Proposed Model In Context Research Articles

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

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.

ON THE CHALLENGE OF SERVICE RECOMMENDATION TO MOBILE USERS IN SMART CITIES: CONTEXT AND ARCHITECTURE

Abstract. The industrial and academic interest of the research on mobile service recommendation systems based on a wide range of potential applications has significantly increased, owing to the rapid progress of mobile technologies. These systems aim to recommend the right product, service or information to the right mobile users at anytime and anywhere. In smart cities, recommending such services becomes more interesting but also more challenging due to the wide range of information that can be obtained on the user and his surrounding. This quantity and variety of information create problems in terms of processing as well as the problem of choosing the right information to use to offer services. We consider that to provide personalized mobile services in a smart city and know which information is relevant for the recommendation process, identifying and understanding the context of the mobile user is the key.This paper aims to address the issue of recommending personalized mobile services in smart cities by considering two steps: defining the context of the mobile user and designing an architecture of a system that can collect and process context data. Firstly, we propose an UML-based context model to show the contextual parameters to consider in recommending mobile services in a smart city. The model is based on three main classes from which others are divided: the user, his device and the environment. Secondly, we describe a general architecture based on the proposed context model for the collection and processing of context data.

Additional lossless compression of JPEG images based on BPG

The task of additional lossless compression of JPEG images is considered. We propose to decode JPEG image and recompress it using lossy BPG (Better Portable Graphics) codec based on a subset of the HEVC open video compression standard. Then the decompressed and smoothed BPG image is used for calculation and quantization of DCT coefficients in 8x8 image blocks using quantization tables of the source JPEG image. A difference between obtained quantized DCT coefficients and quantized DCT coefficients of the source JPEG image (prediction error) is calculated. The difference is lossless compressed by a proposed context modeling and arithmetical coding. In this way the source JPEG image is replaced by two files: compressed BPG image and the compressed difference which needed for lossless restoration of the source JPEG image. It is shown that the proposed approach provides compression ratios comparable with state of the art PAQ8, WinZip and STUFFIT file archivers. At the same time BPG images may be used for fast preview of compressed JPEG images.

3LConOnt: a three-level ontology for context modelling in context-aware computing

Context-aware computing is the ability of Services and applications to adapt and react to context changes. Context modelling is a core feature of context-aware computing. Although a lot of research has been made in the field of context modelling, most of the context-aware computing proposals prefer to design their own customized context model instead of reusing an existing one. The main reason for this behaviour is that current context models present some problems concerning reusability, extensibility and adaptation. To contribute solving these issues, in this paper we present 3LConOnt, a three-level context ontology that can be easily reused, extended and adapted for specific or generic purposes. The proposed context model consolidates the context knowledge already available from a modular perspective yielding a clear schema of knowledge reutilization. To do so, we gathered context knowledge pieces from different ontologies to be integrated into standardized and well-defined levels of abstraction and modules. The proposal has been validated considering: (1) reusability, extensibility and adaptation by instantiating different smart scenarios; (2) consistency and reasoning by triggering queries to the proposed model based on some competence questions; and (3) reusability in existing ontologies by importing the needed module or level of the model. Additionally, we also illustrate its usability in context-aware Services by modelling a context-aware framework architecture for supporting the whole context life cycle: acquisition, modelling, reasoning and distribution.

Holistic Context Models for Visual Recognition

A novel framework to context modeling based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availability of robust appearance classifiers. Images are represented by their posterior probabilities with respect to a set of contextual models, built upon the bag-of-features image representation, through two layers of probabilistic modeling. The first layer represents the image in a semantic space, where each dimension encodes an appearance-based posterior probability with respect to a concept. Due to the inherent ambiguity of classifying image patches, this representation suffers from a certain amount of contextual noise. The second layer enables robust inference in the presence of this noise by modeling the distribution of each concept in the semantic space. A thorough and systematic experimental evaluation of the proposed context modeling is presented. It is shown that it captures the contextual “gist” of natural images. Scene classification experiments show that contextual classifiers outperform their appearance-based counterparts, irrespective of the precise choice and accuracy of the latter. The effectiveness of the proposed approach to context modeling is further demonstrated through a comparison to existing approaches on scene classification and image retrieval, on benchmark data sets. In all cases, the proposed approach achieves superior results.

