth of devices and cameras at sports venues and facilities that collect different forms of data. Mining useful insights from such data are crucial for improving the performance of professional athletes. In this paper, we introduce a new interactive tennis analytics framework that can realistically simulate tennis matches using parameters mined from past match data and help ...Show More

Insight Analysis for Tennis Strategy and Tactics

Zhaoyu Liu;Kan Jiang;Zhe Hou;Yun Lin;Jin Song Dong

2023 IEEE International Conference on Data Mining (ICDM)

Year: 2023 | Conference Paper |

Sports Injury Prediction in Professional Tennis

Zhaoyu Liu;Kan Jiang;Jin Song Dong

2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)

Year: 2023 | Conference Paper |

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Each week, there are 2-3 top-level professional tennis tournaments around the world offering attractive prize money and ranking points for the players. Even though match playing is an important part of the professional tennis player’s career development, sports injuries will happen if the player plays too many tournaments. By analyzing historical data and studying cases where players retired durin...Show More

Sports Injury Prediction in Professional Tennis

Zhaoyu Liu;Kan Jiang;Jin Song Dong

2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)

Year: 2023 | Conference Paper |

Recognizing a Sequence of Events from Tennis Video Clips: Addressing Timestep Identification and Subtle Class Differences

Zhaoyu Liu;Jingyu Guo;Mo Wang;Ruicong Wang;Kan Jiang;Jin Song Dong

2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Detecting temporally precise and fine-grained events from tennis videos is important in automatic video annotation. This paper addresses the challenges of recognizing a sequence of events from tennis video clips, focusing on accurate timestep identification and distinguishing subtle class differences. We propose a novel but simple end-to-end event detection network to accurately detect and identif...Show More

Recognizing a Sequence of Events from Tennis Video Clips: Addressing Timestep Identification and Subtle Class Differences

Zhaoyu Liu;Jingyu Guo;Mo Wang;Ruicong Wang;Kan Jiang;Jin Song Dong

2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)

Year: 2023 | Conference Paper |

Supervised Robustness-preserving Data-free Neural Network Pruning

Mark Huasong Meng;Guangdong Bai;Sin G. Teo;Jin Song Dong

2023 27th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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When deploying pre-trained neural network models in real-world applications, model consumers often encounter resource-constraint platforms such as mobile and smart devices. They typically use the pruning technique to reduce the size and complexity of the model, generating a lighter one with less resource consumption. Nonetheless, most existing pruning methods are proposed with a premise that the m...Show More

Supervised Robustness-preserving Data-free Neural Network Pruning

Mark Huasong Meng;Guangdong Bai;Sin G. Teo;Jin Song Dong

2023 27th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2023 | Conference Paper |

DeepArc: Modularizing Neural Networks for the Model Maintenance

Xiaoning Ren;Yun Lin;Yinxing Xue;Ruofan Liu;Jun Sun;Zhiyong Feng;Jin Song Dong

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Neural networks are an emerging data-driven programming paradigm widely used in many areas. Unlike traditional software systems consisting of decomposable modules, a neural network is usually delivered as a monolithic package, raising challenges for some maintenance tasks such as model restructure and re-adaption. In this work, we propose DeepArc, a novel modularization method for neural networks,...Show More

DeepArc: Modularizing Neural Networks for the Model Maintenance

Xiaoning Ren;Yun Lin;Yinxing Xue;Ruofan Liu;Jun Sun;Zhiyong Feng;Jin Song Dong

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

Year: 2023 | Conference Paper |

gDefects4DL: A Dataset of General Real-World Deep Learning Program Defects

Yunkai Liang;Yun Lin;Xuezhi Song;Jun Sun;Zhiyong Feng;Jin Song Dong

2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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The development of deep learning programs, as a new programming paradigm, is observed to suffer from various defects. Emerging research works have been proposed to detect, debug, and repair deep learning bugs, which drive the need to construct the bug benchmarks. In this work, we present gDefects4DL, a dataset for general bugs of deep learning programs. Comparing to existing datasets, gDefects4DL ...Show More

gDefects4DL: A Dataset of General Real-World Deep Learning Program Defects

Yunkai Liang;Yun Lin;Xuezhi Song;Jun Sun;Zhiyong Feng;Jin Song Dong

2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Year: 2022 | Conference Paper |

