Showing papers on "Context awareness published in 2015"

A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion

Abstract: Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a novel hierarchical recurrent encoder-decoder architecture that makes possible to account for sequences of previous queries of arbitrary lengths. As a result, our suggestions are sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that our model outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our architecture is general enough to be used in a variety of other applications.

Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing

Abstract: Creating context-aware ad hoc collaborative systems remains to be one of the primary hurdles hampering the ubiquitous deployment of IT and communication services Especially under mission-critical scenarios, these services must often adhere to strict timing deadlines We believe empowering such realtime collaboration systems requires context-aware application platforms working in conjunction with ultra-low latency data transmissions In this paper, we make a strong case that this could be accomplished by combining the novel communication architectures being proposed for 5G with the principles of Mobile Edge Computing (MEC) We show that combining 5G with MEC would enable inter- and intra-domain use cases that are otherwise not feasible

Going Mobile: Defining Context for On-the-Go Travelers

Abstract: Travel behavior is becoming inherently dynamic and socially connected because of the increasing use of mobile technologies; as such, the concept of context is becoming increasingly important in travel and tourism and particularly within today’s technology-supported mobile environment. This article builds upon existing literature describing recent developments in context-aware system design with the aim of defining the notion of context as it relates to the mobile technological environment for tourism. As part of this effort, a conceptual framework is proposed to describe the structure and fundamental properties of context, and several implications are discussed for tourism research and the design of mobile systems.

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security a privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

A systematic review of scholar context-aware recommender systems

Abstract: We review the relevant articles in the field of scholar recommendations.We explore contextual information influential in scholar recommendations.We examine recommending approaches.Contextual information are categorised in three groups.The most recommending approaches are collaborative filtering, content based, knowledge based and hybrid. Incorporating contextual information in recommender systems is an effective approach to create more accurate and relevant recommendations. This review has been conducted to identify the contextual information and methods used for making recommendations in digital libraries as well as the way researchers understood and used relevant contextual information from the years 2001 to 2013 based on the Kitchenham systematic review methodology. The results indicated that contextual information incorporated into recommendations can be categorised into three contexts, namely users' context, document's context, and environment context. In addition, the classical approaches such as collaborative filtering were employed more than the other approaches. Researchers have understood and exploited relevant contextual information through four ways, including citation of past studies, citation of past definitions, self-definitions, and field-query researches; however, citation of the past studies has been the most popular method. This review highlights the need for more investigations on the concept of context from user viewpoint in scholarly domains. It also discusses the way a context-aware recommender system can be effectively designed and implemented in digital libraries. Additionally, a few recommendations for future investigations on scholarly recommender systems are proposed.

A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Abstract: Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.

Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field

Abstract: When should a machine attempt to communicate with a user? This is a historical problem that has been studied since the rise of personal computing. More recently, the emergence of pervasive technologies such as the smartphone have extended the problem to be ever-present in our daily lives, opening up new opportunities for context awareness through data collection and reasoning. Complementary to this there has been increasing interest in techniques to intelligently synchronise interruptions with human behaviour and cognition. However, it is increasingly challenging to categorise new developments, which are often scenario specific or scope a problem with particular unique features. In this paper we present a meta-analysis of this area, decomposing and comparing historical and recent works that seek to understand and predict how users will perceive and respond to interruptions. In doing so we identify research gaps, questions and opportunities that characterise this important emerging field for pervasive technology.

Emergency situation monitoring service using context motion tracking of chronic disease patients

