检索结果-内蒙古大学图书馆

ieee International symposium on Biomedical Imaging

作者： Ali Keshavarzi Elsa Angelini LTCI Telecom Paris Institut Polytechnique de Paris Palaiseau France

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The lack of large annotated datasets in medical imaging is an intrinsic burden for supervised Deep Learning (DL) segmentation models. Few-shot learning approaches are cost-effective solutions to transfer pre-trained models using only limited annotated data. However, such methods can be prone to overfitting due to limited data diversity especially when segmenting complex, diverse, and sparse tubular structures like airways. Furthermore, crafting informative image representations has played a crucial role in medical imaging, enabling discriminative enhancement of anatomical details. In this paper, we initially train a data-driven sparsification module to enhance airways efficiently in lung CT scans. We then incorporate these sparse representations in a standard supervised segmentation pipeline as a pretraining step to enhance the performance of the DL models. Results presented on the ATM public challenge cohort show the effectiveness of using sparse priors in pre-training, leading to segmentation Dice score increase by 1% to 10% in full-scale and few-shot learning scenarios, respectively.

关键词： Training Image segmentation visualization Atmospheric modeling Computed tomography Lung Encoding

来源：评论

学校读者我要写书评

暂无评论

Visilience: An Interactive visualization Framework for Resilience analysis using Control-Flow Graph

Visilience: An Interactive Visualization Framework for Resil...

引用

Pacific Rim International symposium on Dependable Computing

作者： Hailong Jiang Shaolun Ruan Bo Fang Yong Wang Qiang Guan Kent State University Singapore Management University Pacific Northwest National Laboratory

Soft errors have become one of the main concerns for the resilience of HPC applications, as these errors can cause HPC applications to generate serious outcomes such as silent data corruption (SDC). Many approaches have been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to perform resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of detail, which can create obstacles to compare and explore the resilience of the HPC program execution. To this end, we present Visilience, an interactive VISual resILIENCE analysis framework to allow programmers to facilitate the resilience analysis of HPC applications. In particular, Visilience leverages an effective visualization approach, Control Flow Graph (CFG) to present a function execution. Furthermore, three widely used models for resilience analysis (i.e., Y-Branch, IPAS, and TRIDENT) are seamlessly integrated into the framework for resilience analysis and result comparison. Multiple case studies have been conducted to demonstrate the effectiveness of our proposed framework Visilience.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

引用

ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2021年第11期43卷 4037-4058页

作者： Jing, Longlong Tian, Yingli CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA CUNY City Coll Dept Elect Engn New York NY 10031 USA CUNY Grad Ctr Dept Comp Sci New York NY 10031 USA

large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods, self-supervised learning methods are proposed to learn general image and video features from large-scale unlabeled data without using any human-annotated labels. This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos. First, the motivation, general pipeline, and terminologies of this field are described. Then the common deep neural network architectures that used for self-supervised learning are summarized. Next, the schema and evaluation metrics of self-supervised learning methods are reviewed followed by the commonly used datasets for images, videos, audios, and 3D data, as well as the existing self-supervised visual feature learning methods. Finally, quantitative performance comparisons of the reviewed methods on benchmark datasets are summarized and discussed for both image and video feature learning. At last, this paper is concluded and lists a set of promising future directions for self-supervised visual feature learning.

关键词： Task analysis visualization Videos Training Learning systems Feature extraction Annotations Self-supervised learning unsupervised learning convolutional neural network transfer learning deep learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding

引用

ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2022年第12期44卷 9434-9445页

作者： Monfort, Mathew Pan, Bowen Ramakrishnan, Kandan Andonian, Alex McNamara, Barry A. Lascelles, Alex Fan, Quanfu Gutfreund, Dan Feris, Rogerio Schmidt Oliva, Aude MIT CSAIL Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA 02142 USA IBM Res Cambridge MA 02142 USA

Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds. However, most large-scale datasets built to train models for action recognition in video only provide a single label per video. Consequently, models can be incorrectly penalized for classifying actions that exist in the videos but are not explicitly labeled and do not learn the full spectrum of information present in each video in training. Towards this goal, we present the Multi-Moments in Time dataset (M-MiT) which includes over two million action labels for over one million three second videos. This multi-label dataset introduces novel challenges on how to train and analyze models for multi-action detection. Here, we present baseline results for multi-action recognition using loss functions adapted for long tail multi-label learning, provide improved methods for visualizing and interpreting models trained for multi-label action detection and show the strength of transferring models trained on M-MiT to smaller datasets.

