检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

来源：评论

学校读者我要写书评

暂无评论

BitterEN: A novel ensemble model for the identification of bitter peptide

引用

Computers in biology and medicine 2025年 195卷

作者： Md Fahim Sultan Tasmin Karim Md Shazzad Hossain Shaon Md Mamun Ali Sobhy M Ibrahim Mst Shapna Akter Kawsar Ahmed Francis M Bui Mohammad Ali Moni Department of Computer Science and Engineering Oakland University Rochester MI 48309 USA. Electronic address: fahim15-3416@diu.edu.bd. Department of Computer Science and Engineering Oakland University Rochester MI 48309 USA. Electronic address: tasmin15-2920@diu.edu.bd. Department of Computer Science and Engineering Oakland University Rochester MI 48309 USA. Electronic address: shazzad15-3404@diu.edu.bd. Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada Department of Software Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh. Electronic address: m.ali@usask.ca. Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh 11451 Saudi Arabia. Electronic address: syakout@ksu.edu.sa. Department of Computer Science and Engineering Oakland University Rochester MI 48309 USA. Electronic address: akter@oakland.edu. Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh. Electronic address: k.ahmed.bd@***. Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon SK S7N 5A9 Canada. Electronic address: francis.bui@usask.ca. AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Sturt University Bathurst NSW 2795 Australia AI & Digital Health Technology Rural Health Research Institute Charles Sturt University Orange NSW 2800 Australia. Electronic address: mmoni@csu.edu.au.

Bitter peptides are short amino acid chains that produce a bitter taste. These peptides are made primarily in food processing through the chemical reduction of peptides. The bitterness arises from the specific sequence of amino acids in peptides, which interact with the bitter taste receptors on the human tongue. These peptides influence nutrition and health, offering insights into protein digestion and bioactive advantages. Hence, correctly identifying bitter peptides is pivotal for revealing the biochemical properties of efficient medication. The computational approach is most suitable for identifying bitterness, where most studies obtained insufficient outcomes. Therefore, the current study developed an ensemble-based framework called "BitterEN", where we integrate the Gradient Boosting (GB) and Multi-layer Perception (MLP) methods. Our proposed method improved more than 3 % of accuracy compare to all of the state-of-the-arts methods, where the proposed approach achieved 0.995 accuracy in merged feature extractions with the Random Forest (RF) feature selection method. We used 50 iterations over the performance evaluation phases to enable a more exact generalization of model performance. In addition, we provided a convenient GitHub-based version of our bitter peptide identification. It highlights the practical applicability of these findings. We are optimistic that the proposed approach might benefit many fields, including healthcare development and nutritional science.

关键词： Bitter peptides Ensemble learning Feature extractions Healthcare Nutritional science

来源：评论

学校读者我要写书评

暂无评论

Modularizing code by use cases and tests for better maintainability 17

Modularizing code by use cases and tests for better maintain...

引用

1st International Conference on the Art, Science and engineering of Programming, Programming 2017

作者： Bystrický, Michal Vranic, Valentino Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovak University of Technology in Bratislava Ilkovicova 2 Bratislava Slovakia

No abstract available.

ISBN: (纸本)9781450348362

No abstract available.

关键词： use case modularization user acceptance tests test driven development Cucumber

来源：评论

学校读者我要写书评

暂无评论

Preface

引用

CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

来源：评论

学校读者我要写书评

暂无评论

The Carbon Footprint of Bitcoin

SSRN

引用

SSRN 2019年

作者： Stoll, Christian Klaaßen, Lena Gallersdörfer, Ulrich MIT Center for Energy and Environmental Policy Research Massachusetts Institute of Technology CambridgeMA02139 United States TUM Center for Energy Markets TUM School of Management Technical University of Munich Germany TUM School of Management Technical University of Munich Germany TUM Software Engineering for Business Information Systems Department of Informatics Technical University of Munich Germany

Participation in the Bitcoin blockchain validation process requires specialized hardware and vast amounts of electricity, which translate into a significant carbon footprint. Here we demonstrate a methodology for estimating the power consumption associated with Bitcoin’s blockchain based on IPO filings of major hardware manufacturers, insights on mining facility operations, and mining pool compositions. We then translate our power consumption estimate into carbon emissions, using the localization of IP-addresses. We determine the annual electricity consumption of Bitcoin, as of November 2018, to be 48.2 TWh, and estimate that annual carbon emissions range from 23.6 to 28.8 MtCO2. This means that the level of emissions produced by Bitcoin sits between the levels produced by the nations of Jordan and Mongolia. With this article, we aim to gauge the external costs of Bitcoin, and inform the broader debate on the costs and benefits of cryptocurrencies. The externalities we discuss here may help policy-makers in setting the right rules as the adoption journey of blockchain has just started. © 2019, The Authors. All rights reserved.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

NetSim – The framework for complex network generator

引用

Procedia Computer Science 2018年 126卷 547-556页

作者： Akanda Wahid-Ul-Ashraf Marcin Budka Katarzyna Musial Department of Computing and Informatics Bournemouth University Fern Barrow Poole BH12 5BB UK Advanced Analytics Institute School of Software Faculty of Engineering and IT University of Technology Sydney Australia

Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. However, in real-world networks, it’s hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edges. We propose a flexible and extensible NetSim framework to understand how properties in different types of networks change with varying number of edges and vertices. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networks.