A Digital Twin Runtime Verification Framework for Protecting Satellites Systems from Cyber Attacks

Zhé Hóu;Qinyi Li;Ernest Foo;Jin Song Dong;Paulo de Souza

2022 26th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2022 | Conference Paper |

Cited by: Papers (8)

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This paper presents the conceptualisation of a framework that combines digital twins with runtime verification and applies the techniques in the context of security monitoring and verification for satellites. We focus on special considerations needed for space missions and satellites, and we discuss how digital twins in such applications can be developed and how the states of the twins should be s...Show More

A Digital Twin Runtime Verification Framework for Protecting Satellites Systems from Cyber Attacks

Zhé Hóu;Qinyi Li;Ernest Foo;Jin Song Dong;Paulo de Souza

2022 26th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2022 | Conference Paper |

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling

Peixin Zhang;Jingyi Wang;Jun Sun;Xinyu Wang;Guoliang Dong;Xingen Wang;Ting Dai;Jin Song Dong

IEEE Transactions on Software Engineering

Year: 2022 | Volume: 48, Issue: 9 | Journal Article |

Cited by: Papers (10)

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Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning applications with societal impact is fairness (i.e., non-discrimination). Unfortunately, discrimination might be intrinsically embedded into the models due to the discrimination in the training data. As a countermeasure, fai...Show More

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling

Peixin Zhang;Jingyi Wang;Jun Sun;Xinyu Wang;Guoliang Dong;Xingen Wang;Ting Dai;Jin Song Dong

IEEE Transactions on Software Engineering

Year: 2022 | Volume: 48, Issue: 9 | Journal Article |

A First Look at the Effect of Deep Learning in Coverage-guided Fuzzing

Siqi Li;Yun Lin;Xiaofei Xie;Yuekang Li;Xiaohong Li;Weimin Ge;Yang Liu;Jinsong Dong

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2021 | Conference Paper |

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Fuzzing has been a widely-used technique for discovering software vulnerabilities. Many existing fuzzers leverage coverage-feedback to evolve seeds to maximize (optimize) program branch coverage. Recently, some techniques propose to train deep learning models to predict the branch coverage of an arbitrary input. Those techniques have proved their success in improving coverage and discovering bugs ...Show More

A First Look at the Effect of Deep Learning in Coverage-guided Fuzzing

Siqi Li;Yun Lin;Xiaofei Xie;Yuekang Li;Xiaohong Li;Weimin Ge;Yang Liu;Jinsong Dong

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2021 | Conference Paper |

Self-Checking Deep Neural Networks in Deployment

Yan Xiao;Ivan Beschastnikh;David S. Rosenblum;Changsheng Sun;Sebastian Elbaum;Yun Lin;Jin Song Dong

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

Year: 2021 | Conference Paper |

Cited by: Papers (15)

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The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make mistakes some of the time, and in settings like self-driving vehicles these mistakes must be quickly detected and properly dealt with in deployment. Just as our community has developed effective techniques and mechanisms to moni...Show More

Self-Checking Deep Neural Networks in Deployment

Yan Xiao;Ivan Beschastnikh;David S. Rosenblum;Changsheng Sun;Sebastian Elbaum;Yun Lin;Jin Song Dong

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

Year: 2021 | Conference Paper |

Adversarial Adaptive Neighborhood With Feature Importance-Aware Convex Interpolation

Qian Li;Yong Qi;Qingyuan Hu;Saiyu Qi;Yun Lin;Jin Song Dong

IEEE Transactions on Information Forensics and Security

Year: 2021 | Volume: 16 | Journal Article |

Cited by: Papers (13)

