The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which invo...The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which involve on-board units and road side units in the edge network,as well as the concept of cloud computing,which involves the data center that provides service.The security issues are always an important concern in the design of IoV architecture.To achieve a secure IoV architecture,some security measures are necessary for the cloud computing and fog computing associated with the vehicular network.In this paper,we summarize some research works on the security schemes in the vehicular network and cloud-fog-dew computing platforms which the IoV depends on.展开更多
Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)systems.By breaking up the blockchain network into smaller partiti...Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)systems.By 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 scalability,thereby enhancing the rescue potential.However,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR systems.Adaptability refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission level.To address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding mechanism.Apart from the benefits in scalability 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 shards.We implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the performance.The results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.展开更多
Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category...Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.展开更多
Fuzz testing is crucial for identifying software vulnerabilities,with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection.However,as the need for targeted detection grows,directed grey-bo...Fuzz testing is crucial for identifying software vulnerabilities,with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection.However,as the need for targeted detection grows,directed grey-box fuzzing(DGF)has become essential,focusing on specific vulnerabilities.The initial seed corpus,which consists of carefully selected input samples that the fuzzer uses as a starting point,is fundamental in determining the paths that the fuzzer explores.A well-designed seed corpus can guide the fuzzer more effectively towards critical areas of the code,improving the efficiency and success of the fuzzing process.Even with its importance,much work concentrates on refining guidance mechanisms while paying less attention to optimizing the initial seed corpus.In this paper,we introduce ISC4DGF,a novel approach to generating optimized initial seed corpus for DGF using large language models(LLMs).By leveraging LLMs’deep understanding of software and refined user inputs,ISC4DGF creates a precise seed corpus that efficiently triggers specific vulnerabilities through a multi-round validation process.Implemented on AFL and tested against state-of-the-art fuzzers such as Titan,BEACON,AFLGo,FairFuzz,and Entropic using the Magma benchmark,ISC4DGF achieves a 25.03x speedup with fewer target reaches.Moreover,ISC4DGF improves target vulnerabilities detection accuracy while narrowing the detection scope and reducing code coverage.展开更多
Background:MRI segmentation offers crucial insights for automatic analysis.Although deep learningbased segmentation methods have attained cutting-edge performance,their efffcacy heavily relies on vast sets of meticulo...Background:MRI segmentation offers crucial insights for automatic analysis.Although deep learningbased segmentation methods have attained cutting-edge performance,their efffcacy heavily relies on vast sets of meticulously annotated data.Methods:In this study,we propose a novel semi-supervised MRI segmentation model that is able to explore unlabeled data in multiple aspects based on various semi-supervised learning technologies.Results:We compared the performance of our proposed method with other deep learning-based methods on 2 public datasets,and the results demonstrated that we have achieved Dice scores of 90.3%and 89.4%on the LA and ACDC datasets,respectively.Conclusions:We explored the synergy of various semi-supervised learning technologies for MRI segmentation,and our investigation will inspire research that focuses on designing MRI segmentation models.展开更多
Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make...Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps,such as AI agents and high-level reinforcement learning.Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision,language,and sensory data—into a cohesive decision-making process.Foundation models(FMs)have become pivotal in science and industry,transforming decision-making and research capabilities.Their large-scale,multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare,life sciences,and education.This survey examines IDM’s evolution,advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains,highlighting the challenges and opportunities in building efficient,adaptive,and ethical decision systems.展开更多
Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of method...Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.展开更多
With the advancement of new information technologies,a revolution is being taken place to bring the industry into a new era of intelligent manufacturing.One of the key requirements of intelligent manufacturing is the ...With the advancement of new information technologies,a revolution is being taken place to bring the industry into a new era of intelligent manufacturing.One of the key requirements of intelligent manufacturing is the interoperability of industrial applications.However,it is challenging to realize the interoperability for legacy industrial applications due to 1)the deficient semantic information of data transmitted over heterogeneous communication protocols,2)the difficulty to understand the complex process of business logic with no source code,and 3)the high cost and potential risk of reengineering the applications.To address the issues,in this paper,we propose an approach named SmartPipe to exposing existing functionalities of an industrial application as APIs without source code while simultaneously allowing the application to remain unchanged.We design a behavioral runtime model(BRM)as the self-representation of the industrial applications,based on which a computational reflection framework is designed to flexibly construct the model and generate APIs that encapsulate specific functionalities.We validate SmartPipe on a real industrial application that controls the spin-draw winding machine.Results show that our approach is effective and more suitable for industrial scenes compared with traditional approaches.展开更多
