scholarly journals A Proposed Ontology-Based Generic Context Model for Ubiquitous Learning

2021 ◽  
Vol 16 (3) ◽  
pp. 47-64
Author(s):  
Benmesbah Ouissem ◽  
Mahnane Lamia ◽  
Mohamed Hafidi
Keyword(s):  
Comparative Study ◽  
Intelligent System ◽  
Context Aware ◽  
Context Modeling ◽  
Ubiquitous Learning ◽  
Context Model ◽  
Specific Domain ◽  
Context Models ◽  
Common Understanding ◽  
Model Context

Context modeling is the keystone to enable the intelligent system to adapt its functionalities properly to different situations. As such, a representation mechanism that allows an adequate manipulation of this kind of information is required, and diverse approaches have been introduced; however, what takes more value and is being positioned as a standard is the ontology-based context modeling because it presents a common understanding vocabulary for a specific domain. Hence, it might be beneficial to have a generic ontology to model context in this area. However, according to diverse works, there is no proposal of a generic context model for context-aware learning. For addressing this problem, several existing context models are studied to identify the essentials of context modeling, whereby an ontology-based generic context model is presented. The proposed ontology is evaluated in two ways. Firstly, scenarios are used to justify the feasibility of the model; then a comparative study and evaluation metrics are applied to assess the proposal.

Meta Model Context Based Space for Ubiquitous Computing

2015 ◽  
Vol 7 (2) ◽  
pp. 51-66
Author(s):  
Mohammed Fethi Khalfi ◽  
Sidi Mohamed Benslimane
Keyword(s):  
Pervasive Computing ◽  
Situation Awareness ◽  
Context Awareness ◽  
Real Life ◽  
Context Aware ◽  
Context Modeling ◽  
Context Model ◽  
Meta Model ◽  
Domain Specific ◽  
Model Context

Pervasive computing is a paradigm that focuses on the availability of computer resources anytime anywhere for any application and supports integration of computing services into everyday life. Context awareness is the core feature of pervasive computing. High-level context awareness can be enhanced by situation awareness that represents the ability to detect and reason about the real-life situations. In this paper, in order to deal with the problem in context-aware modeling in pervasive computing environments, the authors present a comprehensive and integrated approach for context modeling. They first propose a Meta model context based on ontology for Pervasive Computing aiming firstly to overcome the limitations of the existing ontologies, and secondly extend its capabilities by adding new environmental aspects. They divide the context model into Meta Ontology level and Domain-specific Ontology level according to the abstraction hierarchy. The Meta Ontology is the high abstract level which extracting the basic elements of the context knowledge. The Domain-specific Ontology is the lower abstract lever which focusing on different domains knowledge, directed by the Meta Ontology. The advantage is that it can provide a flexible modeling mechanism for multiple applications of context-aware pervasive computing. A case study of HealthCare domain is given to illustrate the practicality of the authors' Model.

Context-Based Scene Understanding

2018 ◽  
pp. 754-773
Author(s):  
Esfandiar Zolghadr ◽  
Borko Furht
Keyword(s):  
Object Detection ◽  
Latent Dirichlet Allocation ◽  
Model Performance ◽  
High Order ◽  
Multiple Sources ◽  
Context Model ◽  
Unified Framework ◽  
Context Models ◽  
Scene Content ◽  
Model Context

Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order). In this paper, a new unified framework is presented that combines multiple sources of context in high-order relations to encode semantical coherence and consistency of the scenes. This framework introduces a new descriptor called context relevance score to model context-based distribution of the response variables and apply it to two distributions. First model incorporates context descriptor along with annotation response into a supervised Latent Dirichlet Allocation (LDA) built on multi-variate Bernoulli distribution called Context-Based LDA (CBLDA). The second model is based on multi-variate Wallenius' non-central Hyper-geometric distribution and is called Wallenius LDA (WLDA). WLDA incorporates context knowledge as bias parameter. Scene context is modeled as a graph and effectively used in object detection framework to maximize semantical consistency of the scene. The graph can also be used in recognition of out-of-context objects. Annotation metadata of Sun397 dataset is used to construct the context model. Performance of the proposed approaches was evaluated on ImageNet dataset. Comparison between proposed approaches and state-of-art multi-class object annotation algorithm shows superiority of presented approach in labeling of scene content.

A Broader View on Context Models towards Supporting Business Process Agility

2011 ◽  
pp. 337-358 ◽  
Author(s):  
Barbara Thönssen ◽  
Daniela Wolff
Keyword(s):  
Business Process ◽  
Enterprise Architecture ◽  
Business Processes ◽  
Ad Hoc ◽  
Workflow Engine ◽  
Context Aware ◽  
Context Model ◽  
Model Based ◽  
Run Time ◽  
Context Models

Today’s enterprises need to be agile, to be able to cope with unexpected changes, to increasingly be dynamic, and to continually deal with change. Change affecting business processes may range from ad hoc modification to process evolution. In this chapter we present dimensions of change concentrating on a specific ability of an enterprise to deal with change. To support business in being agile we propose a semantically enriched context model based on well known enterprise architecture. We present a context aware workflow engine basing on the context model and on rules which trigger process adaptations during run time.

scholarly journals A Context-Aware Middleware for Context Modeling and Reasoning: A Case-Study in Smart Cultural Spaces

