Context Information Model Research Articles

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

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.

Context-Aware Answer Sentence Selection With Hierarchical Gated Recurrent Neural Networks

In this paper, we study the task of reading comprehension style answer sentence selection that aims to select the best sentence from a given passage to answer a question. Unlike most previous works that match the question and each candidate sentence separately, we observe that the context information among sentences in the same passage plays a vital role in this task. We propose modeling context information with hierarchical gated recurrent neural networks. Specifically, we first apply a word level recurrent neural network to model the context independent matching between the question and each candidate sentence. We then employ a sentence level recurrent neural network to incorporate the context information among all candidate sentences. Moreover, we introduce the gate mechanism to select matching information before feeding into recurrent neural networks at both word and sentence level. Experiments on the WikiQA and SQuAD datasets show that our model outperforms state-of-the-art methods.

Learning communication patterns for malware discovery in HTTPs data

Encrypted communication on the Internet using the HTTPs protocol represents a challenging task for network intrusion detection systems. While it significantly helps to preserve users’ privacy, it also limits a detection system’s ability to understand the traffic and effectively identify malicious activities. In this work, we propose a method for modeling and representation of encrypted communication from logs of web communication. The idea is based on introducing communication snapshots of individual users’ activity that model contextual information of the encrypted requests. This helps to compensate the information hidden by the encryption. We then propose statistical descriptors of the communication snapshots that can be consumed by various machine learning algorithms for either supervised or unsupervised analysis of the data. In the experimental evaluation, we show that the presented approach can be used even on a large corpus of network traffic logs as the process of creation of the descriptors can be effectively implemented on a Hadoop cluster.

Protein Secondary Structure Prediction Based on Deep Learning

Prediction of protein secondary structure from the amino acid sequence is a classical bioinformatics problem in computational biology. For accurate predicting the sequence-structure mapping relationship between protein secondary structure and features, a novel deep learning prediction model is proposed by combining convolutional neural network (CNN) and bi-directional recurrent neural network (BRNN) with long short-term memory cells (Bi-directional LSTM RNN). In order to draw eight classes (Q8) protein secondary structure prediction results, we first utilize CNN to filter and sample amino acid sequences, and then use Bi-directional LSTM RNN to model context information interaction between amino acids in protein. Experimental results show that the prediction accuracy of the proposed model is about 1-3% higher than that of the existing prediction models, and the prediction accuracy of 69.4% is obtained.

Information Model and Architecture Specification for Context Awareness Interaction Decision Support in Cyber-Physical Human–Machine Systems

This paper aims to contribute to situation, activity, and goal awareness in cyber-physical human–machine systems (HMS) by presenting a new information model and specifications for a decision-making component that can be integrated in current system architectures. The objective of this work is to improve the efficacy, acceptance, adaptability, and overall performance of HMS and human–system interaction (HSI) applications using a context-based approach. Our hypothesis is that we can enhance current interaction functionalities by integrating context and interaction information models into a decision-making component that behaves as a supervision process for controlling interaction. In HSI, we aim to define a general human model that may lead to principles and algorithms, allowing more natural and effective interaction between humans and artificial agents. The approach was implemented and tested targeting application in the domain of active and assisted living. The challenge of user acceptance is of vital importance for future solutions and is still one of the major reasons for reluctance to adopt cyber-physical systems in this domain.

A Hybrid DBN and CRF Model for Spectral-Spatial Classification of Hyperspectral Images

Hyperspectral image classification plays an important role in remote sensing image analysis. Recent techniques have attempted to investigate the capabilities of deep learning approaches to tackle the hyperspectral image classification. This work shows how to further improve the hyperspectral image classification through using both a deep representation and contextual information. To implement this objective, this work proposes a new Conditional Random Field (CRF) model (named DBN-CRF) with the potentials defined over the deep features produced by a Deep Belief Network (DBN). The newly formulated DBN-CRF model takes advantage of the strength of DBNs in learning a good representation and the ability of CRFs to model contextual (spatial) information in both the observations and labels. Within a piecewise training framework, an efficient training method is proposed to train the whole DBN-CRF model end-to-end. This means that the parameters in DBN and CRF can be jointly trained and thus the proposed method can fully use the strength of both DBN and CRF. Moreover, in the proposed training method, the end-to-end training can be implemented with a standard back-propagation algorithm, avoiding the repeated inference usually involved in CRF training and thus is computationally efficient. Experiments on real-world hyperspectral data show that our method outperforms the most recent approaches in hyperspectral image classification.

