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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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DTDMat: A Comprehensive SVBRDF Dataset with Detailed Text Descriptions 19

DTDMat: A Comprehensive SVBRDF Dataset with Detailed Text De...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Chen, Mufan Wang, Yanxiang Hu, Detao Zhu, Pengfei Guo, Jie Guo, Yanwen State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400713484

In this paper, we designed an automatic annotation tool to generate full descriptions to solve material datasets lacking essential text information challenge. This tool can extract six aspect tags from BRDF maps: intrinsic type, texture, color, roughness, lightness, and other relevant attributes. We applied this tool to both open-source material datasets and our own dataset to create DTDMat, which consists of 14,919 high-resolution Physically Based Rendering materials, each accompanied by a detailed text description. DTDMat covers 20 intrinsic material types and 22 texture structures. It stands out as the most diverse dataset in this domain and represents the largest texture dataset with associated text, offering a wide range of categories and diverse descriptions. We then trained a text-to-material generation framework based on DTDMat, yielding multiple generated BRDF maps that satisfy the input text. © 2024 ACM.

关键词： Large datasets

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DUNKS: Chunking and Summarizing Large and Heterogeneous Data for Dataset Search 23rd

DUNKS: Chunking and Summarizing Large and Heterogeneous Da...

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23rd International Semantic Web Conference, ISWC 2024

作者： Chen, Qiaosheng Zhou, Xiao Zhang, Zhiyang Cheng, Gong State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783031778490

With the vast influx of open data on the Web, dataset search has become a trending research problem which is crucial to data discovery and reuse. Existing methods for dataset search either employ only the unstructured metadata of datasets but ignore their actual data, or cater to structured data in a single format such as RDF despite the diverse formats of open data. In this paper, to address the magnitude of large datasets, we decompose RDF data into data chunks, and then, to accommodate big chunks to the limited input capacity of dense ranking models based on pre-trained language models, we propose a multi-chunk summarization method that extracts representative data from representative chunks. Moreover, to handle heterogeneous data formats beyond RDF, we transform other formats into chunks to be processed in a uniform way. Experiments on two test collections for dataset search demonstrate the effectiveness of our dense ranking over summarized data chunks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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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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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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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalab.lity in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalab.lity,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalab.lity brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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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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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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Transferring Foundation Models for Generalizable Robotic Manipulation

Transferring Foundation Models for Generalizable Robotic Man...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Yang, Jiange Tan, Wenhui Jin, Chuhao Yao, Keling Liu, Bei Fu, Jianlong Song, Ruihua Wu, Gangshan Wang, Limin Renmin University of China China The Chinese University of HongKong Shenzhen China Microsoft Research United States Nanjing University State Key Laboratory for Novel Software Technology China Shanghai Ai Lab China

ISBN: (纸本)9798331510831

Improving the generalization capabilities of general-purpose robotic manipulation in real world has long been a significant challenge. Existing approaches often rely on collecting large-scale robotic data which is costly and time-consuming. However, due to insufficient diversity of data, they typically suffer from limiting their capability in open-domain scenarios with new objects and diverse environments. In this paper, we propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models, to condition robot manipulation tasks. By integrating the mask modality, which incorporates semantic, geometric, and temporal correlation priors derived from vision foundation models, into the end-to-end policy model, our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning, including new object instances, semantic categories, and unseen backgrounds. We first introduce a series of foundation models to ground natural language demands across multiple tasks. Secondly, we develop a two-stream 2D policy model based on imitation learning, which processes raw images and object masks to predict robot actions with a local-global perception manner. Extensive real-world experiments conducted on a Franka Emika robot and a low-cost dual-arm robot demonstrate the effectiveness of our proposed paradigm and policy. Demos can be found in link 1 or link 2 and our code will be released at https://***/MCG-NJU/TPM. © 2025 IEEE.

关键词： Robotic arms

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