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Synthesizing Invariants for Polynomial Programs by Semidefinite Programming

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

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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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ESA: Example Sieve Approach for Multi-Positive and Unlab.led Learning 25

ESA: Example Sieve Approach for Multi-Positive and Unlabeled...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Li, Zhongnian Wei, Meng Ying, Peng Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400713293

Learning from Multi-Positive and Unlab.led (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.1. In this paper, to alleviate the shifting of minimum risk problem, we propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier. Specifically, we sieve out some examples by utilizing the Certain Loss (CL) value of each example in the training stage and analyze the consistency of the proposed risk estimator. Besides, we show that the estimation error of proposed ESA obtains the optimal parametric convergence rate. Extensive experiments on various real-world datasets show the proposed approach outperforms previous methods. Source code is availab.e at https://***/WilsonMqz/ESA. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Poster Abstract: IMU-assisted Image Stitching for Scenes with Obstructions Based on Camera Motion Sensing 25

Poster Abstract: IMU-assisted Image Stitching for Scenes wit...

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Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems

作者： Saibing Han Yanling Bu Yanchao Zhao Lei Xie College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400714795

Image stitching is often affected by obstructions such as pedestrians and vehicles. Traditional methods often ignore obstructions or retain them, leading to artifacts. They also cause edge distortion due to fixed-perspective stitching. To address above issues, this poster presents IIS, an IMU-assisted Image Stitching method. It uses IMU data for perspective pre-alignment, projecting images to an intermediate perspective to reduce edge distortion. It simultaneously removes obstructions and mitigating their impact. A weighted target scene stitching strategy is integrated to further enhance stitching quality. IIS effectively reduces artifacts and maintains high computational efficiency.

关键词： image stitching

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Toward Covert and Reliable Communication for Anti-eavesdropping Transmission in V2X Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Rui Li, Gaolei Wu, Jun Li, Jianhua Zhao, Yue Liu, Yuchen Chen, Mingzhe Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200030 China Xidian University State Key Lab. of ISN School of Telecommunication Engineering Shaanxi 710071 China North Carolina State University Department of Computer Science Raleigh27607 United States University of Miami Department of Electrical and Computer Engineering Coral Gables33156 United States

The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collab.ratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

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POSTER: STAR (Space-Time Adaptive and Reductive) Algorithms for Real-World Space-Time Optimality

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ACM SIGPLAN Notices 2017年第8期52卷 455-456页

作者： Tang, Yuan You, Ronghui School of Computer Science School of Software Fudan University Shanghai Key Lab. of Intelligent Information Processing State Key Lab. of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

It's important to hit a space-time balance for a real-world algorithm to achieve high performance on modern shared-memory multi-core or many-core systems. However, a large class of dynamic programs with more than $O(1)$ dependency achieve optimality either in space or time, but not both. In the literature, the problem is known as the fundamental space-time tradeoff. By exploiting properly on the runtime system, we show that our STAR (Space-Time Adaptive and Reductive) technique can help these dynamic programs to achieve sublinear parallel time bounds while still maintaining work-, space-, and cache-optimality in a processor- and cache-oblivious fashion. © 2017 ACM.

关键词： Real time systems

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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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Adaptive foreground and shadow segmentation using hidden conditional random fields

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第4期8卷 586-592页

作者： CHU Yi-ping YE Xiu-zi QIAN Jiang ZHANG Yin ZHANG San-yuan School of Computer Science State Key Lab. of CAD ＆ CG Zhejiang University Hangzhou 310027 China

Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).

关键词： Video segmentation Shadow elimination Hidden conditional random fields (HCRFs) On-line learning

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