检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Zhao, Haotian Chen, Ruifeng Yan, Jing Feng, Juan Xiang, Jun Chen, Yang Liang, Dong Li, Yinsheng Research Center for Medical Artificial Intelligence Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China X-Ray Department United Imaging Healthcare Limited Company Shanghai China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China Research Center for Medical Artificial Intelligence Key Laboratory of Biomedical Imaging Science and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

—To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm conebeam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling density of typical C-arm CBCT are much poorer than those of multi-detector-row CT in the diagnostic imaging suite. The current quantitative perfusion imaging includes two cascaded steps, time-resolved image reconstruction and perfusion parametric estimation. For time-resolved image reconstruction, technical challenge imposed by poor temporal resolution and poor sampling density causes inaccurate quantification of the temporal variation of cerebral artery and tissue attenuation values. For perfusion parametric estimation, it remains a technical challenge to appropriately design the handcrafted regularization for better solving the associated deconvolution problem. These two challenges together prevent from obtaining quantitatively accurate perfusion images using C-arm CBCT. The purpose of this work is to simultaneously address these two challenges via combining the two cascaded steps into a single joint optimization problem and reconstructing quantitative perfusion images directly from the measured sinogram data. In the developed direct cerebral perfusion parametric image reconstruction technique, TRAINER in short, the quantitative perfusion images have been represented as a subject-specific conditional generative model trained under the constraint of the time-resolved CT forward model, perfusion convolutional model, and the subject’s own measured sinogram data. Results shown in this paper demonstrated that using TRAINER, quantitative cerebral perfusion images can be accurately obtained using C-arm CBCT in the interventional suite. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Instance-dependent partial label learning

arXiv

引用

arXiv 2021年

作者： Xu, Ning Qiao, Congyu Geng, Xin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China MOE Key Laboratory of Computer Network and Information Integration Ministry of Education China

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, this assumption is not realistic since the candidate labels are always instance-dependent. In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature. The incorrect label with a high degree is more likely to be annotated as the candidate label. Therefore, the latent label distribution is the essential labeling information in partially labeled examples and worth being leveraged for predictive model training. Motivated by this consideration, we propose a novel PLL method that recovers the label distribution as a label enhancement (LE) process and trains the predictive model iteratively in every epoch. Specifically, we assume the true posterior density of the latent label distribution takes on the variational approximate Dirichlet density parameterized by an inference model. Then the evidence lower bound is deduced for optimizing the inference model and the label distributions generated from the variational posterior are utilized for training the predictive model. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method. Source code is available at https://***/palm-ml/valen. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

NCAGC: A Neighborhood Contrast Framework for Attributed Graph Clustering

arXiv

引用

arXiv 2022年

作者： Wang, Tong Yang, Guanyu He, Qijia Zhang, Zhenquan Wu, Junhua The Laboratory of Image Science and Technology The Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China The CAS Key Laboratory of FAST National Astronomical Observatory Chinese Academy of Sciences Beijing100101 China The Department of School of Computer Science and Technology Nanjing Tech University Jiangsu211816 China

Attributed graph clustering is one of the most fundamental tasks among graph learning field, the goal of which is to group nodes with similar representations into the same cluster without human annotations. Recent studies based on graph contrastive learning method have achieved remarkable results when exploit graph-structured data. However, most existing methods 1) do not directly address the clustering task, since the representation learning and clustering process are separated;2) depend too much on data augmentation, which greatly limits the capability of contrastive learning;3) ignore the contrastive message for clustering tasks, which adversely degenerate the clustering results. In this paper, we propose a Neighborhood Contrast Framework for Attributed Graph Clustering, namely NCAGC, seeking for conquering the aforementioned limitations. Specifically, by leveraging the Neighborhood Contrast Module, the representation of neighbor nodes will be'push closer' and become clustering-oriented with the neighborhood contrast loss. Moreover, a Contrastive Self-Expression Module is built by minimizing the node representation before and after the self-expression layer to constraint the learning of self-expression matrix. All the modules of NCAGC are optimized in a unified framework, so the learned node representation contains clustering-oriented messages. Extensive experimental results on four attributed graph datasets demonstrate the promising performance of NCAGC compared with 16 state-of-the-art clustering methods. The code is available at https://***/wangtong627/NCAGC. Copyright © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia university datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion information exchange Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

Ontology Revision based on Pre-trained Language Models

arXiv

引用

arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Student Loss: Towards the Probability Assumption in Inaccurate Supervision

TechRxiv

引用

TechRxiv 2023年

作者： Zhang, Shuo Li, Jianqing Fujita, Hamido Li, Yuwen Wang, Dengbao Zhu, Tingting Zhang, Minling Liu, Chengyu The School of Instrument Science and Engineering The State Key Laboratory of Bioelectronics The School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing210096 China HUTECH University Ho Chi Minh City Viet Nam University of Granada Granada Spain Regional Research Center Iwate Prefectural University Iwate Japan The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom

