检索结果-内蒙古大学图书馆

Findings of the Association for Computational Linguistics, ACL 2023

作者： Qin, Libo Huang, Shijue Chen, Qiguang Cai, Chenran Zhang, Yudi Liang, Bin Che, Wanxiang Xu, Ruifeng School of Computer Science and Engineering Central South University China Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Research Center for SCIR Harbin Institute of Technology Harbin China

ISBN: (纸本)9781959429623

Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-modal sarcasm detection system: (1) There are some spurious cues in MMSD, leading to the model bias learning;(2) The negative samples in MMSD are not always reasonable. To solve the aforementioned issues, we introduce MMSD2.0, a correction dataset that fixes the shortcomings of MMSD, by removing the spurious cues and re-annotating the unreasonable samples. Meanwhile, we present a novel framework called multi-view CLIP that is capable of leveraging multi-grained cues from multiple perspectives (i.e., text, image, and text-image interaction view) for multi-modal sarcasm detection. Extensive experiments show that MMSD2.0 is a valuable benchmark for building reliable multi-modal sarcasm detection systems and multi-view CLIP can significantly outperform the previous best baselines. © 2023 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GAN Augmentation-Based Continuous Authentication for Vehicular Digital Twin

GAN Augmentation-Based Continuous Authentication for Vehicul...

引用

IEEE International Conference on Communications (ICC)

作者： Chengzhe Lai Xinwei Zhang Haodi Zhang Guanjie Li Yong Yu Dong Zheng School of Cyberspace Security Xi'an University of Posts and Telecommunications Xi'an China National Engineering Research Center for Secured Wireless Xi'an China School of Cyber Engineering Xidian University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, to secure the communication between autonomous vehicle and its digital representative in the vehicular digital twin system, we propose a GAN augmentation-based continuous authentication scheme. Specifically, in the proposed scheme, we first introduce a data augmentation technique based Generative Adversarial Network (GAN) that provides the augmentation of raw data from vehicle sensors. We then present the efficient authentication: 1) We train a Convolutional Neural Network (CNN) using raw and augmented data; 2) Deep features are extracted through a combination of Principal Component Analysis (PCA) and CNN; 3) We train the OC-SVM classifier during the registration to ensure the legality of vehicle in the authentication phase. Performance evaluations via extensive simulations demonstrate the efficiency and effectiveness of the proposed scheme in terms of GAN loss and accuracy.

关键词： Performance evaluation Accuracy Authentication Generative adversarial networks Feature extraction Data augmentation Digital twins

来源：评论

学校读者我要写书评

暂无评论

Fragment Distance-Guided Dual-Stream Learning for Automatic Pelvic Fracture Segmentation

SSRN

引用

SSRN 2024年

作者： Zeng, Bolun Wang, Huixiang Joskowicz, Leo Chen, Xiaojun Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Department of Orthopedics National Center for Orthopedics Shanghai Sixth People’s Hospital Shanghai Jiao Tong University School of Medicine Shanghai China School of Computer Science and Engineering Edmond and Lily Safra Center for Brain Sciences Hebrew University of Jerusalem Jerusalem Israel Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

Pelvic fracture is a complex and severe injury. Accurate diagnosis and treatment planning require the segmentation of the pelvic structure and the fractured fragments from preoperative CT scans. However, this segmentation is a challenging task, as the fragments from a pelvic fracture typically exhibit considerable variability and irregularity in the morphologies, locations, and quantities. In this study, a novel dual-stream learning framework is proposed for the automatic segmentation and category labeling of pelvic fractures. First, we develop a dual-branch architecture leveraging distance learning from bone fragments for enhanced spatial information, aiding in fracture segmentation and improving the identification of bone fragments with variable positioning. Second, we implement a multi-size feature fusion module that adaptively aggregates features from diverse receptive fields tailored to targets of different sizes and shapes, thus boosting segmentation performance. Extensive experiments on two pelvic fracture datasets from different medical centers demonstrated the accuracy and generalizability of the proposed method. It achieves a mean Dice coefficient and mean F1-score of 0.935[[EQUATION]]0.068 and 0.930[[EQUATION]]0.057 in the dataset FracCLINIC, and 0.955[[EQUATION]]0.072 and 0.912[[EQUATION]]0.129 in the dataset FracSegData, which are superior than other comparing methods. Our method optimizes the process of pelvic fracture segmentation, potentially serving as an effective tool for preoperative planning in the clinical management of pelvic fractures. © 2024, The Authors. All rights reserved.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

引用

arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

来源：评论

学校读者我要写书评

暂无评论

Cross-cycle Transformer-based Stitching Method for Low-resolution Borehole Images

Cross-cycle Transformer-based Stitching Method for Low-resol...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Jia Chen Zhenpeng Fu Fei Fang Mingfu Xiong Xinrong Hu Tao Peng School of Computer Science and Artificial Intelligence Wuhan Textile University Engineering Research Center of Hubei Province for Clothing Information Wuhan China

The stitching of borehole images has an important predictive role in safety analysis in the field of geotechnical engineering and intelligent geological exploration. Applying traditional image stitching methods that designed specifically for high-resolution images to low-resolution images will lead to blurred stitching results, stitching seams, fewer matched feature points and difficulties in massive image stitching. To address these problems, we propose an autoencoder-based coarse-to-fine feature extraction network, which can extract image features with high semantic and improves the accuracy of the feature point matching. Besides, we design a cross-cycle Transformer-based image stitching framework, which increase the number of matching feature points by Cross-QuadTree attention and stitch image by affine transformation. Experimental results show that the proposed method can effectively stitch low-resolution geotechnical borehole images with satisfactory visual quality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

vPlanSim: An Open Source Graphical Interface for the Visualisation and Simulation of AI Systems 31

vPlanSim: An Open Source Graphical Interface for the Visuali...

