DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT : MLA THROUGH N



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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :  MLA THROUGH N


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GOOGLE SITES CIS RESEARCH GUIDE SITE MAP DATABASES

 

AMOUNT OF CIS CONTENT IN A WIDE RANGE OF DATABASES 
WITH CONTENT SAMPLES FOR SOME

 

 

 

DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE 
FROM SELECTED SEARCH ENGINES DISCOVERY TOOLS

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :   A THROUGH  B

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :   C   PART ONE

http://tinyurl.com/y9my4ppp

 

 

DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :   C    PART TWO

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT:   D

http://tinyurl.com/yay8pkc3

 

 

DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT:   E    PART ONE

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT:    E    PART TWO

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT:     F THROUGH G

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT: 
H THROUGH INGENTA
 

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :    INSPEC THROUGH  L

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :    M THROUGH MLA

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :    MLA THROUGH N

http://tinyurl.com/ydeyt2d7

 

 

DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :   O

http://tinyurl.com/y8kh4r5u

 

 

DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :   P   PART ONE

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :   P   PART TWO
POLLUTION THROUGH PROQUEST STATISTICAL INSIGHT

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DATABASE SEARCH RESULTS FOR COMPUTER AND INFORMATION SCIENCE CONTENT :    PSY THROUGH PZ

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DATABASES USEFUL FOR COMPUTER SCIENCE AND INFORMATION SCIENCE RESEARCH:   R

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DATABASES USEFUL FOR COMPUTER SCIENCE AND INFORMATION SCIENCE RESEARCH:    S THROUGH SCOPUS

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DATABASES USEFUL FOR COMPUTER SCIENCE AND INFORMATION SCIENCE RESEARCH:
SEARCH.BIOPREPRINT THROUGH SZ

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DATABASES USEFUL FOR COMPUTER SCIENCE AND INFORMATION SCIENCE RESEARCH:     T THROUGH Z

http://tinyurl.com/ya2qprg5

 

 

 




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"computer science" OR "information science" OR "computer programming" OR informatics OR "data management" OR "information security" OR "network security" OR forensics OR "visual computing" OR "data mining" OR "machine learning" OR "multimedia databases" OR "artificial intelligence"



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MLA International Bibliography 

Results:  2,093



Content Sample

  • The Mechanical Turk: Enduring Misapprehensions concerning Artificial Intelligence


    By: Ashford, David; Cambridge Quarterly, 2017 June; 46 (2): 119-139. (journal article)

    Subjects: artificial intelligencecomputer; agency; human identity

    plumx




  • 2.

    Academic Journal




    Imitative Identity, Imitative Art, and AI: Artificial Intelligence


    By: Jackson, Tony E.; Mosaic: An Interdisciplinary Critical Journal, 2017 June; 50 (2): 47-63. (journal article)

    Subjects: science fiction film; Spielberg, Steven; imitation; neuroscience







  • 3.

    Academic Journal




    Critical News Reading with Twitter? Exploring Data-Mining Practices and Their Impact on Societal Discourse


    By: Verbeke, Mathias; Berendt, Bettina; d'Haenens, Leen; Opgenhaffen, Michaël; Communications: The European Journal of Communication Research, 2017; 42 (2): 127-149. (journal article)

    Subjects: public discourse; Twitter; news reporting; computer programs; data collection


    plumx




  • 4.

    Academic Journal





    Dying for Sex: Cultural and Forensic Narratives of Autoerotic Death


    By: Downing, Lisa; New Formations: A Journal of Culture/Theory/Politics, 2017; 89-90: 137-152. (journal article)

    Subjects: autoerotic asphyxiation; death; sexuality


    plumx




  • 5.

    Book





    Coding Literacy: How Computer Programming Is Changing Writing


    By: Vee, Annette Cambridge, MA: MIT; 2017. xi, 361 pp. (book)

    Subjects: literacy; coding






  • 6.

    Academic Journal




    Literary Pattern Recognition: Modernism between Close Reading and Machine Learning

    By: Long, Hoyt; So, Richard Jean; Critical Inquiry, 2016 Winter; 42 (2): 235-267. (journal article)

    Subjects: modernist poets; close reading; computer-assisted research; sociohistorical approach





  • 7.

    Academic Journal




    Morgue Porn: A Female Gaze in Forensic Television


    By: Risson, Toni; TEXT: Journal of Writing and Writing Courses, 2016 Oct; ([Supplement 35]): [no pagination]. (journal article)

    Subjects: CSI: Crime Scene Investigation; female protagonist; female gaze; misogyny






  • 8.

    Dissertation/ Thesis




    Characterization of Prose by Rhetorical Structure for Machine Learning Classification


    By: Java, James; Dissertation Abstracts International, 2016 Aug; 77 (2). Nova Southeastern U, 2015 (dissertation abstract)

    Subjects: automatic text classification







  • 9.

