Composite Context Information Model for Adaptive Human Computing

2011 ◽  
pp. 52-56
Author(s):  
Sukyoung Kim ◽  
Eungha Kim ◽  
Youngil Choi
Keyword(s):  
Information Model ◽  
Context Information ◽  
Human Computing

scholarly journals Air Quality Context Information Model for Ubiquitous Public Access to Geographic Information

2018 ◽  
Vol 7 (8) ◽  
pp. 316 ◽  
Author(s):  
Sungchul Hong
Keyword(s):  
Information System ◽  
Air Quality ◽  
Information Model ◽  
Heterogeneous Data ◽  
Geographic Information ◽  
Information Services ◽  
Public Access ◽  
Quality Information ◽  
Context Information ◽  
New Paradigm

The advance in Information Communication Technology (ICT) has contributed to global challenges of improving urban air quality. Ubiquitous computing technology enables citizens to easily access air quality information services without spatial or temporal limitations. Citizens are also encouraged to participate in air quality assessment and environmental governance. These societal and technical changes require a new paradigm to develop an air quality information system and its services. An air quality information system needs to integrate varied types of air quality information from heterogeneous data sources as well as allow citizens to express their concerns about air quality. Thus, a standardized manner is necessary to develop an air quality information system. In this regard, an air quality context information model was designed according to the Ubiquitous Public Access (UPA) context information model defined in the International Organization for Standard (ISO) 19154. For validation and verification purposes, the air quality context information model was implemented in a geographic information system (GIS)-based air quality information system. Implementation results showed that spatially relevant air quality information services were generated from the system, depending on the location and air quality situations near a specific user. Also, citizens can contribute air quality information at their current regions.

Ontology Based Context Information Model for u-Healthcare Service

2011 ◽  
Author(s):  
Joong-Kyung Ryu ◽  
Jong-Hun Kim ◽  
Kyung-Yong Chung ◽  
Kee-Wook Rim ◽  
Jung-Hyun Lee
Keyword(s):  
Healthcare Service ◽  
Information Model ◽  
Context Information

scholarly journals Ontology-Based Context Mashup Model for Context-Aware Services in IoT Environments

2018 ◽  
Author(s):  
Yonggoo Choi ◽  
Ilkyeun Ra
Keyword(s):  
Common Knowledge ◽  
Information Model ◽  
Context Information ◽  
Context Model ◽  
Semantic Relationships ◽  
Open World ◽  
Information Models ◽  
Multiple Context ◽  
Service Oriented ◽  
Verification Model

In an open and dynamic IoT (the Internet of Things) environment, a common context information model is essential for active things to share common knowledge, reason their situations, and support adaptive interoperability with each other. There have been many studies on the IoT context information models based on semantic technology, but most of them have assumed a static situation based on a service-oriented information model suitable for specific applications of the IoT. In the case of applying their models to an open and dynamic IoT environment, two issues have been observed: Most of the models ignore (a) the mashup of the open-world semantics of context information generated by multiple context sources and (b) the reconciliation of the semantic relationships between multiple context entities under dynamic situation changes. Therefore, in this paper, we propose a context information model that is flexible enough to express complex and diverse semantic relationships between context information generated from a variety of context information sources in the IoT. The main background of this proposal is to propose an adaptive context model that can effectively mash up various context classes that use ontology in open and dynamic IoT environments. In this paper, we also show the effectiveness of the proposed model through an adequate verification model and a practical example.

Automatic Stereotype Activation Is Context Dependent

2010 ◽  
Vol 41 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Catharina Casper ◽  
Klaus Rothermund ◽  
Dirk Wentura
Keyword(s):  
Lexical Decision ◽  
Context Information ◽  
Decision Task ◽  
Social Categories ◽  
Priming Effects ◽  
Stereotype Activation ◽  
Social Stereotypes ◽  
Priming Paradigm ◽  
Automatic Activation ◽  
Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

Impact of Context Information on Metaphor Elaboration

2018 ◽  
Vol 65 (6) ◽  
pp. 370-384 ◽  
Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss
Keyword(s):  
Diffusion Model ◽  
Processing Speed ◽  
A Priori ◽  
Model Fit ◽  
Verbal Task ◽  
Context Information ◽  
Speed Of Processing ◽  
Good Account ◽  
Dependent Variables ◽  
Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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