Relevant bibliographies by topics / Context-aware computing

Academic literature on the topic 'Context-aware computing'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Context-aware computing"

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Chang, Edward Y. "Context-aware computing." Proceedings of the VLDB Endowment 6, no. 11 (August 27, 2013): 1172–73. http://dx.doi.org/10.14778/2536222.2536245.

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Zhou, Zhenji, Lifa Wu, and Zheng Hong. "Context-Aware Access Control Model for Cloud Computing." International Journal of Grid and Distributed Computing 6, no. 6 (December 31, 2013): 1–12. http://dx.doi.org/10.14257/ijgdc.2013.6.6.01.

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Vinh, Phan Cong. "Context-Aware Computing and Nature-Inspired Computing." Mobile Networks and Applications 26, no. 3 (May 4, 2021): 1267–69. http://dx.doi.org/10.1007/s11036-021-01766-w.

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Vinh, Phan Cong. "Context-Aware Computing and Nature-Inspired Computing." Mobile Networks and Applications 25, no. 3 (April 20, 2020): 1122–24. http://dx.doi.org/10.1007/s11036-020-01534-2.

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Yan, Zheng, Xixun Yu, and Wenxiu Ding. "Context-Aware Verifiable Cloud Computing." IEEE Access 5 (2017): 2211–27. http://dx.doi.org/10.1109/access.2017.2666839.

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Rohn, Eli. "Predicting context aware computing performance." Ubiquity 2003, February (February 2003): 1–17. http://dx.doi.org/10.1145/764010.764011.

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Rohn, Eli. "Predicting context aware computing performance." Ubiquity 2003, February (February 2003): 1–17. http://dx.doi.org/10.1145/764014.764011.

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Clarke, S., and C. Driver. "Context-aware trails [mobile computing." Computer 37, no. 8 (August 2004): 97–99. http://dx.doi.org/10.1109/mc.2004.89.

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B., Siva Rama Krishna, and Sreenivasa Reddy E. "Improved Context Aware PSO Task Scheduling in Cloud Computing." Webology 19, no. 1 (January 20, 2022): 3709–21. http://dx.doi.org/10.14704/web/v19i1/web19244.

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One of the major advantages of switching to the clouds is the scalability ability of the applications. Contrary to the grids, the ability to scale the cloud resources allows their real-time provisioning so as to meet the application constraints Generation of optimal schedule for given set of tasks and machines. Different experiments show that although having an optimum solution is almost impossible but having a sub-optimal solution using heuristic algorithms seems possible. In this paper, we propose a Context Aware PSO Task Scheduling scheme to analyze various scenarios with different parameters in cloud computing system corresponding to APSO In each scenario, we change one parameter and keep other parameters constant.
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Nykänen, Ossi Antero, and Alejandro Rivero Rodriguez. "Problems in Context-Aware Semantic Computing." International Journal of Interactive Mobile Technologies (iJIM) 8, no. 3 (June 8, 2014): 32. http://dx.doi.org/10.3991/ijim.v8i3.3870.

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Dissertations / Theses on the topic "Context-aware computing"

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Nicolini, Andrea. "Context-Aware Computing e Tecnologie Wearable." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10556/.