    Academic Journal




    Digital Analytical Bibliography: Ballad Sheet Forensics, Preservation, and the Digital Archive


    By: Stahmer, Carl G.; Huntington Library Quarterly: Studies in English and American History and Literature, 2016 Summer; 79 (2): 263-278. (journal article)

    Subjects: automatic recognition; image; analytical bibliography





  • 10.

    Academic Journal




    Blushing on Cue: The Forensics of the Blush in Early Modern Drama


    By: Dunne, Derek; Shakespeare Bulletin: A Journal of Performance Criticism and Scholarship, 2016 Summer; 34 (2): 233-252. (journal article)

    Subjects: blushing; facial expression; criminality; forensic sciences; physiognomy; conscience; shame





  • 11.

    Dissertation/ Thesis




    Latent Semantic Analysis, Corpus Stylistics and Machine Learning Stylometry for Translational and Authorial Style Analysis: The Case of Denys Johnson-Davies' Translations into English


    By: Al Batineh, Mohammed; Dissertation Abstracts International, 2016 May; 76 (11). Kent State U, 2015 (dissertation abstract)

    Subjects: English language translation; North African literature; West Asian literature; Arabic language literature; Arabic language (Modern); latent semantic analysis





  • 12.

    Book





    Information
     Science, Terminology and Translation Studies: Adaptation, Collaboration, Integration


    By: Bowker, Lynne; Delsey, Tom pp. 73-95 IN: Gambier, Yves (ed. and introd.); Doorslaer, Luc van (ed. and introd.) Border Crossings: Translation Studies and Other Disciplines. Amsterdam, Netherlands: Benjamins; 2016. xvi, 380 pp. (book article)

    Subjects: mathematical linguistics; computational linguistics; information science; terminological database; translationplumx





  • 13.

    Book





    Computer
     Science and Translation: Natural Languages and Machine Translation


    By: Giammarresi, Salvatore; Lapalme, Guy pp. 205-224 IN: Gambier, Yves (ed. and introd.); Doorslaer, Luc van (ed. and introd.) Border Crossings: Translation Studies and Other Disciplines. Amsterdam, Netherlands: Benjamins; 2016. xvi, 380 pp. (book article)

    Subjects: machine translation




  • 14.

    Academic Journal





    Christine Brooke-Rose and the Science of Fiction: Rethinking Conceptual Writing and Artificial Intelligence in Xorandor


    By: Vechinski, Matthew James; Critique: Studies in Contemporary Fiction, 2016; 57 (4): 448-463. (journal article)

    Subjects: dialogue; adolescent narrator


    Academic Journal

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Music and Performing Arts Collection 
1,110 Results


Content Sample



  • Artificial Intelligence and Music: An Artificial Intelligence Approach to Sound Design

    Miranda, Eduardo Reck. Computer Music Journal; Menlo Park, Calif., etc.19.2 (Summer 1995): 59.

    •  
      

  • Artificial Intelligence and Music: On the Role of Artificial Intelligence in Music Research

    Camurri, Antonio. Journal of New Music Research; Lisse19.2-3 (1990): 219.

    Cited by (‎1)    

     

     

  • Multimedia Databases for Public Service in Music Libraries

    Duggan, Mary Kay. Fontes Artis Musicae - Journal of the International Association of Music Libraries, Archives, and Documentation Centres; Middleton, Wis.38.1 (Jan 1991): 49.
     

  • Report on the ECAI Workshop on Artificial Intelligence and Music, held during the 9th European Conference on Artificial Intelligence (ECAI-90), Stockholm, Sweden, Aug. 6-10, 1990

    Widmer, Gerhard. Journal of New Music Research; Lisse19.4 (1990): 293.
     

  • Feature: Computer Programming

    Stuart, Phyllis. Back Stage West, Drama-Logue; New York, N. Y.7.22 (Jun 1, 2000-Jun 7, 2000): 8-9.

     

  • ARTIFICIAL INTELLIGENCE

    Pryor, Gregor. Music Week; London (Mar 20, 2017): 20.
     

  • ARTIFICIAL INTELLIGENCE
    Evans, Suzy. American Theatre; New York, N. Y.31.7 (Sep 2014): 25.

     

  • Reviews: Artificial Intelligence

    Fry, C. Computer Music Journal; Menlo Park, Calif., etc.4.3 (Fall 1980): 61.
     

  • Will Wright's artificial intelligence
    Kushner, David. Rolling Stone; New York 877 (Sep 13, 2001): 102.




    Artificial IntelligenceTravers, Peter. 
    Rolling Stone; New York 873 (Jul 19, 2001): 54.



  • SEX, LIES AND ARTIFICIAL INTELLIGENCE
    Hiatt, Brian. Rolling Stone; New York 1233 (Apr 23, 2015): 18.


     

  • Reviews: "A.I. Artificial Intelligence"

    Strick, Philip. Sight and Sound; London11.10 (Oct 2001): 38-39.
  • Preview 

  • The Ultimate Trip: Artificial Intelligence

    Empire; London, England 305 (Nov 2014): 79.
  •  

  • Artificial
     Intelligence and Music

    Roads, C. Computer Music Journal; Menlo Park, Calif., etc.4.2 (Summer 1980): 13.