关键词： visualization Annotations Training Analytical models Three-dimensional displays Semantics Convolutional neural networks Computer vision machine learning video vision and scene understanding benchmarking multi-modal recognition modeling from video methods of data collection neural nets

来源：评论

学校读者我要写书评

暂无评论

VulnEx: Exploring Open-Source Software Vulnerabilities in large Development Organizations to Understand Risk Exposure

VulnEx: Exploring Open-Source Software Vulnerabilities in La...

引用

2021 ieee symposium on visualization for Cyber Security, VizSec 2021

作者： Dennig, Frederik L. Cakmak, Eren Plate, Henrik Keim, Daniel A. University of Konstanz Germany Sap Security Research France

ISBN: (纸本)9781665420853

The prevalent usage of open-source software (OSS) has led to an increased interest in resolving potential third-party security risks by fixing common vulnerabilities and exposures (CVEs). However, even with automated code analysis tools in place, security analysts often lack the means to obtain an overview of vulnerable OSS reuse in large software organizations. In this design study, we propose VULNEX (Vulnerability Explorer), a tool to audit entire software development organizations. We introduce three complementary table based representations to identify and assess vulnerability exposures due to OSS, which we designed in collaboration with security analysts. The presented tool allows examining problematic projects and applications (repositories), third-party libraries, and vulnerabilities across a software organization. We show the applicability of our tool through a use case and preliminary expert feedback. © 2021 ieee.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

An analysis of Crypto Gas Wars in Ethereum 23

An Analysis of Crypto Gas Wars in Ethereum

引用

23rd Asia-Pacific Network Operations and Management symposium (APNOMS)

作者： Ko, Kyungchan Jeong, Taeyeol Woo, Jongsoo Hong, James Won-Ki POSTECH Dept Comp Sci & Engn Pohang South Korea POSTECH Grad Sch Informat Technol Pohang South Korea

ISBN: (数字)9784885523397

ISBN: (纸本)9784885523397

Several years after NFTs first appeared, people began to use NFT to prove ownership of assets in diverse domains. Then the values of NFTs rapidly increased and accordingly the interests in NFTs have grown. Because Ethereum has the most active transactions and has a lot of users, many projects use Ethereum to deploy their NFT smart contracts. However, Ethereum has a chronic disadvantage of low scalability. During the NFT drop period for famous and popular NFTs, users are crowding the event, resulting in a large number of transactions to get NFTs. The low scalability leads to a fierce competition called the Crypto Gas War. In this work, we choose three famous NFT drop events to collect on-chain data of Ethereum over the drop period in order to analyze the impact of the Crypto Gas War on the Ethereum network in detail. In addition, we analyze the collected data to uncover critical and hidden problems, and present insights to solve them.

关键词： Blockchain Ethereum Non-Fungible Tokens Crypto Gas War Minting

来源：评论

学校读者我要写书评

暂无评论

Contrast-Reconstruction Representation Learning for Self-Supervised Skeleton-Based Action Recognition

引用

ieee TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 6224-6238页

作者： Wang, Peng Wen, Jun Si, Chenyang Qian, Yuntao Wang, Liang Zhejiang Univ Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China Harvard Med Sch Boston MA 02115 USA Chinese Acad Sci CASIA Ctr Res Intelligent Percept & Comp CRIPAC Inst Automat Natl Lab Pattern Recognit NLPR Beijing 100190 Peoples R China

Skeleton-based action recognition is widely used in varied areas, e.g., surveillance and human-machine interaction. Existing models are mainly learned in a supervised manner, thus heavily depending on large-scale labeled data, which could be infeasible when labels are prohibitively expensive. In this paper, we propose a novel Contrast-Reconstruction Representation Learning network (CRRL) that simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition. It consists of three parts: Sequence Reconstructor (SER), Contrastive Motion Learner (CML), and Information Fuser (INF). SER learns representation from skeleton coordinate sequence via reconstruction. However the learned representation tends to focus on trivial postural coordinates and be hesitant in motion learning. To enhance the learning of motions, CML performs contrastive learning between the representation learned from coordinate sequences and additional velocity sequences, respectively. Finally, in the INF module, we explore varied strategies to combine SER and CML, and propose to couple postures and motions via a knowledge-distillation based fusion strategy which transfers the motion learning from CML to SER. Experimental results on several benchmarks, i.e., NTU RGB+D 60/120, PKU-MMD, CMU, and NW-UCLA, demonstrate the promise of the our method by outperforming state-of-the-art approaches.

