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Improving prompt tuning-based software vulnerability assessment by fusing source code and vulnerability description

Automated Software Engineering 2025年第2期32卷 1-29页

作者： Wang, Jiyu Chen, Xiang Pei, Wenlong Yang, Shaoyu School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nanjing China

To effectively allocate resources for vulnerability remediation, it is crucial to prioritize vulnerability fixes based on vulnerability severity. With the increasingnumber of vulnerabilities in recent years, there is an urgent need for automated methods for software vulnerability assessment (SVA). Most of the previous SVA studies mainly rely on traditional machine learning methods. Recently, fine-tuning pre-trained language models has emerged as an intuitive method for improving performance. However, there is a gap between pre-training and fine-tuning, and their performance heavily depends on the dataset’s quality of the downstream task. Therefore, we propose a prompt tuning-based method PT-SVA. Different from the fine-tuning paradigm, the prompt-tuning paradigm involves adding prompts to make the training process similar to pre-training, thereby better adapting to downstream tasks. Moreover, previous research aimed to automatically predict severity by only analyzing either the vulnerability descriptions or the source code of the vulnerability. Therefore, we further consider both types of vulnerability information for designing hybrid prompts (i.e., a combination of hard and soft prompts). To evaluate PT-SVA, we construct the SVA dataset based on the CVSS V3 standard, while previous SVA studies only consider the CVSS V2 standard. Experimental results show that PT-SVA outperforms ten state-of-the-art SVA baselines, such as by 13.7% to 42.1% in terms of MCC. Finally, our ablation experiments confirm the effectiveness of PT-SVA’s design, specifically in replacing fine-tuning with prompt tuning, incorporating both types of vulnerability information, and adopting hybrid prompts. Our promising results indicate that prompt tuning-based SVA is a promising direction and needs more follow-up studies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Bi-modal information Hybrid prompt Pre-trained language model Prompt tuning Software vulnerability assessment

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Synthesizing Invariants for Polynomial Programs by Semidefinite Programming

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ACM Transactions on Programming Languages and systems 2025年第1期47卷 1-35页

作者： Wu, Hao Wang, Qiuye Xue, Bai Zhan, Naijun Zhi, Lihong Yang, Zhi-Hong State Key Lab. of Computer Sciences Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Fermat Labs Huawei Inc. Dongguan China Key Lab. of System Software and State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technology School of Computer Science Peking University Beijing China School of Mathematics and Statistics Central South University Changsha China

Constraint-solving-based program invariant synthesis takes a parametric invariant template and encodes the (inductive) invariant conditions into constraints. The problem of characterizing the set of all valid parameter assignments is referred to as the strong invariant synthesis problem, while the problem of finding a concrete valid parameter assignment is called the weak invariant synthesis problem. For both problems, the challenge lies in solving or reducing the encoded constraints, which are generally non-convex and lack efficient solvers. In this article, we propose two novel algorithms for synthesizing invariants of polynomial programs using semidefinite programming (SDP): (1) The Cluster algorithm targets the strong invariant synthesis problem for polynomial invariant templates. Leveraging robust optimization techniques, it solves a series of SDP relaxations and yields a sequence of increasingly precise under-approximations of the set of valid parameter assignments. We prove the algorithm’s soundness, convergence, and weak completeness under a specific robustness assumption on templates. Moreover, the outputs can simplify the weak invariant synthesis problem. (2) The Mask algorithm addresses the weak invariant synthesis problem in scenarios where the aforementioned robustness assumption does not hold, rendering the Cluster algorithm ineffective. It identifies a specific subclass of invariant templates, termed masked templates, involving parameterized polynomial equalities and known inequalities. By applying variable substitution, the algorithm transforms constraints into an equivalent form amenable to SDP relaxations. Both algorithms have been implemented and demonstrated superior performance compared to state-of-the-art methods in our empirical evaluation. © 2025 Copyright held by the owner/author(s).

关键词： Polynomial approximation

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Research on 1050 nm high-power narrow linewidth fiber laser based on tapered ytterbium-doped fiber 7

Research on 1050 nm high-power narrow linewidth fiber laser ...

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7th Optics Young Scientist Summit, OYSS 2024

作者： Ye, Yun Wang, Xiaolin Wang, Yanbin Zhou, Xuanfeng Jiang, Chenglong Zhang, Defeng Xi, Xiaoming Xu, Xiaojun State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang471003 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

Compared to conventional uniform fibers, long tapered Yb-doped fibers (T-YDF) have outstanding potential to alleviate the detrimental nonlinear effects due to their unique mode field characteristics along the length direction. In this work, an T-YDF-based short-wavelength narrow linewidth monolithic fiber amplifier was constructed through one-stage MOPA configuration. The fiber amplifier emits at the central wavelength of ~1050 nm, and the output lasing performance is carefully studied for different pumping scheme. The experimental results indicated that compared with the co-pumping scheme, the counter-pump one holds a better properties involving spectral broadening suppression, SRS suppression and TMI threshold enhancement in the 1050 nm narrow-linewidth fiber lasers. As a result, an output power of 3.56 kW was achieved with a slope efficiency of ~83.6% by adopting bidirectional pumping scheme. At the maximum lasing power, the 3dB spectral linewidth was measured ~0.58 nm, and the SRS suppression ratio was greater than ~27.3dB. This work could offer a promising approach for extending the operating wavelength of high-power narrow linewidth fiber lasers. © 2025 SPIE.