A Model-Driven Based Methodology for the Generation of Context-Aware Medical Interfaces from Open EHR Archetypes

Purpose: Different medical computer science standards, such as ISO\EN 13606, openEHR and HL7 CDA, use the dual model approach to support interoperability between medical information systems. However, use of these standards encounters some obstacles: (i) lack methodological tools to facilitate design of Electronic Health Record (EHR) systems in accordance to two conceptual levels, (ii) Lack of methodologies to perform semantic activities on EHR content in the semantic web environment, and (iii) Absence of models to adapt medical knowledge, modelled by means of archetypes, in different contexts of use. This paper presents a model-driven based methodology for the generation of context-aware medical interfaces. This methodology, entitled OpenEHR modelling Methodology: OpenEHR-MM includes concepts, models, rules, processes and tools aimed to assist EHR designer in the generation process of context-aware medical interfaces. Methods: OpenEHR-MM is based on the use of the model driven architecture technological space, and it includes the following four steps: 1. Analysis and specification of requirements: This first step aims to guide EHR interface designer in the definition of users’ needs. 2. Conceptualization: This step aims to define tools, concepts and approaches that assist EHR designer in the two conceptual levels modelling process. At this step, we define an approach to provide the context information modelling. 3. Ontologization: This step aims to assist the designer to integrate the semantic dimension in the EHR systems modeled in accordance to the dual model approach. 4. Implementation: Finally, OpenEHR-MM provides an adaptive approach to enable medical interface configuration with respect to contexts settings. Results: A model driven-based methodology for context-aware medical interfaces generation process. The experimentation of this methodology is performed on the EHR related to patients affected by cerebral palsy.

Stream cipher based key derivation function

A key derivation function (KDF) is used to generate one or more cryptographic keys from a private string together with some public information. To date, most of the existing KDF proposals are designed using hash functions and block ciphers. This paper proposes alternative designs for constructing KDFs using stream ciphers. We show that these designs are secure in relation to the adaptive chosen context information model with single salt (CCS) security model from Krawczyk based on the assumption that the underlying stream ciphers are secure. We simulate instances of these stream cipher based KDFs using three eStream finalists: Trivium, Sosemanuk and Rabbit. The simulation results indicate that the stream cipher based KDFs offer significant efficiency advantages (in terms of running time) over hash function and block cipher based KDFs.

Design of air quality information service based upon geographic context information model in ISO19154

Advance on the information and communication technology requires a new paradigm to develop the air quality information system for monitoring air qualities in real-time and for encouraging citizens’ participation to assess air qualities. Thus, in this research, the GIS-based air quality information system is developed for citizens to easily access the air quality information service and to provide their perceptions of air quality as social data. In the system, the geographic context information model is a basis for defining the contextual relationships between a user and air quality data so that air quality information service can be generated meeting users’ contextual request. Implementation results show that the system provides proper air quality information services, depending on user’s location and air qualities situation around the users. Users can also assess air qualities at their current locations or in regions of interest.

Modeling context-aware and intention-aware in-car infotainment systems

It is fundamental to understand users' intentions to support them when operating a computer system with a dynamically varying set of functions, e.g., within an in-car infotainment system. The system needs to have sufficient information about its own and the user's context to predict those intentions. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling contextual information and user intentions is currently not supported. However, manually creating software that understands the current context and predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user's intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible user intentions according to the current context. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user's behavior. We demonstrate feasibility of our approach using an industrial case study of an intention-aware in-car infotainment system. Finally, we show how modeling of contextual information and modeling user intentions can be combined by using model transformation.

Robust Multi-Focus Image Fusion Using Multi-Task Sparse Representation and Spatial Context.