    Cited by (‎3)    

     

  • Reviews: "A.I. Artificial Intelligence"

    Major, Wade. Boxoffice; Chicago, Ill.137.9 (Sep 2001): 152.
      

  • Artificial Intelligence in Organised Sound

    Miranda, Eduardo R; Williams, Duncan. Organised Sound; Cambridge, Eng.20.1 (Apr 2015): 76-81.
     

  • "A.I.: Artificial Intelligence"

    Bond, Jeff. Film Score Monthly; Vineyard Haven, Mass.6.6 (Jul 2001): 43.
     

  • A.I.: Artificial Intelligence

    Kreider, Tim. Film Quarterly; Berkeley56.2 (Winter 2002/2003): 32-39.
    Images (1) 
     

  • Artificial Intelligence in Organised Sound

    Miranda, Eduardo R; Williams, Duncan. Organised Sound, suppl. Special issue: 
    Organised Sound Celebrates 20 Years; Cambridge20.1 (Apr 

  • Going With the Flow (Charting) in Computer Programming

    Neiman, Marcus L. The Instrumentalist; Northfield, Ill.42.8 (Mar 1988): 54.
      

  • Artificial intelligence and its discontents
    The Village Voice; New York [New York]07 Dec 1993: 60.

    Preview 

  • A.I. Artificial Intelligence

    Sight and Sound; London21.6 (Jun 2011): 17.Preview 

  • WILLIAMS: A.I. Artificial intelligence

    Lintgen, Arthur. Fanfare; Tenafly39.2 (Nov/Dec 2015): 540-541.
    •  
    Preview 

  • *

    Music Index Online

    Results:  3,017



    Content Sample

    • 1.
      Automatic sociophonetics: Exploring corpora with a forensic accent recognition system.



      By: Brown, Georgina; Wormald, Jessica. Journal of the Acoustical Society of America. Jul2017, Vol. 142 Issue 1, p422-433. 12p. 3 Diagrams, 4 Charts, 1 Graph. DOI: 10.1121/1.4991330.

      Subjects: AUTOMATIC speech recognition; ACCENTS & accentuation; CORPORA (Linguistics); PHONETICS; SOCIOLINGUISTICS; FORENSIC sciences



    • 2.
      ARTIFICIAL
       INTELLIGENCE.



      By: PRYOR, GREGOR. Music Week. 3/20/2017, p20-20. 1p.

      Subjects: ARTIFICIAL intelligence -- Musical applications; ELECTRONIC data processing; MUSIC industry; 

    • MACHINE theory; COPYRIGHT infringement -- Lawsuits & claims






    • 3.





      Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.



      By: Monaghan, Jessica J. M.; Goehring, Tobias; Xin Yang; Bolner, Federico; Shangqiguo Wang; Wright, Matthew C. M.; Bleeck, Stefan. Journal of the Acoustical Society of America. Mar2017, Vol. 141 Issue 3, p1985-1998. 14p. DOI: 10.1121/1.4977197.

      Subjects: MACHINE learning; INTELLIGIBILITY of speech; HEARING impaired; SPEECH enhancement (Speech processing systems); SPEECH perception


      Cited References: (57)



    • 4. Expectations versus Reality of Artificial Intelligence.

      Detail Only Available

      By: TORRE, GIUSEPPE. Leonardo. 2017, Vol. 50 Issue 1, p31-35. 5p. DOI: 10.1162/LEON_a_01342.

      Subjects: SMALL art works; ARTIFICIAL intelligence research; CONCEPT art (Illustration); ONTOLOGIES (Information retrieval); ILLUSTRATION (Art); EDUCATION research; UNIVERSITY research; ARTS -- Computer network resources


      Cited References: (11)




    • 5. BOULEZ, INFORMATION SCIENCE AND IRCAM.



      By: Maconie, Robin. Tempo. Jan2017, Vol. 71 Issue 279, p38-50. 13p. DOI: 10.1017/S0040298216000309.

      Subjects: BELL Telephone Laboratories Inc.; INFORMATION science; TELECOMMUNICATION -- Congresses; MEMORIES & Commentaries (Book); BOULEZ, Pierre, 1925-2016


      Cited References: (27)




    • 6.




      Compositional Forensics: Examining "Skyfall".



      By: Kaschub, Michele; Smith, Janice P. Illinois Music Educator. Fall2016, Vol. 77 Issue 1, p43-44. 2p.

      Subjects: MUSIC teachers -- Societies, etc.; MUSIC -- Instruction & study





    • 7.




      Machine
       learning and music generation.



      By: Iñesta, José M.; Conklin, Darrell; Ramírez, Rafael. Journal of Mathematics & Music. Jul2016, Vol. 10 Issue 2, p87-91. 5p. DOI: 10.1080/17459737.2016.1216369.

