Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective

2004 ◽  
Vol 19 (3) ◽  
pp. 213-233 ◽  
Author(s):  
SENG W LOKE
Keyword(s):  
Logic Programming ◽  
Pervasive Computing ◽  
Contextual Information ◽  
Physical World ◽  
Context Aware ◽  
Pervasive Systems ◽  
Computational Environment ◽  
Meta Programming ◽  
Context Aware System ◽  
High Level

Context-aware pervasive systems are emerging as an important class of applications. Such systems can respond intelligently to contextual information about the physical world acquired via sensors and information about the computational environment. A declarative approach to building context-aware pervasive systems is presented, and the notion of the situation program is introduced, which highlights the primacy of the situation abstraction for building context-aware pervasive systems. There is also a demonstration of how to manipulate situation programs using meta-programming within an extension of the Prolog logic programming language which is called LogicCAP. Such meta-reasoning enables complex situations to be described in terms of other situations. Furthermore, a discussion is given on how the design of situation programs can affect the properties of a context-aware system. The approach encourages a high-level of abstraction for representing and reasoning with situations, and supports building context-aware systems incrementally by providing modularity and separation of concerns.

ConChi

2016 ◽  
pp. 798-820
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users' requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

ConChi

2014 ◽  
pp. 263-285
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users’ requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

Ontological Representation and an Architecture for Homecare Pervasive Systems

2013 ◽  
pp. 215-234
Author(s):  
Leandro Freitas ◽  
Rafael T. Pereira ◽  
Henrique G. G. Pereira ◽  
Ricardo Martini ◽  
Bruno A. Mozzaquatro ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Pervasive Computing ◽  
World Population ◽  
Access To Information ◽  
Pervasive Systems ◽  
High Level ◽  
Representation Of Knowledge ◽  
Patient Attendance

Queues in hospitals grow due to, among others, the increasing world population and delay in patient attendance. One way of solving this problem is developing systems to provide treatment directly in the homes of patients. These systems help to decrease queues, improving the attendance to those looking for assistance. In this chapter, the authors present an ontological representation of knowledge of homecare environments and the modeling of an architecture for pervasive systems to this kind of domain. Systems with this modeling aim to improve services provided by professionals during treatment of patients located in their houses. The authors used concepts of pervasive computing to provide access to information anytime and wherever the user is, once a homecare environment has a high level of dynamicity. The knowledge representation is done through ontologies due to the possibility of reuse of information stored, as well as the interoperability of information among different computational devices.

Toward a Knowledge-Driven Context-Aware System for Surgical Assistance

2017 ◽  
Vol 02 (03) ◽  
pp. 1740007 ◽  
Author(s):  
Hirenkumar Nakawala ◽  
Giancarlo Ferrigno ◽  
Elena De Momi
Keyword(s):  
Image Processing ◽  
Surgical Training ◽  
Contextual Information ◽  
Surgical Instruments ◽  
Context Aware ◽  
Semantic Technologies ◽  
Surgical Instrument ◽  
Test Scenario ◽  
Surgical Assistance ◽  
Context Aware System

Complex surgeries complications are increasing, thus making an efficient surgical assistance is a real need. In this work, an ontology-based context-aware system was developed for surgical training/assistance during Thoracentesis by using image processing and semantic technologies. We evaluated the Thoracentesis ontology and implemented a paradigmatic test scenario to check the efficacy of the system by recognizing contextual information, e.g. the presence of surgical instruments on the table. The framework was able to retrieve contextual information about current surgical activity along with information on the need or presence of a surgical instrument.

scholarly journals Context-Aware Image Inpainting with Learned Semantic Priors

2021 ◽  
Author(s):  
Wendong Zhang ◽  
Junwei Zhu ◽  
Ying Tai ◽  
Yunbo Wang ◽  
Wenqing Chu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Contextual Information ◽  
Image Inpainting ◽  
Context Aware ◽  
Global Context ◽  
Low Level ◽  
Complex Scenes ◽  
Knowledge Distillation ◽  
Image Generator ◽  
High Level

Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still challenging to restore reasonable contents as the contextual information within the missing regions tends to be ambiguous. To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. In particular, we perform knowledge distillation on pretext models and adapt the features to image inpainting. The learned semantic priors ought to be partially invariant between the high-level pretext task and low-level image inpainting, which not only help to understand the global context but also provide structural guidance for the restoration of local textures. Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator. The semantic learner and the image generator are trained in an end-to-end manner. We name the model SPL to highlight its ability to learn and leverage semantic priors. It achieves the state of the art on Places2, CelebA, and Paris StreetView datasets

A survey on pervasive computing over context-aware system

2020 ◽  
Vol 2 (2) ◽  
pp. 79-85 ◽  
Author(s):  
S. G. Gollagi ◽  
M. M. Math ◽  
A. A. Daptardar
Keyword(s):  
Pervasive Computing ◽  
Context Aware ◽  
Context Aware System

Pervasive Computing in the Supermarket

2010 ◽  
Vol 2 (3) ◽  
pp. 31-43 ◽  
Author(s):  
Darren Black ◽  
Nils Jakob Clemmensen ◽  
Mikael B. Skov
Keyword(s):  
Empirical Study ◽  
Pervasive Computing ◽  
Real World ◽  
Context Awareness ◽  
Empirical Evaluation ◽  
Context Aware ◽  
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.

Bigraphical Reactive Systems Based Approaches for Modeling Context-Aware Systems

2014 ◽  
Vol 5 (4) ◽  
pp. 1-19
Author(s):  
Taha Abdelmoutaleb Cherfia ◽  
Faïza Belala
Keyword(s):  
Pervasive Computing ◽  
Reactive Systems ◽  
Critical Issue ◽  
Context Aware ◽  
Context Aware Computing ◽  
The Past ◽  
Everyday Activities ◽  
Modeling Process ◽  
Ubiquitous System ◽  
Context Aware System

In the past few years, context-aware computing has become one of the most promising topics of ubiquitous (pervasive) computing where computers are integrated and vanish in the background of users everyday activities. A context-aware system is a ubiquitous system, which is able to adapt its behavior automatically according to the gathered context information. However, due to the increasing complexity and diversity of such systems, the modeling process has become a major challenge for the ubiquitous computing community. In order to address this critical issue, different bigraphical reactive systems based approaches have been proposed to ease the modeling of some aspects of context-aware systems. Therefore, this paper presents a study attempting to show how bigraphs work under these approaches, and to illustrate the efficiency of our proposed approach in terms of addressing various aspects of context-aware systems.

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