Background： Patients with major depressive disorder （MDD） usually have high risk of suicidality. Few studies have investigated the effects of stressful life events （SLEs） on the risk of suicide in Chinese patient...Background： Patients with major depressive disorder （MDD） usually have high risk of suicidality. Few studies have investigated the effects of stressful life events （SLEs） on the risk of suicide in Chinese patients who have developed MDD. This study aimed to investigate the impact of SLEs on suicidal risk in Chinese patients with MDD. Methods： In total, 1029 patients with MDD were included from nine psychiatric hospitals to evaluate the impact of SLEs on suicidal risk. Patients fulfilling the Mini-International Neuropsychiatric Interview （MINI） criteria for MDD were included in the study. Patients were excluded if they had lifetime or current diagnoses of psychotic disorder, bipolar disorder, and alcohol or substance dependence. Depressive symptoms were assessed by the 17-item Harnilton Depression Scale （HAMD-17）. The suicidal risk of MDD patients was determined by the suicide risk module of MINI. SLEs were assessed by the Life Events Scale. Results： No gender difference was found for suicidal risk in MDD patients. Patients with suicidal risk had younger ages, lower education levels, more drinking behavior, and lower marriage rate, and fewer people had child and more severe depressive symptoms than nonsuicidal risk group. High-level perceived stressfulness （HPS） and number of SLEs that patients were exposed to were significantly greater in patients with suicidal risk than patients without. In multivariate logistic analysis, HPS of SLEs （odds ratio [OR] = 1.54, 95% confidence interval [C1]： 1.16-2.05, P = 0.003） and depressive symptoms （OR = 1.08.95% CI： 1.05-1.11, P 〈 0.001 ） were associated with suicidal risk even after adjustment of gender, age, marriage, drinking behavior, and childless. Conclusions： HPS of SLEs is associated with suicide risk in Chinese patients with MDD. Further suicide prevention programs targeting this risk factor are needed. Trial Registration： ClinicalTrials.gov： NCT02023567; https：//clinicaltrials.gov/ct2/show/NCTO2023567？term=NCTO2023567＆rank=l.展开更多
Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attac...Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].展开更多
基金supported by National Natural Science Foundation of China under Grant No.61672060.
文摘The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which involve on-board units and road side units in the edge network,as well as the concept of cloud computing,which involves the data center that provides service.The security issues are always an important concern in the design of IoV architecture.To achieve a secure IoV architecture,some security measures are necessary for the cloud computing and fog computing associated with the vehicular network.In this paper,we summarize some research works on the security schemes in the vehicular network and cloud-fog-dew computing platforms which the IoV depends on.
基金supported by the National Key R&D Program of China(2022YFB2703200)the National Natural Science Foundation of China(Grant Nos.62202011,62172010).
文摘Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)systems.By 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 scalability,thereby enhancing the rescue potential.However,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR systems.Adaptability refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission level.To address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding mechanism.Apart from the benefits in scalability 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 shards.We implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the performance.The results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.
基金supported by the National Natural Science Foundation of China under Grant No.62036001.
文摘Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.
基金supported by the National Key Research and Development Program of China under Grant No.2021YFB3101802.
文摘Fuzz testing is crucial for identifying software vulnerabilities,with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection.However,as the need for targeted detection grows,directed grey-box fuzzing(DGF)has become essential,focusing on specific vulnerabilities.The initial seed corpus,which consists of carefully selected input samples that the fuzzer uses as a starting point,is fundamental in determining the paths that the fuzzer explores.A well-designed seed corpus can guide the fuzzer more effectively towards critical areas of the code,improving the efficiency and success of the fuzzing process.Even with its importance,much work concentrates on refining guidance mechanisms while paying less attention to optimizing the initial seed corpus.In this paper,we introduce ISC4DGF,a novel approach to generating optimized initial seed corpus for DGF using large language models(LLMs).By leveraging LLMs’deep understanding of software and refined user inputs,ISC4DGF creates a precise seed corpus that efficiently triggers specific vulnerabilities through a multi-round validation process.Implemented on AFL and tested against state-of-the-art fuzzers such as Titan,BEACON,AFLGo,FairFuzz,and Entropic using the Magma benchmark,ISC4DGF achieves a 25.03x speedup with fewer target reaches.Moreover,ISC4DGF improves target vulnerabilities detection accuracy while narrowing the detection scope and reducing code coverage.
基金funded by the Proof of Concept Program of Zhongguancun Science City and Peking University TTird Hospital(HDCXZHKC2022212)China Postdoctoral Science Foundation(2023M740079 and GZC20230058)+1 种基金Major Program of National Natural Science Foundation of China(62394310 and 62394314)Beijing Natural Science Foun dation(L222026).
文摘Background:MRI segmentation offers crucial insights for automatic analysis.Although deep learningbased segmentation methods have attained cutting-edge performance,their efffcacy heavily relies on vast sets of meticulously annotated data.Methods:In this study,we propose a novel semi-supervised MRI segmentation model that is able to explore unlabeled data in multiple aspects based on various semi-supervised learning technologies.Results:We compared the performance of our proposed method with other deep learning-based methods on 2 public datasets,and the results demonstrated that we have achieved Dice scores of 90.3%and 89.4%on the LA and ACDC datasets,respectively.Conclusions:We explored the synergy of various semi-supervised learning technologies for MRI segmentation,and our investigation will inspire research that focuses on designing MRI segmentation models.