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Quest' ultimo ventennio ha visto una vera e propria rivoluzione dei dispositivi, partendo dal computer desktop, passando ai laptop fino ad arrivare agli smartphone. Oggi giorno invece si parla di computer indossabili, i dispositivi stanno diventando sempre più piccoli e integrati in oggetti di moda come possono essere degli orologi, occhiali e orecchini.Questi sono connessi in rete con migliaia di dispositivi e con computer più grandi, con i quali, gli utenti nel corso della giornata interagiscono continuamente senza nemmeno rendersene conto scambiandosi migliaia di piccole informazioni: quando si cammina per strada, in centro città quando si fanno compere, quando si è in casa a guardare la TV. Questo ha portato quindi alla nascita di una nuova tipologia di sistemi, in risposta ai cambiamenti portati da questa rivoluzione, i così detti "Sistemi Context-Aware".Il context di un utente può essere descritto come la relazione che vi è tra i suoi dispositivi elettronici, e l' ambiente che lo circonda, a seconda di dove si trova esso dovrà dare delle risposte opportune, e compiere quindi autonomamente certe azioni, tal volta ad insaputa dell' utente. Le applicazioni che usano quindi questo sistema, vengono continuamente messe a conoscenza dei cambiamenti che vengono apportati all' ambiente circostante, regolandosi e reagendo di conseguenza in autonomia. Ad esempio, il nostro dispositivo scopre tramite la rete, la presenza di un amico nelle vicinanze, mentre stiamo passeggiano per strada, allora potrebbe inviarci un messaggio mostrandoci chi è, e dove si trova, con il tragitto da percorrere per raggiungerlo. Le migliaia di informazioni che vengono quindi scambiate in rete andranno a creare “un ambiente intelligente”, con il quale gli utenti interagiscono inviando informazioni sul proprio conto, senza nemmeno accorgersene, in modo da avere una risposta personalizzata, da parte dell' ambiente.
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Ergüt, Salih. "Context-aware computing for wireless networks." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3402341.

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Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed May 14, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Hübinette, Daniel. "Occupancy Sensor System : For Context-aware Computing." Thesis, KTH, Kommunikationssystem, CoS, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91936.

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This masters thesis project, "Occupancy Sensor System", was conducted at Kungliga Tekniska Högskolan (KTH), Stockholm, Sweden, during the period 2007-04-24 – 2007-12-17. The goal of the project was to design an occupancy sensor system that determines if there exists more than one person in a defined region. The output of this system is for use in a context-aware system at the KTH Center for Wireless Systems (Wireless@KTH). The system is important because there is a need for specific input to context-aware systems concerning occupancy of spaces and because this thesis has focused on a problem that enables new complex and interesting services. Additionally, the specific problem of determining not only occupancy, but if this occupancy is zero, one, many has not been widely examined previously. The significance of zero occupants indicating an empty room has already been recognized as having economic and environmental value in terms of heating, ventilating, air-conditioning, and lighting. However, there has not been an effort to differentiate between a person being alone or if more than one person is present. A context-aware system might be able to use this later information to infer that a meeting is taking place in a meeting room, a class taking place in a classroom or that an individual is alone in a conference room, class room, etc. Thus enabling context-aware services to change their behavior based upon the differences in these situations. An occupancy sensor system prototype was designed to monitor a boundary by using a thermal detector, gumstix computer, an analog to digital converter prototype board, laptop computer, and a context broker. The testing and evaluation of the system, proved it to be sound. However, there are still further improvements and tests to be made. These improvements include: dynamic configuration of the system, communication between the different system entities, detection algorithms, and code improvements. Tests measuring accuracy of a detection algorithm and determining optimal detector placement need to be performed. The next step is to design applications that use the context information provided from the occupancy sensor system and expand the system to use multiple detectors.