A unified context assessing model for object categorization

In this paper, we investigate how to appropriately use contextual information for object categorization tasks, especially in the statistical bag-of-features (BoF) framework. Our main contributions are two-folds. Firstly, we propose a context measuring mechanism which could explicitly assess roles of context features for different object recognition tasks. By analyzing information entropy and data ambiguity, only the useful and confident context information would have a final impact on categorization. Secondly, based on the context assessing results, under the BoF framework, we design unified object representations that incorporate the object appearance and contextual information from multiple spatial levels without the need of prior scene segmentations or context annotations. We evaluate the proposed method by the PASCAL object categorization task. The experimental results demonstrate that the proposed context modeling approach improves object categorization significantly and outperforms several state-of-the-art context models.

Context-Aware Workflow Management For Intelligent Navigation Applications In Pervasive Environments

Abstract Pervasive computing is auser-centric distributed computing paradigm, allowing users to access their preferred services even while moving around. To make this vision a reality, context-aware workflow management is one of key issues because the context of pervasive applications is changing dynamically. In this paper, we propose a context model for intelligent navigation applications and then present acontext-aware workflow management algorithm (CAWM) which can adaptively adjust workflow execution behaviours based on current context information. The correctness of the CAWM algorithm has been also verified theoretically by formulating it as a Petri-net model. Furthermore, the proposed context model and workflow management algorithm can apply to other applications by simply revising the corresponding context structures only.

Generating multimodal user interfaces for Web services

Web services are gaining momentum as a standard interface to a rich variety of services in ubiquitous computing environments. To improve access to Web services, convenient user interfaces should be provided. This paper presents a method that automatically constructs multimodal user interfaces from WSDL files of Web services. The proposed method appropriately produces user interfaces based on context information. To describe context information, including the characteristics of devices, the proposed context model is developed based on the CC/PP standard. The proposed method extracts operations and their input message types, which are in the form of XML Schema, and chooses the most suitable input controls according to input message types and context information. Experiment results with a usability test show that the proposed method successfully generates multimodal user interfaces from the description files of Web services.

지능적인 홈을 위한 상황인식 미들웨어에 대한 연구

무선네트워크와 각종 감지 센서로 통합된 스마트 홈은 우리의 삶의 일부분으로 자리 잡을 것이다. 이 논문은 사용자의 선호도에 근거하여 자동적인 흠 서비스를 제공하는 상황인식 미들웨어에 대하여 설명한다. 상황인식 미들웨어는 사용자의 선호도에 대한 학습과 예측 알고리즘을 수행하기 위하여 6가지의 기본 데이터를 이용하고 제시되는 6가지의 기본 데이터는 맥박, 체온, 얼굴표정, 실내온도, 시간, 사용자 위치이다. 6개의 데이터는 컨텍스트 모델을 구성하고 컨텍스트 매니저 모듈에 의해 기본 데이터로 사용된다. 사용자에 의해서 선택되어진 컨텐츠에 대한 정보를 유지하는 로그매니저가 제시되고 사용자에게 적절한 홈서비스를 제공하기 위해 신경망에 근거한 학습 및 예측 알고리즘을 제시한다. 실험결과는 개인의 선호도 패턴이 연구된 컨텍스트 모델에 의해서 효과적으로 예측되고 평가되는 것을 보여준다. Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a user's preference. The context-aware middle-ware utilizes 6 basic data for learning and predicting the user's preference on the multimedia content : the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The log manager module maintains history information for multimedia content chosen by the user. The user-pattern learning and pre-dicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an in-dividual's preferences can be effectively evaluated and predicted by adopting the proposed context model.

Issues of representing context illustrated by video-surveillance applications

This paper tackles several issues of context representation in knowledge-based systems. First, we propose a definition of context through the description of the different types of information manipulated by a process. Thanks to this definition we explain the role of the granularity level of processing and the role of the abstraction level of application in modelling context. Based on this definition two main issues related to context are tackled: how context representation can be built and organized and how context contents can be re-used for other applications. Then we propose several solutions to deal with these issues: using a multi-viewpoint representation and describing context through symbolic information. We illustrate the proposed context model with the process of dynamic scene interpretation. After explaining the reasons why this process is particularly concerned with the use of contextual information, we describe the context representation and its implementation for this specific process. Finally, we give an example illustrating the utilization of the context representation and we describe the software we have developed to ease the acquisition stage of context contents.