Добірка наукової літератури з теми "Social and multimedia data"
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Статті в журналах з теми "Social and multimedia data"
Yu, Chen, Yiwen Zhong, Thomas Smith, Ikhyun Park, and Weixia Huang. "Visual Data Mining of Multimedia Data for Social and Behavioral Studies." Information Visualization 8, no. 1 (January 2009): 56–70. http://dx.doi.org/10.1057/ivs.2008.32.
Повний текст джерелаNaaman, Mor. "Social multimedia: highlighting opportunities for search and mining of multimedia data in social media applications." Multimedia Tools and Applications 56, no. 1 (May 21, 2010): 9–34. http://dx.doi.org/10.1007/s11042-010-0538-7.
Повний текст джерелаSperlì, Giancarlo, Flora Amato, Vincenzo Moscato, and Antonio Picariello. "Multimedia Social Network Modeling using Hypergraphs." International Journal of Multimedia Data Engineering and Management 7, no. 3 (July 2016): 53–77. http://dx.doi.org/10.4018/ijmdem.2016070104.
Повний текст джерелаYang, Qing, Tigang Jiang, Wenjia Li, Guangchi Liu, Danda B. Rawat, and Jun Wu. "Editorial: Multimedia and Social Data Processing in Vehicular Networks." Mobile Networks and Applications 25, no. 2 (December 14, 2019): 620–22. http://dx.doi.org/10.1007/s11036-019-01432-2.
Повний текст джерелаSang, Jitao, Yue Gao, Bing-kun Bao, Cees Snoek, and Qionghai Dai. "Recent advances in social multimedia big data mining and applications." Multimedia Systems 22, no. 1 (September 28, 2015): 1–3. http://dx.doi.org/10.1007/s00530-015-0482-5.
Повний текст джерелаKim, Sul-Ho, Kwon-Jae An, Seok-Woo Jang, and Gye-Young Kim. "Texture feature-based text region segmentation in social multimedia data." Multimedia Tools and Applications 75, no. 20 (January 27, 2016): 12815–29. http://dx.doi.org/10.1007/s11042-015-3237-6.
Повний текст джерелаYadav, Snehlata, and Namita Tiwari. "Privacy preserving data sharing method for social media platforms." PLOS ONE 18, no. 1 (January 20, 2023): e0280182. http://dx.doi.org/10.1371/journal.pone.0280182.
Повний текст джерелаGarg, Muskan, and Mukesh Kumar. "Review on event detection techniques in social multimedia." Online Information Review 40, no. 3 (June 13, 2016): 347–61. http://dx.doi.org/10.1108/oir-08-2015-0281.
Повний текст джерелаAmato, Flora, Giovanni Cozzolino, and Giancarlo Sperlì. "A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks." Information 10, no. 6 (May 28, 2019): 183. http://dx.doi.org/10.3390/info10060183.
Повний текст джерелаGupta, B. B., and Somya Ranjan Sahoo. "Fake profile detection in multimedia big data on online social networks." International Journal of Information and Computer Security 12, no. 2/3 (2020): 303. http://dx.doi.org/10.1504/ijics.2020.10026785.
Повний текст джерелаДисертації з теми "Social and multimedia data"
Wong, León Kevin, and Valdivia Diego Eduardo Antonio Rodríguez. "Distributed Social Media System - Multimedia Data Linkage." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2014. http://hdl.handle.net/10757/324525.
Повний текст джерелаTesis
Bracamonte, Nole Teresa Jacqueline. "Improving web multimedia information retrieval using social data." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168681.