Examensarbetet "Occupancy Sensor System" genomfördes på Kungliga Tekniska Högskolan (KTH), Stockholm, Sverige, under perioden 2007-04-24 – 2007-12-17. Målet med examensarbetet var att designa ett sensorsystem, som avgör om ett rum är befolkat med fler än en person i ett definierat område. Resultatet av detta system är till för användning i ett kontextmedvetet system som finns i KTH Center for Wireless Systems (Wireless@KTH). Systemet är viktigt eftersom det finns ett behov för specifik input till kontextmedvetna system som berör befolkning av rum och eftersom detta examensarbete har fokuserat på ett problem som möjliggör nya komplexa och intressanta tjänster. Dessutom har det inte tidigare undersökts i vidare bemärkelse hur man kan avgöra om ett rum befolkats av noll, en eller flera personer. Betydelsen av att ett rum är obefolkat har redan ansetts ha ekonomiskt och miljöbetingat värde vad gäller uppvärming, ventilation, luftkonditionering och belysning. Däremot har det inte gjorts ansträngningar att differentiera mellan att en ensam person eller flera är närvarande. Ett kontextmedvetet system skulle kunna använda den senare nämnda informationen för att dra slutsatsen att ett möte pågår i ett mötesrum, en lektion är igång i ett klassrum o.s.v. Detta möjliggör i sin tur för kontextmedvetna tjänster att ändra på sina beteenden baserat på skillnaderna i dessa situationer. En prototyp utvecklades för att övervaka en gräns genom användningen av en termisk detektor, gumstixdator, analog till digital signalkonverterare, bärbar dator och en context broker (kontextförmedlare). Testningar och utvärderingar av systemet visade att systemet var dugligt. Flera förbättringar och tester behöver dock göras i framtiden. Dessa förbättringar inkluderar: dynamisk konfiguration av systemet, kommunikation mellan de olika systementiteterna, detektionsalgoritmer och kodförbättringar. Återstående tester inkluderar mätning av en detektionsalgoritms tillförlitlighet samt optimal placering av detektorer. Nästa steg är att utveckla applikationer som använder kontextinformationen från systemet samt att utveckla systemet till att kunna använda flera detektorer.
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Vieira, André Fonseca dos Santos Dias. "Context-aware personalization environment for mobile computing." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8649.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Currently, we live in a world where the amount of on-line information vastly outstrips any individual’s capability to survey it. Filtering that information in order to obtain only useful and interesting information is a solution to this problem. The mobile computing area proposes to integrate computation in users’ daily activities in an unobtrusive way, in order to guarantee an improvement in their experience and quality of life. Furthermore, it is crucial to develop smaller and more intelligent devices to achieve this area’s goals, such as mobility and energy savings. This computing area reinforces the necessity to filter information towards personalization due to its humancentred paradigm. In order to attend to this personalization necessity, it is desired to have a solution that is able to learn the users preferences and needs, resulting in the generation of profiles that represent each style of interaction between a user and an application’s resources(e.g. buttons and menus). Those profiles can be obtained by using machine learning algorithms that use data derived from the user interaction with the application, combined with context data and explicit user preferences. This work proposes an environment with a generic context-aware personalization model and a machine learning module. It is provided the possibility to personalize an application, based on user profiles obtained from data, collected from implicit and explicit user interaction. Using a provided personalization API (Application Programming Interface) and other configuration modules, the environment was tested on LEY (Less energy Empowers You), a persuasive mobile-based serious game to help people understand domestic energy usage.
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Alharthi, Raneem. "Context–Aware Stress Prediction System." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34486.