基金supported by the National Natural Science Foundation of China under grant nos.62372470,72225011,62402414,U23B2059,62173034,32222070,62402017,72421002,62206303,62476264,62406312,62102266,52173241,and U23A20468the National Key Research and Development Program of China(2023YFD1900604)+8 种基金the Strategic Priority Research Program of the Chinese Academy of Science(XDB0680301)the Youth Innovation Promotion Association CAS(2023112)the National High Level Hospital Clinical Research funding(2022-PUMCH-A-014),the Beijing Natural Science Foundation(4244098)the Science and Technology Innovation Program of Hunan Province(2023RC3009)the Key Research and Development Program of Yunnan Province(202202AE090034)the MNR Key Laboratory for Geo-Environmental Monitoring of Greater Bay Area(GEMLab-2023001)the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2024TIAD-STX0024)the China National Postdoctoral Program for Innovative Talents(BX20240385)the River Talent Recruitment Program of Guangdong Province(2019ZT08X603).
文摘Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps,such as AI agents and high-level reinforcement learning.Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision,language,and sensory data—into a cohesive decision-making process.Foundation models(FMs)have become pivotal in science and industry,transforming decision-making and research capabilities.Their large-scale,multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare,life sciences,and education.This survey examines IDM’s evolution,advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains,highlighting the challenges and opportunities in building efficient,adaptive,and ethical decision systems.
基金developed by the NLP601 group at School of Electronics Engineering and Computer Science, Peking University, within the National Natural Science Foundation of China (No. 61672046)
文摘Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.
基金This work was supported by the National Key Research and Development Program of China under Grant No.2018YFB1004800the National Natural Science Foundation of China under Grant No.61725201+1 种基金Beijing Municipal Science and Technology Project under Grant No.Z171100005117002Tianjin Municipal People's Government Port Service Office(Project on Cross-Border E-Commerce Big Data Analysis and Display System).
文摘With the advancement of new information technologies,a revolution is being taken place to bring the industry into a new era of intelligent manufacturing.One of the key requirements of intelligent manufacturing is the interoperability of industrial applications.However,it is challenging to realize the interoperability for legacy industrial applications due to 1)the deficient semantic information of data transmitted over heterogeneous communication protocols,2)the difficulty to understand the complex process of business logic with no source code,and 3)the high cost and potential risk of reengineering the applications.To address the issues,in this paper,we propose an approach named SmartPipe to exposing existing functionalities of an industrial application as APIs without source code while simultaneously allowing the application to remain unchanged.We design a behavioral runtime model(BRM)as the self-representation of the industrial applications,based on which a computational reflection framework is designed to flexibly construct the model and generate APIs that encapsulate specific functionalities.We validate SmartPipe on a real industrial application that controls the spin-draw winding machine.Results show that our approach is effective and more suitable for industrial scenes compared with traditional approaches.
文摘Background： Patients with major depressive disorder （MDD） usually have high risk of suicidality. Few studies have investigated the effects of stressful life events （SLEs） on the risk of suicide in Chinese patients who have developed MDD. This study aimed to investigate the impact of SLEs on suicidal risk in Chinese patients with MDD. Methods： In total, 1029 patients with MDD were included from nine psychiatric hospitals to evaluate the impact of SLEs on suicidal risk. Patients fulfilling the Mini-International Neuropsychiatric Interview （MINI） criteria for MDD were included in the study. Patients were excluded if they had lifetime or current diagnoses of psychotic disorder, bipolar disorder, and alcohol or substance dependence. Depressive symptoms were assessed by the 17-item Harnilton Depression Scale （HAMD-17）. The suicidal risk of MDD patients was determined by the suicide risk module of MINI. SLEs were assessed by the Life Events Scale. Results： No gender difference was found for suicidal risk in MDD patients. Patients with suicidal risk had younger ages, lower education levels, more drinking behavior, and lower marriage rate, and fewer people had child and more severe depressive symptoms than nonsuicidal risk group. High-level perceived stressfulness （HPS） and number of SLEs that patients were exposed to were significantly greater in patients with suicidal risk than patients without. In multivariate logistic analysis, HPS of SLEs （odds ratio [OR] = 1.54, 95% confidence interval [C1]： 1.16-2.05, P = 0.003） and depressive symptoms （OR = 1.08.95% CI： 1.05-1.11, P 〈 0.001 ） were associated with suicidal risk even after adjustment of gender, age, marriage, drinking behavior, and childless. Conclusions： HPS of SLEs is associated with suicide risk in Chinese patients with MDD. Further suicide prevention programs targeting this risk factor are needed. Trial Registration： ClinicalTrials.gov： NCT02023567; https：//clinicaltrials.gov/ct2/show/NCTO2023567？term=NCTO2023567＆rank=l.
基金This work was supported by the MoST Science and Technology Innovation Project of Xiong'an(2022XAGG0115)the National Natural Science Foundation of China(Grant Nos.62202011,62172010).
文摘Federated learning(FL)is a decentralized machine learning paradigm,which has significant advantages in protecting data privacy[1].However,FL is vulnerable to poisoning attacks that malicious participants perform attacks by injecting dirty data or abnormal model parameters during the local model training and aim to manipulate the performance of the global model[2].