Повний текст джерелаBuscar contenido multimedia es una de las tareas más comunes que los usuarios realizan en la Web. Actualmente, los motores de búsqueda en la Web han mejorado la precisión de sus búsquedas de contenido multimedia y ahora brindan una mejor experiencia de usuarios. Sin embargo, estos motores aún no logran obtener resultados precisos para consultas que no son comunes, y consultas que se refieren a conceptos abstractos. En ambos escenarios, la razón principal es la falta de información preliminar. Esta tesis se enfoca en mejorar la recuperación de información multimedia en la Web usando datos generados a partir de la interacción entre usuarios y recursos multimedia. Para eso, se propone mejorar la recuperación de información multimedia desde dos perspectivas: (1) extrayendo conceptos relevantes a los recursos multimedia, y (2) mejorando las descripciones multimedia con datos generados por el usuario. En ambos casos, proponemos sistemas que funcionan independientemente del tipo de multimedia, y del idioma de los datos de entrada. En cuanto a la identificación de conceptos relacionados a objetos multimedia, desarrollamos un sistema que va desde los resultados de búsqueda específicos de la consulta hasta los conceptos detectados para dicha consulta. Nuestro enfoque demuestra que podemos aprovechar la vista parcial de una gran colección de documentos multimedia para detectar conceptos relevantes para una consulta determinada. Además, diseñamos una evaluación basada en usuarios que demuestra que nuestro algoritmo de detección de conceptos es más sólido que otros enfoques similares basados en detección de comunidades. Para mejorar la descripción multimedia, desarrollamos un sistema que combina contenido audio-visual de documentos multimedia con información de su contexto para mejorar y generar nuevas anotaciones para los documentos multimedia. Específicamente, extraemos datos de clicks de los registros de consultas y usamos las consultas como sustitutos para las anotaciones manuales. Tras una primera inspección, demostramos que las consultas proporcionan una descripción concisa de los documentos multimedia. El objetivo principal de esta tesis es demostrar la relevancia del contexto asociado a documentos multimedia para mejorar el proceso de recuperación de documentos multimedia en la Web. Además, mostramos que los grafos proporcionan una forma natural de modelar problemas multimedia.
Fondef D09I-1185, CONICYT-PCHA/Doctorado Nacional/2013-63130260, Apoyo a estadías corta de la Escuela de Postgrado de la U. de Chile, y el Núcleo Milenio CIWS
Li, John Zhong. "Modeling and querying multimedia data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ29063.pdf.
Повний текст джерелаAmornraksa, Thumrongrat. "Data security for multimedia communications." Thesis, University of Surrey, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298091.
Повний текст джерелаPortnoy, Michael, and Hsueh-Szu Yang. "NETWORK DATA ACQUISITION AND PLAYBACK OF MULTIMEDIA DATA." International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604246.
Повний текст джерелаTraditional data acquisition systems have relied on physical connections between data sources and data receivers to handle the routing of acquired data streams. However, these systems grow exponentially in complexity as the number of data sources and receivers increases. New techniques are needed to address the ever increasing complexity of data acquisition. Furthermore, more advanced mechanisms are needed that move past the limitations of traditional data models that connect each data source to exactly one data receiver. This paper presents a software framework for the playback of multiplexed data acquired from a network acquisition system. This framework uses multicast technologies to connect data sources with multiple data receivers. The network acquisition system is briefly introduced before the software framework is discussed. Both the challenges and advantages involved with creating such a system are presented. Finally, this framework is applied to an aviation telemetry example.
CARABALLO, ALEXANDER ARTURO MERA. "PUBLISHING ANNOTATED MULTIMEDIA DEEP WEB DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23714@1.
Повний текст джерелаCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Nos últimos anos, temos assistido um enorme crescimento de dados multimídia na Web. Novas tecnologias de menor custo e maior largura de banda têm permitido que a Web evolua para um formato multimídia. No entanto, a falta de ferramentas que podem tornar o formato multimídia disponível na Web nos levou a um conjunto de dados não-pesquisável e não indexável da Web, também conhecido como Deep Web. Desta forma, esta dissertação aborda o problema de como publicar conteúdo de áudio e vídeo na Web. Apresentamos uma ferramenta e uma nova abordagem que facilita a indexação e recuperação dos objetos com a ajuda das maquinas de busca tradicionais. A ferramenta gera automaticamente páginas Web estáticas que descrevem o conteúdo dos objetos e organizar esse conteúdo para facilitar a localização de segmentos do áudio ou vídeo que correspondem às descrições. As páginas Web estáticas podem ser traduzidos para outras línguas para atingir outras populações de usuários. Um processo de anotação também é realizado para incorporar dados legíveis pelas máquinas nas páginas Web. A dissertação também apresenta um experimento completo, publicando objetos de aprendizagem baseados em áudio e vídeo para avaliar a eficácia da abordagem.