2021 ◽  
Vol 11 (13) ◽  
pp. 5770
Author(s):  
Konstantinos Michalakis ◽  
Yannis Christodoulou ◽  
George Caridakis ◽  
Yorghos Voutos ◽  
Phivos Mylonas
Keyword(s):  
Context Awareness ◽  
Context Aware ◽  
Context Modeling ◽  
Context Model ◽  
Case Scenario ◽  
Preventive Conservation ◽  
Context Aware Computing ◽  
Hybrid Reasoning ◽  
Smart Things ◽  
High Level

The proliferation of smart things and the subsequent emergence of the Internet of Things has motivated the deployment of intelligent spaces that provide automated services to users. Context-awareness refers to the ability of the system to be aware of the virtual and physical environment, allowing more efficient personalization. Context modeling and reasoning are two important aspects of context-aware computing, since they enable the representation of contextual data and inference of high-level, meaningful information. Context-awareness middleware systems integrate context modeling and reasoning, providing abstraction and supporting heterogeneous context streams. In this work, such a context-awareness middleware system is presented, which integrates a proposed context model based on the adaptation and combination of the most prominent context categorization schemata. A hybrid reasoning procedure, which combines multiple techniques, is also proposed and integrated. The proposed system was evaluated in a real-case-scenario cultural space, which supports preventive conservation. The evaluation showed that the proposed system efficiently addressed both conceptual aspects, through means of representation and reasoning, and implementation aspects, through means of performance.

Developing Context-Aware System using the Conceptual Context Model

2006 ◽  
Author(s):  
Chung-seong Hong ◽  
Kang-woo Lee ◽  
Young-ho Suh ◽  
Hyoung-sun Kim ◽  
Hyun Kim ◽  
...  
Keyword(s):  
Context Aware ◽  
Context Model ◽  
Context Aware System

scholarly journals Context modeling based on feature models expressed as views on ontologies via mappings

2015 ◽  
Vol 12 (3) ◽  
pp. 961-977 ◽  
Author(s):  
Sinisa Neskovic ◽  
Rade Matic
Keyword(s):  
Contextual Information ◽  
Product Line ◽  
Feature Model ◽  
Local Context ◽  
Context Modeling ◽  
Feature Models ◽  
Context Model ◽  
Global Context ◽  
Software Product ◽  
Global And Local

This paper presents an approach for context modeling in complex self adapted systems consisting of many independent context-aware applications. The contextual information used for adaptation of all system applications is described by an ontology treated as a global context model. A local context model tailored to the specific needs of a particular application is defined as a view over the global context in the form of a feature model. Feature models and their configurations derived from the global context state are then used by a specific dynamic software product line in order to adapt applications at runtime. The main focus of the paper is on the realization of mappings between global and local contexts. The paper describes an overall model architecture and provides corresponding metamodels as well as rules for a mapping between feature models and ontologies.

scholarly journals Preserving User Privacy and Security in Context-Aware Mobile Platforms

2019 ◽  
pp. 1203-1230 ◽  
Author(s):  
Prajit Kumar Das ◽  
Dibyajyoti Ghosh ◽  
Pramod Jagtap ◽  
Anupam Joshi ◽  
Tim Finin
Keyword(s):  
Context Aware ◽  
Context Modeling ◽  
User Privacy ◽  
Mobile Platforms ◽  
Privacy And Security ◽  
Semantic Web Technologies ◽  
Fine Grained ◽  
New Class ◽  
Context Data ◽  
Collaborative Context

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

scholarly journals A retrieved context model of the emotional modulation of memory

2017 ◽  
Author(s):  
Deborah Talmi ◽  
Lynn J. Lohnas ◽  
Nathaniel D. Daw
Keyword(s):  
Emotional Memory ◽  
Temporal Context ◽  
Composition Effect ◽  
Context Model ◽  
Emotional Modulation ◽  
Emotional Information ◽  
Retrieval Processes ◽  
The Rich ◽  
Context Models ◽  
Evolutionary Fitness

AbstractEmotion enhances episodic memory, an effect thought to be an adaptation to prioritise the memories that best serve evolutionary fitness. But viewing this effect largely in terms of prioritising what to encode or consolidate neglects broader rational considerations about what sorts of associations should be formed at encoding, and which should be retrieved later. Although neurobiological investigations have provided many mechanistic clues about how emotional arousal modulates item memory, these effects have not been wholly integrated with the cognitive and computational neuroscience of memory more generally.Here we apply the Context Maintenance and Retrieval Model (CMR, Polyn, Norman & Kahana, 2009) to this problem by extending it to describe the way people may represent and process emotional information. A number of ways to operationalise the effect of emotion were tested. The winning emotional CMR (eCMR) model reconceptualises emotional memory effects as arising from the modulation of a process by which memories become bound to ever-changing temporal and emotional contexts. eCMR provides a good qualitative fit for the emotional list-composition effect and the emotional oddball effect, illuminating how these effects are jointly determined by the interplay of encoding and retrieval processes. eCMR explains the increased advantage of emotional memories in delayed memory tests through the limited ability of retrieval to reinstate the temporal context of encoding.By leveraging the rich tradition of temporal context models, eCMR helps integrate existing effects of emotion and provides a powerful tool to test mechanisms by which emotion affects memory in a broad range of paradigms.

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