Abstract: Nowadays, great attention is paid to studies on fusion health and medical care combined of IT and BT for chronic disease patients due to westernization of dietary life, increase in stress, decrease in physical activities, and others. In reality, full recovery of chronic disease is difficult to achieve as its cause is diverse and complex. Therefore, the necessity for continuous management is proposed rather than approach to treat the disease. It is urgent to come up with the countermeasure since the lengthening of life expectancy in the aging society brings about the increase in chronic diseases and such increased medical expense becomes a big burden in socioeconomic activities. Companies are promoting test-projects in association with health management together with nationwide health management business for chronic diseases. In this study, we proposed the emergency situation monitoring service using context motion tracking for chronic disease patients. Proposed service diagnoses current status of patient based on contextual information collected and it provides information necessary for chronic disease management by analyzing life habits. Bio status recognition can provide proper service through the extraction of contextual data relevant to chronic disease patients. The context motion tracking provides emergency situation monitoring service accordingly with alert and symptom level in case of detecting symptoms through measured results and analysis. Semantic inference engine for context awareness conducts active and intelligent analysis on health condition and life patterns. Since it can properly correspond to extraordinary circumstances, it provides necessary service environment for emergency situation or symptoms. The cameras, speakers, and sensors are installed accordingly with the structure of indoor living space of user and the contextual information is transmitted from them. Considering the user convenience, motion history image is used for the motion recognition and continuous tracking from video. The system detects the patterns of expertise based on life pattern and psychological state through life log based motion detection and provides the service accordingly. It provides health related information and emergency situation monitoring service to user at anytime and anywhere and it is easy to use with simple handling. As a result, this system has the advantage of being able to detect emergency situations realistically and intuitively.

Identifying Opportunities for Valuable Encounters: Toward Context-Aware Social Matching Systems

Abstract: Mobile social matching systems have the potential to transform the way we make new social ties, but only if we are able to overcome the many challenges that exist as to how systems can utilize contextual data to recommend interesting and relevant people to users and facilitate valuable encounters between strangers. This article outlines how context and mobility influence people's motivations to meet new people and presents innovative design concepts for mediating mobile encounters through context-aware social matching systems. Findings from two studies are presented. The first, a survey study (n = 117) explored the concept of contextual rarity of shared user attributes as a measure to improve desirability in mobile social matches. The second, an interview study (n = 58) explored people's motivations to meet others in various contexts. From these studies we derived a set of novel context-aware social matching concepts, including contextual sociability and familiarity as an indicator of opportune social context; contextual engagement as an indicator of opportune personal context; and contextual rarity, oddity, and activity partnering as an indicator of opportune relational context. The findings of these studies establish the importance of different contextual factors and frame the design space of context-aware social matching systems.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition

Abstract: The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities. Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology. The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application. We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data representation in the field on context awareness and thus toward unified benchmark data sets.

A Multi-Temporal Context-aware System for Competences Management

Abstract: The evolution of computing technology and wireless networks has contributed to the miniaturization of mobile devices and their increase in power, providing services anywhere and anytime. In this scenario, applications have considered the user’s contexts to make decisions (Context Awareness). Context-aware applications have enabled new opportunities in different areas, for example, education, games and entertainment, commerce, and competence management. In this article, we present MultCComp, a multi-temporal context-aware system for competences management. The main system contribution is to take advantage of the workers’ present and past contexts to help them to develop their competences. We define as multi-temporal context awareness the joint use of workers’ present and past contexts to assist them in the development of their competences. We developed a prototype and conducted two experiments with it in an evaluation environment. The first experiment aimed to demonstrate the system functionalities. It consisted of two evaluation scenarios that were followed by two users. The second experiment focused on evaluating the acceptance of the system. It comprised a scenario that was followed by 21 users, who filled out a questionnaire at the end of the test.

Concepts of context in music therapy

Abstract: In contemporary music therapy as well as in related interdisciplinary fields, the importance of context in relation to theory, research, and practice has been emphasized. However, the word context seems to be used in several different ways and conceptualizations of contextual approaches vary too. The objective of this theoretical article is to clarify traditions of language use in relation to context in music therapy. In reviewing and discussing the literature, we focus on the field of mental health care. When discussing issues related to context, this literature partly focuses on the surroundings of music therapy practice, partly on the ecology of reciprocal influences within and between situations or systems. On this basis, three types of context awareness in music therapy are identified: music therapy in context; music therapy as context; and music therapy as interacting contexts. The identified types of context awareness are exemplified through references to music therapy literature and then discussed in relation to two very different metaphors, namely context as frame and context as link. Implications for practice, research, and theory development in music therapy are suggested.