引用

31st International Conference on Automated Planning and Scheduling, ICAPS 2021

作者： Roberts, Jamie O. Mastorakis, Georgios Lazaruk, Brad Franco, Santiago Stokes, Adam A. Bernardini, Sara School of Engineering Institute for Integrated Micro and Nano Systems University of Edinburgh United Kingdom EPSRC CDT in Robotics and Autonomous Systems Edinburgh Centre for Robotics United Kingdom Department of Computer Science Royal Holloway University of London United Kingdom

ISBN: (纸本)9781713832317

We introduce vPlanSim, an open source tool to aid in AI PDDL development. This tool is primarily aimed at researchers and developers who need a visual representation of their planning problem so that they can make useful insights into the performance of their system, and also to naturally convey their system to others. It is an open-source tool which allows a user to quickly and easily visualise a target environment to generate the problem files and also to visualise a plan. It is particularly well suited to spatial planning problems. This paper will demonstrate vPlanSim on 2D and 3D planning problems. vPlanSim is based on a small and carefully considered set of dependencies such as VTK and PyQt. It can be set up on different platforms and compiled from source with minimal effort. The code is and maintained via a clear code review mechanism. We welcome contributions from the open-source community. Copyright © 2021, Proceedings of the 31st International Conference on Automated Planning and Scheduling. All rights reserved.

关键词： Open systems

来源：评论

学校读者我要写书评

暂无评论

Internet of Things (IoT) in Smart Cities: A Statistical Survey

Internet of Things (IoT) in Smart Cities: A Statistical Surv...

引用

Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Muhammad Waseem Iqbal Khowla Khaliq Nidal A. Al-Dmour Muhammad Aqeel Nasir Ali K. Hamid Department of Software Engineering Superior University Lahore Pakistan Department of Computer Sciecnce Superior University Lahore Pakistan Department of Computer Engineering College of Engineering Mutah University Jordan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Superior University Lahore Pakistan

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I’ve presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain.

关键词： Semantic Web COVID-19 Smart cities Scalability Semantics Transportation Medical services

来源：评论

学校读者我要写书评

暂无评论

Deep Q-learning Sampling Based on Advantages 5

Deep Q-learning Sampling Based on Advantages

引用

5th International Conference on Intelligent robotics and Control engineering, IRCE 2022

作者： Xie, Ming Ren, Xinrui Yu, Jianbo Shu, Feng Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement Academy for Engineering and Technology Fudan University Shanghai China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Microelectronics Fudan University State Key Laboratory of ASIC and System Shanghai200433 China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

Deep Q-learning (DQN) has shown recent success on a wide range of complicated sequential decision-making issues, especially in the classic control area. However, in most DQN training, the sampling policies, particularly the ϵ-greedy policy and its derivation rules, produce noise erroneously due to the unsteadiness and unreliability of Q values utilized for searching for optimum actions. We begin with a fundamental hypothesis: complete advantages-based exploitation may be employed to help in successful exploration, and the gap between advantages on outstanding and poor actions tends to extend during the early phases of training before stabilizing. We then present a novel DQN architecture with a superior sampling policy that is totally and directly based on advantage values. This new DQN is named A-sampling DQN, and it requires minor code changes based on the original DQN codes. In most cases, A-sampling DQN and its derivatives outperformed the traditional DQN method. © 2022 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Honeytoken-Detector: A Symbolic Execution-Based Honeypot Token Detection Tool

Honeytoken-Detector: A Symbolic Execution-Based Honeypot Tok...

引用

Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Yi Liu Lizhi Cai School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350345865

ISBN: (纸本)9798350345872

Smart contracts have emerged as one of the most successful applications in the blockchain domain, playing a significant role in various blockchain ecosystems. Inspired by smart contracts, a multitude of cryptographic assets have been created. To standardize these assets, industry standards such as ERC20 (Ethereum Request for Comments 20), ERC721, and ERC1155 have been proposed. In recent years, smart contracts have frequently fallen victim to attacks. Honeypot contracts, disguised as ERC20-compliant tokens, are widely prevalent on the blockchain, enticing victims to make purchases. Such malicious smart contracts exhibiting deceptive behavior are collectively referred to as honeypot tokens. This paper focuses on ERC20-compliant smart contracts and defines six common types of honeypot issues. Building upon advancements in smart contract vulnerability detection, we propose an enhanced symbolic execution-based detection tool called Honeytoken-Detector. We conduct experiments on both contracts known to have similar issues and actual token contracts from the real world. The experimental results demonstrate the effectiveness of our tool in identifying vulnerabilities.

关键词： Industries Source coding Smart contracts Ecosystems Buildings Blockchains Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Robust Iterative Permittivity Inversion Method Based on Reduced Order Models

Robust Iterative Permittivity Inversion Method Based on Redu...

引用

Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Tianchen Shao Zekui Jia Maokun Li Fan Yang Shengheng Xu Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

To mitigate the nonlinearity and cycle-skipping effects inherent in inverse scattering problems (ISPs), we propose a robust permittivity inversion method using the iterative data to Born (DtB) process. The DtB process is based on reduced order models (ROMs) obtained by the Galerkin approximation of wave operators. We incorporate the layer-peeling (LP) strategy to ensure efficient and stable convergence. Furthermore, two regularization techniques are implemented to improve the robustness of the inversion algorithm in noisy environments. The inversion algorithm is validated through a 2D example.

关键词： Scattering Approximation algorithms Robustness Reduced order systems Iterative methods Noise measurement Permittivity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：