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Stress is now recognized as one of the major causes of physical and psychological illness. It is known as a reaction to surrounding environmental threats and the best way to manage it is to understand its triggers. Although people continuously react to their surrounding environments, they sometimes are not aware that certain elements in their environment are considered to be stressors. Based on this fact, researchers have recently proposed context-aware stress management systems. Most of the proposed systems use context data to provide real time stress monitoring and visualization, along with intervention techniques. However, these interventions are limited to the second and tertiary stages and very little attention has been given to the primary stage. In this thesis, we introduce a system called CASP. The system’s objective is to provide stress status predictions based on a user’s current contextual data. Therefore, a detection method is developed using heart rate variability (HRV) as a stress indicator to deliver personalized context-aware stress reports. Based on the predicted status, the system provides users with stress interventions at an early stage in order to help avoid and/or eliminate the occurrence of stress. Our evaluation results show that the CASP system is able to predict the stress status of a user with an averaged accuracy of 78.23% through our limited activity, when compare to a stress status measured using physiological signals. Moreover, it provides prediction models that adapt to the changing nature of both the user’s stress status and the surrounding environment.
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Daruwala, Yohann. "A Constructive Memory Architecture for Context Awareness." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/2250.

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Context-aware computing is a mobile computing paradigm in which applications can discover, use, and take advantage of contextual information, such as the location, tasks and preferences of the user, in order to adapt their behaviour in response to changing operating environments and user requirements. A problem that arises is the inability to respond to contextual information that cannot be classified into any known context. Many context-aware applications require all discovered contextual information to exactly match a type of context, otherwise the application will not react responsively. The ability to learn and recall contexts based on the contextual information discovered has not been very well addressed by previous context-aware applications and research. The aim of this thesis is to develop a component middleware technology for mobile computing devices for the discovery and capture of contextual information, using the situated reasoning concept of constructive memory. The research contribution of this thesis lies in developing a modified architecture for context-aware systems, using a constructive memory model as a way to learn and recall contexts from previous experiences and application interactions. Using a constructive memory model, previous experiences can be induced to construct potential contexts, given a small amount of learning and interaction. The learning process is able to map the many variations of contextual information currently discovered by the user with a predicted type of context based on what the application has stored and seen previously. It only requires a small amount of contextual information to predict a context, something common context-aware systems lack, as they require all information before a type of context is assigned. Additionally, some mechanism to reason about the contextual information being discovered from past application interactions will be beneficial to induce contexts for future experiences.
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Daruwala, Yohann. "A Constructive Memory Architecture for Context Awareness." University of Sydney, 2008. http://hdl.handle.net/2123/2250.

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Master of Philosophy (Architecture)
Context-aware computing is a mobile computing paradigm in which applications can discover, use, and take advantage of contextual information, such as the location, tasks and preferences of the user, in order to adapt their behaviour in response to changing operating environments and user requirements. A problem that arises is the inability to respond to contextual information that cannot be classified into any known context. Many context-aware applications require all discovered contextual information to exactly match a type of context, otherwise the application will not react responsively. The ability to learn and recall contexts based on the contextual information discovered has not been very well addressed by previous context-aware applications and research. The aim of this thesis is to develop a component middleware technology for mobile computing devices for the discovery and capture of contextual information, using the situated reasoning concept of constructive memory. The research contribution of this thesis lies in developing a modified architecture for context-aware systems, using a constructive memory model as a way to learn and recall contexts from previous experiences and application interactions. Using a constructive memory model, previous experiences can be induced to construct potential contexts, given a small amount of learning and interaction. The learning process is able to map the many variations of contextual information currently discovered by the user with a predicted type of context based on what the application has stored and seen previously. It only requires a small amount of contextual information to predict a context, something common context-aware systems lack, as they require all information before a type of context is assigned. Additionally, some mechanism to reason about the contextual information being discovered from past application interactions will be beneficial to induce contexts for future experiences.
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Simons, Christof. "Context aware applications in mobile distributed systems /." Aachen : Shaker, 2008. http://d-nb.info/987900757/04.

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Drakatos, Stylianos. "Context-aware data caching for mobile computing environments." FIU Digital Commons, 2006. http://digitalcommons.fiu.edu/etd/3082.

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The deployment of wireless communications coupled with the popularity of portable devices has led to significant research in the area of mobile data caching. Prior research has focused on the development of solutions that allow applications to run in wireless environments using proxy based techniques. Most of these approaches are semantic based and do not provide adequate support for representing the context of a user (i.e., the interpreted human intention.). Although the context may be treated implicitly it is still crucial to data management. In order to address this challenge this dissertation focuses on two characteristics: how to predict (i) the future location of the user and (ii) locations of the fetched data where the queried data item has valid answers. Using this approach, more complete information about the dynamics of an application environment is maintained. The contribution of this dissertation is a novel data caching mechanism for pervasive computing environments that can adapt dynamically to a mobile user's context. In this dissertation, we design and develop a conceptual model and context aware protocols for wireless data caching management. Our replacement policy uses the validity of the data fetched from the server and the neighboring locations to decide which of the cache entries is less likely to be needed in the future, and therefore a good candidate for eviction when cache space is needed. The context aware driven prefetching algorithm exploits the query context to effectively guide the prefetching process. The query context is defined using a mobile user's movement pattern and requested information context. Numerical results and simulations show that the proposed prefetching and replacement policies significantly outperform conventional ones. Anticipated applications of these solutions include biomedical engineering, telehealth, medical information systems and business.
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Ahmed, Ali Ahmed Ali. "Context-aware access control in ubiquitous computing (CRAAC)." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/contextaware-access-control-in-ubiquitous-computing-craac(1eaa4390-7062-4af5-8aa5-d645d91fa547).html.

