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Qualitative and Quantitative Model Checking Against Recurrent Neural Networks

Journal of computer science & technology 2024年第6期39卷 1292-1311页

作者： Zhen Liang Wan-Wei Liu Fu Song Bai Xue Wen-Jing Yang Ji Wang Zheng-Bin Pang Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Institute of Software Chinese Academy of SciencesBeijing 100190China

Recurrent neural networks (RNNs) have been heavily used in applications relying on sequence data such as time series and natural languages. As a matter of fact, their behaviors lack rigorous quality assurance due to the black-box nature of deep learning. It is an urgent and challenging task to formally reason about the behaviors of RNNs. To this end, we first present an extension of linear-time temporal logic to reason about properties with respect to RNNs, such as local robustness, reachability, and some temporal properties. Based on the proposed logic, we formalize the verification obligation as a Hoare-like triple, from both qualitative and quantitative perspectives. The former concerns whether all the outputs resulting from the inputs fulfilling the pre-condition satisfy the post-condition, whereas the latter is to compute the probability that the post-condition is satisfied on the premise that the inputs fulfill the pre-condition. To tackle these problems, we develop a systematic verification framework, mainly based on polyhedron propagation, dimension-preserving abstraction, and the Monte Carlo sampling. We also implement our algorithm with a prototype tool and conduct experiments to demonstrate its feasibility and efficiency.

关键词： recurrent neural network model checking temporal logic qualitative/quantitative verification

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DOS: DIVERSE OUTLIER SAMPLING FOR OUT-OF-DISTRIBUTION DETECTION 12

DOS: DIVERSE OUTLIER SAMPLING FOR OUT-OF-DISTRIBUTION DETECT...

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12th International Conference on Learning Representations, ICLR 2024

作者： Jiang, Wenyu Cheng, Hao Chen, Mingcai Wang, Chongjun Wei, Hongxin Department of Statistics and Data Science Southern University of Science and Technology China State Key Laboratory for Novel Software Technology Nanjing University China

Modern neural networks are known to give overconfident predictions for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model during training, and recent studies emphasize the role of uncertainty in designing the sampling strategy for outlier datasets. However, the OOD samples selected solely based on predictive uncertainty can be biased towards certain types, which may fail to capture the full outlier distribution. In this work, we empirically show that diversity is critical in sampling outliers for OOD detection performance. Motivated by the observation, we propose a straightforward and novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse and informative outliers. Specifically, we cluster the normalized features at each iteration, and the most informative outlier from each cluster is selected for model training with absent category loss. With DOS, the sampled outliers efficiently shape a globally compact decision boundary between ID and OOD data. Extensive experiments demonstrate the superiority of DOS, reducing the average FPR95 by up to 25.79% on CIFAR-100 with TI-300K. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Statistics

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Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

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computers, Materials & Continua 2024年第11期81卷 2517-2545页

作者： Wenming Wang Zhiquan Liu Shumin Zhang Guijiang Liu School of Computer and Information Anqing Normal UniversityAnqing246133China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210023China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and EconomicsKunming650221China

Advancements in the vehicular network technology enable real-time interconnection,data sharing,and intelligent cooperative driving among ***,malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle *** mechanisms serve as an effective solution to this *** recent years,many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes,incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the *** this paper,we propose a distributed vehicular network scheme based on trust ***,the designed architecture partitions multiple vehicle regions into ***,cloud supervision systems(CSSs)verify the accuracy of the information transmitted by ***,the trust scores for vehicles are calculated to reward or penalize them based on the trust evaluation *** proposed scheme demonstrates good scalability and effectively addresses the main cause of malicious information distribution among *** theoretical and experimental analysis show that our scheme outperforms the compared schemes.

关键词： Distributed trust mechanism vehicular network privacy protection

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LPW: an efficient data-aware cache replacement strategy for Apache Spark

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science China(Information sciences) 2023年第1期66卷 77-96页

作者： Hui LI Shuping JI Hua ZHONG Wei WANG Lijie XU Zhen TANG Jun WEI Tao HUANG State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Nanjing Institute of Software Technology

Caching is one of the most important techniques for the popular distributed big data processing framework Spark. For this big data parallel computing framework, which is designed to support various applications based on in-memory computing, it is not possible to cache every intermediate result due to the memory size limitation. The arbitrariness of cache application programming interface(API) usage,the diversity of application characteristics, and the variability of memory resources constitute challenges to achieving high system execution performance. Inefficient cache replacement strategies may cause different performance problems such as long application execution time, low memory utilization, high replacement frequency, and even program execution failure resulting from out of memory. The cache replacement strategy currently adopted by Spark is the least recently used(LRU) strategy. Although LRU is a classical algorithm and has been widely used, it lacks consideration for the environment and workloads. As a result, it cannot achieve good performance under many scenarios. In this paper, we propose a novel cache replacement algorithm, least partition weight(LPW). LPW takes comprehensive consideration of different factors affecting system performance, such as partition size, computational cost, and reference count. The LPW algorithm was implemented in Spark and compared against the LRU as well as other state-of-the-art mechanisms. Our detailed experiments indicate that LPW obviously outperforms its counterparts and can reduce the execution time by up to 75% under typical workloads. Furthermore, the decreasing eviction frequency also shows the LPW algorithm can generate more reasonable predictions.

