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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Zhaowei Zhang Wenjing Yu Zibin Wang Youlin Xiang National Key Laboratory of Science and Technology on Information System Security Beijing China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing.

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On the Exploitation of Control Knowledge for Enhancing Automated Planning

SSRN

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SSRN 2023年

作者： Lu, Xu Yu, Bin Chen, Chu Tian, Cong Duan, Zhenhua Institute of Computing Theory and Technology State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China School of Computer Science Qufu Normal University Qufu273165 China

Heuristic search provides an efficient way to automatically explore the state space in planning, while Control Knowledge (CK) also has the potential to significantly increase the performance of planners. Currently, most of the state-of-the-art planners primarily rely on sophisticated heuristic mechanisms. However, these planners fail to scale up and to provide (high-quality) solutions in a range of problems. Appropriate CK may be capable of making hard problems easy to solve, thus leading planners to achieve much higher efficiency. In this paper, we propose a planning approach by incorporating CK with heuristic search in order to leverage the advantages of both. To achieve this, we encode CK specified by a novel variant of Linear Temporal Logic (LTL), referred to as LTL$_{\mathcal{P}}$, into standard planning models. Consequently, we can directly use existing heuristic planners to solve the augmented problem, and avoid tailoring the planners in order to deal with CK implicitly. Experiments demonstrate that our approach shows highly competitive results versus heuristic search and other techniques on many intractable benchmark problems, benefiting in improving the coverage and quality of plans. © 2023, The Authors. All rights reserved.

关键词： Temporal logic

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Named Entity Driven Zero-Shot Image Manipulation

Named Entity Driven Zero-Shot Image Manipulation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhida Feng Li Chen Jing Tian JiaXiang Liu Shikun Feng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Baidu Inc. Institute of Systems Science National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduced StyleEntity, a zero-shot image manipulation model that utilizes named entities as proxies during its training phase. This strategy enables our model to manipulate images using unseen textual descriptions during inference, all within a single training phase. Additionally, we proposed an inference technique termed Prompt Ensemble Latent Averaging (PELA). PELA averages the manipulation directions derived from various named entities during inference, effectively eliminating the noise directions, thus achieving stable manipulation. In our experiments, StyleEntity exhibited superior performance in a zero-shot setting compared to other methods. The code, model weights, and datasets are available at https://***/feng-zhida/StyleEntity.

关键词： Training Adaptation models computer vision Codes Computational modeling Noise Stability analysis

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ODLPIM: A Write-Optimized and Long-Lifetime ReRAM-Based Accelerator for Online Deep Learning

ODLPIM: A Write-Optimized and Long-Lifetime ReRAM-Based Acce...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Heng Zhou Bing Wu Huan Cheng Wei Zhao Xueliang Wei Jinpeng Liu Dan Feng Wei Tong Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan China

ReRAM-based Processing-In-Memory (PIM) architectures have demonstrated high energy efficiency and performance in deep neural network (DNN) acceleration. Most of the existing PIM accelerators for DNN focus on offline batch learning (OBL) which requires the whole dataset to be available before training. However, in the real world, data instances arrive in sequential settings, and even the data pattern may change, which calls concept drift. OBL requires expensive retraining to solve concept drift, whereas online deep learning (ODL) is evidenced to be a better solution to keep the model evolving over streaming data. Unfortunately, when ODL optimizes models over a large-scale data stream in the PIM system, unbalanced writes are more severe than OBL, due to the heavier weight updates, resulting in the amplification of unbalanced writes and lifetime deterioration. In this work, we propose ODLPIM, an online deep learning PIM accelerator that extends the system lifetime through algorithm-hardware co-optimization. ODLPIM adopts a novel write-optimized parameter update (WARP) scheme that reduces the non-critical weight updates in hidden layers. Besides, a table-based inter-crossbar wear-leveling (TIWL) scheme is proposed and applied to the hardware controller to achieve wear-leveling between crossbars for lifetime improvement. Experiments show that WARP reduces weight updates on average to 15.25% and up to 24% compared to that without WARP, and eventually prolongs system lifetime on average to 9.65% and up to 26.81%, with a negligible rise in cumulative error rate (up to 0.31%). By combining WARP with TIWL, the lifetime of ODLPIM is improved by an average of $\mathbf{12}.\mathbf{59}\times$ and up to $\mathbf{17}.\mathbf{73}\times$ .