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Ubiquitous computing (UbiComp) envisions a new computing environment, where computing devices and related technology are widespread (i.e. everywhere) and services are provided at anytime. The technology is embedded discreetly in the environment to raise users' awareness. UbiComp environments support the proliferation of heterogeneous devices such as embedded computing devices, personal digital assistants (PDAs), wearable computers, mobile phones, laptops, office desktops (PCs), and hardware sensors. These devices may be interconnected by common networks (e.g. wired, wireless), and may have different levels of capabilities (i.e. computational power, storage, power consumption, etc). They are seamlessly integrated and interoperated to provide smart services (i.e. adaptive services). A UbiComp environment provides smart services to users based on the users' and/or system's current contexts. It provides the services to users unobtrusively and in turn the user's interactions with the environment should be as non-intrusive and as transparent as possible. Access to such smart services and devices must be controlled by an effective access control system that adapts its decisions based on the changes in the surrounding contextual information. This thesis aims at designing an adaptive fine-grained access control solution that seamlessly fits into UbiComp environments. The solution should be flexible in supporting the use of different contextual information and efficient, in terms of access delays, in controlling access to resources with divergent levels of sensitivity. The main contribution of this thesis is the proposal of the Context-Risk-Aware Access Control (CRAAC) model. CRAAC achieves fine-grained access control based upon the risk level in the underlying access environment and/or the sensitivity level of the requested resource object. CRAAC makes new contributions to the access control field, those include 1) introducing the concept of level of assurance based access control, 2) providing a method to convert the contextual attributes values into the corresponding level of assurance, 3) Proposing two methods to aggregate the set of level of assurance into one requester level of assurance, 4) supporting four modes of working each suits a different application context and/or access control requirements, 5) a comprehensive access control architecture that supports the CRAAC four modes of working, and 6) an evaluation of the CRAAC performance at runtime.
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Books on the topic "Context-aware computing"

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Gay, Geri. Context-Aware Mobile Computing. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02187-9.

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Laitakari, Juhani. Dynamic context monitoring for adaptive and context-aware applications. [Espoo, Finland]: VTT Technical Research Centre of Finland, 2007.

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Markus, Endler, ed. Context management for distributed and dynamic context-aware computing. London: Springer, 2012.

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da Rocha, Ricardo Couto Antunes, and Markus Endler. Context Management for Distributed and Dynamic Context-Aware Computing. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4020-7.

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Waltenegus, Dargie, ed. Context-aware computing and self-managing systems. Boca Raton: Chapman & Hall/CRC, 2009.

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Kessler, Carsten. Context-aware semantics-based information retrieval. Amsterdam: IOS Press, 2010.

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Curran, Kevin. Recent advances in ambient intelligence and context-aware computing. Hershey, PA: Information Science Reference, 2015.

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Varuna, Godara, ed. Strategic pervasive computing applications: Emerging trends. Hershey, PA: Information Science Reference, 2010.

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1969-, Stojanovic Dragan, ed. Context-aware mobile and ubiquitous computing for enhanced usability: Adaptive technologies and applications. Hershey PA: Information Science Reference, 2009.

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Temdee, Punnarumol, and Ramjee Prasad. Context-Aware Communication and Computing: Applications for Smart Environment. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59035-6.

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Book chapters on the topic "Context-aware computing"

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Kale, Vivek. "Context-Aware Computing." In Digital Transformation of Enterprise Architecture, 395–412. Boca Raton, Florida : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9781351029148-21.

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Gay, Geri. "Space, Place, and Context." In Context-Aware Mobile Computing, 5–25. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02187-9_2.

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Gay, Geri. "Creating a Sense of Presence and Awareness with Mobile Tools." In Context-Aware Mobile Computing, 27–43. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02187-9_3.