关键词： Skeleton Dynamics Representation learning Image reconstruction Task analysis Computational modeling visualization Skeleton-based action recognition unsupervised representation learning contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale System Reliability analysis of Multi-Layer Network Infrastructures 71

Multi-Scale System Reliability Analysis of Multi-Layer Netwo...

引用

71st Annual Reliability and Maintainability symposium, RAMS 2025

作者： Huynh, Phat K. Hoang, Ca Nguyen, Duy Truong, Tien Le, Trung Q. Department of Industrial and Systems Engineering North Carolina A&T State University 1121 E. Market Street 419 McNair Hall GreensboroNC27411 United States Department of Industrial & Management Systems Engineering University of South Florida 4202 E. Fowler Avenue TampaFL33620 United States Faculty of Telecommunication Engineer Posts and Telecommunications Institute of Technology 122 Hoang Quoc Viet Cau Giay District Hanoi122000 Viet Nam

ISBN: (纸本)9798350367744

This study presents a novel framework that integrates dynamic multi-layer graph theory with cascading failure dynamics to analyze the resilience and reliability of multi-layer network infrastructures. These infrastructures include critical systems such as power grids, which are inherently susceptible to cascading failures due to their complex, interconnected structures. Our research introduces a sophisticated model that dynamically captures the multi-dimensional interactions of the systems across various scales. Each layer of the network, from physical components to control systems, is continually updated with real-time operational data and historical incident records, enhancing the model’s predictive accuracy and responsiveness to emerging threats. The core of our multi-scale system reliability analysis is the evaluation of Node-Centric and Edge-Centric Subgraph Reliability Metrics (N-SRM and E-SRM), examining both intra-layer and inter-layer dynamics. Through extensive simulations under two critical scenarios-cyberphysical attacks and extreme weather conditions-we quantitatively assessed the network's resilience. For cyber-physical attacks, rapid propagation of failures was observed, significantly impacting the network's operational integrity. The N-SRM values across the layers exhibited steep declines within a short duration, demonstrating pronounced susceptibility to these attacks. In contrast, the impact of extreme weather was characterized by a gradual decline in reliability, suggesting a robustness that allows for effective adaptation strategies over an extended period. In addition, the inter-layer connections, particularly in Scenario B, demonstrated higher resilience with slower rates of reliability decline, underscoring the structural integrity and operational capability of the network. © 2025 ieee.

关键词： large scale systems

来源：评论

学校读者我要写书评

暂无评论

Image analysis for Historical Knowledge Discovery and Preservation

Image Analysis for Historical Knowledge Discovery and Preser...

引用

2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Sakthivel, K. Ashwin, J. Poongodi, K. Oviya, S. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Namakkal India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Digitized archival collections offer rich sources for cultural heritage research. Current systems, though, fail to effectively integrate heterogeneous data forms like images, textual descriptions, and metadata. This study offers a sophisticated framework that blends image recognition, natural language processing (NLP), and knowledge graph methods to facilitate access, enable discovery, and reveal concealed historical patterns. The system comprises several modules such as a data input module, preprocessing pipeline, image recognition, NLP analysis, knowledge graph generation, semantic search engine, and an interactive user interface. Testing the system on a large historical archive dataset proved high accuracy, efficiency, and usability for all modules. Major takeaways are 93.4% accuracy in object detection for images, 94.8% accuracy for named entity recognition, and 95.5% search accuracy in semantic search feature. © 2025 ieee.

关键词： Usability engineering

来源：评论

学校读者我要写书评

暂无评论

Link Prediction Research Based on Visual analysis: Expansion of the LERTR Index and System Validation

Link Prediction Research Based on Visual Analysis: Expansion...

引用

Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Yilei He Jiansu Pu Yu Zhang Hanlin Lan Boyang Gao Yanlin Zhu University of Electronic Science and Technology of China Chengdu Advanced Metal Materials Industry Technology Research Institute Co.

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Link prediction is widely used in areas like social networks, bioinformatics, recommendation systems, IoT, and healthcare to uncover latent information in complex networks. While similarity-based methods are simple and interpretable, they often overlook high-order structures and additional attributes, limiting their performance. To address this, we developed LPExplorer, a system that integrates the LERTR index (combining RA and LCP principles) for interpretable and accurate predictions across three-hop paths. LPExplorer visually represents network structures, resource flows, and parameter impacts, allowing users to sort and filter predictions effectively. Experimental results demonstrate its strong performance in analyzing and exploring complex networks.

关键词： visualization Accuracy Social networking (online) data visualization Complex networks Robustness Indexes System validation Recommender systems Tuning

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：