Showing papers on "Context awareness published in 2012"

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Abstract: Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

A survey of context data distribution for mobile ubiquitous systems

Abstract: The capacity to gather and timely deliver to the service level any relevant information that can characterize the service-provisioning environment, such as computing resources/capabilities, physical device location, user preferences, and time constraints, usually defined as context-awareness, is widely recognized as a core function for the development of modern ubiquitous and mobile systems. Much work has been done to enable context-awareness and to ease the diffusion of context-aware services; at the same time, several middleware solutions have been designed to transparently implement context management and provisioning in the mobile system. However, to the best of our knowledge, an in-depth analysis of the context data distribution, namely, the function in charge of distributing context data to interested entities, is still missing. Starting from the core assumption that only effective and efficient context data distribution can pave the way to the deployment of truly context-aware services, this article aims at putting together current research efforts to derive an original and holistic view of the existing literature. We present a unified architectural model and a new taxonomy for context data distribution by considering and comparing a large number of solutions. Finally, based on our analysis, we draw some of the research challenges still unsolved and identify some possible directions for future work.

Understanding and prediction of mobile application usage for smart phones

Abstract: It is becoming harder to find an app on one's smart phone due to the increasing number of apps available and installed on smart phones today. We collect sensory data including app use from smart phones, to perform a comprehensive analysis of the context related to mobile app use, and build prediction models that calculate the probability of an app in the current context. Based on these models, we developed a dynamic home screen application that presents icons for the most probable apps on the main screen of the phone and highlights the most probable one. Our models outperformed other strategies, and, in particular, improved prediction accuracy by 8% over Most Frequently Used from 79.8% to 87.8% (for 9 candidate apps). Also, we found that the dynamic home screen improved accessibility to apps on the phone, compared to the conventional static home screen in terms of accuracy, required touch input and app selection time.

Context-aware mobile music recommendation for daily activities

Abstract: Existing music recommendation systems rely on collaborative filtering or content-based technologies to satisfy users' long-term music playing needs. Given the popularity of mobile music devices with rich sensing and wireless communication capabilities, we present in this paper a novel approach to employ contextual information collected with mobile devices for satisfying users' short-term music playing needs. We present a probabilistic model to integrate contextual information with music content analysis to offer music recommendation for daily activities, and we present a prototype implementation of the model. Finally, we present evaluation results demonstrating good accuracy and usability of the model and prototype.

Spatial models for context-aware indoor navigation systems: A survey

Abstract: This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, consid- ering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environ- ments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation pur- poses, and by taking into account the context dimensions.

A Comprehensive Ontology for Knowledge Representation in the Internet of Things

Abstract: Semantic modeling for the Internet of Things has become fundamental to resolve the problem of interoperability given the distributed and heterogeneous nature of the "Things". Most of the current research has primarily focused on devices and resources modeling while paid less attention on access and utilisation of the information generated by the things. The idea that things are able to expose standard service interfaces coincides with the service oriented computing and more importantly, represents a scalable means for business services and applications that need context awareness and intelligence to access and consume the physical world information. We present the design of a comprehensive description ontology for knowledge representation in the domain of Internet of Things and discuss how it can be used to support tasks such as service discovery, testing and dynamic composition.

Research challenges in computation, communication, and context awareness for ubiquitous healthcare

Abstract: A new paradigm for ubiquitous healthcare characterized by pervasive continuous vital sign data collection, real-time processing of monitored data to derive meaningful physiological parameters, and context-aware data- and patient-centric decision making, is central to deliver personalized healthcare solutions to the elderly and the physically challenged. However, this new paradigm requires real-time processing of wirelessly collected vital signs using inherently complex physiological models and analysis of the processed information under context (e.g., location, ambient conditions, current physical activity) to extract knowledge about the health condition of patients. As the computational capabilities of biomedical sensor nodes are insufficient to run these models, this article presents an innovative resource provisioning framework that organizes and harnesses the computing capabilities of under-utilized electronic devices in the vicinity (e.g., laptops, tablets, PDAs, DVRs, medical terminals) in home and hospital settings. Novel wireless communication solutions for reliable vital sign transmission and algorithms for acquiring context awareness to support this framework are also discussed.

From Context Awareness to Socially Aware Computing

Abstract: The new generation of smartphones has realized the early vision of context awareness. The next step is facilitating real-world impact of more complex recognition, moving toward next-generation opportunistic recognition configurations and large-scale ensembles of networked subsystems interacting with communities of users.