Reconfigurable Wireless Networks

Abstract: Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Access Control Issues in Utilizing Fog Computing for Transport Infrastructure

Abstract: The integration of the information and communication technologies of cloud computing, Software Defined Networking (SDN) and Internet of Things (IoT) into traditional transportation infrastructures enables the evolution of Intelligent Transportation Systems (ITS). Moreover, the specific requirements for real-time applications and service provision near to consumers introduce the utilization of fog computing as an extension of cloud. However, such a movement affects security aspects and poses new access control challenges. In this paper, we study the operational characteristics of a proposed ITS paradigm utilizing fog computing and identify corresponding access control issues. To address these issues in such a versatile and highly distributed environment, we present the key pointers of an attribute-based access control scheme suitable for fog computing. This paper aims to set a basis for further work in refining, verifying and validating the proposed solution.

Context-Adaptive Privacy: Leveraging Context Awareness to Support Privacy Decision Making

Abstract: Ubiquitous computing applications introduce multiple privacy challenges due to their sensing and actuation capabilities, which are often combined with ubiquitous interconnectivity. Because of the complexity of many ubicomp systems, users might have difficulties estimating the privacy implications of their actions and decisions. In this article, the authors discuss leveraging awareness about a user's context and respective context changes to dynamically support privacy decision making. They present an operationalization of Irwin Altman's privacy regulation theory for this purpose, describing how individual phases of the process can be supported. They also report on their experiences in developing context-adaptive privacy mechanisms for different applications and domains. This article is part of a special issue on privacy and security.

Context-Aware Mobile Cloud Computing and Its Challenges

Abstract: Cloud computing is gaining popularity due to virtually unlimited resources, low capital cost, ease of adoption, flexible resource provisioning, and high scalability. Considering these benefits, researchers envision the usage of cloud computing for mobile devices to overcome the ever-increasing computational and energy demands of smartphone applications. However, this requires specialized context-ware application development models that can facilitate the development of cloud-enabled applications capable of making context-aware computation offloading decisions. This article provides an overview of mobile cloud computing technology, focusing on its context-awareness aspects and challenges.

COT: contextual operating tensor for context-aware recommender systems

Abstract: With rapid growth of information on the internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot capture the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this work, we propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector. Then, to model the semantic operation of a context combination, we generate contextual operating matrix from the contextual operating tensor and latent vectors of contexts. Thus latent vectors of users and items can be operated by the contextual operating matrices. Experimental results show that the proposed COT model yields significant improvements over the competitive compared methods on three typical datasets, i.e., Food, Adom and Movielens-1M datasets.

Trends and Potentials of the Smart Grid Infrastructure: From ICT Sub-System to SDN-Enabled Smart Grid Architecture

Abstract: Context and situational awareness are key features and trends of the smart grid and enable adaptable, flexible and extendable smart grid services. However, the traditional hardware-dependent communication infrastructure is not designed to identify the flow and context of data, and it focuses only on packet forwarding using a pre-defined network configuration profile. Thus, the current network infrastructure may not dynamically adapt the various business models and services of the smart grid system. To solve this problem, software-defined networking (SDN) is being considered in the smart grid, but the design, architecture and system model need to be optimized for the smart grid environment. In this paper, we investigate the state-of-the-art smart grid information subsystem, communication infrastructure and its emerging trends and potentials, called an SDN-enabled smart grid. We present an abstract business model, candidate SDN applications and common architecture of the SDN-enabled smart grid. Further, we compare recent studies into the SDN-enabled smart grid depending on its service functionalities, and we describe further challenges of the SDN-enabled smart grid network infrastructure.

Push or Delay? Decomposing Smartphone Notification Response Behaviour

Abstract: Smartphone notifications are often delivered without considering user interruptibility, potentially causing frustration for the recipient. Therefore research in this area has concerned finding contexts where interruptions are better received. The typical convention for monitoring interruption behaviour assumes binary actions, where a response is either completed or not at all. However, in reality a user may partially respond to an interruption, such as reacting to an audible alert or exploring which application caused it. Consequently we present a multi-step model of interruptibility that allows assessment of both partial and complete notification responses. Through a 6-month in-the-wild case study of 11,346 to-do list reminders from 93 users, we find support for reducing false-negative classification of interruptibility. Additionally, we find that different response behaviour is correlated with different contexts and that these behaviours are predictable with similar accuracy to complete responses.

