检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Sun, Shaoxiong Folarin, Amos A. Zhang, Yuezhou Cummins, Nicholas Garcia-Dias, Rafael Stewart, Callum Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Laiou, Petroula Sankesara, Heet Matcham, Faith Leightley, Daniel White, Katie M. Oetzmann, Carolin Ivan, Alina Lamers, Femke Siddi, Sara Simblett, Sara Nica, Raluca Rintala, Aki Mohr, David C. Myin-Germeys, Inez Wykes, Til Haro, Josep Maria Penninx, Brenda W.J.H. Vairavan, Srinivasan Narayan, Vaibhav A. Annas, Peter Hotopf, Matthew Dobson, Richard J.B. Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Faculty of Medicine and Health Sciences Universitat de Barcelona Barcelona Spain Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom RADAR-CNS Patient Advisory Board King's College London London United Kingdom The Romanian League for Mental Health Bucharest Romania Department of Neurosciences Center for Contextual Psychiatry Katholieke Universiteit Leuven Leuven Belgium Physical Activity and Functional Capacity Research Group Faculty of Health Care and Social Services LAB University of Applied Sciences Lahti Finland Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States South London and Maudsley NHS Foundation Trust London United Ki

Background: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data;distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk;and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. Objective: We aimed to address these 3 challenges to inform future work in stratified analyses. Methods: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

引用

作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Stroke Lesion Detection Using Convolutional Neural Networks

Stroke Lesion Detection Using Convolutional Neural Networks

引用

International Joint Conference on Neural Networks

作者： Danillo Roberto Pereira Pedro P. Reboucas Filho Gustavo Henrique de Rosa Joao Paulo Papa Victor Hugo C. de Albuquerque Department of Computing São Paulo State University Bauru SP Brazil Federal Institute of Education Science and Technology of Ceará CE Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm Optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results.

关键词： Computed tomography Hemorrhaging Brain Attenuation Blood Image segmentation Cranium

来源：评论

学校读者我要写书评

暂无评论

Task assignment to distributed teams based on a qualitative multi-criteria approach

Task assignment to distributed teams based on a qualitative ...

引用

Iberian Conference on Information Systems and Technologies (CISTI)

作者： Marum Simão Filho Plácido Rogério Pinheiro Adriano Bessa Albuquerque Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

ISBN: (纸本)9781509050475

The high growth of companies in recent years has caused the transformation of local markets into global ones. The software industry is not stranger to this transformation and has been looking for effective ways to work in a distributed scenario. A critical activity in distributed projects concerns the assignment of tasks to remote teams. This is a multicriteria decision-making problem and can be supported by Verbal Decision Analysis approaches. This paper describes an approach based on Verbal Decision Analysis methods to aid decision on factors that influence the assignment of tasks to distrubuted teams. The approach is based on the ORCLASS and ZAPROS III-i methods. The first results showed that factors such as "low turnover rate" and "team maturity" are among those considered more important, and the factors "personal trust", "low labor cost", "cultural similarities" and "time zone" were considered less relevant in DSD context.

关键词： Software Resource management Object oriented modeling Companies Decision analysis Project management

来源：评论

学校读者我要写书评

暂无评论

Toward a prescriptive catalog for it value 10

Toward a prescriptive catalog for it value

引用

10th IADIS International Conference on Information Systems 2017, IS 2017

作者： Gandelman, Ricardo Tulio Cappelli, Claudia Santoro, Flavia Maria Federal University of the State of Rio de Janeiro UNIRIO Rio de Janeiro Brazil Postgraduate Program in Informatics Department of Applied Informatics Av. Pasteur 458 Térreo Urca Rio de Janeiro RJ22.290-240 Brazil

ISBN: (纸本)9789898533623

There is no longer any question as to whether or not IT can deliver value to the organizations: This is already considered a fact. IT value is one of the most researched topics, but little is said about what is "value", most of the research is about IT investments evaluation methods and their classifications. We lack a better understanding of what IT value is, not a single definition but what are the elements that contribute to the IT value. We present our research in progress, whose main objective is to build an IT value Catalog revealing its composition and prescriptions (operacionalizations) on how to obtain contributions to the IT value from each model's element. © 2017 IADIS Press.