In recent years, we witnessed a huge growth of multimedia data on the Web. New lower-cost technologies and greater bandwidth allowed the Web to evolve into a multimedia format. However, the lack of tools that can make multimedia format easily accessible on the Web led us to a non-searchable and non-indexable data of the Web, also known as Deep Web. In line with these observations, this dissertation addresses the problem of how to publish audio and video content on the Web. We present a tool and a novel approach that facilitates the indexing and retrieval of the objects with the help of traditional search engines. The tool automatically generates static Web pages that describe the content of the objects and organize this content to facilitate locating segments of the audio or video which correspond to the descriptions. The static Web pages can be translated to others languages to reach other user populations. An annotation process is also performed to embed machine-readable data into the Web pages. The dissertation also presents an in-depth experiment, publishing learning objects based on audio and video, to assess the efficacy of the technique.
Klaghstan, Merza. "Multimedia data dissemination in opportunistic systems." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI125/document.
Повний текст джерелаOpportunistic networks are human-centric mobile ad-hoc networks, in which neither the topology nor the participating nodes are known in advance. Routing is dynamically planned following the store-carry-and-forward paradigm, which takes advantage of people mobility. This widens the range of communication and supports indirect end-to-end data delivery. But due to individuals’ mobility, OppNets are characterized by frequent communication disruptions and uncertain data delivery. Hence, these networks are mostly used for exchanging small messages like disaster alarms or traffic notifications. Other scenarios that require the exchange of larger data are still challenging due to the characteristics of this kind of networks. However, there are still multimedia sharing scenarios where a user might need switching to an ad-hoc alternative. Examples are the cases of 1) absence of infrastructural networks in far rural areas, 2) high costs due limited data volumes or 3) undesirable censorship by third parties while exchanging sensitive content. Consequently, we target in this thesis a video dissemination scheme in OppNets. For the video delivery problem in the sparse opportunistic networks, we propose a solution that encloses three contributions. The first one is given by granulating the videos at the source node into smaller parts, and associating them with unequal redundancy degrees. This is technically based on using the Scalable Video Coding (SVC), which encodes a video into several layers of unequal importance for viewing the content at different quality levels. Layers are routed using the Spray-and-Wait routing protocol, with different redundancy factors for the different layers depending on their importance degree. In this context as well, a video viewing QoE metric is proposed, which takes the values of the perceived video quality, delivery delay and network overhead into consideration, and on a scalable basis. Second, we take advantage of the small units of the Network Abstraction Layer (NAL), which compose SVC layers. NAL units are packetized together under specific size constraints to optimize granularity. Packets sizes are tuned in an adaptive way, with regard to the dynamic network conditions. Each node is enabled to record a history of environmental information regarding the contacts and forwarding opportunities, and use this history to predict future opportunities and optimize the sizes accordingly. Lastly, the receiver node is pushed into action by reacting to missing data parts in a composite backward loss concealment mechanism. So, the receiver asks first for the missing data from other nodes in the network in the form of request-response. Then, since the transmission is concerned with video content, video frame loss error concealment techniques are also exploited at the receiver side. Consequently, we propose to combine the two techniques in the loss concealment mechanism, which is enabled then to react to missing data parts
Lin, Lin. "Multimedia Data Mining and Retrieval for Multimedia Databases Using Associations and Correlations." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/434.
Повний текст джерелаGibbons, Paul C. "Telecommunications services for multimedia data exchange support." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA271704.
Повний текст джерелаFu, Haohuan. "Efficient multimedia data transmission over heterogeneous networks /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-cs-b19887218a.pdf.
Повний текст джерела"Submitted to Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Philosophy." Includes bibliographical references (leaves 105-108).
