检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

108,400 篇 会议
15,727 册 图书
11,121 篇 期刊文献
3 篇 学位论文

馆藏范围

135,109 篇 电子文献
170 种 纸本馆藏

日期分布

学科分类号

107,605 篇 工学
- 93,057 篇 计算机科学与技术...
- 66,619 篇 软件工程
- 23,096 篇 信息与通信工程
- 12,539 篇 电气工程
- 8,337 篇 控制科学与工程
- 8,184 篇 生物工程
- 8,077 篇 生物医学工程（可授...
- 6,286 篇 光学工程
- 4,393 篇 电子科学与技术（可...
- 3,853 篇 机械工程
- 3,609 篇 化学工程与技术
- 2,781 篇 建筑学
- 2,343 篇 动力工程及工程热...
- 2,310 篇 土木工程
- 2,012 篇 安全科学与工程
55,651 篇 理学
- 41,048 篇 数学
- 9,539 篇 生物学
- 8,447 篇 物理学
- 7,806 篇 统计学（可授理学、...
- 4,825 篇 系统科学
- 3,126 篇 化学
22,326 篇 管理学
- 13,183 篇 图书情报与档案管...
- 10,141 篇 管理科学与工程(可...
- 6,929 篇 工商管理
5,722 篇 医学
- 5,266 篇 临床医学
- 4,540 篇 基础医学(可授医学...
- 3,132 篇 药学(可授医学、理...
3,695 篇 法学
- 3,129 篇 社会学
2,458 篇 教育学
- 2,416 篇 教育学
2,158 篇 经济学
- 2,152 篇 应用经济学
1,824 篇 农学
171 篇 军事学
107 篇 文学
74 篇 艺术学
17 篇 哲学
2 篇 历史学

主题

8,057 篇 artificial intel...
4,840 篇 computer communi...
3,516 篇 information syst...
2,779 篇 algorithm analys...
2,432 篇 software enginee...
2,006 篇 image processing...
1,992 篇 user interfaces ...
1,973 篇 semantics
1,939 篇 computers
1,925 篇 information stor...
1,665 篇 computer applica...
1,526 篇 pattern recognit...
1,469 篇 data mining and ...
1,433 篇 computer vision
1,426 篇 software enginee...
1,371 篇 database managem...
1,251 篇 distributed comp...
1,225 篇 theory of comput...
1,216 篇 computer circuit...
1,181 篇 computer systems...

机构

202 篇 university of sc...
152 篇 huazhong univers...
151 篇 department of co...
145 篇 institute of inf...
121 篇 department of co...
120 篇 tsinghua univers...
116 篇 shanghai jiao to...
112 篇 national institu...
96 篇 school of comput...
93 篇 chinese academy ...
85 篇 école des ponts ...
83 篇 department of co...
81 篇 university of el...
73 篇 karnataka bangal...
72 篇 department of co...
71 篇 college of compu...
68 篇 school of comput...
67 篇 university of ch...
63 篇 school of comput...
63 篇 college of compu...

作者

73 篇 vardi moshe y.
70 篇 wang lei
60 篇 zhang lei
59 篇 pnueli amir
47 篇 hancock edwin r.
41 篇 huang yueh-min
40 篇 li xin
40 篇 marina l. gavril...
38 篇 man ka lok
37 篇 jiang xiaoyi
37 篇 li yang
36 篇 stuckey peter j.
35 篇 zhang tao
34 篇 yu yong
34 篇 liu bo
33 篇 panayiotis zaphi...
33 篇 jianying zhou
32 篇 peleg david
31 篇 hsu tsan-sheng
31 篇 lee dong chun

语言

127,590 篇 英文
5,876 篇 其他
1,495 篇 中文
284 篇 德文
4 篇 法文
1 篇 加泰罗尼亚文
1 篇 俄文

检索条件"任意字段=Lecture Notes in Computer Science"

共 135251 条记录，以下是1571-1580 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Multi-scale Value-Density Transformer with Medical Semantic Guidance for Disease Risk Prediction Based on Clinical Time Series 27th