Noisy labels are often encountered in datasets, but learning with them is challenging. Although natural discrepancies between clean and mislabeled samples in a noisy category exist, most techniques in this field still gather them indiscriminately, which leads to their performances being partially robust. In this paper, we reveal both empirically and theoretically that the learning robustness can be improved by assuming deep features with the same labels follow a student distribution, resulting in a more intuitive method called student loss. By embedding the student distribution and exploiting the sharpness of its curve, our method is naturally data-selective. This ability makes clean samples aggregate tightly in the center, while mislabeled samples scatter, even if they share the same label. Additionally, we employ the metric learning strategy and develop a large-margin student (LT) loss for better capability. It should be noted that our approach is the first work that adopts the prior probability assumption in feature representation to decrease the contributions of mislabeled samples. This strategy can enhance various losses to join the student loss family, even if they have been robust losses. Experiments demonstrate that our approach is more effective in inaccurate supervision. Enhanced LT losses significantly outperform various state-of-the-art methods in most cases. Even huge improvements of over 50% can be obtained under certain conditions. An implementation of the main codes is available at https://***/Zhangshuojackpot/Student-Loss. © 2023, CC BY.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Detection of Virtual Machines Based on Thread Scheduling 7th

Detection of Virtual Machines Based on Thread Scheduling

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Lin, Zhi Song, Yubo Wang, Junbo School of Information Science and Engineering Southeast University Nanjing211111 China Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Nanjing211111 China Purple Mountain Laboratories Nanjing211111 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China

ISBN: (纸本)9783030786113

With the rapid development of cloud computing, virtual machines are now attracting more and more attention. Virtual machines used at malicious motivation cause enormous threats to the security of computer systems. Virtual machine detection is crucial for honeypot systems and software that provide free trials. Various strategies based on local register values affected by virtualization have been proposed. However, these strategies have a limited scope of application since they can only run natively. What’s more, the values they depend on can be modified with ease. In this paper, we propose a new remote virtual machine detection strategy applying to different types of virtual machines and different operating systems based on time difference in thread scheduling. Our main contribution is to set up a probability-based thread scheduling analysis model to describe the time difference between physical machines and virtual machines. This paper shows that the probability distribution of execution time of a piece of CPU-bound code in virtual machines has higher variance along with lower kurtosis and skewness, which make up our index system for detection. Results of Numeric simulation and real test show good agreement and provide a clear criterion for detection. In the real test all the virtual machines and 97.2% of the physical machines were identified correctly. © 2021, Springer Nature Switzerland AG.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Enhancing Interest Forwarding for Fast Recovery from Unanticipated Data Access Failure in NDN

引用

China Communications 2019年第7期16卷 120-130页

作者： Xiaoyan Hu Xuhui Liu Lixia Zhao Jian Gong Guang Cheng School of Cyber Science & Engineering Southeast University Nanjing 211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing China

We show that an aggregated Interest in Named Data networking (NDN) may fail to retrieve desired data since the Interest previously sent upstream for the same content is judged as a duplicate one and then dropped by an upstream node due to its multipath forwarding. Furthermore, we propose NDRUDAF, a NACK based mechanism that enhances the Interest forwarding and enables Detection and fast Recovery from such Unanticipated Data Access Failure. In the NDN enhanced with NDRUDAF, the router that aggregates the Interest detects such unanticipated data access failure based on a negative acknowledgement from the upstream node that judges the Interest as a duplicate one. Then the router retransmits the Interest as soon as possible on behalf of the requester whose Interest is aggregated to fast recover from the data access failure. We qualitatively and quantitatively analyze the performance of the NDN enhanced with our proposed NDRUDAF and compare it with that of the present NDN. Our experimental results validate that NDRUDAF improves the system performance in case of such unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers.

关键词： named data networking interest aggregation multipath forwarding data access failure negative acknowledgement

来源：评论

学校读者我要写书评

暂无评论

Topic-aware evidence reasoning and stance-aware aggregation for fact verification

arXiv

引用

arXiv 2021年

作者： Si, Jiasheng Zhou, Deyu Li, Tongzhe Shi, Xingyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Computer Science University of Warwick United Kingdom

Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. Existing approaches typically (i) explore the semantic interaction between the claim and evidence at different granularity levels but fail to capture their topical consistency during the reasoning process, which we believe is crucial for verification;(ii) aggregate multiple pieces of evidence equally without considering their implicit stances to the claim, thereby introducing spurious information. To alleviate the above issues, we propose a novel topic-aware evidence reasoning and stance-aware aggregation model for more accurate fact verification, with the following four key properties: 1) checking topical consistency between the claim and evidence;2) maintaining topical coherence among multiple pieces of evidence;3) ensuring semantic similarity between the global topic information and the semantic representation of evidence;4) aggregating evidence based on their implicit stances to the claim. Extensive experiments conducted on the two benchmark datasets demonstrate the superiority of the proposed model over several state-of-the-art approaches for fact verification. The source code can be obtained from https://***/jasenchn/TARSA. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Topic-driven and knowledge-aware transformer for dialogue emotion detection

arXiv

引用

arXiv 2021年

作者： Zhu, Lixing Pergola, Gabriele Gui, Lin Zhou, Deyu He, Yulan Department of Computer Science University of Warwick United Kingdom School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge based systems

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：