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Adversarial Examples threaten to fool deep learning models to output erroneous predictions with high confidence. Optimization-based methods for constructing such samples have been extensively studied. While being effective in terms of aggression, they typically lack clear interpretation and constraint about their underlying generation process, which thus hinders us from leveraging the produced adv...Show More

Adversarial Adaptive Neighborhood With Feature Importance-Aware Convex Interpolation

Qian Li;Yong Qi;Qingyuan Hu;Saiyu Qi;Yun Lin;Jin Song Dong

IEEE Transactions on Information Forensics and Security

Year: 2021 | Volume: 16 | Journal Article |

Scrutinizing Implementations of Smart Home Integrations

Kulani Mahadewa;Kailong Wang;Guangdong Bai;Ling Shi;Yan Liu;Jin Song Dong;Zhenkai Liang

IEEE Transactions on Software Engineering

Year: 2021 | Volume: 47, Issue: 12 | Journal Article |

Cited by: Papers (5)

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A key feature of the booming smart home is the integration of a wide assortment of technologies, including various standards, proprietary communication protocols and heterogeneous platforms. Due to customization, unsatisfied assumptions and incompatibility in the integration, critical security vulnerabilities are likely to be introduced by the integration. Hence, this work addresses the security p...Show More

Scrutinizing Implementations of Smart Home Integrations

Kulani Mahadewa;Kailong Wang;Guangdong Bai;Ling Shi;Yan Liu;Jin Song Dong;Zhenkai Liang

IEEE Transactions on Software Engineering

Year: 2021 | Volume: 47, Issue: 12 | Journal Article |

Explaining Regressions via Alignment Slicing and Mending

Haijun Wang;Yun Lin;Zijiang Yang;Jun Sun;Yang Liu;Jinsong Dong;Qinghua Zheng;Ting Liu

IEEE Transactions on Software Engineering

Year: 2021 | Volume: 47, Issue: 11 | Journal Article |

Cited by: Papers (11)

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Regression faults, which make working code stop functioning, are often introduced when developers make changes to the software. Many regression fault localization techniques have been proposed. However, issues like inaccuracy and lack of explanation are still obstacles for their practical application. In this work, we propose a trace-based approach to identifying not only where the root cause of a...Show More

Explaining Regressions via Alignment Slicing and Mending

Haijun Wang;Yun Lin;Zijiang Yang;Jun Sun;Yang Liu;Jinsong Dong;Qinghua Zheng;Ting Liu

IEEE Transactions on Software Engineering

Year: 2021 | Volume: 47, Issue: 11 | Journal Article |

An Empirical Study on Correlation between Coverage and Robustness for Deep Neural Networks

Yizhen Dong;Peixin Zhang;Jingyi Wang;Shuang Liu;Jun Sun;Jianye Hao;Xinyu Wang;Li Wang;Jinsong Dong;Ting Dai

2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2020 | Conference Paper |

Cited by: Papers (18)

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Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs....Show More

An Empirical Study on Correlation between Coverage and Robustness for Deep Neural Networks

Yizhen Dong;Peixin Zhang;Jingyi Wang;Shuang Liu;Jun Sun;Jianye Hao;Xinyu Wang;Li Wang;Jinsong Dong;Ting Dai

2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2020 | Conference Paper |

Deep Learning Application in Broadcast Tennis Video Annotation

Kan Jiang;Masoumeh Izadi;Zhaoyu Liu;Jin Song Dong

2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2020 | Conference Paper |

Cited by: Papers (5)

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We are in the era that sport is increasingly defined by data. Rich data is a powerful enabling foundation for novel insights on the game and on player actions, and consequently used for fan engagement and better decision making. Detailed data that has specifications of each shot is currently missing in tennis, as the game dynamics is fast and it is beyond human ability to manually record all speci...Show More

