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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
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Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children &#8211; Working Memory (CABC-WM)
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Efficient context-aware computing: a systematic model for dynamic working memory updates in context-aware computing.

Mumtaz Ali1, Muhammad Arshad1, Ijaz Uddin1

  • 1Department of Computer Science, City University of Science and Information Technology, Peshawar, Pakistan.

Peerj. Computer Science
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for dynamic working memory management in resource-constrained devices. It optimizes memory usage for intelligent context-aware computing systems.

Keywords:
Context-aware systemsRule-based reasoningSmart spacesWorking memory

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Artificial Intelligence

Background:

  • The proliferation of smart devices enables ubiquitous computing, offering intelligent services contextually.
  • Context-aware systems on mobile and wearable devices face challenges with memory management.
  • Rule-based reasoning is a common approach but struggles with efficient working memory control.

Purpose of the Study:

  • To address the dynamic working memory challenge in memory-constrained devices.
  • To introduce a systematic method for content removal to optimize memory utilization.
  • To enhance the development of intelligent systems for resource-constrained environments.

Main Methods:

  • Developing a systematic approach for dynamic content removal from working memory.
  • Implementing and evaluating the method on resource-constrained devices.
  • Focusing on rule-based reasoning systems within context-aware computing.

Main Results:

  • Demonstrated improved memory utilization in resource-constrained systems.
  • Showcased the effectiveness of the systematic content removal method.
  • Facilitated more efficient context-aware computing on mobile and wearable devices.

Conclusions:

  • The proposed method effectively manages dynamic working memory in constrained devices.
  • Optimized memory management is crucial for advanced intelligent systems on wearables.
  • This work advances context-aware computing through efficient resource utilization.