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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Li, Zexin Zhu, Yi Zhao, Li Zhao, Ying School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

With the increasing visibility of autonomous vehicles, the assurance of safe and precise behavioral decisions presents a substantial challenge within the realm of autonomous driving. The complexity of physical space, uncertainty in duration-space data, and human subjectivity complicate the assurance of existing behavior decision-making methods. To address these challenges, a safety decision method for autonomous driving based on Bayesian network is proposed. Firstly, semantic relations among risk elements in driving scenario are represented using knowledge graph, and the hierarchical structure and node information of the Bayesian network are determined based on expert knowledge and historical experience. Behavior decision actions are derived through Bayesian network inference. Secondly, leveraging the Bayesian network structure and inference results, a Duration-space Resource Timed Communicating Sequential process is construct to verify the security of time and space resources. Thirdly, the constructed process is converted into Bigraphs and use the CPS MODEL CHECKER for security verification. Finally, the effectiveness of the proposed method is verified using a highway lane change overtaking scenario. © 2024 IEEE.

关键词： Autonomous vehicles

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A processing method for inconsistent answer set programs based on minimal principle

A processing method for inconsistent answer set programs bas...

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International Conference on Automatic Control and Artificial Intelligence, ACAI 2012

作者： Zhu, Tao Zhang, Zhizheng Zhai, Yuqing Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou China

ISBN: (纸本)9781849195379

Answer set programming (ASP) has become an important tool for knowledge representation and reasoning. Inconsistency processing in ASP provides a way for reasoning of inconsistent knowledge. In this paper, we present a minimal principle based method to process inconsistency in ASP. The method is able to ensure maximum retention of certain knowledge by removing the fewest defeasible rules preferentially. Then, we propose corresponding algorithms for simple logic programs and extended logic programs, and analyze the complexity of our method. After that, we compare this method with related work. Finally, we conclude and indicate the prospect of the further research.

关键词： processing

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Exploiting Unlabeled Data for Neural Grammatical Error Detection

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Journal of computer Science & technology 2017年第4期32卷 758-767页

作者： Zhuo-Ran Liu Yang Liu School of Software Beihang University Beijing 100191 China State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Jiangsu Collaborative Innovation Center for Language Competence Xuzhou 221009 China

Identifying and correcting grammatical errors in the text written by non-native writers have received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVM and convolutional networks with fixed-size context window.

关键词： unlabeled data grammatical error detection neural network

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A Model-Agnostic Interpretability Approach Based on Enhanced Hierarchical Clustering 24

A Model-Agnostic Interpretability Approach Based on Enhanced...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Ding, Xi Zhu, Yi Yu, Qiao Huang, Chongyang School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

The Local Interpretable Model-Agnostic Explanations (LIME) is a relatively novel technique for explaining black-box models. LIME generates perturbed instances of the model based on a random instance, and then approximates the original model with a linear model derived from the weighted perturbed instances, thereby providing an explanation for individual predictions. While LIME effectively addresses the opacity of black-box models, the randomness in instance generation often leads to unstable and inaccurate predictions. For a given prediction, different explanations may be generated, posing critical issues, particularly in security-sensitive domains. In response to these challenges, we propose a stable version of LIME, named DDLIME(Density and Distance-based LIME). DDLIME leverages improved hierarchical clustering and KNN algorithms to select instances that are most similar to the data, thus reducing the impact of perturbations. Additionally, the enhanced hierarchical clustering, incorporating density and distance metrics, enables DDLIME to address the limitations of traditional hierarchical clustering in clustering non-spherical clusters. Consequently, DDLIME can effectively capture complex structures and patterns within the data. The experimental results on three binary public datasets from the UCI repository demonstrate that DDLIME provides more stable explanations compared to LIME. Furthermore, compared to both baseline models, DDLIME exhibits superior performance across various evaluation metrics. © 2024 IEEE.

关键词： Hierarchical clustering

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A Dynamic Interest-Aware Message-Passing GCN for Recommendation 24

A Dynamic Interest-Aware Message-Passing GCN for Recommendat...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： He, Weichao Zhu, Yi Song, Mei Wu, Zixin Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Jiangsu Engineering Technology Research Center of Ict in Education Jiangsu Xuzhou China Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

Over the past few years, Graph Convolutional Networks (GCN) have emerged as a promising tool in the field of recommendation. They have the ability to effectively learn user and item embeddings by leveraging collaborative signals from higher-order neighbors. Nevertheless, the recommendation models based on GCN also face a common issue. As more layers are added, the node embeddings start to become increasingly similar and eventually become indistin-guishable, leading to a decline in performance. To address the issues at hand, we present a recommendation model called DIMP-GCN. This model utilizes a Dynamic Interest-aware Message-Passing approach, where users and interacted items are organized into distinct subgraphs based on their interests. Within each subgraph, single-layer graph convolution operations are performed. As a result, the users who have been updated are divided based on their interests once again, leading to dynamic changes in subgraph partitioning as the network layers stack. Nodes are able to acquire more accurate representations through this process. We conduct extensive experiments on three established datasets, showcasing the superior performance of our DIMP-GCN compared to other state-of-the-art GCN-based recommendation models. © 2024 IEEE.

