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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Adversarial training via multi-guidance and historical memory enhancement

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Neurocomputing 2025年 619卷

作者： Zhao, Chenyu Qian, Yaguan Wang, Bin Gu, Zhaoquan Ji, Shouling Wang, Wei Zhang, Yanchun School of Science Zhejiang University of Science and Technology China Network and Data Security China China School of Computer Science and Technology Zhejiang University China Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University China Institute for Sustainable Industries and Liveable Cities Victoria University Australia

Deep neural networks (DNNs) are often susceptible to the influence of adversarial examples, potentially leading to severe security issues. Adversarial training stands out as one of the most effective defenses. In this paper, we empirically demonstrate that weakly robust models maintain resilience to adversarial examples generated on a fully trained robust model. Building on this observation, we introduce a novel weight ensemble training method named Multi-Guided Adversarial Training (MGAT). MGAT improves both clean and adversarial accuracy through two guiding strategies: self-guidance and expert-guidance. Self-guidance promotes diversity in decision boundaries among member models, while expert-guidance aligns them more closely with natural decision boundaries. Additionally, MGAT enhances the historical memory of model weights using an exponential moving average (EMA) with a memory factor, thereby better memorizing the weights of weakly robust models. Our experiments demonstrate that MGAT is highly effective in defending against a range of adversarial attacks. Notably, MGAT achieves an accuracy of 55.48% against AutoAttack, marking a 10.92% improvement over standard adversarial training on CIFAR-10. © 2024 Elsevier B.V.

关键词： Generative adversarial networks

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TrustRAG: An Information Assistant with Retrieval Augmented Generation

arXiv

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arXiv 2025年

作者： Fan, Yixing Yan, Qiang Wang, Wenshan Guo, Jiafeng Zhang, Ruqing Cheng, Xueqi Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Retrieval-Augmented Generation (RAG) has emerged as a crucial technique for enhancing large models with real-time and domain-specific knowledge. While numerous improvements and open-source tools have been proposed to refine the RAG framework for accuracy, relatively little attention has been given to improving the trustworthiness of generated results. To address this gap, we introduce TrustRAG, a novel framework that enhances RAG from three perspectives: indexing, retrieval, and generation. Specifically, in the indexing stage, we propose a semantic-enhanced chunking strategy that incorporates hierarchical indexing to supplement each chunk with contextual information, ensuring semantic completeness. In the retrieval stage, we introduce a utility-based filtering mechanism to identify high-quality information, supporting answer generation while reducing input length. In the generation stage, we propose fine-grained citation enhancement, which detects opinion-bearing sentences in responses and infers citation relationships at the sentence-level, thereby improving citation accuracy. We open-source the TrustRAG framework and provide a demonstration studio designed for excerpt-based question answering tasks 1. Based on these, we aim to help researchers: 1) systematically enhancing the trustworthiness of RAG systems and (2) developing their own RAG systems with more reliable outputs. © 2025, CC BY.

关键词： Semantics

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Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank

arXiv

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arXiv 2024年

作者： Yu, Lulu Bi, Keping Ni, Shiyu Guo, Jiafeng CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences Beijing China

Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods has primarily been validated on synthetic datasets. However, their performance on real-world click data remains unclear. Recently, Baidu released a large publicly available dataset of their web search logs. Subsequently, the NTCIR-17 ULTRE-2 task released a subset dataset extracted from it. We conduct experiments on commonly used or effective ULTR methods on this subset to determine whether they maintain their effectiveness. In this paper, we propose a Contextual Dual Learning Algorithm with Listwise Distillation (CDLA-LD) to simultaneously address both position bias and contextual bias. We utilize a listwise-input ranking model to obtain reconstructed feature vectors incorporating local contextual information and employ the Dual Learning Algorithm (DLA) method to jointly train this ranking model and a propensity model to address position bias. As this ranking model learns the interaction information within the documents list of the training set, to enhance the ranking model’s generalization ability, we additionally train a pointwise-input ranking model to learn the listwise-input ranking model’s capability for relevance judgment in a listwise manner. Extensive experiments and analysis confirm the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine

RoKEPG: RoBERTa and Knowledge Enhancement for Prescription G...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Pu, Hua Mi, Jiacong Lu, Shan He, Jieyue Southeast University School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Moe Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd Jiangsu Nanjing China

ISBN: (纸本)9798350337488

Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F1 metric by about 2% over the baseline model with the best results. © 2023 IEEE.

关键词： Deep learning Pre-trained language models Traditional Chinese medicine prescription generation

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Low-input redoxomics facilitates global identification of metabolic regulators of oxidative stress in the gut

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Signal Transduction and Targeted Therapy 2025年第2期10卷 885-902页

作者： Xina Xiao Meng Hu Li Gao Huan Yuan Baochen Chong Yu Liu Rou Zhang Yanqiu Gong Dan Du Yong Zhang Hao Yang Xiaohui Liu Yan Zhang Huiyuan Zhang Heng Xu Yi Zhao Wenbo Meng Dan Xie Peng Lei Shiqian Qi Yong Peng Tao Tan Yang Yu Hongbo Hu Biao Dong Lunzhi Dai National Clinical Research Center for Geriatrics State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina Advanced Mass Spectrometry Center Research Core FacilityFrontiers Science Center for Disease-Related Molecular NetworkNHC Key Lab of Transplant Engineering and ImmunologyWest China HospitalSichuan UniversityChengduChina School of Life Sciences Tsinghua UniversityBeijingChina Department of Rheumatology and Immunology West China HospitalSichuan UniversityChengduChina The First School of Clinical Medicine Lanzhou UniversityLanzhouChina State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingChina Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology and Key Laboratory of Assisted Reproduction Ministry of EducationCenter for Reproductive MedicineDepartment of Obstetrics and GynecologyPeking University Third HospitalBeijingChina Frontiers Medical Center Tianfu Jincheng LaboratoryChengduChina