      Subjects: MACHINE learning; MUSICAL analysis




    • 8.




      machine learning approach to ornamentation modeling and synthesis in jazz guitar.



      By: Giraldo, Sergio; Ramírez, Rafael. Journal of Mathematics & Music. Jul2016, Vol. 10 Issue 2, p107-126. 20p. DOI: 10.1080/17459737.2016.1207814.

      Subjects: MACHINE learning; JAZZ; AFRICAN Americans -- Music; MACHINE theory; REGRESSION analysis; MELODY



    • 9.
      Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis.



      By: Kirlin, Phillip B.; Yust, Jason. Journal of Mathematics & Music. Jul2016, Vol. 10 Issue 2, p127-148. 22p. DOI: 10.1080/17459737.2016.1209588.

      Subjects: MACHINE learning; SCHENKERIAN analysis; HARMONIC analysis (Music theory); MUSICAL analysis; ALGORITHMS; HARMONY in music



    • 10.
      Mapping between dynamic markings and performed loudness: a machine learning approach

      Academic Journal

      By: Kosta, Katerina; Ramírez, Rafael; Bandtlow, Oscar F.; Chew, Elaine. Journal of Mathematics & Music. Jul2016, Vol. 10 Issue 2, p149-172. 24p. DOI: 10.1080/17459737.2016.1193237.

      Subjects: LOUDNESS; MACHINE learning; AUDITORY perception; SUPPORT vector machines; DECISION trees; PIANISTS



    • 11.
      The development of Sonic Pi and its use in educational partnerships: Co-creating pedagogies for learning computerprogramming.



      By: Aaron, Samuel; Blackwell, Alan F.; Burnard, Pamela. Journal of Music, Technology & Education. May2016, Vol. 9 Issue 1, p75-94. 20p. DOI: 10.1386/jmte.9.1.75_1.

      Subjects: GREAT Britain; COMPUTER music -- Study & teaching; COMPUTER programmingCOMPUTER scientists; SCHOOL children; SECONDARY education



    • 12.
      Forensic
       EYE Katie.


      Periodical

      By: DUCHEN, JESSICA. Opera News. May2016, Vol. 80 Issue 11, p40-43. 4p.

      Subjects: THEATRICAL producers & directors; OPERAS; PERFORMING arts; WRITTEN on Skin (Theatrical production); MITCHELL, Katie, 1964-






    • 13.





      Development of the therapeutic relationship in music therapy with forensic psychiatric inpatients: a mixed method case study


      By: Frederiksen, Britta. Nordic Journal of Music Therapy. 2016 Supplement, Vol. 25, p24-24. 1p. DOI: 10.1080/08098131.2016.1179907.



    • 14.
      Bringing 'Musicque into the tableture': machine-learning models for polyphonic transcription of 16th-century lute tablature.



      By: de Valk, Reinier; Weyde, Tillman. Early Music. Nov2015, Vol. 43 Issue 4, p563-576. 14p. DOI: 10.1093/em/cau102.

      Subjects: TABLATURE (Music notation); MUSIC -- Manuscripts; COMPUTERS in musicology; ARRANGEMENT (Musical composition); MUSICAL notation









    • 15.






      A.I. 


      Artificial
       intelligence.



      By: Lintgen, Arthur. Fanfare: The Magazine for Serious Record Collectors. Nov/Dec2015, Vol. 39 Issue 2, p540-541. 2p.

      Subjects: WILLIAMS, John; BONNEY, Barbara; FABIAN, Lara







    • 16.





      ARTIFICIAL
       INTELLIGENCE— WHAT COULD GO WRONG?


      Periodical

      By: Vaughn, David. Sound & Vision. Nov2015, Vol. 80 Issue 9, p64-64. 1/2p. 2 Color Photographs.

      Subjects: SCIENCE fiction films -- Reviews; EX Machina (Film); GLEESON, Domhnall, 1983-







    • 17.





      An investigation of supervector regression for forensic voice comparison on small data.



      By: Huang, Chee; Epps, Julien; Thiruvaran, Tharmarajah. EURASIP Journal on Audio Speech & Music Processing. 9/8/2015, Vol. 2015 Issue 1, p1-15. 15p. DOI: 10.1186/s13636-014-0048-z.

      Subjects: GAUSSIAN mixture models (Statistics); BAYESIAN analysis; FINITE Gaussian mixture models (Statistics); LIKELIHOOD ratio tests; MATHEMATICAL optimization

       





    • 18.





      Rap Music Therapy in Forensic Psychiatry: Emphasis on the Musical Approach to Rap.



      By: HAKVOORT, LAURIEN. Music Therapy Perspectives. 2015, Vol. 33 Issue 2, p184-192. 9p. DOI: 10.1093/mtp/miv003.







    • 19.