关键词： Information systems

来源：评论

学校读者我要写书评

暂无评论

Functional outlier detection and taxonomy by sequential transformations

arXiv

引用

arXiv 2018年

作者： Dai, Wenlin Mrkvička, Tomáš Sun, Ying Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Applied Mathematics and Informatics Faculty of Economics University of South Bohemia Studentská 13 České Budějovice37005 Czech Republic

Functional data analysis can be seriously impaired by abnormal observations, which can be classified as either magnitude or shape outliers based on their way of deviating from the bulk of data. Identifying magnitude outliers is relatively easy, while detecting shape outliers is much more challenging. We propose turning the shape outliers into magnitude outliers through data transformation and detecting them using the functional boxplot. Besides easing the detection procedure, applying several transformations sequentially provides a reasonable taxonomy for the flagged outliers. A joint functional ranking, which consists of several transformations, is also defined here. Simulation studies are carried out to evaluate the performance of the proposed method using different functional depth notions. Interesting results are obtained in several practical applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

MAGIC observation of BL Lacertae flaring period in 2020 38

MAGIC observation of BL Lacertae flaring period in 2020

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Imazawa, R. Strišković, J. Jormanainen, J. Truzzi, S. Lindfors, E. Dominis Prester, D. Bonnoli, G. Fukazawa, Y. Suda, Y. Abe, H. Abe, S. Abhir, J. Acciari, V.A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bošnjak, Ž. Burelli, I. Busetto, G. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Ceribella, G. Chai, Y. Chilingarian, A. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Emery, G. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Grau, R. Green, D. Green, J.G. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hrupec, D. Hütten, M. Imazawa, R. Inada, T. Iotov, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez Lezáun, M. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado de Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Mang, N. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molero González, M. Molina, E. Mondal, H.A. Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Josip Juraj Strossmayer University of Osijek Department of Physics Osijek31500 Croatia Finnish Centre for Astronomy with ESO University of Turku TurkuFI-20014 Finland Università di Siena INFN Pisa SienaI-53100 Italy INFN Sezione di Bari Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari BariI-70125 Italy RomeI-00136 Italy The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN PadovaI-35131 Italy Barcelona BellaterraE-08193 Spain Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Armenian MAGIC Group A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Catania Dipartimento di Fisica e Astronomia University of Catania CataniaI-95123 Italy INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy INFN MAGIC Group INF

BL Lacertae (BL Lac) is one of the low-frequency peaked BL Lac object (LBL). Since August 2020, BL Lac has shown a historically high activity in HE (high-energy, 0.1 GeV 100 GeV) gamma-ray range, BL Lac was detected in several nights during an especially bright flare simultaneous with a HE flare. In the long-term light curves, X-ray and HE gamma-ray flux overall show correlation, but sometimes only HE gamma-ray was bright. These multi-wavelength (MWL) correlations may relate to the emission mechanisms and the origin of the observed flaring activity. In this contribution we will present some of the preliminary results on the MWL campaign of BL Lac in September 2020, with a focus on the X-ray and gamma-ray activity, based on the data collected by Swift-XRT, MAGIC and Fermi-LAT telescopes. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

Ontology Design of Influential People Identification Using Centrality

引用

Journal of Physics: Conference Series 2018年第1期1007卷 012012-012012页

作者： Rolly Maulana Awangga Yusril, Muhammad Setyawan, Helmi Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Indonesia

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Understanding the origin of the extended γ-ray emission and the physical nature of HESS J1841-055 using observations at TeV energies with the MAGIC telescopes 37

