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2022 2nd International Conference on Communication technology and Information technology, ICCTIT 2022

作者： Li, Shuang Zhang, Xin Zhou, Yue Zhang, Meng School of Computer Science Fudan University 200433 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology 201112 China

With the increasing requirement of people, the functions of in-vehicle infotainment systems are becoming more and more abundant, and their security also affects the safety of vehicles. Therefore, it is more and more important to evaluate the security of the IVI system. This paper proposes a security evaluation framework for in-vehicle infotainment systems based on threat analyses and penetration tests. By constructing the data flow diagram of application scenarios, analyzing threats, combing the attack link diagram, combining white-box audit and black-box test, we use the characteristics of high efficiency of automatic tools and high accuracy of manual methods to set factor sets, and then the whole IVI system is evaluated by Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. © Published under licence by IOP Publishing Ltd.

关键词： Data flow analysis

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DeepTD: Diversity-Guided Deep Neural Network Test Generation 9th

DeepTD: Diversity-Guided Deep Neural Network Test Generation

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9th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Zhu, Jin Tao, Chuanqi Guo, Hongjing Ju, Yue College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China State Key Laboratory for Novel Software Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9789819986637

Coverage-guided Fuzz Testing (CGF) techniques have been applied to deep neural network (DNN) testing in recent years, generating a significant number of test samples to uncover inherent defects in DNN models. However, the effectiveness of CGF techniques that utilize structured coverage metrics as coverage criteria is currently being questioned. A few unstructured coverage metrics, such as surprise adequacy, only take into account the diversity of the test samples against the training set, while ignoring the diversity of the test samples themselves. In addition to this, the existing surprise adequacy metrics have some limitations in their applications. Therefore, this paper proposes DeepTD, a diversity-guided deep neural networks test generation method. Firstly, DeepTD selects high-loss test samples from each class on average, ensuring these test seeds possess a strong ability to reveal model errors. Then, DeepTD transforms these test seeds to enhance the diversity of the generated samples. Finally, Cluster-based Surprise Adequacy is designed to guide the generation of test samples. To evaluate the effectiveness of DeepTD, six DNN models are selected as subjects, covering two well-known image datasets. Experimental results demonstrate that the Cluster-based Surprise Adequacy outperforms the two existing metrics not only in computational efficiency but also in discovering more model defects. What’s more, the test samples generated by DeepTD are on average 6.04% and 3.24% more effective for model retraining in MNIST and CIFAR10 compared to baseline methods, respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coverage-guided fuzz testing Deep neural network Test generation Test samples diversity

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Introduction to the Special Issue on Formal Methods and Models for System Design

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Brandt, Jens Saha, Indranil Zhang, Lijun Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

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SyNet:Medical image anomaly detection with noise synthesis network 10

SyNet:Medical image anomaly detection with noise synthesis n...

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Liu, Jingnian Liang, Wei Chen, Yuxiang Jin, Kai Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtang China

ISBN: (纸本)9798350389524

Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to supervised learning not being able to obtain sufficient training. While existing unsupervised learning is mainly based on reconstruction and generation as well as feature embedding, these methods do not take into account the problem of data type bias during domain migration and often face the problem of loose judgment boundaries. To this end, this paper proposes a novel unsupervised learning method, SyNet, based on noisy anomaly synthesis. First, the problem at domain migration is well solved by aggregating the range of features within the feature map, fusing the multiscale features at the intermediate level, and adding feature adapters. Then, anomaly samples are synthesized by adding noise in the feature space, which is more efficient and stable than generating samples in the image space. Finally, normal and abnormal data are distinguished by training the discriminator. In this paper, experiments are conducted on ISIC and brain MRI datasets, and SyNet achieves nearly 20% improvement in AUC, ACC and other metrics compared with the current mainstream methods, and also has good inference efficiency. © 2024 IEEE.

关键词： Anomaly detection

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LAMB: Label-Induced Mixed-Level Blending for Multimodal Multi-label Emotion Detection 19th

LAMB: Label-Induced Mixed-Level Blending for Multimodal Mul...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Qian, Shuwei Guo, Ming Fan, Zhicheng Chen, Mingcai Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031545276

To better understand complex human emotions, there is growing interest in utilizing heterogeneous sensory data to detect multiple co-occurring emotions. However, existing studies have focused on extracting static information from each modality, while overlooking various interactions within and between modalities. Additionally, the label-to-modality and label-to-label dependencies still lack exploration. In this paper, we propose LAbel-induced Mixed-level Blending (LAMB) to address these challenges. Mixed-level blending leverages shallow but manifold self-attention and cross-attention encoders in parallel to model unimodal context dependency and cross-modal interaction simultaneously. This is in contrast to previous works either use one of them or cascade them successively, which ignores the diversity of interaction in multimodal data. LAMB also employs label-induced aggregation to allow different labels to attend to the most relevant blended tokens adaptively using a transformer-based decoder, which facilitates the exploration of label-to-modality dependency. Unlike common low-order strategies in multi-label learning, correlations among multiple labels can be learned by self-attention in label embedding space before being treated as queries. Comprehensive experiments demonstrate the effectiveness of our methods for multimodal multi-label emotion detection. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Emotion Recognition

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Time Series Data Cleaning under Expressive Constraints on Both Rows and Columns 40

Time Series Data Cleaning under Expressive Constraints on Bo...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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The Computational Advantage of MIP∗ Vanishes in the Presence of Noise 39

The Computational Advantage of MIP∗ Vanishes in the Presenc...