      SEX, LIES AND ARTIFICIAL INTELLIGENCE

       

      By: HIATT, BRIAN. Rolling Stone. 4/23/2015, Issue 1233, p18-18. 3/8p. 2 Color Photographs.

      Subjects: ARTIFICIAL intelligence -- Moral & ethical aspects; EX Machina (Film); ISAAC, Oscar, 1979-





    • 20.





      Data
       Mining The Digital Gold Rush: 4 COMPANIES THAT GET IT.



      By: LEVINE, ROBERT. Billboard. 4/11/2015, Vol. 127 Issue 10, p14-15. 2p. 4 Color Photographs.

      Subjects: NEXT Big Sound Inc.; UNIVERSAL Music Group Inc.; ECHO Nest (Company); SPOTIFY Ltd.; BIG data; SOCIAL media; STREAMING audio; DATABASES







    • 21.





      Artificial
       Intelligence in Organised Sound.



      By: Miranda, Eduardo R.; Williams, Duncan. Organised Sound. Apr2015, Vol. 20 Issue 1, p76-81. 6p. DOI: 10.1017/S1355771814000454.

      Subjects: ARTIFICIAL intelligence; DIGITAL computer simulation; ELECTRONIC music analysis; MUSICAL analysis; COMPOSITION (Musical composition)







    • 22.





      CRITICAL AESTHETICS AND COMPOSITIONAL INFORMATICS.



      By: Holter, Colin. Leonardo. 2015, Vol. 48 Issue 2, p185-185. 1p. DOI: 10.1162/LEON_a_00981.

      Subjects: 21ST century art; AESTHETICS; COMPOSITION (Art); HUBER, Nicolaus A.; MANOVICH, Lev; ADORNO, Theodor W., 1903-1969






    • 23.




      New Directions in Music and Machine Learning.


      By: Conklin, Darrell; Ramirez, Rafael; Iñesta, José M. Journal of New Music Research. Sep2014, Vol. 43 Issue 3, p251-254. 4p. DOI: 10.1080/09298215.2014.930496.

      Subjects: MACHINE learning; CONDUCTORS (Musicians); MUSICAL analysis; SOUND recordings; DIGITAL sound recording







    • 24.





      The Sense of Ensemble: a Machine Learning Approach to Expressive Performance Modelling in String Quartets.



      By: Marchini, Marco; Ramirez, Rafael; Papiotis, Panos; Maestre, Esteban. Journal of New Music Research. Sep2014, Vol. 43 Issue 3, p303-317. 15p. DOI: 10.1080/09298215.2014.922999.

      Subjects: STRING quartets (Musical groups); MUSIC -- Interpretation (Phrasing, dynamics, etc.); MACHINE learning; MUSICIANS; PERFORMANCE evaluation








    • 25.





      Supporting students in music technology higher education to learn computer programming.



      By: Moore, David. Journal of Music, Technology & Education. May2014, Vol. 7 Issue 1, p75-92. 18p. DOI: 10.1386/jmte.7.1.75_1.

      Subjects: MUSIC & technology -- Study & teaching (Higher); MUSIC & technology; COMPUTER programming education in universities & colleges; COLLABORATIVE learning; EXPERIENTIAL learning; HIGHER education







    • 26.





      Issues in Forensic Voice.



      By: Hollien, Harry; Huntley Bahr, Ruth; Harnsberger, James D. Journal of Voice. Mar2014, Vol. 28 Issue 2, p170-184. 15p. DOI: 10.1016/j.jvoice.2013.06.011.

      Subjects: MEDICAL jurisprudence; PSYCHOACOUSTICS; SPEECH; INTELLIGIBILITY of speech; BIOSURVEILLANCE







    • 27.





      Classification of large acoustic datasets using machine learning and crowdsourcing: Application to whale calls.



      By: Shamir, Lior; Yerby, Carol; Simpson, Robert; von Benda-Beckmann, Alexander M.; Tyack, Peter; Samarra, Filipa; Miller, Patrick; Wallin, John. Journal of the Acoustical Society of America. Feb2014, Vol. 135 Issue 2, p953-962. 10p. DOI: 10.1121/1.4861348.

      Subjects: SOUND production by animals; KILLER whale; MACHINE learning research; CROWDSOURCING research; ACOUSTICS (Physical sciences) research


      Cited References: (43) Times

      Cited in this Database: (2)






    • 28.





      Forensic
       music therapy: a treatment for men & women in secure hospital settings.



      By: Frederiksen, Britta. Nordic Journal of Music Therapy. 2014, Vol. 23 Issue 1, p90-91. 2p. DOI: 10.1080/08098131.2013.863800.

      Subjects: MUSIC therapy









    • 29.






      BLAST FROM THE PAST: KODÁLY ENVOY DECEMBER 1976 !G !K2- !M3:4 (2E.U (0)): Folk Song Research and ComputerScience.



      By: Locke, Eleanor G. Kodaly Envoy. Winter2014, Vol. 40 Issue 2, p10-11. 2p.