关键词： Fiber lasers

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Automated Design of Complex Analog Circuits with Multiagent Based Reinforcement Learning 23

Automated Design of Complex Analog Circuits with Multiagent ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Jinxin Zhang Jiarui Bao Zhangcheng Huang Xuan Zeng Ye Lu State Key Lab. of ASIC & System School of Information Science and Technology Frontier Institute of Chip and System State Key Lab. of ASIC & System Microelectronics Department Fudan University Shanghai China

ISBN: (纸本)9798350323481

Despite the effort of analog circuit design automation, currently complex analog circuit design still requires extensive manual iterations, making it lab.r intensive and time-consuming. Recently, reinforcement learning (RL) algorithms have been demonstrated successfully for the analog circuit design optimization. However, a robust and highly efficient RL method to design analog circuits with complex design space has not been fully explored yet. In this work, inspired by multiagent planning theory as well as human expert design practice, we propose a multiagent based RL (MA-RL) framework to tackle this issue. Particularly, we (i) partition the complex analog circuits into several sub-blocks based on topology information and effectively reduce the complexity of design search space; (ii) leverage MA-RL for the circuit optimization, where each agent corresponds to a single sub-block, and the interactions between agents delicately mimic the best design tradeoffs between circuit sub-blocks by human experts; (iii) introduce the multiagent twin-delayed techniques to further boost training stability and accomplish higher performances. Experiments on two different analog circuit topologies and knowledge transfers between two technology nodes are demonstrated. It's shown that MA-RL framework can achieve the best FoM for complex analog circuits design. This work shines the light for future large scale analog circuit system design automation.

关键词： complex analog circuits

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Navigating the Unknown: A Chat-Based Collab.rative Interface for Personalized Exploratory Tasks 25

Navigating the Unknown: A Chat-Based Collaborative Interface...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Peng, Yingzhe Qin, Xiaoting Zhang, Zhiyang Zhang, Jue Lin, Qingwei Yang, Xu Zhang, Dongmei Rajmohan, Saravan Zhang, Qi Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Nanjing China Microsoft Beijing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China Microsoft RedmondWA United States

ISBN: (纸本)9798400713064

The rise of large language models (LLMs) has revolutionized user interactions with knowledge-based systems, enabling chatbots to synthesize vast amounts of information and assist with complex, exploratory tasks. However, LLM-based chatbots often struggle to provide personalized support, particularly when users start with vague queries or lack sufficient contextual information. This paper introduces the Collab.rative Assistant for Personalized Exploration (CARE), a system designed to enhance personalization in exploratory tasks by combining a multi-agent LLM framework with a structured user interface. CARE's interface consists of a Chat Panel, Solution Panel, and Needs Panel, enabling iterative query refinement and dynamic solution generation. The multi-agent framework collab.rates to identify both explicit and implicit user needs, delivering tailored, actionable solutions. In a within-subject user study with 22 participants, CARE was consistently preferred over a baseline LLM chatbot, with users praising its ability to reduce cognitive load, inspire creativity, and provide more tailored solutions. Our findings highlight CARE's potential to transform LLM-based systems from passive information retrievers to proactive partners in personalized problem-solving and exploration. The code will be made availab.e at https://***/chatbot-care. © 2025 Copyright held by the owner/author(s).

关键词： Chatbots

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Cross-Sensor SAR Image Target Detection Based on Dynamic Feature Discrimination and Center-Aware Calibration

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Xianghui Zhang, Siqian Sun, Zhongzhen Liu, Chenfang Sun, Yuli Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

In practical synthetic aperture radar (SAR) target detection applications, it is often encountered that the training and testing data come from different SAR sensors, leading to a decline in the SAR target detection performance. Although domain adaptation methods can achieve model generalization through feature transfer, the change of scattering characteristics for the same target and the difference in feature distribution, caused by cross sensor, cannot be ignored in SAR images. It is inevitable to lead to the escalation of the offset in the bounding box regression and deviation of the feature alignment. To address these issues, a cross-sensor SAR image target detection method based on the dynamic feature discrimination and center-aware calibration is proposed. Based on the domain adaptation framework, initially, a dynamic feature discrimination module (DFDM) is introduced to address the exacerbated offset in the regression. A bidirectional spatial feature aggregation (BSFA) mechanism is employed to aggregate features in both horizontal and vertical directions, and a multiscale structure is adopted to enhance the scattering and semantic features, which can dynamically constrain the target position while improving target discrimination capability. Then, the center-aware calibration module (CACM) is designed to address the alignment deviation in feature transfer. The target salience relationship is modeled based on the distance between different positions and the target center to suppress the background clutter interference. The perception center of the target is focused by combining the centerness map and classification map, which can calibrate the domain-invariant features and alleviate misalignment. Finally, the proposed method is tested on two datasets, MiniSAR and FARAD, and compared with the latest domain adaption methods. Both mAP and F1 values have improved by more than 6%-20%, verifying the effectiveness of the proposed method. © 1980-2012 IEEE.

