检索结果-内蒙古大学图书馆

15th International Conference on Parallel and Distributed Systems, ICPADS '09

作者： Lu, Wenjun Wu, Yue Liu, Zongtian Li, Qing Luo, Xiangfeng School of Computer Engineering and Science Shanghai University Shanghai China High Performance Computing Center Shanghai University Shanghai China

ISBN: (纸本)9780769539003

Malignant tumor or cancer is one of serious diseases threatening human health. It is a challenging issue on how to diagnose the malignant tumor earlier and more accurately. A feasible way on cancer diagnosis is efficiently to share and analyze tumor related data. We work on a cyberinfrastructure which presents data sharing and data analysis solutions to reveal the relationship between tumor and gene. © 2009 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Surgical video workflow analysis via visual-language learning

npj Health Systems

引用

npj Health Systems 2025年第1期2卷 1-12页

作者： Pengpeng Li Xiangbo Shu Jinhui Tang Chun-Mei Feng Yifei Feng Wangmeng Zuo School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore Republic of Singapore The First School of Clinical Medicine Nanjing Medical University Nanjing China Department of General Surgery The First Affiliated Hospital of Nanjing Medical University Nanjing China Faculty of Computing Harbin Institute of Technology Harbin China

Surgical video workflow analysis has made intensive development in computer-assisted surgery by combining deep learning models, aiming to enhance surgical scene analysis and decision-making. However, previous research has primarily focused on coarse-grained analysis of surgical videos, e.g., phase recognition, instrument recognition, and triplet recognition that only considers relationships within surgical triplets. In order to provide a more comprehensive fine-grained analysis of surgical videos, this work focuses on accurately identifying triplets from surgical videos. Specifically, we propose a vision-language deep learning framework that incorporates intra- and inter- triplet modeling, termed I2TM, to explore the relationships among triplets and leverage the model understanding of the entire surgical process, thereby enhancing the accuracy and robustness of recognition. Besides, we also develop a new surgical triplet semantic enhancer (TSE) to establish semantic relationships, both intra- and inter-triplets, across visual and textual modalities. Extensive experimental results on surgical video benchmark datasets demonstrate that our approach can capture finer semantics, achieve effective surgical video understanding and analysis, with potential for widespread medical applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction 37

ES-Mask: Evolutionary Strip Mask for Explaining Time Series ...

引用

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Sun, Yifei Song, Cheng Lu, Feng Li, Wei Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia

ISBN: (纸本)9781577358800

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

An e-learning platform based on grid architecture

An e-learning platform based on grid architecture

引用

作者： Yang, Chao-Tung Ho, Hsin-Chuan High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 407 Taiwan Department of Computer Science and Information Engineering Tunghai University Lizen High School Taichung Taiwan

The overall popularity of the Internet has helped e-learning become a hot method for learning in recent years. Over the Internet, learners can freely absorb new knowledge without restrictions on time or place. Many companies have adopted e-learning to train their employees. An e-learning system can make an enterprise more competitive by increasing the knowledge of its employees. E-learning has been shown to have impressive potential in e-commerce. At present, most e-learning environment architectures use single computers or servers as their structural foundations. As soon as their work loads increase, their software and hardware must be updated or renewed. This is a big burden on organizations that lack sufficient funds. Thus, in this study we employ a kind of Grid computing technology, called the "Data Grid" to integrate idle computer resources in enterprises into e-learning platforms, thus eliminating the need to purchase costly high-level servers and other equipment.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

A carbon 2.0 framework based on cloud computing

A carbon 2.0 framework based on cloud computing

引用

IEEE/International Association for computer and Information science International Conference on computer and Information science

作者： Li, Jiandun Zhang, Wu Peng, Junjie Lei, Zhou Lei, Yu School of Computer Engineering and Science High Performance Computing Center Shanghai University Shanghai China Department of Computer Science and Engineering University of Texas at Arlington Arlington TX United States

ISBN: (纸本)9780769541471

The Copenhagen climate conference 2009 has concluded that we have to change the way how we live. Currently, several carbon management systems are being developed to meet enterprises' requirement. In addition, a number of carbon footprint calculation tools have been developed to fulfill carbon calculation needs from industries, households and individuals. However, under the current circumstance, it is still a challenging issue on how to take advantage of the latest IT technologies to improve carbon calculations with comprehensive functionality. In this paper, we extend carbon 2.0, the concept proposed by previous studies, and introduce a new carbon framework based on cloud computing. This new framework is massively scalable and can configure and deliver carbon-related services dynamically and on demand. Moreover, it is a real low-carbon framework itself! © 2010 IEEE.

关键词： Greenhouse gases

来源：评论

学校读者我要写书评

暂无评论

BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable Data with Item- and Attribute-level Signals

引用

ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Text knowledge representation model based on human concept learning

Text knowledge representation model based on human concept l...