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39th Computational Complexity Conference, CCC 2024

作者： Dong, Yangjing Fu, Honghao Natarajan, Anand Qin, Minglong Xu, Haochen Yao, Penghui State Key Laboratory for Novel Software Technology Nanjing University China Massachusetts Institute of Technology CambridgeMA United States State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Hefei National Laboratory China

ISBN: (纸本)9783959773317

The class MIP∗ of quantum multiprover interactive proof systems with entanglement is much more powerful than its classical counterpart MIP [7, 29, 28]: while MIP = NEXP, the quantum class MIP∗ is equal to RE, a class including the halting problem. This is because the provers in MIP∗ can share unbounded quantum entanglement. However, recent works [46, 47] have shown that this advantage is significantly reduced if the provers’ shared state contains noise. This paper attempts to exactly characterize the effect of noise on the computational power of quantum multiprover interactive proof systems. We investigate the quantum two-prover one-round interactive system MIP∗ [poly, O(1)], where the verifier sends polynomially many bits to the provers and the provers send back constantly many bits. We show noise completely destroys the computational advantage given by shared entanglement in this model. Specifically, we show that if the provers are allowed to share arbitrarily many EPR states, where each EPR state is affected by an arbitrarily small constant amount of noise, the resulting complexity class is equivalent to NEXP = MIP. This improves significantly on the previous best-known bound of NEEEXP (nondeterministic triply exponential time) [46]. We also show that this collapse in power is due to the noise, rather than the O(1) answer size, by showing that allowing for noiseless EPR states gives the class the full power of RE = MIP∗ [poly, poly]. Along the way, we develop two technical tools of independent interest. First, we give a new, deterministic tester for the positivity of an exponentially large matrix, provided it has a low-degree Fourier decomposition in terms of Pauli matrices. Secondly, we develop a new invariance principle for smooth matrix functions having bounded third-order Fréchet derivatives or which are Lipschitz continuous. © Yangjing Dong, Honghao Fu, Anand Natarajan, Minglong Qin, Haochen Xu, and Penghui Yao.

关键词： Fourier analysis

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Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines 30

Recommending Missed Citations Identified by Reviewers: A New...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Long, Kehan Li, Shasha Wang, Pancheng Bao, Chenlong Tang, Jintao Wang, Ting School of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

ISBN: (纸本)9782493814104

Citing comprehensively and appropriately has become a challenging task with the explosive growth of scientific publications. Current citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper. However, none of the existing work focuses on already included citations of full papers, which are imperfect and still have much room for improvement. In the scenario of peer reviewing, it is a common phenomenon that submissions are identified as missing vital citations by reviewers. This may lead to a negative impact on the credibility and validity of the research presented. To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR. We conduct an extensive evaluation of several state-of-the-art methods on CitationR. Furthermore, we propose a new framework RMCNet with an Attentive Reference Encoder module mining the relevance between papers, already-made citations, and missed citations. Empirical results prove that RMC is challenging, with the proposed architecture outperforming previous methods in all metrics. We release our dataset and benchmark models to motivate future research on this challenging new task. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Paper

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Testing Constraint Checking Implementations via Principled Metamorphic Transformations 31

Testing Constraint Checking Implementations via Principled M...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Gao, Mingchen Wang, Huiyan Xu, Chang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350330663

Constraint checking techniques are being widely used for ensuring the consistency of software artifacts during their development and evolution (e.g., detecting inconsistency in an application's running contexts or identifying rule violation in the code being developed). Typically, consistency constraints are formulated and checked upon the changes of software artifacts under checking. When any constraint is violated, an inconsistency is said to occur and then follow-up actions can be taken to remedy the problem. Currently, various constraint checking techniques have been proposed and implemented with sophisticated mechanisms for higher efficiency and scalability. However, these implementations could be far from being well tested due to their oracle problems, i.e., hardly able to tell what the checking result should be, given any software artifacts and their consistency constraints to check. In this paper, we leverage metamorphic testing and propose a family of metamorphic relations catered for testing constraint checking implementations. We dedicatedly design these relations by following the sensitivity principle via a fine-granularity control and the diversity principle via input-oriented transformations. Our experiments reported promising results (disclosing 80 % mutation bugs and five real bugs) without the need of any manual labeling. © 2024 IEEE.

关键词： software testing

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Adversarial image detection based on the maximum channel of saliency maps

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Optoelectronics Letters 2022年第5期18卷 307-312页

作者： FU Haoran WANG Chundong LIN Hao HAO Qingbo Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Studies have shown that deep neural networks(DNNs) are vulnerable to adversarial examples(AEs) that induce incorrect behaviors. To defend these AEs, various detection techniques have been developed. However, most of them only appear to be effective against specific AEs and cannot generalize well to different AEs. We propose a new detection method against AEs based on the maximum channel of saliency maps(MCSM). The proposed method can alter the structure of adversarial perturbations and preserve the statistical properties of images at the same time. We conduct a complete evaluation on AEs generated by 6 prominent adversarial attacks on the Image Net large scale visual recognition challenge(ILSVRC) 2012 validation sets. The experimental results show that our method performs well on detecting various AEs.

关键词： networks neural maps

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