      Subjects: FOLK songs -- United States; MUSIC -- Data processing; MUSIC -- Instruction & study; FOLK music; MUSICOLOGY







    • 30.





      An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics.



      By: Sturm, Bob L. Computer Music Journal. Winter2013, Vol. 37 Issue 4, p90-91. 2p. DOI: 10.1162/COMJ_r_00208.

      Subjects: SIGNAL processing; NONFICTION; INTRODUCTION to Audio Content Analysis: Applications in Signal Processing & Music Informatics, An (Book); LERCH, Alexander



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    41 Results

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    1,728 Results

    CONTENT SAMPLE

    • 1

      1. COMPUTER SCIENCE AND TECHNOLOGY -
      THE NETWORK SECURITY CENTER -
      A SYSTEM LEVEL APPROACH TO COMPUTER NETWORKSECURITY




    • 2. COMPUTER
       SCIENCE AND TECHNOLOGY - DESIGN ALTERNATIVES FOR COMPUTER NETWORK SECURITY

      COLE, G D.  (1978).

      Cited by (‎1)    

      Preview

       

    • 3. Information Security:
      Comments on the Proposed Government Information Security Act of 1999

      Brock, Jack L.. 2000: 13

    • 4.

      Artificial Intelligence, Expert Systems, Microcomputers and Law Enforcement

      Cameron, J. Police Chief57.3 (March 1990): 36-41.


    • 5.

      Artificial Intelligence: Expert Systems, Microcomputers and Law Enforcement

      Cameron, J. Law and Order 36.3: 58-66. (March 1988)


    • 6.

      Corrections Data Mining

      (2004): 3.

    • 7.

      Dig Into Data Mining

      Kanable, Rebecca. Law Enforcement Technology34.3 (March 2007): 62,64-68, 70.
    • 8.

      Handbook on Artificial Intelligence and Expert Systems in Law Enforcement

      Ratledge, E C; Jacoby, J E. (1989): 194.



      9. ISSUES IN COMPUTER 
      NETWORK SECURITY
      HINCKLEY, A C; MITCHELL, J.  (1978).

    • 10.

      Drugs, Guns and Violence: Data Mining

      McCue, Colleen; McNulty, Paul J.. Law and Order51.12 (December 2003): 34-36.


    • 11.

      Data Mining and Value-Added Analysis

      McCue, Colleen; Stone, Emily S. W; Gooch, Teresa P.. FBI Law Enfo

    • 12.

      How to Manage the Network Security Problem

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    • 13.

      Network Security: Safeguarding Systems Against the Latest Threats

      Moore, Eric. Police Chief50.2 (February 2003): 23-31.

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      INFORMATION SECURITY BASICS

      Kabay, M E. NCSA News4.5 (September/October 1993): 14-15, 19.
       

    • 15.

      Information Security in Banks

      Shrinath, B. Journal of Financial Crime5.1 (August 1997): 65-71.

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      INFORMATION SCIENCE - LAW ENFORCEMENT APPLICATIONS

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      Data-Mining Client Concerns in Adolescent Mental Health Services:
      Clinical and Program Implications

      Peake, Ken; Surko, Michael; Epstein, Irwin; Medeiros, Daniel. 
      Social Work in Mental Health3.3 (2005): 287-304.

      Cited by (‎2)    References (‎22)    


    • 18.

      Assessment of Data Mining Methods
      for Forensic Case Data Analysis

      Terrettaz-Zufferey, Anne-Laure; Ratle, Frederic;
      Ribaux, Olivier; Esseiva, Pierre; Kanevski, Mikhail. 
      Journal of Criminal Justice and Security
      8.3,4 (December 2006): 350-355.


    • 19.

      Collaborative Data-Mining in an Adolescent Mental Health Service:
      Clinicians Speak of Their Experience

      Ciro, Dianne; Nembhard, Michael. Social Work in Mental Health
      3.3 (2005): 305-317.

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    • 20.

      Connecting the Dots: Data Mining and
      Predictive Analytics in Law Enforcement and Intelligence Analysis

      McCue, Colleen; Parker, Andre. Police Chief
      70.10 (October 2003): 115-118,120, 122.

    • 21.

      Twenty-four/Seven Crime Analysis: Web-based Data Mining,
      Predictive Analytics

      McCue, Colleen; Parker, Andre. Law Enforcement Technology
      31.2 (February 2004): 92,94-99.

       
    • 22.

      Modus Operandi Modelling of Group Offending:
      Data-mining Case Study

      Adderley, Richard; Musgrove, Peter. 
      International Journal of Police Science & Management
      5.4 (Winter 2003): 265-276.

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    • 23.

      Application of Machine Learning to Toolmarks:
      Statistically Based Methods for Impression Pattern Comparisons

      Petraco, Nicholas D. K.; Chan, Helen B.A.;
      De Forest, D.Crim.,Peter R.; Diaczuk, Peter; Gambino, Carol; et al. 
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    • 24.