Multi-scale Value-Density Transformer with Medical Semantic...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Jingwen Wei, Xiaoge Yuen, Pong C. Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031783463

Clinical time series are ubiquitous in healthcare for accurate disease risk prediction. The recent Transformer models have demonstrated superior performance in time series learning. However, these methods focus on global temporal dependency and widely utilize channel-mix or channel-independent tokenization. They ignore local dependency in clinical time series and are limited to capturing the intrinsic clinical variable correlations. To address the above issues, we present an Multi-scale Value-Density Transformer with Medical Semantic Guidance (MVMformer), which takes irregularity-aware segment-wise modeling for clinical time series and correlate diverse clinical variates based on their medical semantic affinity. Specifically, MVMformer introduces a Multi-scale Value-Density Attention to capture intra-segment characteristics in both value trends and temporal density while accommodating multi-length segments. Furthermore, MVMformer constructs a Hierarchical Medical Semantic Graph to analyze complicated variable relationships starting from detailed measurements to associated organs. Experimental results on three medical datasets demonstrate the superiority of MVMformer over existing state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

DT-UPD: User Privacy Data Protection Through Distribution Transformation in Unlearning Cloud Service 24th

DT-UPD: User Privacy Data Protection Through Distribution Tr...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Sun, Shouyue Wu, Feng Zhang, Yingzhi Cui, Lei Gao, Longxiang Yu, Shui Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science University of Technology Sydney Ultimo Australia

ISBN: (纸本)9789819615476

Providing machine unlearning services to users in the cloud, known as Machine Unlearning as a Service (MUaaS), has become a prominent privacy protection strategy. However, existing methods primarily focus on the effectiveness of the unlearning algorithm itself, while neglecting the privacy of the data users upload to the cloud for unlearning. To address this problem, we propose a distribution transformation method that maps users’ private data to a distribution (DT-UPD) that is functionally parallel and orthogonal to the original distribution. We then sample from this transformed distribution and use the sampled data instead of the original data to obtain the unlearning service. In detail, DT-UPD first employs the information bottleneck theory to extract the most relevant features for the unlearning service and then maps these features to a normal distribution. Low-dimensional features are then randomly sampled from this normal distribution and expanded into a high-dimensional space using adversarial reconstruction. This sampling and adversarial reconstruction process removes most of the information from the original private data (including unlearning-related and irrelevant information), ensuring that the final reconstructed data used for the unlearning service has a privacy protection effect. Additionally, we introduce Elastic Weight Consolidation (EWC) loss, which effectively avoids ‘catastrophic unlearning’ during the unlearning process. Extensive experimental results show that DT-UPD can achieve a forgetting accuracy (forget ACC) of 18.22% in the forgetting set with no more than a 6% accuracy loss in the test set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Multi-views Enhanced Spatio-Temporal Adaptive Transformer for Urban Traffic Prediction 27th

Multi-views Enhanced Spatio-Temporal Adaptive Transformer fo...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Lin, Jiaqi Ren, Qianqian Xu, Hui Department of Computer Science and Technology Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031781681

Traffic prediction is a fundamental research problem in traffic planning and management, with wide-ranging applications. The task is challenging due to: (1) Most methods in traffic prediction emphasize short-term forecasting tasks and encounter difficulties in accurately modeling both short-term and long-term dependencies simultaneously. (2) Existing methods do not delve deeply enough into capturing spatial dependencies and lack sufficient exploration of spatial information in traffic data, resulting in suboptimal effectiveness. To address these issues, this paper proposes the Multi-views Enhanced Spatio-Temporal Adaptive Transformer(MESTformer) for Urban Traffic Prediction. Specifically, the network designs spatial attention and temporal attention to explicitly model the spatio-temporal dependencies. And two spatial enhanced views to delve into the spatial information of traffic data from local geographic and global sematic perspectives. By combining spatial enhanced views with spatial attention, the model can more fully learn spatial features. In addition, we manually insert uniform attention between the two ST blocks to ensure coverage of a broader range of information. We conduct extensive experiments on four prominent traffic benchmark datasets to evaluate the effectiveness and efficiency of MESTformer. The experimental results indicate that our proposed MESTformer achieves state-of-the-art performance compared to competitive baselines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Towards Robust Object Detection: Identifying and Removing Backdoors via Module Inconsistency Analysis 27th