引用

IEEE International Conference on Cognitive Informatics

作者： Luo, Xiangfeng Cai, Chuanliang Hu, Qingliang School of Computer Engineering and Science Shanghai University Shanghai 200072 China High Performance Computing Center Shanghai University Shanghai 200072 China

ISBN: (纸本)9781424480401

The essential abilities of text knowledge representation, such as automatic construction, carrying abundant semantics and flexible reasoning, should be held due to the rapid growth of web resources and the requirements of the reasoning-based web services. However, current text knowledge representation models either lose many textual semantics or cannot be constructed automatically. To solve the above issues, text knowledge representation model is proposed based on the concept algebra of human concept learning. Then, the degree-2 power series hypothesis is developed and the reasoning ability of text representation is proposed. Finally, the results compared with current knowledge representation models show that our model performs better than other models in representing text knowledge. © 2010 IEEE.

关键词： Knowledge representation

来源：评论

学校读者我要写书评

暂无评论

performance Analysis of Distributed Deep Learning using Horovod for Image Classification 6

Performance Analysis of Distributed Deep Learning using Horo...

引用

6th International Conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Rakshith, R.M. Lokur, Vineet Hongal, Prateek Janamatti, Vivek Chickerur, Satyadhyan KLE Technological University School of Computer Science and Engineering Hubballi India KLE Technological University Centre for High Performance Computing Hubballi India

ISBN: (纸本)9781665410359

Recent advances in unsupervised feature learning and deep learning methodologies have shown that training large models may significantly improve performance. This research study reviews the topic of training a deep neural network in a distributed manner. The proposed study uses the Horovod to train large models on compute clusters with thousands of computers. It can be viewed that the image classification and segmentation are computationally expensive jobs, with more segmentation. Lower-level tasks may be fine-tuned for enhanced efficiency, but the study demonstrates that the proposed research study can work better and more effectively in categorization by exploiting the distributed deep learning idea. The proposed study demonstrate the application of Horovod on Image classification using CNNs, and our works serve as a benchmark for performance analysis on standard datasets;CIFAR 10 and Cat Vs Dog. © 2022 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

performance Analysis of Change Detection Algorithms on Multispectral Imagery 12

Performance Analysis of Change Detection Algorithms on Multi...

引用

12th International Conference on Computational Intelligence and Communication Networks, CICN 2020

作者： Bidari, Indira Chickerur, Satyadhyan Talikoti, Rekha.M. Kapali, Smita.S. Talawar, Sushmita Sangam, Soumya Kle Technological University School of Computer Science and Engineering Hubballi India Kle Technological University Center for High Performance Computing Hubballi India

ISBN: (纸本)9781728193939

Due to growing human population and technology, huge climate change occurs which impact the environment and lives. Satellite images are profound for monitoring the ground surface. Due to the enormous availability of multispectral images, various applications based on classification, change detection have emerged. Researchers have developed lots of Multispectral Change Detection Methods (MSCD) till date. These methods show essential sub-pixel level details, such as the abundance variation of each underlying material at a given location, or the shift in material distribution within the scene, with time or as a result of significant events such as a natural disaster. The main aim of the proposed work is to present a comparative study of various unsupervised methods for detecting binary changes in multispectral imagery. Through experimental study, we provide a comparative analysis of the algorithms. The various algorithms considered for our study are (1) Principal Component Analysis (PCA), (2) PCA with K-means clustering, (3) Multivariate Alteration Detection (MAD), (4) Iteratively Reweighted Multivariate Alteration Detection (IRMAD). Using real-world multi-temporal multispectral imaging dataset, we assess and compare the performance of all these algorithms and their time efficiency on Central Processing Unit (CPU) and Graphics Processing Unit (GPU). The empirical findings, accompanied by a description of each algorithms pros and cons, are intended to help researchers pick the procedures with the good features for MSCD applications. © 2020 IEEE.

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

Hybrid Convolutional Neural Network with Change Detection on Hyperspectral Imagery 1

Hybrid Convolutional Neural Network with Change Detection on...

引用

1st International Conference on Industrial Electronics Research and Applications, ICIERA 2021

作者： Bidari, Indira Chickerur, Satyadhyan Thm, Abhishek School of Computer Science and Engineering KLE Technological University Hubballi India Centre for High Performance Computing KLE Technological University Hubballi India

ISBN: (纸本)9781665435420

Change Detection (CD) is one of the major research areas in the field of remote sensing. Hyperspectral Images (HSI's) boosted the change detection technology with their high spectral resolution features. Traditional CD techniques process the low dimensional images not well suited for high dimensional HSI's. Even tiny details can be captured using hyperspectral images, but processing these images is difficult because of their complex high-dimensional data. HSI contains noise and redundancy that affect the spectral features of hyperspectral imagery. We present a hybrid convolution neural network method to address processing complex high dimensional data of hyperspectral images. The proposed network extracts the spectral-spatial information of hyperspectral images by decomposing the change Tensor in three directions. The spectral dimension of the change Tensor is reduced by using I-D Convolution' and then the Tensor is decomposed from two spatial dimensions. A 2-D convolution is applied to extract the spectral and spatial features along different spatial dimensions to improve accuracy. The results on three hyperspectral image datasets illustrate the performance improvement than most state-of-The-Art techniques. © 2021 IEEE.

关键词： Change detection

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：