      Information Security Directory -- A Cybercitizen Initiative,
      Third Edition (2001).


    • 25.

      Cyber Citizen: Information Security Resource Directory,
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      GLOBALIZATION OF COMPUTER CRIME AND INFORMATION SECURITY

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    • 27.

      INTERNAL AUDITOR'S INFORMATION SECURITY HANDBOOK

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      Information Security: Challenges in Using Biometrics

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    • 29.

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    • 30.

      Information Security: Technologies To Secure Federal Systems

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    18,411 Results


    CONTENT SAMPLE

    1. Reviews | 28 June 2012

      Biological imaging software tools

      • ...of available software for bioimage informatics, focusing on open-source...
      • Kevin W Eliceiri
      • Michael R Berthold
      • […]
      • Anne E Carpenter
      Nature Methods 9, 697–710

    2. Reviews | 27 May 2015

      Probabilistic machine learning and artificial intelligence

      • ...for expressing probabilistic models as computer programs and which could have a...
      • Zoubin Ghahramani
      Nature 521, 452–459


    3. Reviews | 07 October 2013

      Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

      • ...approach for data analysis termed data mining (DM). It enables...
      • R Shouval
      • O Bondi
      • […]
      • A Nagler
    4. Comments and Opinion | 15 April 2004

      Table of suppliers

      Nature 428, 778–779

    5. Comments and Opinion | 11 September 2003

      Table of suppliers

      Nature 425, 216–217

    6. Comments and Opinion | 17 October 2002

      table of suppliers

      Nature 419, 759–761

    7. Reviews | 15 January 2010

      Informatics for practicing anatomical pathologists: marking a new era in pathology practice

      • Informatics can be defined as using... ...patient diagnosis or management. Pathology informatics had evolved as a response... ...most commonly used tools of informatics can be classified into digital... ...well as Internet and electronic data mining. Digital imaging is the... ...of informatics in the...
      • Manal Y Gabril
      •  & George M Yousef
      Modern Pathology 23, 349–358

    8. Research | 01 November 2002

      Bioinformatics and genomic medicine

      • ...challenges in computational science. Clinical informatics has long developed methodologies to... ...and clinical information systems. The informatics revolution in both bioinformatics and... clinical informatics will eventually change the current... .... Postgenome informatics, powered...
      Genetics in Medicine 4, 62S–65S

    9. Reviews | 10 January 2012

      Machine learning and data mining: strategies for hypothesis generation

      • ...incremental pace. Machine learning and data mining are alternative approaches to identifying... ...standard techniques through implementation of data-mining and machine-learning strategies....
      • M A Oquendo
      • E Baca-Garcia
      • […]
      • N Duan
      Molecular Psychiatry 17, 956–959

    10. Research | 17 February 2017 | OPEN

      Learning from data to design functional materials without inversion symmetry

      • ...problem. Here we develop an informatics-guided ab initio approach to... ...The workflow integrates group theory, informatics and density-functional theory to... ...interplay break inversion symmetry, while informatics tools learn from available data...
      • Prasanna V. Balachandran
      • Joshua Young
      • […]
      • James M. Rondinelli
    11. Comments and Opinion | 29 September 1988

      Building Bacon's new engine

      • ...The eager worker writes a computer program. Having elicited some cute...
      • Donald Michie
      Nature 335, 461–462

    12. Research | 01 October 2000

      Data mining

      • Data mining has been defined as “... ...the life sciences and healthcare, data mining is a huge industry,... ...industries that rely daily on data mining for a number of their...
      Nature Biotechnology 18, IT35–IT36

    13. Research | 22 March 2008

      Better informed in clinical practice – a brief overview of dental informatics

      • Uptake of dental informatics has been hampered by technical... ...the longer term, future dental informatics tools will incorporate dynamic,...
      • P. A. Reynolds
      • J. Harper
      •  & S. Dunne
      British Dental Journal 204, 313–317

    14. News and Views | 12 October 2016

      Artificial intelligence: Deep neural reasoning

      • A neural network/computer program hybrid
      • Herbert Jaeger
      Nature 538, 467–468

    15. Reviews | 02 May 2012

      Mining electronic health records: towards better research applications and clinical care

      • ...research increasingly uses methods from data mining, machine learning and text...
      • Peter B. Jensen
      • Lars J. Jensen
      •  & Søren Brunak
    16. Comments and Opinion | 01 June 2002

      Dealing with the data deluge

      • ...it would be surprising if informatics was not already heavily involved... ...fully exploited the capabilities of informatics is perhaps more questionable.... ...isn't to say that informatics will replace or supersede existing... ...be a growing reliance on informatics to improve efficiency, to...
      • Jerry Lanfear
    17. Research | 26 August 2016 | OPEN

      A general-purpose machine learning framework for predicting properties of inorganic materials

      • The materials informatics literature is full of successful...
      • Logan Ward
      • Ankit Agrawal
      • […]
      • Christopher Wolverton
    18. News and Views | 02 June 1977

      What can artificial intelligence offer psychology?