关键词： Message passing

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Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

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Tsinghua Science and technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie Xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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A decoupled hybrid structure for active noise control with uncorrelatednarrowband disturbances 43

A decoupled hybrid structure for active noise control with u...

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43rd International Congress on Noise Control Engineering: Improving the World Through Noise Control, INTERNOISE 2014

作者： Wu, Lifu Qiu, Xiaojun Burnett, Ian S. Cheng, Eva Guo, Yecai Royal Melbourne Institute of Technology School of Electrical and Computer Engineering Australia Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology 210044 China

ISBN: (纸本)9780909882037

In real active noise control (ANC)applications,the following situations frequently occur, one isthat disturbances only present at the error sensor and havelowcorrelation with reference signal, the other is thatthere is no enough space or ideal position for locating the reference sensor to satisfy causality condition. Thusthe residual noise after feedforward control can be seen as uncorrelated narrowband disturbancesin these situationsand ahybrid adaptive feedforward and feedback structure is often utilized to cope with this *** efforts have been paid to improve the performance of the hybrid ANC system, nevertheless, few interests are concerned about the combination method between the feedforward and feedback structure. After investigating the conventional combination method of hybrid feedforward and feedback system, this paper introduces analternate combination method for hybrid ANC systemwhich featuresthat itavoidsthe coupling between the feedforward and feedback structures and both structures are concatenated to attenuate the ambient noise. Simulations are carried out to validatethe effectiveness of the introduced methodfor ANCwith uncorrelated narrowband disturbances.

关键词： Hybrid systems

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Reliable neighbors-based collaborative filtering for recommendation systems 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Sun, Xiaohan Cheng, Jiangfei Li, Zepeng School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Collaborative filtering

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Suppression of deep‑level traps via semicarbazide hydrochloride additives for high‑performance tin‑based perovskite solar cells

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Frontiers of Optoelectronics 2023年第4期16卷 205-213页

作者： Wenbo Jia Yi Jing Han Zhang Baoyan Tian Huabo Huang Changlei Wang Ligang Xu Key Laboratory for Organic Electronics and Information Displays(KLOEID)and Jiangsu Key Laboratory for Biosensors Institute of Advanced Materials(IAM)Nanjing University of Posts and TelecommunicationsNanjing 210023China Hubei Key Laboratory of Plasma Chemistry and Advanced Materials Key Laboratory for Green Chemical Process of Ministry of EducationSchool of Materials Science and EngineeringWuhan Institute of TechnologyWuhan 430205China School of Optoelectronic Science and Engineering and Collaborative Innovation Center of Suzhou Nano Science and Technology Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province and Key Lab of Modern Optical Technologies of Education Ministry of ChinaSoochow UniversitySuzhou 215006China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China

Tin perovskites with exemplary optoelectronic properties ofer potential application in lead-free perovskite solar ***,Sn vacancies and undercoordinated Sn ions on the tin perovskite surfaces can create deep-level traps,leading to nonradiative recombination and absorption of nucleophilic O_(2)molecules,impeding further device efciency and ***,in this study,a new additive of semicarbazide hydrochloride(SEM-HCl)with a N–C=O functional group was introduced into the perovskite precursor to fabricate high-quality flms with a low concentration of deep-level trap ***,in turn,serves to prevent undesirable interaction between photogenerated carriers and adsorbed oxygen molecules in the device’s operational environment,ultimately reducing the proliferation of superoxide *** the result,the SEM-HCl-derived devices show a peak efciency of 10.9%with improved device *** unencapsulated devices maintain almost 100%of their initial efciencies after working for 100 h under continuous AM1.5 illumination conditions.

关键词： Lead-free perovskite solar cells Deep-level traps Power conversion efciency Semicarbazide hydrochloride Stability

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Semi-supervised feature selection using sparse laplacian support vector machine 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Zheng, Xiaohan Xu, Zhiqiang School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

Semi-supervised feature selection is an active topic in machine learning and data mining. Laplacian support vector machine (LapSVM) has been successfully applied to semi-supervised learning. However, LapSVM cannot be directly applied to feature selection. To remedy it, we propose a sparse Laplacian support vector machine (SLapSVM) and apply it to semi-supervised feature selection. On the basis of LapSVM, SLapSVM introduces the 1-norm regularization, which means the solution of SLapSVM has sparsity. In addition, the training procedure of SLapSVM can be formulated as solving a quadratic programming problem, which indicates that the solution of SLapSVM is unique and global. SLapSVM can perform feature selection and classification at the same time. Experimental results on semi-supervised classification problems show the feasibility and effectiveness of the proposed semi-supervised learning algorithms. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Support vector machines

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