关键词： Spark memory cache replacement least partition weight data-aware

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USee: Ultrasound-based Device-free Eye Movement Sensing 21

USee: Ultrasound-based Device-free Eye Movement Sensing

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Cheng, Wen Pang, Mingzhi Wan, Haoran Dong, Shichen Liu, Dongxu Wang, Wei State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Princeton University Princeton United States

ISBN: (纸本)9798331519186

Eye movements play a significant role in human-computer interaction and are widely recognized as an essential health indicator, making their detection both appealing and technically challenging. In this paper, we present a system named USEE that achieves high-precision capture of weak and aperiodic eye movements by utilizing fine-grained and ubiquitous ultrasound signals, capturing both blinking and more subtle saccades. We first identify signal changes associated with eye movements by capturing the unique impact of blinking. Further, we establish a pioneering relationship between the residuals from signal decomposition and subtle eye movements. Utilizing innovative signal processing architectures, we mitigate interference and effectively extract eye movement features. Subsequently, we employ one-dimensional convolutional operations in place of signal cross-correlation, designing filters for motion category identification and a lightweight convolutional neural network for saccade direction classification. This enables our system to serve as a foundational sensing layer for eye movement tracking, applicable across diverse applications. We implement USEE on both a research-purpose platform and a commodity Raspberry Pi. Extensive experimental results demonstrate the effectiveness of our system, achieving 91% accuracy in saccade recognition and 94% in blink detection. The system proves robust, even in challenging scenarios with strong interference, such as the presence of moving pedestrians. © 2024 IEEE.

关键词： Motion sensors

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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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： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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RandoMix: a mixed sample data augmentation method with multiple mixed modes

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Multimedia Tools and Applications 2025年第8期84卷 4343-4359页

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior erformance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data augmentation Diversity Mixed-sample RandoMix Robustness

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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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Scalable Multi-Source Pre-training for Graph Neural Networks 24

Scalable Multi-Source Pre-training for Graph Neural Networks

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lin, Mingkai Li, Wenzhong Hong, Xiaobin Lu, Sanglu State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China

ISBN: (纸本)9798400706868

Graph Neural Networks (GNNs) have proven effective in various scenarios. A key strategy involves pre-training existing graphs to extract knowledge that can be transferred to improve performance on downstream tasks, reducing the need for extensive labeled data. However, previous works commonly assumed that pre-training and fine-tuning occur in the same or closely related domains. A limitation is that for each individual graph without accessible pre-training data, a GNN must be trained from scratch, imposing high training overhead and hindering the ability of generalization. In this paper, we address the GNN multi-domain pre-training problem, which intends to pre-train a transferable GNN model from heterogeneous multi-source graph domains and then apply it in an unseen one with minor fine-tuning costs. To this end, we propose a scaLA ble Multi-source Pre-training (LAMP) method. For pre-training, LAMP presents a graph dual-distillation approach to distill massive knowledge from various graph domains to form synthetic homogeneous graphs. Simultaneously, high-level meta-knowledge from the synthetic graphs is extracted to train the GNN model, whose capability can be adjusted according to target graph contexts through a co-training modulation architecture. For fine-tuning, LAMP respectively aligns the target graph distribution, graph context, and graph task with the pretext so that the downstream task in the unseen domain can be reshaped to leverage the transferable knowledge efficiently. Extensive experiments on four different graph domain datasets show the superiority of LAMP. © 2024 ACM.

关键词： Graph neural networks

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Fine-SE: Integrating Semantic Features and Expert Features for software Effort Estimation 24

Fine-SE: Integrating Semantic Features and Expert Features f...

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46th IEEE/ACM International Conference on software Engineering, ICSE 2024

作者： Li, Yue Ren, Zhong Wang, Zhiqi Yang, Lanxin Dong, Liming Zhong, Chenxing Zhang, He State Key Laboratory of Novel Software Technology Software Institute Nanjing University Jiangsu Nanjing China

ISBN: (纸本)9798400702174

Reliable effort estimation is of paramount importance to software planning and management, especially in industry that requires effective and on-time delivery. Although various estimation approaches have been proposed (e.g., planning poker and analogy), they may be manual and/or subjective, which are difficult to apply to other projects. In recent years, deep learning approaches for effort estimation that rely on learning expert features or semantic features respectively have been extensively studied and have been found to be promising. Semantic features and expert features describe software tasks from different perspectives, however, in the literature, the best combination of these two features has not been explored to enhance effort estimation. Additionally, there are a few studies that discuss which expert features are useful for estimating effort in the industry. To this end, we investigate the potential 13 expert features that can be used to estimate effort by interviewing 26 enterprise employees. Based on that, we propose a novel model, called Fine-SE, that leverages semantic features and expert features for effort estimation. To validate our model, a series of evaluations are conducted on more than 30,000 software tasks from 17 industrial projects of a global ICT enterprise and four open-source software (OSS) projects. The evaluation results indicate that Fine-SE provides higher performance than the baselines on evaluation measures (i.e., mean absolute error, mean magnitude of relative error, and performance indicator), particularly in industrial projects with large amounts of software tasks, which implies a significant improvement in effort estimation. In comparison with expert estimation, Fine-SE improves the performance of evaluation measures by 32.0%-45.2% in within-project estimation. In comparison with the state-of-the-art models, Deep-SE and GPT2SP, it also achieves an improvement of 8.9%-91.4% in industrial projects. The experimental results reveal th

关键词： Open source software

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