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Stephen SenerDavid J. BentremWalley TempleGerald P. MurphyJames GraceMichelle BaymanPerry ShenJoel BaumgartnerClyde EllisJesús EsquivelJason HarveshpMogal AviramNissan KonstanI VotanopoulosBiomarkers JanardanD KhandekarW. G. CarsonSteven LibuHemant K. RoyMichael Sabel BiostaElizabeth CsYong-Fang HahnEfstathia KuoBreast PolychronopoulouIsabel KlimbergBenjamín RubioCletus AndersonHarry ArcieroKirby BearFrederick BlandThomas P. GreeneMarcos JulianMoshe MoraesDavid PapaDavid P. WinchesterKatharine WinchesterAnthony Yao ColorectalTheodor SenagoreBradley AsgeirssonKyle ChampagneEric CologneW. Lewis JohnsonKarin HalabiSco HardimanMary Steele CutaneousKimberly BradyErnest BrownL. Jean CampGeorge MelstromCharles PoultsidesHari ScogginsCharles ShuklaJen VollmerYeh JenH AmerNancy Zureikat MedicalNeil KemenyDaniel AbboFlavio G. RochaMarko ŠimunovićChing‐Wei D. TzengSandra WongMichel W.J.M. WoutersPa EntLisa M. BrownAmy RadiaThomas DelaneyPrajnan DasJames B. YuReconstruc VeKetan SurgeryJoseph PatelDavid CareyEdward Yi ChangMing-Huei ChangJoseph ChengMa DisaHanasono HewJoon JpStephen HongJaume KovachMaurice MasiaJus NahabedianM SacksHani SbitanyMark SiscoOrthopedic LorRandall MichaelJ MonumentEdmund BartleKlaus J. BusamDavid GyorkiDale HanAndrew HayesJames W. JakubGiorgios KarakousisMichael LoweKishwer S. NehalOmgo E. NiewegAndrew J. SpillaneJeff ReyWayne EndocrineEren BerberJennifer RosénYouben FanMichele MinutoRodney F. PommierCarmen C. SolórzanoChandra AreT PeterKingham GynecologyYukio SonodaEric EisenhauerSarah FergusonKris NeZano OliverZivanovic HeadJa NeckPravin ShahAshok Shaha AlfiFerlito KepalNitesh V. PatelBhuvanesh SinghSinha HepatobiliaryPancreas ClarkGamblin HjKim DavidA KoobyShishir K. MaithelSyed A. AhmadOliver F. BatheMichael BouvetWilliam W. TsengKenneth CardonaTimothy A. DamronJason FosterAlessandro GronchiHarald HoekstraHans IwenofuWilliam KraybillJoel MayersonEhud MendelRaphael E. PollockPeter S. RoseThomas StomachUpper GiHiroyuki KuwanoCarl SchmidtCiaran McnameeMaria C. RussellVivian E. StrongAlan ThomayMasahiko Yano SurgicalMelissa E. HoggDouglas ConstanPetros C. KarakousisThoracic GillMichael T. JaklitschAnthony KimLuis Argote-GreeneMarcelo DasilvaJames DonahuePhilippe NaKazu OkabeSco AtayStacey SuAndrea UrologyRobert BahnsonJosh MeeksMarcus L. QuekMichael Woods
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Dear Reader! We express our sincere appreciation to all the authors, reviewers, members of the editorial board, and the editorial staff for their tireless contributions during these difficult years 2020-2021. The preparation of each issue of "The Proceedings of the Shevchenko Scientific Society: Medical Sciences” ensures its continuing development. The Journal is accepted by the Content Selection & Advisory Board of Scopus to the international indexing Scopus; to the international catalog of journals Ulrichsweb & Ulrichs; and to the Norsk Center for Forsknings data. As an open-access journal that undergoes peer review, the Editors have received multiple manuscripts from multiple countries. Information from the website shows that we had readers in 134 countries.