      • ...design, implementation and analysis of computer programs which exhibit intelligent behaviour.... ...described fully or partially implemented computer programs. These papers were probably... ...could best be realised as computer programs. He illustrated his argument... ...a computer program which...
      • Tim O'Shea
      Nature 267, 396–397

    19. Research | 03 June 2010

      Machine Learning in Medicine: A Primer for Physicians

      • ...that do some form of data mining or machine learning, recognizing...
      • Akbar K Waljee
      •  & Peter D R Higgins
    20. Research | 08 March 2008

      Better informed: an overview of health informatics

      • Healthcare informatics is increasing in importance both... ...Over the longer term, future informatics tools, combined with other...
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      • J. Harper
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      • S. Dunne
      British Dental Journal 204, 259–264

    21. Research | 01 February 2000

      Data mining in biotechnology

      • Data mining has been defined as “... ...the life sciences and healthcare, data mining is a huge industry,... ...industries that rely daily on data mining for a number of their...
      • Aris Persidis
      Nature Biotechnology 18, 237–238

    22. News | 23 August 2017

      How machine learning could help to improve climate forecasts

      • ...the field known as climate informatics. Work in this area...
      • Nicola Jones
      Nature News 548, 379–380

    23. Special Features | 22 December 2010

      Rise of the Robo Scientists

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      Scientific American 304, 72–77



    24. Research | 21 August 2017 | OPEN

      A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study

    25. Reviews | 27 May 2015

      Reinforcement learning improves behaviour from evaluative feedback

      • ...in board games. If a computer program is playing against a human... ...as noughts and crosses, a computer program can exhaustively enumerate all the...
      • Michael L. Littman
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    edition.

    Patrias K, author; Wendling D, editor.

    Bethesda (MD): National Library of Medicine (US); 2007-.

    Select item 14575083.
    SNP FAQ Archive [Internet].

    SNP FAQ Archive [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2005-.

    Select item 21836404.
    Select item 25577825.
    Select item 24476406.
    The GenBank Submissions Handbook [Internet].

    The GenBank Submissions Handbook [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2011-.

    Select item 18957887.
    Molecular Biology of the Cell. 4th edition.

    Molecular Biology of the Cell. 4th edition.

    Alberts B, Johnson A, Lewis J, et al.

    New York: Garland Science; 2002.

    Select item 15947528.
    UMLS® Reference Manual [Internet].

    UMLS® Reference Manual [Internet].

    Bethesda (MD): National Library of Medicine (US); 2009 Sep-.

    Select item 15027929.
    Molecular Imaging and Contrast Agent Database (MICAD) [Internet].

    Molecular Imaging and Contrast Agent Database (MICAD) [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2004-2013.

    Select item 301216010.
    NCBI Video Vault [Internet].

    NCBI Video Vault [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2013-.

    Select item 141528011.
    Select item 137180812.
    LinkOut Help [Internet].

    LinkOut Help [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2006-.

    Select item 261902613.
    Biosocial Surveys.

    Biosocial Surveys.

    National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys; Weinstein M, Vaupel JW, Wachter KW, editors.

    Washington (DC): National Academies Press (US); 2008.

    Select item 235805214.
    Probe Reports from the NIH Molecular Libraries Program [Internet].

    Probe Reports from the NIH Molecular Libraries Program [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2010-.

    Select item 234888415.
    SRA Handbook [Internet].

    SRA Handbook [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2010-.

    Select item 282152416.
    Select item 139768817.
    GaP FAQ Archive [Internet].

    GaP FAQ Archive [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2009-.

    Select item 327542018.
    Science Needs for Microbial Forensics: Developing Initial International Research Priorities.

    Science Needs for Microbial Forensics: Developing Initial International Research Priorities.

    Committee on Science Needs for Microbial Forensics: Developing an Initial International Roadmap; Board on Life Sciences; Division on Earth and Life Studies; National Research Council.

    Washington (DC): National Academies Press (US); 2014 Jul 25.

    Select item 315267219.
    Emerging and Readily Available Technologies and National Security: A Framework for Addressing Ethical, Legal, and Societal Issues.

    Emerging and Readily Available Technologies and National Security: A Framework for Addressing Ethical, Legal, and Societal Issues.

    Committee on Ethical and Societal Implications of Advances in Militarily Significant Technologies that are Rapidly Changing and Increasingly Globally Accessible; Computer Science and Telecommunications Board; Board on Life Sciences; Committee on Science, Technology, and Law; Center for Engineering, Ethics, and Society; National Research Council; National Academy of Engineering; Chameau JL, Ballhaus WF, Lin HS, editors.

    Washington (DC): National Academies Press (US); 2014 Mar 12.

    Select item 235748420.
    SRA Application Notes [Internet].

    SRA Application Notes [Internet].

    Bethesda (MD): National Center for Biotechnology Information (US); 2010-.


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