We present a novel fusion method based on a multi-task robust sparse representation (MRSR) model and spatial context information to address the fusion of multi-focus gray-level images with misregistration. First, we present a robust sparse representation (RSR) model by replacing the conventional least-squared reconstruction error by a sparse reconstruction error. We then propose a multi-task version of the RSR model, viz., the MRSR model. The latter is then applied to multi-focus image fusion by employing the detailed information regarding each image patch and its spatial neighbors to collaboratively determine both the focused and defocused regions in the input images. To achieve this, we formulate the problem of extracting details from multiple image patches as a joint multi-task sparsity pursuit based on the MRSR model. Experimental results demonstrate that the suggested algorithm is competitive with the current state-of-the-art and superior to some approaches that use traditional sparse representation methods when input images are misregistered.

Data-driven contextual modeling for 3D scene understanding

The recent development of fast depth map fusion technique enables the realtime, detailed scene reconstruction using commodity depth camera, making the indoor scene understanding more possible than ever. To address the specific challenges in object analysis at subscene level, this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts. Our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing the objects in the scene a priori. The key idea is that while contextual information could benefit the detection of either individual objects or object groups, both can contribute to object extraction when objects are unknown.Our method starts with a robust segmentation and partitions a subscene into segments, each of which represents either an independent object or a part of some object. A set of classifiers are trained for both individual objects and object groups, using a database of 3D scene models. We employ the multiple kernel learning (MKL) to learn per-category optimized classifiers for objects and object groups. Finally, we perform a graph matching to extract objects using the classifiers, thus grouping the segments into either an object or an object group. The output is an object-level labeled segmentation of the input subscene. Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes.

Decision support for the usage of mobile information services: A context-aware service selection approach that considers the effects of context interdependencies

In mobile business, context information is utilised to select services mostly tailored to a user’s current situation and preferences. In existing context-aware service selection approaches, a service utility is determined by comparing its non-functional properties with current context information but without considering its integration in a service composition. This may cause suboptimal selection results, as context information and thus the determined utility of a certain service are usually dependent on its preceding and succeeding services. The latter we denote as context interdependencies. In this paper, we investigate how the effects of context interdependencies can be modelled for the context-aware service selection at planning time (i.e. before starting to accomplish a service composition). To develop this approach, we use the concept of states to model context information for the selection. In our evaluation, we find that our approach leads to superior results compared to current context-aware service selection approaches.

상황인지모델을 이용한 GIS 기반의 대기오염 정보시스템 개발

Due to the rapid advance in web and mobile computing technologies, normal users have become to produce, provide, and share a varied form of spatial data and information. In the domain of spatial information, numerous researches on GIS have been conducted to provide spatial information services based on a geo-sensor network and a data integration and processing technology. However, to provide user-oriented information, a context information model is necessary to associate GIS data with web and sensor data. Context awareness services is designed to provide specific information, minimizing users' interference. For which, the context information model expresses the relationship of various data from sensor networks and mobile applications and provides a user-specific information considering location and area of interest. Thus, this research aims to develops a context information model based air-pollution information system that obtains and analyses air pollution data and reflects the analysis results on an air-pollution policy. Also, this system aims to raise citizens' awareness on air-pollution and to promote citizens' participatory to improve city's air quality.

From Ambient Sensing to IoT-based Context Computing: An Open Framework for End to End QoC Management.

Quality of Context (QoC) awareness is recognized as a key point for the success of context-aware computing. At the time where the combination of the Internet of Things, Cloud Computing, and Ambient Intelligence paradigms offer together new opportunities for managing richer context data, the next generation of Distributed Context Managers (DCM) is facing new challenges concerning QoC management. This paper presents our model-driven QoCIM framework. QoCIM is the acronym for Quality of Context Information Model. We show how it can help application developers to manage the whole QoC life-cycle by providing genericity, openness and uniformity. Its usages are illustrated, both at design time and at runtime, in the case of an urban pollution context- and QoC-aware scenario.

A Context-Aware Framework to Provide Personalized Mobile Assessment

In mobile learning environment, context-aware systems refer to applications that employs contextual information to provide appropriate services to the leaners or other applications to perform a specific task. An important challenge in such applications is context modeling, using ontologies to model context information and to reason about context at a semantic level has attracted a lot of interest in the research community. Semantic Web technologies have been applied in recent years with different purposes in education. But, their applications for generating useful personalized mobile assessment resources have not been researched enough so far.In this paper, we introduce a context-aware approach that makes use of Semantic Web technologies to support personalized assessment in mobile environments. We propose a Service-based framework for bringing assessment techniques to mobile environment. We provide a formal description for our mobile assessment framework and detail the functionalities of its various layers. We have carried out also an experiment with computer science university students to evaluate our mobile assessment framework.