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Citation Recommendation is very interesting research area. Many algorithms and methods are proposed for better citation recommendation. Recently, the growth of information technology is high. So the digital libraries are there such as IEEE Xplore and ACM Digital library. The online publications of research papers and conferences are increasing day by day. This makes citation recommendation is a very challenging one. In this paper, propose a citation recommendation method that uses citation relations and similarity between many other papers. The basic method consists of recommend citations by cross references. If one paper is co-occurred in two or more citing papers, then they are similar to some extent. After that, these citing papers are pairwise compared with their contents to get similarities between them. Here, evaluate the proposed method in real word datasets such as IEEE journals.
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With the tremendous amount of citations available in digital library, how to suggest citations automatically, to meet the information needs of researchers has become an important problem. In this paper, we propose a model which treats citation recommendation as a special retrieval task to address this challenge. First, users provide a target paper with some metadata to our system. Second, the system retrieves a relevant candidate citation set. Then the candidate citations are reranked by well-chosen citation evidence, such as publication time preference, self-citation preference, co-citation preference and publication reputation preference. Especially, various measures are introduced to integrate the evidence. We experimented with the proposed model on an established bibliographic corpus-ACL Anthology Network, the results show that the model is valuable in practice, and citation recommendation can be significantly improved using proposed evidences.
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OpenURL links provide access to full-text articles from citation databases; however, end users who have found citations outside of library databases must find the full text of their journal articles another way. Many OpenURL link resolvers offer a citation finder service which allows the end user to search for specific known items at the article level. This paper studies the usability of the “citation linker” search from Ex Libris' SFX®. Twenty-one volunteers tested finding known journal articles using both a standard A-to-Z list and SFX' citation linker search. The researchers compare results from the two search interfaces, document the problems found, recommend which interface makes the best default search interface, and suggest improvements to the citation linker interface.
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OpenURL links provide access to full-text articles from citation databases; however, end users who have found citations outside of library databases must find the full text of their journal articles another way. Many OpenURL link resolvers offer a citation finder service which allows the end user to search for specific known items at the article level. This paper studies the usability of the “citation linker” search from Ex Libris' SFX®. Twenty-one volunteers tested finding known journal articles using both a standard A-to-Z list and SFX' citation linker search. The researchers compare results from the two search interfaces, document the problems found, recommend which interface makes the best default search interface, and suggest improvements to the citation linker interface.
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Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text , "National Science Foundation, National Academy of Sciences—National Research Council, and American Documentation Institute Jointly Announce Plans to Establish an International Conference on Scientific Information," J. Opt. Soc. Am. 47, 258_3-259 (1957) Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
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As the volume of publications has increased dramatically, an urgent need has developed to assist researchers in locating high-quality, candidate-cited papers from a research repository. Traditional scholarly-recommendation approaches ignore the chronological nature of citation recommendations. In this study, we propose a novel method called "Chronological Citation Recommendation" which assumes initial user information needs could shift while users are searching for papers in different time slices. We model the information-need shifts with two-level modeling: dynamic time-related ranking feature construction and dynamic evolving feature weight training. In more detail, we employed a supervised document influence model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that encapsulates dynamic topic-based information, time-decay paper/topic citation information, and word-based information. We applied multiple meta-paths for different ranking hypotheses which carried different types of information for citation recommendation in various time slices, along with information-need shifting. We also used multiple learning-to-rank models to optimize the feature weights for different time slices to generate the final "Chronological Citation Recommendation" rankings. The use of Chronological Citation Recommendation suggests time-series ranking lists based on initial user textual information need and characterizes the information-need shifting. Experiments on the ACM corpus show that Chronological Citation Recommendation can significantly enhance citation recommendation performance.
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This editorial describes the main improvements Oecologia Australis has gone through from 2017 to 2018. The journal renewed its Editorial Board and has now a Deputy Editor-in-Chief and a Graphic Editor. We also updated its OLS submission system and it is now hosted on the Federal University of Rio de Janeiro's webpage ( https://revistas.ufrj.br/index.php/oa/login ). In 2017, Oecologia Australis reached Qualis-Capes B3 in the BIODIVERSITY area and B1 in the ENVIRONMENTAL SCIENCES area, and it was indexed and abstracted in the Zoological Record , Biological Abstracts e BIOSIS Previews. In 2018, the new editor-in-Chief Dr. Ana Claudia Delciellos and the editorial board will keep working to make Oecologia Australis a reference journal in Ecology and related subjects.
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