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Gay, Geri. "Mobile Computing: A Tool for Social Influence to Change Behavior." In Context-Aware Mobile Computing, 45–56. Cham: Springer International Publishing, 2009. http://dx.doi.org/10.1007/978-3-031-02187-9_4.

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Satoh, Ichiro. "Context-Aware Service Composition." In Ubiquitous Intelligence and Computing, 267–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23641-9_23.

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Madhavapeddy, Anil, David Scott, and Richard Sharp. "Context-Aware Computing with Sound." In UbiComp 2003: Ubiquitous Computing, 315–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39653-6_25.

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Colman, Alan, Mahmoud Hussein, Jun Han, and Malinda Kapuruge. "Context Aware and Adaptive Systems." In Context in Computing, 63–82. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1887-4_5.

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Vahdat-Nejad, Hamed. "Context-Aware Middleware: A Review." In Context in Computing, 83–96. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1887-4_6.

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Mahalle, Parikshit N., and Prashant S. Dhotre. "Context-Aware Computing and Personalization." In Intelligent Systems Reference Library, 67–90. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9952-8_4.

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Mahalle, Parikshit N., and Prashant S. Dhotre. "Context-Aware Computing: Telecom Perspective." In Intelligent Systems Reference Library, 109–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9952-8_6.

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Conference papers on the topic "Context-aware computing"

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Schilit, B., N. Adams, and R. Want. "Context-Aware Computing Applications." In 1994 First Workshop on Mobile Computing Systems and Applications (WMCSA). IEEE, 1994. http://dx.doi.org/10.1109/wmcsa.1994.16.

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Li, Yuanping, and Ling Feng. "A Quality-Aware Context Middleware Specification for Context-Aware Computing." In 2009 33rd Annual IEEE International Computer Software and Applications Conference. IEEE, 2009. http://dx.doi.org/10.1109/compsac.2009.135.

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Noh, Eun Sang, Hyun Ko, Chang Hoon Lyu, and Hee Yong Youn. "Hybrid Context Matchmaking for Fast Context-Aware Computing." In 2009 International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2009. http://dx.doi.org/10.1109/waina.2009.86.

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Maciel, Carlos A. V., Jose Anderson S. N. Filho, Gabriella A. B. Barros, Thun Pin T. F. Chiu, Patricia C. A. R. Tedesco, Antonio L. O. Cavalcanti, Angelica A. Mascaro, Fabio Q. B. Da Silva, and Andre L. M. Santos. "Context-Aware Collector." In 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2013. http://dx.doi.org/10.1109/hpcc.and.euc.2013.312.

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Habib, Azhar, and Ahmed Hakim. "Context aware augmentational marketing." In 2016 SAI Computing Conference (SAI). IEEE, 2016. http://dx.doi.org/10.1109/sai.2016.7556135.

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Olivia Snooks, Kim, Joseph Lindley, Daniel Richards, and Roger Whitham. "Context-Aware Wearables." In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411763.3450367.

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Gandodhar, Pooja S., and S. M. Chaware. "Context Aware Computing Systems: A survey." In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2018. http://dx.doi.org/10.1109/i-smac.2018.8653786.

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8

Sobie, Randall J. "Context-aware cloud computing for HEP." In International Symposium on Grids and Clouds (ISGC) 2016. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.270.0033.

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Han, Li, Salomaa Jyri, Jian Ma, and Kuifei Yu. "Research on Context-Aware Mobile Computing." In 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008). IEEE, 2008. http://dx.doi.org/10.1109/waina.2008.115.

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Xue, Wenwei, Hungkeng Pung, Paulito P. Palmes, and Tao Gu. "Schema matching for context-aware computing." In the 10th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1409635.1409674.

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Reports on the topic "Context-aware computing"

1

Chen, Harry, Tim Finin, and Anupam Joshi. An Ontology for Context-Aware Pervasive Computing Environments. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439610.

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2

Chen, Guanling, and David Kotz. Solar: A Pervasive-Computing Infrastructure for Context-Aware Mobile Applications. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada440303.

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