Dynamic Social Structure of Things: A Contextual Approach in CPSS

Abstract: The emergence of cyber-physical-social systems (CPSS) and context-aware technologies has helped boost a growing interest in building frameworks for adaptive smart services that hide heterogeneity in the infrastructure and support services by seamlessly integrating the cyber, physical, and social worlds. However, this entails an enormous amount of computational and networking contextual complexity. Here, the proposed smart services framework in CPSS (called Dynamic Social Structure of Things, or DSSoT) boosts sociality and narrows down the contextual complexity based on situational awareness. DSSoT monitors spatiotemporal situations and, depending on users' individual goals and other social aspects, induces and structures relevant social objects and smart services in a temporal network of interactions. An application using DSSoT, called Airport Dynamic Social, provides a proof of concept.

MobiByte: An Application Development Model for Mobile Cloud Computing

Abstract: Mobile cloud computing presents an effective solution to overcome smartphone constraints, such as limited computational power, storage, and energy. As the traditional mobile application development models do not support computation offloading, mobile cloud computing requires novel application development models that can facilitate the development of cloud enabled mobile applications. This paper presents a mobile cloud application development model, named MobiByte, to enhance mobile device applications' performance, energy efficiency, and execution support. MobiByte is a context-aware application model that uses multiple data offloading techniques to support a wide range of applications. The proposed model is validated using prototype applications and detailed results are presented. Moreover, MobiByte is compared with the most recent application models with a conclusion that it outperforms the existing application models in many aspects like energy efficiency, performance, generality, context awareness, and privacy.

Research on IoT based Cyber Physical System for Industrial big data Analytics

Abstract: The purpose of this paper is to provide a comprehensive solution for industry through research and development of an Internet of Things (IoT) based Cyber Physical System for Industrial Informatics Analytics with the following objectives. This study conducted a review regarding big data analytics in industry and designed a cyber physical system with the integration of various existing and proprietary data analytics systems based on their business needs so that themodules can be reconfigurable and interchangeable. The paper designs a new context intelligence framework to handle industrial informatics regarding location, sensor and unstructured data for big data mining. A case study isused to illustrate the concept of the proposed cyberphysical system. Further study on system integration and migration from existing factories to smart factories should be conducted so as to realize the next industrial paradigm shift.

Using Contextual Information to Understand Searching and Browsing Behavior

Abstract: There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications.

Social Access vs. Privacy in Wearable Computing: A Case Study of Autism

Abstract: People with high-functioning autism face challenges in communication and social interaction. This article considers the possibility, and perhaps inevitability, of wearable devices such as Google Glass being used as real-time assistive technologies for this group, with the intent of enabling them to better access our complex social world. Social impairments, by their very nature, highlight issues of communication, personal information, and social judgment. In considering such assistive technology in this context, the authors explore new tensions between privacy issues and assistive technologies, especially those of a do-it-yourself nature, which are not immediately solvable within our current privacy frameworks. This article is part of a special issue on privacy and security.

A context and user aware smart notification system

Abstract: Nowadays, notifications are increasingly gaining momentum in our society. New smart devices and appliances are developed everyday with the ability to generate, send and show messages about their status, acquired data and/or information received from other devices and users. Consequently, the number of notifications received by a user is growing and the tolerance to them could decrease in a short time. This paper presents a smart notification system that uses machine learning algorithms to adequately manage incoming notifications. According to context awareness and user habits, the system decides: a) who should receive an incoming notification; b) what is the best moment to show the notification to the chosen user(s); c) on which device(s) the chosen user(s) should receive the notification; d) which is the best way to notify the incoming notification. After the design of a general architecture, as a first step in building such a system, three different machine learning algorithms were compared in the task of establishing the best device on which the incoming notification should be delivered. The algorithms were applied to a dataset derived from real data provided by the MIT Media Laboratory Reality Mining project, enriched with additional synthetic information.