Understanding the origin of the extended γ-ray emission and...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Acciari, V.A. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Becerra González, J. Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bökenkamp, H. Bonnoli, G. Bošnjak, Ž. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S.M. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Do Souto Espiñeira, E. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Fallah Ramazani, V. Fattorini, A. Fonseca, M.V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Green, David Green, J.G. Hadasch, D. Hahn, A. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jiménez Martínez, I. Jormanainen, J. Jouvin, L. Karjalainen, M. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, Alicia Loporchio, S. Machado de Oliveira Fraga, B. Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martínez, M. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J.M. Pavletić, L. Peñil, P. Persic, M. Pihet, M. Prada Moroni, P.G. Prandini, E. Priyadarshi, C. Brazil Spain Croatian MAGIC Group Ruđer Bošković Institute Croatia Croatia Croatian MAGIC Group Josip Juraj Strossmayer University of Osijek Department of Physics Croatia Croatian MAGIC Group University of Rijeka Department of Physics Croatia Croatia Germany Technische Universität Dortmund Germany ETH Zürich Switzerland Finnish MAGIC Group Finnish Centre for Astronomy with ESO University of Turku Finland Finnish MAGIC Group Astronomy Research Unit University of Oulu Finland Instituto de Astrofísica de Andalucía-CSIC Spain Instituto de Astrofísica de Canarias La LagunaE-38200 Spain Universidad de La Laguna Dpto. Astrofísica La Laguna TenerifeE-38206 Spain Spain INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari Italy INFN MAGIC Group INFN Sezione di Perugia Italy INFN MAGIC Group INFN Roma Tor Vergata The University of Tokyo Japan Japanese MAGIC Group Department of Physics Kyoto University Japan Japanese MAGIC Group Department of Physics Tokai University Japan Japanese MAGIC Group: Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan Japanese MAGIC Group Department of Physics Yamagata University Japan Japanese MAGIC Group Department of Physics Konan University Japan University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Poland IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN Italy Università di Pisa INFN Pisa Italy Università di Siena INFN Pisa Italy Saha Institute of Nuclear Physics India Inst. for Nucl. Research and Nucl. Energy Bulgarian Academy of Sciences Bulgaria Departament de Física CERES-IEEC Universitat Autónoma de Barcelona Spain Universitat de Barcelona ICCUB IEEC-UB Spain Università

With the improved sensitivity with respect to the previous generation, current space-borne and ground-based gamma-ray telescopes have made the number of γ-ray sources detected at GeV-TeV energies increase many folds over the last decade. Many of the detected extended gamma-ray sources are not associated with any known sources at other wavelengths. Understanding the nature of these sources and the origin of the observed high energy gamma-ray emission remains a great challenge. Using the MAGIC telescopes, we have observed one such unassociated gamma-ray source, named HESS J1841-055 at TeV energies. We investigate the physical nature and origin of the γ-ray emission from this extended source. In this paper, we present the results of our detailed investigation on this source using MAGIC data and other multi-waveband information on nearby sources. The results included in this proceeding and shown in this presentation were published in ref. [1]. The details about the analysis, interpretation and modelling can be found in the aforementioned reference. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Escada: Predicting Virtual Machine Network Bandwidth Demands for Elastic Provisioning in IaaS Clouds 4

Escada: Predicting Virtual Machine Network Bandwidth Demands...

引用

4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017

作者： Marques, Jonatas Adilson Obelheiro, Rafael Rodrigues Institute of Informatics Federal University of Rio Grande Do sul Porto Alegre Brazil Graduate Program in Applied Computing Department of Computer Science Santa Catarina State University Joinville Brazil

ISBN: (纸本)9781538619391

In Infrastructure as a Service clouds, customers lease virtual resources (e.g., CPU, memory, network) offered by cloud providers, paying for the allocated capacity of resources, regardless of their effective use. In this context, it is in the interest of the customers to reserve resources with sufficient capacity so that their applications achieve good performance while, at the same time, minimizing their expenses with idle capacities. The correct provisioning of resources is also invaluable to providers, who seek to maximize the resource usage of their physical infrastructures, avoiding spending maintenance costs with unused hardware such as high capacity servers and networking devices. We introduce Escada, a novel model for network bandwidth prediction to guide the provisioning of virtual machines. Escada is application-oblivious and dynamically adapts to changes in a virtual machine workload profile. The network flows of a virtual machine are monitored and used to estimate its total required bandwidth over a time period. A prediction algorithm is then applied to the entire history of estimates to predict the virtual machine future required bandwidth. Our experimental evaluation shows that the estimates provided by Escada are useful for guiding the prediction of network bandwidth requirements, providing accurate bandwidth estimates even when the network is under-provisioned. Furthermore, Escada correctly adjusts its prediction algorithm faster than the state-of-the-art model and predicts more reliably during the adjusting period. © 2017 IEEE.

关键词： Virtual machine

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：