关键词： complex networks simulation random graphs scale-free networks small-world networks

来源：评论

学校读者我要写书评

暂无评论

Correction to: Development and Validation of a Colorectal Cancer Prediction Model: A Nationwide Cohort‑Based Study

引用

Digestive diseases and sciences 2025年第3期70卷 1250页

作者： Ofer Isakov Dan Riesel Michael Leshchinsky Galit Shaham Ben Y Reis Dan Keret Zohar Levi Baruch Brenner Ran Balicer Noa Dagan Samah Hayek Innovation Division Clalit Research Institute Clalit Health Services Tel Aviv Israel. Department of Biomedical Informatics Harvard Medical School Boston MA USA. The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute Boston MA USA. Predictive Medicine Group Boston Children's Hospital Boston MA USA. Harvard Medical School Boston MA USA. Gastroenterology and Hepatology Department Clalit Health Services Jerusalem Israel. Department of Gastroenterology Beilinson Medical Center Faculty of Medicine Tel Aviv University Tel Aviv Israel. Institute of Oncology Davidoff Cancer Center Rabin Medical Center Beilinson Campus Petah Tikva Israel. Faculty of Medicine Tel Aviv University Tel Aviv Israel. School of Public Health Faculty of Health Sciences Ben Gurion University of the Negev Be'er Sheva Israel. Software and Information Systems Engineering Ben Gurion University of the Negev Be'er Sheva Israel. Innovation Division Clalit Research Institute Clalit Health Services Tel Aviv Israel. Samahhayek@tauex.tau.ac.il. Department of Epidemiology and Preventive Medicine School of Public Health Faculty of Medicine Tel Aviv University Tel Aviv Israel. Samahhayek@tauex.tau.ac.il.

来源：评论

学校读者我要写书评

暂无评论

Cover Image, Volume 59, Issue 3

引用

Molecular Carcinogenesis 2020年第3期59卷

作者： Guosen Zhang Qiang Wang Mengsi Yang Xitong Yao Xinlei Qi Yang An Huan Dong Lu Zhang Wan Zhu Yongqiang Li Xiangqian Guo Department of Predictive Medicine Institute of Biomedical Informatics Cell Signal Transduction Laboratory Bioinformatics Center Henan Provincial Engineering Center for Tumor Molecular Medicine School of Software School of Basic Medical Sciences Henan University Kaifeng China Department of Anesthesia Stanford University Stanford California

来源：评论

学校读者我要写书评

暂无评论

P-S Instance Retrieval via early elimination and late expansion 1

P-S Instance Retrieval via early elimination and late expans...

引用

1st ACM Multimedia Workshops on Visual Analysis for Smart and Connected Communities, VSCC 2017

作者： Lan, Jiamei Chen, Jun Wang, Zheng Liang, Chao Satoh, Shin'ichi National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China National Institute of Informatics Japan

ISBN: (纸本)9781450355063

In daily life, it is common that viewers want to quickly browse scenes with their idols in TV series. In 2016, the TRECVID INS (Instance Search) task started to focus on identifying a specific target person in a target location. In this paper, we name this kind of task as P-S (Person-Scene) Instance Retrieval. As we know, most approaches handle this task by separately obtaining the person instance and the scene instance retrieval results, and directly combining them together. However, we find that the person and scene instance retrieval modules are not always effective at the same time, which will decrease the accuracy if the results are aggregated directly. To solve this problem, we attempt to achieve the results in two steps. (1) Early Elimination. There are many noisy data making person/scene instance retrieval score solely high, such as the occluded person or scene shots. Corresponding scores of these shots should be eliminated rather than calculated with noise. (2) Late Expansion. Considering the video's continuity, person or scene in adjacent shots is likely to be the same one, hence we try to expand the results of those eliminated shots. On this basis, we propose an early elimination and late expansion method to improve the accuracy of P-S Instance Retrieval. Experimental results on the large-scale TRECVID INS dataset demonstrate the effectiveness of the proposed method. 2017 ACM.

关键词： Large-scale Person-scene instance retrieval Results aggregation

来源：评论

学校读者我要写书评

暂无评论

Research of modern opportunities for obtaining the grant support for innovative projects financing

引用

IOP Conference Series: Earth and Environmental Science 2021年第2期677卷

作者： A L Zolkin T G Aygumov A I Pakhomova V V Bobkov Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics L. Tolstogo Street 23 Samara 443010 Russia Department of computer engineering software and automated systems Dagestan State Technical University I. Shamyl Street 70 Makhachkala 367000 Russia Department of Economics and management Institute of service and entrepreneurship (branch) of DSTU in Shakhty Shevchenko Street 147 Shakhty 346500 Russia Department of Physical Education and Sports Gubkin Russian State University of Oil and Gas Leninsky prospect 65 Moscow 119991 Russia

This paper examines innovative, including social entrepreneurship and modern opportunities for obtaining grant support for financing innovative projects, including social ones. The possibility of receiving grants from various funds is being considered. Detailed attention is paid to the stages of project development for drawing up an application for grant funding. Attention is drawn to certain nuances in the writing and processing of grant applications. We also conduct research on current conditions and factors that influence the development of innovative entrepreneurship, and compare Russian indicators with foreign ones. Detailed attention is paid to economic and innovative conditions, educational factors, measures to promote competition and tax incentives.

关键词：

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：