Towards Robust Object Detection: Identifying and Removing Ba...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Xianda Liang, Siyuan Li, Chengyang School of Computer Science Peking University Beijing China National University of Singapore Singapore Singapore College of Artificial Intelligence China University of Petroleum Beijing China

ISBN: (纸本)9783031783821

Object detection models, widely used in security-critical applications, are vulnerable to backdoor attacks that cause targeted misclassifications when triggered by specific patterns. Existing backdoor defense techniques, primarily designed for simpler models like image classifiers, often fail to effectively detect and remove backdoors in object detectors. We propose a backdoor defense framework tailored to object detection models, based on the observation that backdoor attacks cause significant inconsistencies between local modules’ behaviors, such as the Region Proposal Network (RPN) and classification head. By quantifying and analyzing these inconsistencies, we develop an algorithm to detect backdoors. We find that the inconsistent module is usually the main source of backdoor behavior, leading to a removal method that localizes the affected module, resets its parameters, and fine-tunes the model on a small clean dataset. Extensive experiments with state-of-the-art two-stage object detectors show our method achieves a 90% improvement in backdoor removal rate over fine-tuning baselines, while limiting clean data accuracy loss to less than 4%. To the best of our knowledge, this work presents the first approach that addresses both the detection and removal of backdoors in two-stage object detection models, advancing the field of securing these complex systems against backdoor attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

TaylorShift: Shifting the Complexity of Self-attention from Squared to Linear (and Back) Using Taylor-Softmax 27th

TaylorShift: Shifting the Complexity of Self-attention fro...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Nauen, Tobias Christian Palacio, Sebastian Dengel, Andreas Department of Computer Science RPTU Kaiserslautern-Landau Gottlieb-Daimler-Straße Kaiserslautern Germany Trippstadter Str. 122 Kaiserslautern Germany

ISBN: (纸本)9783031781711

The quadratic complexity of the attention mechanism represents one of the biggest hurdles for processing long sequences using Transformers. Current methods, relying on sparse representations or stateful recurrence, sacrifice token-to-token interactions, which ultimately leads to compromises in performance. This paper introduces TaylorShift, a novel reformulation of the Taylor softmax that enables computing full token-to-token interactions in linear time and space. We analytically determine the crossover points where employing TaylorShift becomes more efficient than traditional attention, aligning closely with empirical measurements. Specifically, our findings demonstrate that TaylorShift enhances memory efficiency for sequences as short as 800 tokens and accelerates inference for inputs of approximately 1700 tokens and beyond. For shorter sequences, TaylorShift scales comparably with the vanilla attention. Furthermore, a classification benchmark across five tasks involving long sequences reveals no degradation in accuracy when employing Transformers equipped with TaylorShift. For reproducibility, we provide access to our code under https://***/tobna/TaylorShift. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Efficient Attention Machine Learning Transformer

来源：评论

学校读者我要写书评

暂无评论

Mobile Crowdsourcing Task Assignment Algorithm Based on ConvNeXt and GRU 18th

Mobile Crowdsourcing Task Assignment Algorithm Based on Con...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Fan, Zequn Pan, Qingxian Gao, Zhaolong Luan, Peng Wei, Kai Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China Yantai Institute of Technology Yantai264005 China Yantaishan Hospital Shandong Province Yantai264003 China

ISBN: (纸本)9783031714634

Task assignment is a crucial aspect of mobile crowdsourcing research. The focus is on allocating appropriate perceptual tasks based on task and worker characteristics to optimize perceptual quality. Tasks arrive dynamically in a crowdsourcing system, and the platform cannot assign all tasks at the beginning. This can lead to the task allocation process falling into a local optimal solution. To address the problem, this paper proposes a two-stage prediction algorithm that utilises historical spatio-temporal data of crowdsourcing services. Firstly, the temporal data is converted into image data through Markov Transition Field. Then, the task availability is transformed into a categorisation problem. In the first stage, a ConvNeXt-based network is used to predict the task availability. In the second stage, a GRU-based network is used to predict the task duration. This paper presents the results of several experiments that confirm the effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

引用

3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views 1st

Bilateral Hippocampi Segmentation in Low Field MRIs Using M...