Книги з теми "Social and multimedia data"
Barrett, Edward. The Society of text: Hypertext, hypermedia, and the social construction of information. Cambridge, Mass: MIT Press, 1989.
Знайти повний текст джерелаFlorian, Alt, Michelis Daniel, and SpringerLink (Online service), eds. Pervasive Advertising. London: Springer-Verlag London Limited, 2011.
Знайти повний текст джерелаPoland 2004) International Workshop on Intelligent Media Technology for Communicative Intelligence (2nd Warsaw. International Workshop on Intelligent Media Technology for Communicative Intelligence: Warsaw, Poland, September 13-14, 2004 : proceedings. Warsaw: PJIIT (Polish-Japanese Institute of Information Technology) Publishing House, 2004.
Знайти повний текст джерелаservice), SpringerLink (Online, ed. Handbook of Social Network Technologies and Applications. Boston, MA: Springer Science+Business Media, LLC, 2010.
Знайти повний текст джерелаFacebook nation: Total information awareness. New York, N.Y: Springer, 2013.
Знайти повний текст джерелаPerner, Petra, ed. Data Mining on Multimedia Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36282-7.
Повний текст джерелаRamzan, Naeem. Social Media Retrieval. London: Springer London, 2013.
Знайти повний текст джерелаMotorola. Multimedia device data. Phoenix, AZ: Motorola, 1995.
Знайти повний текст джерелаWu, Min. Multimedia Data Hiding. New York, NY: Springer New York, 2003.
Знайти повний текст джерелаWu, Min, and Bede Liu. Multimedia Data Hiding. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21754-3.
Повний текст джерелаЧастини книг з теми "Social and multimedia data"
Roy, Suman Deb, and Wenjun Zeng. "Revelations from Social Multimedia Data." In Social Multimedia Signals, 135–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09117-4_10.
Повний текст джерелаRoy, Suman Deb, and Wenjun Zeng. "Data Visualization: Gazing at Ripples." In Social Multimedia Signals, 161–74. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09117-4_12.
Повний текст джерелаNiu, Teng, Shiai Zhu, Lei Pang, and Abdulmotaleb El Saddik. "Sentiment Analysis on Multi-View Social Data." In MultiMedia Modeling, 15–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27674-8_2.
Повний текст джерелаKumar, Akshi, Saurabh Raj Sangwan, and Anand Nayyar. "Multimedia Social Big Data: Mining." In Intelligent Systems Reference Library, 289–321. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8759-3_11.
Повний текст джерелаYuan, Jianbo, Quanzeng You, and Jiebo Luo. "Sentiment Analysis Using Social Multimedia." In Multimedia Data Mining and Analytics, 31–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_2.
Повний текст джерелаCao, Liangliang, GuoJun Qi, Shen-Fu Tsai, Min-Hsuan Tsai, Andrey Del Pozo, Thomas S. Huang, Xuemei Zhang, and Suk Hwan Lim. "Multimedia Information Networks in Social Media." In Social Network Data Analytics, 413–45. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-8462-3_15.
Повний текст джерелаBischke, Benjamin, Damian Borth, and Andreas Dengel. "Large-Scale Social Multimedia Analysis." In Big Data Analytics for Large-Scale Multimedia Search, 157–81. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119376996.ch6.
Повний текст джерелаMirkovic, Milan, Dubravko Culibrk, and Vladimir Crnojevic. "Mining Geo-Referenced Community-Contributed Multimedia Data." In Computational Social Networks, 81–102. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_4.
Повний текст джерелаWei, Ling-Yin, Yu Zheng, and Wen-Chih Peng. "Mining Popular Routes from Social Media." In Multimedia Data Mining and Analytics, 93–116. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_4.
Повний текст джерелаAmato, Flora, Giovanni Cozzolino, Francesco Moscato, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperli. "Data Mining in Social Network." In Intelligent Interactive Multimedia Systems and Services, 53–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92231-7_6.
