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arXiv 2023年

作者： Guo, Yucan Li, Zixuan Jin, Xiaolong Liu, Yantao Zeng, Yutao Liu, Wenxuan Li, Xiang Yang, Pan Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Platform and Content Group Tencent

Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts, which brings challenges to existing methods due to task-specific schemas and complex text expressions. Code, as a typical kind of formalized language, is capable of describing structural knowledge under various schemas in a universal way. On the other hand, Large Language Models (LLMs) trained on both codes and texts have demonstrated powerful capabilities of transforming texts into codes, which provides a feasible solution to IE tasks. Therefore, in this paper, we propose a universal retrieval-augmented code generation framework based on LLMs, called Code4UIE, for IE tasks. Specifically, Code4UIE adopts Python classes to define task-specific schemas of various structural knowledge in a universal way. By so doing, extracting knowledge under these schemas can be transformed into generating codes that instantiate the predefined Python classes with the information in texts. To generate these codes more precisely, Code4UIE adopts the in-context learning mechanism to instruct LLMs with examples. In order to obtain appropriate examples for different tasks, Code4UIE explores several example retrieval strategies, which can retrieve examples semantically similar to the given texts. Extensive experiments on five representative IE tasks across nine datasets demonstrate the effectiveness of the Code4UIE framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense

arXiv

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arXiv 2024年

作者： Zhang, Mingkun Bi, Keping Chen, Wei Chen, Quanrun Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology CAS China Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS China School of Statistics University of International Business and Economics China CAS stands for Chinese Academy of Sciences China

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/TMLResearchGroup-CAS/CausalDiff. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic signs

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TCMLLM-PR:evaluation of large language models for prescription recommendation in traditional Chinese medicine

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Digital Chinese Medicine 2024年第4期7卷 343-355页

作者： TIAN Haoyu YANG Kuo DONG Xin ZHAO Chenxi YE Mingwei WANG Hongyan LIU Yiming HU Minjie ZHU Qiang YU Jian ZHANG Lei ZHOU Xuezhong Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science&TechnologyBeijing Jiaotong UniversityBeijing 100044China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical SciencesBeijing 100700China

Objective To develop and evaluate a fine-tuned large language model(LLM)for traditional Chinese medicine(TCM)prescription recommendation named *** First,we constructed an instruction-tuning dataset containing 68654 samples(ap-proximately 10 million tokens)by integrating data from eight sources,including four TCM textbooks,Pharmacopoeia of the People’s Republic of China 2020(CHP),Chinese Medicine Clinical Cases(CMCC),and hospital clinical records covering lung disease,liver disease,stroke,diabetes,and splenic-stomach ***,we trained TCMLLM-PR using Chat-GLM-6B with P-Tuning v2 *** evaluation consisted of three aspects:(i)compari-son with traditional prescription recommendation models(PTM,TCMPR,and PresRecST);(ii)comparison with TCM-specific LLMs(ShenNong,Huatuo,and HuatuoGPT)and general-domain ChatGPT;(iii)assessment of model migration capability across different disease *** employed precision,recall,and F1 score as evaluation *** The experiments showed that TCMLLM-PR significantly outperformed baseline models on TCM textbooks and CHP datasets,with F1@10 improvements of 31.80%and 59.48%,*** cross-dataset validation,the model performed best when migrating from TCM textbooks to liver disease dataset,achieving an F1@10 of *** of real-world cases demonstrated that TCMLLM-PR's prescription recommendations most closely matched actual doctors’*** This study integrated LLMs into TCM prescription recommendations,leverag-ing a tailored instruction-tuning dataset and developing *** study will pub-licly release the best model parameters of TCMLLM-PR to promote the development of the decision-making process in TCM practices(https://***/2020MEAI/TCMLLM).

关键词： Large language models Instruction-tuning Prescription recommendation Traditional Chinese medicine(TCM) Assisted decision-making

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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and Information networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination

On-Line System of Garbage Image-Orientated Intelligent Class...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Jiayin Tian Yaozhi Wang Jiaxin Liu Yan Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi China Shaanxi Key Lab of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an Shaanxi China Shaanxi Key Lab of Big Data Knowledge Engineering School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described.

关键词： Image recognition Heavily-tailed distribution Heuristic algorithms Green products Prototypes Object detection Classification algorithms Object recognition Few shot learning Residual neural networks

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Causal Logic Discovery and Rule Validation Using Evolutionary Integration Model Global Fit U-Net

Causal Logic Discovery and Rule Validation Using Evolutionar...

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Natural Language Processing (ICNLP), International Conference on

作者： Xuelian Yang Fangyuan Du Kunjun Chen Hao Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In order to explore the correlation between different MRI sequences and the results of U-Net segmentation of glioma subregions, this paper proposes an interpretable method based on an evolutionary integration algorithm for logical discovery of the segmentation process of U-Net. Our approach consists of three steps: 1) Global fitting of the U-Net model to the segmentation results of gliomas using a dual evolutionary algorithm to generate a fitted model with both accuracy and interpretability.2) Extracting decision rules from the fitted model according to a specific target interpretable region and generating a complete set of interpretable rules after optimisation.3) Proposing a decision path integrator modeling method for the target region of decision paths for experimental validation. In this study, 293 patients from the BraTS2020 dataset are used as research data, and the accuracy of the fitted model is obtained to be 0.92, which is basically the same as that of Random Forest, but the model in this study has a better and simpler internal structure. At the same time, this study validated the relationship between Flair sequence and the edema region of glioma, and the experimental results showed that our extracted decision paths have a certain auxiliary effect on the segmentation of U-Net, and also proved the effectiveness of our proposed interpretability method.

关键词： Accuracy Correlation Magnetic resonance imaging Fitting Evolutionary computation data models Natural language processing Logic Random forests

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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LFD-RSNet: Lightweight Face Detection network for Rainy Weather Scenes

LFD-RSNet: Lightweight Face Detection Network for Rainy Weat...

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Natural Language Processing (ICNLP), International Conference on

作者： Xiaoying Pan Yamie Ding Nijuan Zhang Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In response to the problem of low detection accuracy of lightweight models for face detection in complex rainy scenes, this paper proposes a face detection network FDL-RSNet for complex rainy scenes. This network integrates an adaptive image denoising module, an optimizer module, an HCA attention module, and a balanced branching strategy. The FDL-RSNet network can effectively address various challenges in complex rainy backgrounds, such as raindrop noise, differences in image lighting and contrast, and face detection challenges caused by low-quality images with dense targets. Experimental results on the UFDD Rain datasets show that FDL-RSNet has superior face detection capabilities in real-world unconstrained complex rainy scenes, Proved its effectiveness and reliability under such highly challenging conditions.

关键词： Adaptation models Rain Adaptive systems Noise Lighting Natural language processing Face detection Reliability Meteorology Image denoising

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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