引用

1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Peiris, Himashi Chen, Zhaolin Department of Data Science and AI Faculty of IT Monash University Melbourne Australia Monash University Melbourne Australia

ISBN: (纸本)9783031830105

Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective, which eliminates the need for sedation in children, though they often suffer from lower image quality. In this paper, we present a novel deep-learning approach for the automatic segmentation of bilateral hippocampi in low-field MRIs. Extending recent advancements in infant brain segmentation to underserved communities through the use of low-field MRIs ensures broader access to essential diagnostic tools, thereby supporting better healthcare outcomes for all children. Inspired by our previous work, Co-BioNet, the proposed model employs a dual-view structure to enable mutual feature learning via high-frequency masking, enhancing segmentation accuracy by leveraging complementary information from different perspectives. Extensive experiments demonstrate that our method provides reliable segmentation outcomes for hippocampal analysis in low-resource settings. The code is publicly available at: https://***/himashi92/LoFiHippSeg. © The Author(s) 2025.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Computational Tumor Progression Analysis via Seriation Based Trajectory Inference 12th

Computational Tumor Progression Analysis via Seriation Base...

引用

12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Moussa, Marmar R. Street, Charles H. Boddeda, Sriram School of Computer Science University of Oklahoma NormanOK United States

ISBN: (纸本)9783031827679

Precise lineage or evolution path determination play a crucial role in discerning the dynamic developmental or temporal progression patterns observed in single cell RNA-Seq data. In this work, we present a novel computational approach for progression pattern inference of normal or tumor cell populations that are actively progressing along a dynamic pathway in single cell resolution. This is achieved via ordering the cellular transcriptional profiles identifying the progression of cell populations along differentiation, signaling, or tumor evolution paths. Here, we developed a seriation-based progression pattern inference method using optimally reordered hierarchies and provide advanced principal-curves-based visualization of the inferred paths in three dimensional latent space representation of scRNA-Seq data. Additionally, we present novel metrics for evaluating the reconstructed order and identified pathways and evaluate our approach using real single cell transcriptomics datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cell signaling

来源：评论

学校读者我要写书评

暂无评论

Stable Single-View 3D Human Digitization via Explicit Geometric Field with Semantic Guidance 17th

Stable Single-View 3D Human Digitization via Explicit Geome...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Liu, Ruizhi Remagnino, Paolo Department of Computer Science Durham University Durham United Kingdom

ISBN: (纸本)9789819609628

We propose a novel single-view human body 3D reconstruction framework guided by the semantic field. We argue that the full visibility of 2D human shape and the alignment between geometric and semantic features are vital for a robust 3D human reconstruction. Due to the single view setting, existing methods fail to predict robust and completed 2D human shapes, which leads to vulnerability against invisibility. The usage of a parametric model could inform the model with a human shape, but the shape prior is too general, resulting in converging to a general body shape, and poor generalization to different types of garments. In response to the aforementioned challenges, we propose a novel framework to reconstruct a photo-realistic 3D human mesh by estimating the overall 2D human shape from the given view via a Human shape predictor, and predicting view-dependent explicit geometric field with estimate semantic field via a novel Semantic-aware explicit geometric field. Then we ensure the queriablity of our view-dependent explicit geometric field via Semantic-aware Geometric Field Integration. Finally, we first propose a cascaded query strategy, termed Semantic-Guided Query Strategy to further combine the 2D and 3D features. Through extensive experiments, our framework surpasses all selected SoTA by a considerable gap in Chamfer and P2S distances on Thuman2.0, CAPE-NFP, and CAPE-FP datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D reconstruction

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 154 155 156 157 158 159 160 161 162 163 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：