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Earthquake Magnitude Prediction Using Lstm Based on Strengthened Elitist Genetic Algorithm and Artificial Immune Network

SSRN

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SSRN 2025年

作者： Zhou, Wen Wang, Han Ye, Zhiwei He, Qiyi Lin, Qiao Gong, Xiuwen Shen, Jun School of Computer Science Hubei University of Technology Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan430068 China School of Computer Science University of Nottingham NottinghamNG7 2RD United Kingdom School of Computer Science The University of Sydney SydneyNSW2006 Australia School of Computing and IT University of Wollongong WollongongNSW2522 Australia

Earthquakes, as a natural disaster, pose a significant threat to human life and property. Currently, earthquake magnitude prediction research is constantly deepening, aiming to improve prediction accuracy and reduce the losses caused by earthquakes. Machine Learning (ML) has been widely applied in earthquake prediction. However, the performance of these earthquake prediction approaches is limited by redundant seismic data features, the difficulty in capturing long-term dependencies in time-series data, and challenges in hyperparameter optimization. To address these issues, we propose an earthquake magnitude prediction method based on Long Short-Term Memory (LSTM) networks enhanced by Strengthened Elitist Genetic Algorithms and Artificial Immune Networks (SEGA-AIN-LSTM). Specifically, a Strengthened Elitist Genetic Algorithm (SEGA), which combines the cloning selection strategy from immunology, is employed for feature selection in seismic data, efficiently selecting the optimal subset of features that are most strongly correlated with earthquake magnitude. To handle the issue of capturing long-term dependencies in time-series data, LSTM is introduced for training and prediction using the selected feature data. Furthermore, for obtaining the optimal hyperparameter combination and improving the model's prediction accuracy, a novel Artificial Immune Network (AIN) incorporating Latin Hypercube Sampling (LHS) and an Adaptive Gaussian Mutation (AGM) strategy is used for LSTM hyperparameter optimization. The seismic acoustic and electromagnetic feature data used in this study are sourced from our self-developed Acoustic \& Electromagnetism to AI (AETA) system. To validate the effectiveness of the proposed method, we conducted training and prediction on six station datasets of AETA. By comparing various methods on evaluation metrics such as RMSE, MAE, and R², experimental results demonstrate that our proposed SEGA-AIN-LSTM earthquake magnitude prediction model outperforms st

关键词： Time series

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A Comprehensive Study of OOP-Related Bugs in C++ Compilers

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IEEE Transactions on Software Engineering 2025年第06期51卷 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

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Reference-Based Iterative Interaction With P2-Matching for Stereo Image Super-Resolution

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 34卷 3779-3789页

作者： Runmin Cong Rongxin Liao Feng Li Ronghui Sheng Huihui Bai Renjie Wan Sam Kwong Wei Zhang School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei China School of Computer Science and Engineering Hefei University of Technology Hefei China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Department of Computer Science Hong Kong Baptist University Hong Kong China School of Data Science Lingnan University Hong Kong China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter-view correspondences in low-resolution (LR) space. The potential of referencing a high-quality SR image of one view benefits the SR for the other is often overlooked, while those with abundant textures contribute to accurate correspondences. Therefore, we propose Reference-based Iterative Interaction (RIISSR), which utilizes reference-based iterative pixel-wise and patch-wise matching, dubbed $P^{2}$ -Matching, to establish cross-view and cross-resolution correspondences for SSR. Specifically, we first design the information perception block (IPB) cascaded in parallel to extract hierarchical contextualized features for different views. Pixel-wise matching is embedded between two parallel IPBs to exploit cross-view interaction in LR space. Iterative patch-wise matching is then executed by utilizing the SR stereo pair as another mutual reference, capitalizing on the cross-scale patch recurrence property to learn high-resolution (HR) correspondences for SSR performance. Moreover, we introduce the supervised side-out modulator (SSOM) to re-weight local intra-view features and produce intermediate SR images, which seamlessly bridge two matching mechanisms. Experimental results demonstrate the superiority of RIISSR against existing state-of-the-art methods.

关键词： Feature extraction Iterative methods Superresolution Electronic mail Image resolution Image reconstruction Modulation Estimation Data mining Merging

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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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Design Automation Conference

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and µ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

arXiv

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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