Towards Activity Context using Software Sensors

Pathan, Kamran Taj; Reiff-Marganiec, Stephan

doi:10.4204/eptcs.2.3

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…However, context may also incorporate logical sensors such as the current CPU load, free RAM or remaining battery power [21]. All this features can be used not only as primary input, but also as a means to adapt application functionality and/or interfaces to user's current activity and environment [4,3].…”

Section: Related Workmentioning

confidence: 99%

Context-aware device self-configuration using self-organizing maps

Batyuk

Scheel

Camtepe

et al. 2011

Proceedings of the 2011 Workshop on Organic Computing

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Modern mobile computing devices are versatile, but bring the burden of constant settings adjustment according to the current conditions of the environment. While until today, this task has to be accomplished by the human user, the variety of sensors usually deployed in such a handset provides enough data for autonomous self-configuration by a learning, adaptive system. However, this data is not fully available at certain points in time, or can contain false values. Handling potentially incomplete sensor data to detect context changes without a semantic layer represents a scientific challenge which we address with our approach. A novel machine learning technique is presented -the Missing-Values-SOM -which solves this problem by predicting setting adjustments based on context information. Our method is centered around a self-organizing map, extending it to provide a means of handling missing values. We demonstrate the performance of our approach on mobile context snapshots, as well as on classical machine learning datasets.

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Section: Related Workmentioning

confidence: 99%

Context-aware device self-configuration using self-organizing maps

Batyuk

Scheel

Camtepe

et al. 2011

Proceedings of the 2011 Workshop on Organic Computing

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User-Centric Advertisement using Software Sensors Technique

Baloch,

Kamran Taj Pathan,

Prof. Dr. Azhar Ali Shah

2023

VFAST trans. softw. eng.

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Contextual advertising is one of the most critical components in the economic system of internet due to increase internet publisher’s income highly dependent on the user-centric advertisement that is displayed on the sites according to the user context during interaction with the multiple sites. Previous contextual advertisement research work generally emphasises on investigating either to the keyword they type, content of the sites or uses any other application from the network hence, this finding has identified work when being extended through the user’s context. In this work we have looked at users’ profile information and user preferences to reach the users according to their context. These smart devices are ready with all capabilities to give useful contexts including information about physical environment, social connection, user internal and external context. These logical contexts beyond just content of the web pages, search keywords, and profile information are well used and organized for user-centric advertising. Here we are also arguing the appearances of the logical contexts which are available on the user browser, profile and visibly define the challenges of results from these logical contexts to improve the advertisement. We present a user-centric advertisement architecture and model that collects to integrate the users’ profile context and activity context to select, generate and to present advertisement with context. Finally, we discuss to implement the aspects of design and one specific application and outline our plans for future.

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Towards Activity Context using Software Sensors

Cited by 2 publications

References 15 publications

Context-aware device self-configuration using self-organizing maps

Context-aware device self-configuration using self-organizing maps

User-Centric Advertisement using Software Sensors Technique

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