Повний текст джерелаТези доповідей конференцій з теми "Social and multimedia data"
Pang, Ran, Agustin Baretto, Henry Kautz, and Jiebo Luo. "Monitoring adolescent alcohol use via multimodal analysis in social multimedia." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363914.
Повний текст джерелаJi, Rongrong, Donglin Cao, and Dazhen Lin. "Cross-Modality Sentiment Analysis for Social Multimedia." In 2015 IEEE International Conference on Multimedia Big Data (BigMM). IEEE, 2015. http://dx.doi.org/10.1109/bigmm.2015.85.
Повний текст джерелаSang, Jitao, and Changsheng Xu. "On Analyzing the 'Variety' of Big Social Multimedia." In 2015 IEEE International Conference on Multimedia Big Data (BigMM). IEEE, 2015. http://dx.doi.org/10.1109/bigmm.2015.60.
Повний текст джерелаJia, Jia. "Mental Health Computing via Harvesting Social Media Data." In MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3267935.3267954.
Повний текст джерелаXu, Dong, Lei Zhang, and Jiebo Luo. "Understanding multimedia content using web scale social media data." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1873951.1874367.
Повний текст джерелаBroniatowski, David A. "Extracting social values and group identities from social media text data." In 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2012. http://dx.doi.org/10.1109/mmsp.2012.6343446.
Повний текст джерелаZhou, Yiheng, Numair Sani, and Jiebo Luo. "Fine-grained mining of illicit drug use patterns using social multimedia data from instagram." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840812.
Повний текст джерелаGao, Yue, Fanglin Wang, Huanbo Luan, and Tat-Seng Chua. "Brand Data Gathering From Live Social Media Streams." In ICMR '14: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2578726.2578748.
Повний текст джерелаAmato, Flora, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperli. "Recommendation in Social Media Networks." In 2017 IEEE Third International Conference on Multimedia Big Data (BigMM). IEEE, 2017. http://dx.doi.org/10.1109/bigmm.2017.55.
Повний текст джерелаChang, Edward Y. "Organizing multimedia data socially." In the 2008 international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1386352.1386355.
Повний текст джерелаЗвіти організацій з теми "Social and multimedia data"
Yatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.
Повний текст джерелаRowe, Neil C., and Eugene J. Guglielmo. Exploiting Captions in Retrieval of Multimedia Data. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada255184.
Повний текст джерелаKim, Kyung-Chang, and Vincent Y. Lum. Towards Intelligent Data Retrieval in Multimedia Databases. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada243323.
Повний текст джерелаDavis, Timothy J. 10 Gbyte Personal Multimedia MEMS ROM Data Storage Card. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada388116.
Повний текст джерелаIatsyshyn, Anna V., Iryna H. Hubeladze, Valeriia O. Kovach, Valentyna V. Kovalenko, Volodymyr O. Artemchuk, Maryna S. Dvornyk, Oleksandr O. Popov, Andrii V. Iatsyshyn, and Arnold E. Kiv. Applying digital technologies for work management of young scientists' councils. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4434.
Повний текст джерелаHa, Kiryong, Padmanabhan Pillai, Grace Lewis, Soumya Simanta, Sarah Clinch, Nigel Davies, and Mahadev Satyanarayanan. The Impact of Mobile Multimedia Applications on Data Center Consolidation. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada570609.
Повний текст джерелаStrenge, D. L., and S. R. Peterson. Chemical data bases for the Multimedia Environmental Pollutant Assessment System (MEPAS). Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/6610163.
Повний текст джерелаKeim, Daniel A., Kyung-Chang Kim, and Vincent Y. Lum. A Friendly and Intelligent Approach to Data Retrieval in a Multimedia DBMS. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada243284.
Повний текст джерелаFeng, Zhuo, Pritam Gundecha, Huan Liu, and Geoffrey Barbier. Provenance Data in Social Media. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ad1007370.
Повний текст джерелаChetty, Raj, and Amy Finkelstein. Social Insurance: Connecting Theory to Data. Cambridge, MA: National Bureau of Economic Research, October 2012. http://dx.doi.org/10.3386/w18433.
Повний текст джерела