scholarly journals A Survey of Context Simulation for Testing Mobile Context-Aware Applications

2020 ◽  
Vol 53 (1) ◽  
pp. 1-39 ◽  
Author(s):  
Chu Luo ◽  
Jorge Goncalves ◽  
Eduardo Velloso ◽  
Vassilis Kostakos
Keyword(s):  
Context Aware ◽  
Mobile Context ◽  
Context Simulation

Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction

2013 ◽  
pp. 218-237 ◽  
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Location Prediction ◽  
Context Aware ◽  
Resonance Theory ◽  
Self Organizing Maps ◽  
Adaptive Resonance ◽  
Proposed Model ◽  
On Line ◽  
Mobile Context

Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.

Context-Aware Presentation of Linked Data on Mobile

2018 ◽  
pp. 1940-1971
Author(s):  
Luca Costabello ◽  
Fabien Gandon
Keyword(s):  
Linked Data ◽  
Edit Distance ◽  
Research Question ◽  
Context Aware ◽  
Graph Edit Distance ◽  
Optimal Error ◽  
Presentation Level ◽  
Rdf Graphs ◽  
Mobile Context ◽  
Proper Context

In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

Pervasive Computing in the Supermarket

2012 ◽  
pp. 172-185 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
Uniform Pattern ◽  
User Attention ◽  
Context Aware System ◽  
Mobile Context ◽  
Interactive Experience

Shopping in the real world is becoming an increasingly interactive experience as stores integrate various technologies to support shoppers. Based on an empirical study of supermarket shoppers, the authors designed a mobile context-aware system called the Context-Aware Shopping Trolley (CAST). The purpose of CAST is to support shopping in supermarkets through context-awareness and acquiring user attention, thus, the authors’ interactive trolley guides and directs shoppers in the handling and finding of groceries. An empirical evaluation showed that shoppers using CAST behaved differently than shoppers using a traditional trolley. Specifically, shoppers using CAST exhibited a more uniform pattern of product collection and found products more easily while travelling a shorter distance. As such, the study finds that CAST supported the supermarket shopping activity.

Context-Awareness and Mobile Devices

2008 ◽  
pp. 205-217 ◽  
Author(s):  
Anind K. Dey ◽  
Jonna Häkkilä
Keyword(s):  
Human Computer Interaction ◽  
Mobile Computing ◽  
Ubiquitous Computing ◽  
Mobile Devices ◽  
Context Awareness ◽  
Design Guidelines ◽  
Context Aware ◽  
Definition Of ◽  
Mobile Context ◽  
Computer Interaction

Context-awareness is a maturing area within the field of ubiquitous computing. It is particularly relevant to the growing sub-field of mobile computing as a user’s context changes more rapidly when a user is mobile, and interacts with more devices and people in a greater number of locations. In this chapter, we present a definition of context and context-awareness and describe its importance to human-computer interaction and mobile computing. We describe some of the difficulties in building context-aware applications and the solutions that have arisen to address these. Despite these solutions, users have difficulties in using and adopting mobile context-aware applications. We discuss these difficulties and present a set of eight design guidelines that can aid application designers in producing more usable and useful mobile context-aware applications.

Context-Awareness and Mobile Devices

2009 ◽  
pp. 3222-3235 ◽  
Author(s):  
Anind K. Dey ◽  
Jonna Häkkilä
Keyword(s):  
Human Computer Interaction ◽  
Mobile Computing ◽  
Ubiquitous Computing ◽  
Mobile Devices ◽  
Context Awareness ◽  
Design Guidelines ◽  
Context Aware ◽  
Definition Of ◽  
Mobile Context ◽  
Computer Interaction

Context-awareness is a maturing area within the field of ubiquitous computing. It is particularly relevant to the growing sub-field of mobile computing as a user’s context changes more rapidly when a user is mobile, and interacts with more devices and people in a greater number of locations. In this chapter, we present a definition of context and context-awareness and describe its importance to human-computer interaction and mobile computing. We describe some of the dif- ficulties in building context-aware applications and the solutions that have arisen to address these. Despite these solutions, users have difficulties in using and adopting mobile context-aware applications. We discuss these difficulties and present a set of eight design guidelines that can aid application designers in producing more usable and useful mobile context-aware applications.

Context-Aware Mobile Capture and Sharing of Video Clips

2009 ◽  
pp. 1080-1095
Author(s):  
Janne Lahti ◽  
Utz Westermann ◽  
Marko Palola ◽  
Johannes Peltola
Keyword(s):  
Mobile Phones ◽  
Video Production ◽  
Context Aware ◽  
Video Content ◽  
Home Video ◽  
Video Clips ◽  
Camera Phones ◽  
Context Data ◽  
Effective Organization ◽  
Mobile Context

Video management research has been neglecting the increased attractiveness of using cameraequipped mobile phones for the production of short home video clips. But specific capabilities of modern phones — especially the availability of rich context data — open up new approaches to traditional video management problems, such as the notorious lack of annotated metadata for home video content. In this chapter, we present MobiCon, a mobile, context-aware home video production tool. MobiCon allows users to capture video clips with their camera phones, to semi-automatically create MPEG-7-conformant annotations by exploiting available context data at capture time, to upload both clips and annotations to the users’ video collections, and to share these clips with friends using OMA DRM. Thereby, MobiCon enables mobile users to effortlessly create richly annotated home video clips with their camera phones, paving the way to a more effective organization of their home video collections.

M-Learning Generations and Interview Study Results of a Mobile Context-Aware Learning Schedule Framework

2011 ◽  
pp. 28-55 ◽  
Author(s):  
Jane Yin-Kim Yau ◽  
Mike Joy
Keyword(s):  
Mobile Learning ◽  
Adaptive Learning ◽  
Interview Study ◽  
Learning Preferences ◽  
Context Aware ◽  
Postgraduate Students ◽  
Learning Contexts ◽  
Proposed Model ◽  
Study Results ◽  
Mobile Context

Mobile learning applications can be categorized into four generations – ‘non-adaptive’, ‘learning-preferences’-based adaptive, ‘learning-contexts’-based adaptive and ‘learning-contexts’-aware adaptive. The research on our Mobile Context-aware and Adaptive Learning Schedule framework is motivated by some of the challenges within the context-aware mobile learning field. These include being able to create and enhance students’ learning opportunities in different locations by considering different learning contexts and using these as the basis for selecting appropriate learning materials for students. The authors have adopted a pedagogical approach for evaluating this framework – an exploratory interview study with potential users consisting of 37 university students. The authors targeted primarily undergraduate computing students, as well as students within other departments and postgraduate students, so that a deep analysis of a wider variety of users’ thoughts regarding the framework can be gained. The observed interview feedback gives us insights into the use of a pedagogical m-learning suggestion framework deploying a learning schedule subject to the five proposed learning contexts. Their data analysis is described and interpreted leading to a personalized suggestion mechanism for each learner and each scenario, and a proposed model for describing mobile learning preferences dimensions.

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