Oxidative stress plays a crucial role in organ aging and related diseases, yet the endogenous regulators involved remain largelyunknown. This work highlights the importance of metabolic homeostasis in protecting against oxidative stress in the large *** developing a low-input and user-friendly pipeline for the simultaneous profiling of five distinct cysteine (Cys) states, includingfree SH, total Cys oxidation (Sto), sulfenic acid (SOH), S-nitrosylation (SNO), and S-glutathionylation (SSG), we shed light on Cysredox modification stoichiometries and signaling with regional resolution in the aging gut of monkeys. Notably, the proteinsmodified by SOH and SSG were associated primarily with cell adhesion. In contrast, SNO-modified proteins were involved inimmunity. Interestingly, we observed that the Sto levels ranged from 0.97% to 99.88%, exhibiting two distinct peaks and increasingwith age. Crosstalk analysis revealed numerous age-related metabolites potentially involved in modulating oxidative stress and Cysmodifications. Notably, we elucidated the role of fumarate in alleviating intestinal oxidative stress in a dextran sulfate sodium (DSS)-induced colitis mouse model. Our findings showed that fumarate treatment promotes the recovery of several cell types, signalingpathways, and genes involved in oxidative stress regulation. Calorie restriction (CR) is a known strategy for alleviating oxidativestress. Two-month CR intervention led to the recovery of many antioxidative metabolites and reshaped the Cys redoxome. Thiswork decodes the complexities of redoxomics during the gut aging of non-human primates and identifies key metabolic regulatorsof oxidative stress and redox signaling.

关键词： metabolic homeostasis total cys oxidation cysteine modifications sulfenic acid gut aging redoxomics oxidative stress

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Generative Retrieval for Book Search

arXiv

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arXiv 2025年

作者： Tang, Yubao Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Liu, Shihao Wang, Shuaiqiang Yin, Dawei Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences University of Amsterdam Amsterdam Netherlands University of Amsterdam Amsterdam Netherlands CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China Baidu Inc Beijing China

In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, where the outline provides hierarchical information between chapters and sections. Generative retrieval (GR) is a new retrieval paradigm that consolidates corpus information into a single model to generate identifiers of documents that are relevant to a given query. How can GR be applied to book search? Directly applying GR to book search is a challenge due to the unique characteristics of book search: (i) The model needs to retain the complex, multi-faceted information of the book, which increases the demand for labeled data. (ii) Splitting book information and treating it as a collection of separate segments for learning might result in a loss of hierarchical information. We propose an effective Generative retrieval framework for Book Search (GBS) that features two main components: (i) data augmentation and (ii) outline-oriented book encoding. For data augmentation, GBS constructs multiple query-book pairs for training;it constructs multiple book identifiers based on the outline, various forms of book contents, and simulates real book retrieval scenarios with varied pseudo-queries. This includes coverage-promoting book identifier augmentation, allowing the model to learn to index effectively, and diversity-enhanced query augmentation, allowing the model to learn to retrieve effectively. Outline-oriented book encoding improves length extrapolation through bi-level positional encoding and retentive attention mechanisms to maintain context over long sequences. Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines, achieving a 9.8% improvement in terms of MRR@20, over the state-of-the-art RIPOR method. Experiments on public datasets confirm the robustness and generalizability of GBS, highlighting its potential to enhance book retrieval. © 2025, CC BY.

关键词： Encoding (symbols)

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Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

arXiv

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arXiv 2024年

作者： Chen, Lu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China

Retrieval-augmented generation (RAG) has emerged as a popular solution to mitigate the hallucination issues of large language models. However, existing studies on RAG seldom address the issue of predictive uncertainty, i.e., how likely it is that a RAG model’s prediction is incorrect, resulting in uncontrollable risks in real-world applications. In this work, we emphasize the importance of risk control, ensuring that RAG models proactively refuse to answer questions with low confidence. Our research identifies two critical latent factors affecting RAG’s confidence in its predictions: the quality of the retrieved results and the manner in which these results are utilized. To guide RAG models in assessing their own confidence based on these two latent factors, we develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers. We also introduce a benchmarking procedure to collect answers with the option to abstain, facilitating a series of experiments. For evaluation, we introduce several risk-related metrics and the experimental results demonstrate the effectiveness of our approach. Our code and benchmark dataset are available at https://***/ictbigdatalab/RC-RAG. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Bending Actuation of Ferromagnetic Soft Continuum Robot under Spatially non-uniform Magnetic Fields 28

Bending Actuation of Ferromagnetic Soft Continuum Robot unde...

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28th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2022

作者： Hao, Lijun Yang, Tangwen Inst. of Info. Sci. and Beijing Key Lab. of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798350334333

Cardiovascular disease seriously affects the health and life quality of human being in nowadays society. In this paper, a new type of ferromagnetic soft continuum robot (FSCR) is devised to realize the flexible maneuver in the blood vessel to treat cardiovascular disease. Its bending actuation performance is studied under the various distance between the robot and a spherical permanent magnet, which generates non-uniform magnetic field, and the different length-diameter ratios of the FSCR. Its bending is calculated respectively by the finite difference (FD) method and the finite element (FE) method. The simulation results of the FD method well agree with the FE method. The workspace of the FSCR is analyzed as well. Consequently, an optimal design and control scheme is established for the FSCR. © 2022 IEEE.

关键词： Finite difference method

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FM-FCN:A Neural network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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