Deep Learning Application in Broadcast Tennis Video Annotation

Kan Jiang;Masoumeh Izadi;Zhaoyu Liu;Jin Song Dong

2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2020 | Conference Paper |

RL: a Language for Formal Engineering

Hadrien Bride;Jin Song Dong;Zhé Hóu;Brendan Mahony;Jim McCarthy

2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2020 | Conference Paper |

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Reflection is a notion that naturally emerges from philosophy, mathematics, and sciences. In short, reflection is the ability of an entity to alter its own behaviour. This paper suggests that reflection is crucial to the development of complex and trustworthy software systems. We present RL, a reflective computational model that aims to support the development of a large-scale framework for modell...Show More

RL: a Language for Formal Engineering

Hadrien Bride;Jin Song Dong;Zhé Hóu;Brendan Mahony;Jim McCarthy

2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS)

Year: 2020 | Conference Paper |

Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction

Guoliang Dong;Jingyi Wang;Jun Sun;Yang Zhang;Xinyu Wang;Ting Dai;Jin Song Dong;Xingen Wang

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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Neural networks are becoming a popular tool for solving many realworld problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, which hinders humans from interpreting and consequently trusting them in making critical decisions. Towards interpreting neural networks, several ap...Show More

Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction

Guoliang Dong;Jingyi Wang;Jun Sun;Yang Zhang;Xinyu Wang;Ting Dai;Jin Song Dong;Xingen Wang

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2020 | Conference Paper |

White-box Fairness Testing through Adversarial Sampling

Peixin Zhang;Jingyi Wang;Jun Sun;Guoliang Dong;Xinyu Wang;Xingen Wang;Jin Song Dong;Ting Dai

2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)

Year: 2020 | Conference Paper |

Cited by: Papers (12)

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Although deep neural networks (DNNs) have demonstrated astonishing performance in many applications, there are still concerns on their dependability. One desirable property of DNN for applications with societal impact is fairness (i.e., non-discrimination). In this work, we propose a scalable approach for searching individual discriminatory instances of DNN. Compared with state-of-the-art methods,...Show More

White-box Fairness Testing through Adversarial Sampling

Peixin Zhang;Jingyi Wang;Jun Sun;Guoliang Dong;Xinyu Wang;Xingen Wang;Jin Song Dong;Ting Dai

2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)

Year: 2020 | Conference Paper |

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Pardeep Kumar;Yun Lin;Guangdong Bai;Andrew Paverd;Jin Song Dong;Andrew Martin

IEEE Communications Surveys & Tutorials

Year: 2019 | Volume: 21, Issue: 3 | Journal Article |

Cited by: Papers (240)

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Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency, and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring, and managing multiple domains in the SG. Despite the salient features of SMI, security, and privacy issues hav...Show More

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Pardeep Kumar;Yun Lin;Guangdong Bai;Andrew Paverd;Jin Song Dong;Andrew Martin

IEEE Communications Surveys & Tutorials

Year: 2019 | Volume: 21, Issue: 3 | Journal Article |

Break the Dead End of Dynamic Slicing: Localizing Data and Control Omission Bug

Yun Lin;Jun Sun;Lyly Tran;Guangdong Bai;Haijun Wang;Jinsong Dong

2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2018 | Conference Paper |

Cited by: Papers (10)

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Dynamic slicing is a common way of identifying the root cause when a program fault is revealed. With the dynamic slicing technique, the programmers can follow data and control flow along the program execution trace to the root cause. However, the technique usually fails to work on omission bugs, i.e., the faults which are caused by missing executing some code. In many cases, dynamic slicing over-s...Show More

Break the Dead End of Dynamic Slicing: Localizing Data and Control Omission Bug

Yun Lin;Jun Sun;Lyly Tran;Guangdong Bai;Haijun Wang;Jinsong Dong

2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)

Year: 2018 | Conference Paper |

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