Secure proximity detection for NFC devices based on ambient sensor data

Abstract: In certain applications, it is important for a remote server to securely determine whether or not two mobile devices are in close physical proximity. In particular, in the context of an NFC transaction, the bank server can validate the transaction if both the NFC phone and reader are precisely at the same location thereby preventing a form of a devastating relay attack against such systems.

Multimodal behavioral analysis for non-invasive stress detection

Abstract: This study presents and approach to measure the levels of acute stress in humans by analysing their behavioral patterns when interacting with technological devices. We study the effects of stress on eight behavioral, physical and cognitive features. The data was collected with the participation of 19 users in different phases, with different levels of stress induced. A non-parametric statistical hypothesis test is used to determine which features show statistically significant differences, for each user, when under stress. It is shown that the features more related to stress are the acceleration and the mean and maximum intensity of the touch. It is also shown that each user is affected by stress in a specific way. Moreover, all the process of estimating stress is undertaken in a non-invasive way. This work constitutes the foundation of a context layer for a virtual environment for conflict resolution. The main objective is to overcome some of the main drawbacks of communicating online, namely the lack of contextual information such as body language or gestures.

Context-Aware Systems—The ʻRightʼ Information, at the ʻRightʼ Time, in the ʻRightʼ Place, in the ʻRightʼ Way, to the ʻRightʼ Person

Abstract: Based on the assumption that the scarce resource for many people in the world of today is not information but human attention, the challenge for future human-centered computer systems is not to deliver more information “to anyone, at anytime, and from anywhere,” but to provide “the ‘right’ information, at the ‘right’ time, in the ‘right’ place, in the ‘right’ way to the ‘right’ person”. This article develops a multidimensional framework for contextaware systems to address this challenge transcending existing frameworks that limited their concerns to particular aspects of context-awareness and paid little attention to potential pitfalls. The framework is based on insights derived from the development and assessment of a variety of different systems that we have developed over the last twenty years to explore different dimensions of context awareness. Specific challenges, guidelines, and design trade-offs (promises and pitfalls) are derived from the framework for designing the next generation of context-aware systems. These systems will support advanced interactions for assisting humans (individuals and groups) to become more knowledgeable, more productive, and more creative by emphasizing context awareness as a fundamental design requirement.

A novel recommendation model of location-based advertising

Abstract: Recommender systems are the efficient and most used tools that prevail over the information overload problem, provide users with the most appropriate content by considering their personal preferences (mostly, ratings). In addition to these preferences, taking into account the interaction context of users will improve the relevancy of the recommendation process. However, only a few prior studies have tried to adopt context-awareness to the recommendation model. Although a number of studies have developed recommendation models using collaborative filtering (CF), few of them have tried to adopt both CF and other artificial intelligence techniques, such as genetic algorithm (GA), as a tool to improve recommendation results.In this paper, we propose a new recommendation model, which we termed Context-Aware Collaborative Filtering using genetic algorithm (CACF-GA), for location-based advertising (LBA) based on both user's preferences and interaction's context. We first defined discrete contexts, and then applied the concept of "context similarity" to conventional CF to create the context-aware recommendation model. The context similarity between two contexts is designed to be optimized using GA. We collect real-world data from mobile users, build a LBA recommendation model using CACF-GA, and then perform an empirical test to validate the usefulness of CACF-GA. Experiments show our proposed model provides the most accurate prediction results compared to comparative ones.

CA4IOT: Context Awareness for Internet of Things

Abstract: Internet of Things (IoT) will connect billions of sensors deployed around the world together. This will create an ideal opportunity to build a sensing-as-a-service platform. Due to large number of sensor deployments, there would be number of sensors that can be used to sense and collect similar information. Further, due to advances in sensor hardware technology, new methods and measurements will be introduced continuously. In the IoT paradigm, selecting the most appropriate sensors which can provide relevant sensor data to address the problems at hand among billions of possibilities would be a challenge for both technical and non-technical users. In this paper, we propose the Context Awareness for Internet of Things (CA4IOT) architecture to help users by automating the task of selecting the sensors according to the problems/tasks at hand. We focus on automated configuration of filtering, fusion and reasoning mechanisms that can be applied to the collected sensor data streams using selected sensors. Our objective is to allow the users to submit their problems, so our proposed architecture understands them and produces more comprehensive and meaningful information than the raw sensor data streams generated by individual sensors.

