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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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SACM: spatial attributes of complex movements in multi-object tracking

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Multimedia Tools and Applications 2025年 1-20页

作者： Li, Hongjun Li, Jiaxin Sun, Xiaohu School of Information Science and Technology Nantong University Jiangsu Nantong226019 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nantong210023 China Nantong Research Institute for Advanced Communication Technologies Jiangsu Nantong226019 China

Multi-object tracking is a crucial task in computer vision, but it faces significant challenges due to complex scenes and interactions between tracked objects. To address these challenges, we propose a complex movement-based spatial attribute tracking model (SACM). First, to enhance target detection, the model extracts both global and local features from the input feature maps to capture detailed spatial location information between different targets. Second, to strengthen the positional association of the same target across consecutive frames, position information from the previous frame is incorporated into the current frame, improving occlusion handling. Finally, a new target matching method is developed within the tracker, leveraging spatial relationships among targets to achieve precise matching of overlapping objects. Experimental results show that, compared to FairMOT, SACM achieves a 3.7% improvement in HOTA and a 4.8% improvement in IDF1 on the DanceTrack dataset. On the MOT17 dataset, SACM also improves HOTA by 3.7% and IDF1 by 4.8%. Furthermore, SACM achieves a processing speed of 30.5 FPS on a single GPU, resulting in improved tracking performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Target tracking

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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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Palmprint Anti-spoofing via Frequency Enhancement and Selection 18th

Palmprint Anti-spoofing via Frequency Enhancement and Select...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ren, Yani Shao, Huikai Zhong, Dexing School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu NanJing210000 China Xi’an Yizhanghui Technology Co. Shannxi Xi’an712000 China Pazhou Lab Guangzhou510335 China Research Institute of Xi’an Jiaotong University Zhejiang311215 China

ISBN: (纸本)9789819610679

Palmprint recognition has attracted considerable interest from researchers as a convenient and secure biometric identification technology. The majority of research on palmprint recognition focuses on enhancing recognition accuracy and speed. However, the security aspects of palmprint recognition are rarely considered. This paper proposed a palmprint anti-spoofing method, called Frequency Enhancement and Selection (FES), based on frequency-domain features. Firstly, the palmprint images are transformed using the discrete cosine transform to obtain frequency-domain features. These features are then enhanced by the frequency-domain enhancement module. Finally, the frequency channels for classification are filtered based on the gate module. In this paper, experiments are conducted on a palmprint anti-spoofing database, and the experimental results demonstrate that our method can obtain the anti-spoofing accuracy of 99.81% and can outperform other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Frequency domain analysis

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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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An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning systems

arXiv

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arXiv 2025年

作者： Tian, Haoxiang Han, Xingshuo Wu, Guoquan Guo, An Zhou, Yuan Zhang, Jie Li, Shuo Wei, Jun Zhang, Tianwei Key Lab of System Software CAS State Key Lab of Computer Science ISCAS Beijing China University of CAS Beijing China Nanyang Technological University Singapore Continental-NTU Corporate Lab Singapore Nanjing Institute of Software Technology University of CAS Nanjing China Nanjing University Nanjing China Zhejiang Sci-Tech University Hangzhou China CFAR IHPC A*STAR Singapore

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes µMOEA, the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), µMOEA promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation to iteratively produce offspring, balancing the evolutionary efficiency and diversity. During the evolutionary process, to navigate away from the local optima, µMOEA integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM's quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate µMOEA in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that µMOEA can significantly improve the efficiency and diversity of the evolutionary search. © 2025, CC BY.

关键词： Adversarial machine learning

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Modeling and analyzing method for CPS software architecture energy consumption

Journal of Software

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Journal of software 2013年第11期8卷 2974-2981页

作者： Zhang, Guangquan Zhang, Kan Zhu, Xueyang Chen, Mingcai Xu, Chengkai Shao, Yuzhen School of Computer Science and Technology Soochow University Suzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China State Key Lab. of Computer Science Institute of Software Chinese Academy of Science Beijing China

CPS is a kind of networked embedded system. Its trustworthiness and cost are impacted by energy consumption. So design a low-power, high trustworthiness CPS has been a major challenge. Modeling and analyzing the energy consumption of CPS software architecture at design stage can help to find the energy consumption design defects. These methods can effectively improve the trustworthiness of the CPS software and reduce development costs. Against this problem, first introduce the concept of the energy consumption time Petri nets;then through analyzing the characteristics of the CPS software energy consumption, use energy consumption time Petri nets to construct the CPS software architecture energy consumption model and discuss the energy consumption problems of CPS software such as lowest energy consumption, largest energy consumption and average energy consumption;Finally, indicate the effectiveness of the proposed method by an instance. © 2013 ACADEMY PUBLISHER.

关键词： Energy utilization

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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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