A Conceptual Architecture for Adaptive Human-Computer Interface of a PT Operation Platform Based on Context-Awareness

We present a conceptual architecture for adaptive human-computer interface of a PT operation platform based on context-awareness. This architecture will form the basis of design for such an interface. This paper describes components, key technologies, and working principles of the architecture. The critical contents covered context information modeling, processing, relationship establishing between contexts and interface design knowledge by use of adaptive knowledge reasoning, and visualization implementing of adaptive interface with the aid of interface tools technology.

A domain-specific language for context modeling in context-aware systems

Context-awareness refers to systems that can both sense and react based on their environment. One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper we present MLContext, a textual Domain-Specific Language (DSL) which is specially tailored for modeling context information. It has been implemented by applying Model-Driven Development (MDD) techniques to automatically generate software artifacts from context models. The MLContext abstract syntax has been defined as a metamodel, and model-to text transformations have been written to generate the desired software artifacts. The concrete syntax has been defined with the EMFText tool, which generates an editor and model injector.MLContext has been designed to provide a high-level abstraction, to be easy to learn, and to promote reuse of context models. A domain analysis has been applied to elicit the requirements and design choices to be taken into account in creating the DSL. As a proof of concept of the proposal, the generative approach has been applied to two different middleware platforms for context management.

Ontology driven interactive healthcare with wearable sensors

Ubiquitous healthcare is the service that offers health-related information and contents to users without any limitations of time and space. Especially, to offer customized services to users, the technology of acquiring context information of users in real time is the most important consideration. In this paper, we researched wearable sensors. We proposed the ontology driven interactive healthcare with wearable sensors (OdIH_WS) to achieve customized healthcare service. For this purpose, wearable-sensor-based smart-wear and methods of data acquisition and processing are being developed. The proposed system has potential value in healthcare. A smart wear using wearable sensors is fabricated as a way of non-tight and comfortable style fitting for the curves of the human body based on clothes to wear in daily life. The design sample of the smart wear uses basic stretch materials and is designed to sustain its wearable property. To offer related information, it establishes an environment-information-based healthcare ontology model needed for inference, and it is composed of inside-outside context information models depending on the users' context. The modeling of the proposed system involved combinations of information streams, focusing on service context information. With the proposed service inference rules, customized information and contents could be drawn by the inference engine. In the established OdIH_WS, real-time health information monitoring was achieved. The results of system performance and users' satisfaction evaluations confirmed that the proposed system is superior to other existing systems.

Context-aware tensor decomposition for relation prediction in social networks

An important task in network modeling is the prediction of relationships between classes of objects, such as friendship between persons, preferences of users for items, or the influence of genes on diseases. Factorizing approaches have proven effective in the modeling of these types of relations. If only a single binary relation is of interest, matrix factorization is typically applied. For ternary relations, tensor factorization has become popular. A typical application of tensor factorization concerns the temporal development of the relationships between objects. There are applications, where models with n-ary relations with n > 3 need to be considered, which is the topic of this paper. These models permit the inclusion of context information that is relevant for relation prediction. Unfortunately, the straightforward application of higher-order tensor models becomes problematic, due to the sparsity of the data and due to the complexity of the computations. In this paper, we discuss two different approaches that both simplify the higher-order tensors using coupled low-order factorization models. While the first approach, the context-aware recommendation tensor decomposition (CARTD), proposes an efficient optimization criterion and decomposition method, the second approach, the context-aware regularized singular value decomposition (CRSVD), introduces a generative probabilistic model and aims at reducing the dimensionality using independence assumptions in graphical models. In this article, we discuss both approaches and compare their ability to model contextual information. We test both models on a social network setting, where the task is to predict preferences based on existing preference patterns, based on the last item selected and based on attributes describing items and users. The experiments are performed using data from the GetGlue social network and the approach is evaluated on the ranking quality of predicted relations. The results indicate that the CARTD is superior in predicting overall rankings for relations, whereas the CRSVD is superior when one is only interested in predicting the top-ranked relations.