01737oam 2200445M 450 991071642370332120200213070542.2(CKB)5470000002521911(OCoLC)1065859794(OCoLC)995470000002521911(EXLCZ)99547000000252191120071213d1927 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierPermitting Maj. Charles Beatty Moore to accept decorations from foreign countries. January 27, 1927. -- Committed to the Committee of the Whole House and ordered to be printed[Washington, D.C.] :[U.S. Government Printing Office],1927.1 online resource (2 pages)House report / 69th Congress, 2nd session. House ;no. 1884[United States congressional serial set] ;[serial no. 8690]Batch processed record: Metadata reviewed, not verified. Some fields updated by batch processes.FDLP item number not assigned.AwardsMilitary decorationsArmed ForcesOfficersLegislative materials.lcgftAwards.Military decorations.Armed ForcesOfficers.Hill Lister1894-1984Democrat (AL)1389925WYUWYUOCLCOOCLCQBOOK9910716423703321Permitting Maj. Charles Beatty Moore to accept decorations from foreign countries. January 27, 1927. -- Committed to the Committee of the Whole House and ordered to be printed3479077UNINA01383oam 2200469zu 450 991013879680332120241212215740.097814244591621424459168(CKB)2400000000002496(SSID)ssj0000452027(PQKBManifestationID)12191881(PQKBTitleCode)TC0000452027(PQKBWorkID)10463421(PQKB)11108676(NjHacI)992400000000002496(EXLCZ)99240000000000249620160829d2010 uy engur|||||||||||txtccr2010 Sixth International Conference on Autonomic and Autonomous Systems[Place of publication not identified]IEEE20101 online resourceBibliographic Level Mode of Issuance: Monograph9780769539706 076953970X 9781424459155 142445915X Autonomic computingCongressesComputer systemsCongressesAutonomic computingComputer systems004IEEE StaffPQKBPROCEEDING99101387968033212010 Sixth International Conference on Autonomic and Autonomous Systems2389782UNINA06969nam 22006735 450 991034928390332120251116220117.03-030-12554-810.1007/978-3-030-12554-7(CKB)4100000009185048(DE-He213)978-3-030-12554-7(MiAaPQ)EBC5892704(PPN)25806434X(EXLCZ)99410000000918504820190906d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierGuide to Mobile Data Analytics in Refugee Scenarios The 'Data for Refugees Challenge' Study /edited by Albert Ali Salah, Alex Pentland, Bruno Lepri, Emmanuel Letouzé1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XVI, 500 p. 169 illus., 149 illus. in color.)3-030-12553-X Chapter 1. Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey -- Chapter 2. Call Detail Records to Obtain Estimates of Forcibly Displaced Populations -- Chapter 3. Measuring Fine-Grained Multidimensional Integration Using Mobile Phone Metadata: The Case of Syrian Refugees in Turkey -- Chapter 4. Integration of Syrian Refugees: Insights from D4R, Media Events and Housing Market Data -- Chapter 5. Mobile Phone Data for Humanitarian Purposes: Challenges and Opportunities -- Chapter 6. Improve Education Opportunities for Better Integration of Syrian Refugees in Turkey -- Chapter 7. Measuring and Mitigating Behavioural Segregation as an Optimisation Problem -- Chapter 8. The Use of Big Mobile Data to Gain Multi-layered Insights for Syrian Refugee Crisis -- Chapter 9. Characterizing the Mobile Phone Use Patterns of Refugee Hosting Provinces in Turkey -- Chapter 10. Towards an Understanding of Refugee Segregation, Isolation, Homophily and Ultimately Integration in Turkey Using Call Detail Records -- Chapter 11. Using Call Data and Stigmergic Similarity to Assess the Integration of Syrian Refugees in Turkey Coding Bootcamps for Refugees -- Chapter 12. Quantified Understanding of Syrian Refugee Integration in Turkey -- Chapter 13. Refugees in Undeclared Employment - A Case Study in Turkey -- Chapter 14. Assessing Refugees' Onward Mobility with Mobile Phone Data - A Case Study of (Syrian) Refugees in Turkey -- Chapter 15. Optimizing the Access to Healthcare Services in Dense Refugee Hosting Urban Areas: A Case for Istanbul -- Chapter 16. A Review of Syrian Refugee Integration in Turkey: Evidence from Call Detail Records -- Chapter 17. Conclusions and Lessons Learned.After the start of the Syrian Civil War in 2011–12, increasing numbers of civilians sought refuge in neighboring countries. By May 2017, Turkey had received over 3 million refugees — the largest r efugee population in the world. Some lived in government-run camps near the Syrian border, but many have moved to cities looking for work and better living conditions. They faced problems of integration, income, welfare, employment, health, education, language, social tension, and discrimination. In order to develop sound policies to solve these interlinked problems, a good understanding of refugee dynamics isnecessary. This book summarizes the most important findings of the Data for Refugees (D4R) Challenge, which was a non-profit project initiated to improve the conditions of the Syrian refugees in Turkey by providing a database for the scientific community to enable research on urgent problems concerning refugees. The database, based on anonymized mobile call detail records (CDRs) of phone calls and SMS messages of one million Turk Telekom customers, indicates the broad activity and mobility patterns of refugees and citizens in Turkey for the year 1 January to 31 December 2017. Over 100 teams from around the globe applied to take part in the challenge, and 61 teams were granted access to the data. This book describes the challenge, and presents selected and revised project reports on the five major themes: unemployment, health, education, social integration, and safety, respectively. These are complemented by additional invited chapters describing related projects from international governmental organizations, technological infrastructure, as well as ethical aspects. The last chapter includes policy recommendations, based on the lessons learned. The book will serve as a guideline for creating innovative data-centered collaborations between industry, academia, government, and non-profit humanitarian agencies to deal with complex problems in refugee scenarios. It illustrates the possibilities of big data analytics in coping with refugee crises and humanitarian responses, by showcasing innovative approaches drawing on multiple data sources, information visualization, pattern analysis, and statistical analysis.It will also provide researchers and students working with mobility data with an excellent coverage across data science, economics, sociology, urban computing, education, migration studies, and more.Data miningBig dataApplication softwareSocial sciences—Data processingSocial sciences—Computer programsEmigration and immigrationData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Computer Appl. in Social and Behavioral Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/I23028Computational Social Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/X34000Migrationhttps://scigraph.springernature.com/ontologies/product-market-codes/X24000Data mining.Big data.Application software.Social sciences—Data processing.Social sciences—Computer programs.Emigration and immigration.Data Mining and Knowledge Discovery.Big Data.Computer Appl. in Social and Behavioral Sciences.Computational Social Sciences.Migration.006.312006.312Salah Albert Aliedthttp://id.loc.gov/vocabulary/relators/edtPentland Alexedthttp://id.loc.gov/vocabulary/relators/edtLepri Brunoedthttp://id.loc.gov/vocabulary/relators/edtLetouzé Emmanueledthttp://id.loc.gov/vocabulary/relators/edtBOOK9910349283903321Guide to Mobile Data Analytics in Refugee Scenarios2515884UNINA