关键词： Calibration

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DCAS-BMT: Dynamic Construction and Adjustment of Skewed Bonsai Merkle Tree for Performance Enhancement in Secure Non-Volatile Memory

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IEEE Transactions on Computers 2025年

作者： Zhang, Yu Chen, Renhai Yan, Hangyu Wu, Hongyue Feng, Zhiyong Tianjin University College of Intelligence and Computing Tianjin300350 China Nanjing University State Key Lab. for Novel Software Technology Nanjing210023 China Yunnan University of Finance and Economics Yunnan Key Lab. of Service Computing Kunming650221 China

Traditional DRAM-based memory solutions face challenges, including high energy consumption and limited scalab.lity. Non-Volatile Memory (NVM) offers low energy consumption and high scalab.lity. However, security challenges, particularly data remanence vulnerabilities, persist. Prevalent methods such as the Bonsai Merkle Tree (BMT) are employed to ensure data security. However, the consistency requirements for integrity tree updates have led to performance issues. It is observed that compared to a secure NVM system without persistent secure metadata, the average overhead for updating and persisting the BMT root with persistent secure metadata is as high as 2.48 times. Therefore, this paper aims to mitigate these inefficiencies by leveraging the principle of memory access locality. We propose the Dynamic Construction and Adjustment of Skewed Bonsai Merkle Tree (DCAS-BMT). The DCAS-BMT is dynamically built and continuously adjusted at runtime according to access weights, ensuring frequently accessed memory blocks reside on shorter paths to the root node. This reduces the verification steps for frequently accessed memory blocks, thereby lowering the overall cost of memory authentication and updates. Experimental results using the USIMM memory simulator demonstrate that compared to the widely used BMT approach, the DCAS-BMT scheme shows a performance improvement of 34.1%. © 1968-2012 IEEE.

关键词： Scalab.lity

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年第6期47卷 4652-4667页

作者： Ziyuan Luo Anderson Rocha Boxin Shi Qing Guo Haoliang Li Renjie Wan Department of Computer Science Hong Kong Baptist University Hong Kong SAR China Artificial Intelligence Lab. Recod.ai Institute of Computing University of Campinas Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China IHPC and CFAR Agency for Science Technology and Research (A*STAR) Singapore Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness.

关键词： Neural radiance field Watermarking Three-dimensional displays Training Robustness Pipelines Computational modeling Visualization Solid modeling Rendering (computer graphics)

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Optimal controller design for a class of networked control systems

Optimal controller design for a class of networked control s...

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HIS 2005: Fifth International Conference on Hybrid Intelligent systems

作者： Hongbo, Li Zengqi, Sun Fengge, Wu Fuchun, Sun State Key Lab. of Intelligence Technology and System Department of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)0769524575

This paper is concerned with the optimal controller design of networked control systems (NCSs). Aiming at a class of NCSs, in which network-induced delay and data packet dropout exist simultaneously from sensor to controller, this paper proposes a novel optimal controller based on a new adaptive genetic algorithm and simulated anneal algorithm (AGASA), guaranteed cost controller (GCC) and state feedback controller (SFC). The proposed controller is less conservative than GCC and SFC, and can be applied to both linear and nonlinear NCSs. Two examples are presented to illustrate the proposed controller.

关键词： systems analysis

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Automatic image annotation based-on model space

Automatic image annotation based-on model space

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2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05

作者： Lu, Jing Ma, Shao-Ping Zhang, Min State Key Lab. of Intelligence Technology and System Department of Computer Science and Technology Tsinghua Univ. Beijing 100084

ISBN: (纸本)0780393619

Automatic image annotation is an important but highly challenging problem in content-based image retrieval. This paper introduces a new procedure for providing images with semantic keywords. To bridge the semantic gap, classified images are used to train a special multi-class classifier which maps the visual image feature into the model space. The model-vectors that construct the model space are more appropriate for the image content and are applied to each individual image. Soft lab.ls are then given to the unannotated images during the propagation procedure, and as a keyword, each lab.l is associated with a membership confidence in probability. Thus conceptualized annotation of images could be provided to users. An empirical study of the COREL image database showed that the proposed model-vectors outperformed visual features by 14.0% in the F-measure for annotation. © 2005 IEEE.

关键词： Image analysis

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