Which App? A recommender system of applications in markets

Abstract: Highlights? We propose a recommender system of applications in Markets. ? It uses five recommendations techniques, more than similar actual recommenders. ? The main input for the recommender is the actual usage of each application made by each user. ? We describe the implemented service for monitoring the users interaction. Users face the information overload problem when downloading applications in markets. This is mainly due to (i) the increasing unmanageable number of applications and (ii) the lack of an accurate and fine-grained categorization of the applications in the markets. To address this issue, we present an integrated solution which recommends to the users applications by considering a big amount of information: that is, according to their previously consumed applications, use pattern, tags used to annotate resources and history of ratings. We focus this paper on the service for monitoring users' interaction.

Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment *

Abstract: Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information-otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human-machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human-machine system. The EEG data from this experiment are available for public download at www.phypa.org.

A social media platform in higher education

Abstract: This paper reports on the successful use of Graasp, a social media platform, by university students for their collaborative work. Graasp features a number of innovations, such as administrator-free creation of collaborative spaces, a context-aware recommendation and privacy management. In the context of a EU-funded project involving large test beds, we have been able to extend this platform with lightweight tools (widgets) aimed for learning and competence development and to validate its usefulness in a collaborative learning context.

Beyond indoor presence monitoring with simple sensors

Abstract: To have buildings that are able to adapt to the user needs and at the same time to operate efficiently, it is essential to know the activity the people are performing. Presence sensors, which are widely deployed in modern buildings, attempt to regulate lighting to the presence of people in indoor spaces. Though, much more in terms of comfort and energy efficiency can be achieved if more detailed information on the activity of the users is detected. In this paper, we provide an initial investigation on detecting indoor activities by using simple sensors (infrared, pressure and acoustic) deliberately avoiding the use of rich sensors such as cameras. The sensors are low-cost, wireless, and retrofittable in existing structures. Our prototype is able to recognize five activities (working at a desk with or without a PC, having a meeting, and the presence/absence in the office) with accuracy of almost 95%, while unaffecting user’s behavior and comfort.

Intuitive Security Policy Configuration in Mobile Devices Using Context Profiling

Abstract: Configuring access control policies in mobile devices can be quite tedious and unintuitive for users. Software designers attempt to address this problem by setting up default policy configurations. But such global defaults may not be sensible for all users. Modern smart phones are capable of sensing a variety of information about the surrounding environment like Bluetooth devices, WiFi access points, temperature, ambient light, sound and location coordinates. We conjecture that profiling this type of contextual information can be used to infer the familiarity and safety of a context and aid in access control decisions. We propose a context profiling framework and describe device locking as an example application where the locking timeout and unlocking method are dynamically decided based on the perceived safety of current context. We report on using datasets from a large scale smart phone data collection campaign to select parameters for the context profiling framework. We also describe a prototype implementation on a smart phone platform. More generally, we hope that our example design and implementation spurs further research on the notion of using context profiling towards automating security policy decisions and identify other applications.

Throughout: Art and Culture Emerging with Ubiquitous Computing

Abstract: Ubiquitous computing and our cultural life promise to become completely interwoven: technical currents feed into our screen culture of digital television, video, home computers, movies, and high-resolution advertising displays. Technology has become at once larger and smaller, mobile and ambient. In Throughout, leading writers on new media--including Jay David Bolter, Mark Hansen, N. Katherine Hayles, and Lev Manovich--take on the crucial challenges that ubiquitous and pervasive computing pose for cultural theory and criticism. The thirty-foure contributing researchers consider the visual sense and sensations of living with a ubicomp culture; electronic sounds from the uncanny to the unremarkable; the effects of ubicomp on communication, including mobility, transmateriality, and infinite availability; general trends and concrete specificities of interaction designs; the affectivity in ubicomp experiences, including performances; context awareness; and claims on the "real" in the use of such terms as "augmented reality" and "mixed reality."

Context-Aware Computing: Beyond Search and Location-Based Services

Abstract: The holy grail of search is intent; of browsing, scent; and of classification, meaning. Each of these pillar technologies of the information age therefore depends critically on extracting and mining human context. Context awareness is the discipline that brings together technologies for extracting data about the larger human context and reasoning with that data at scale. It seeks to make the benefits of contextualization as pervasive in our lives as technology itself.

RFID-driven situation awareness on TangiSense, a table interacting with tangible objects

Abstract: Interactive tables are increasingly present not only in a research context but also in everyday life. One challenge still has to be taken up concerning interaction with several people around an interactive table. The framework of our research relates to an interactive table named TangiSense, which is equipped with RFID technology. The interaction is no longer the result of touching the table, but comes from handling tangible objects placed on the table. The use is closer to the natural use of a table. This technology makes it possible to identify the objects, which can be coupled with the users. Starting from this principle, the article proposes the use of RFID tags to collect the elements of context awareness in order to adapt the workspaces to the various possible situations around a table (work alone or with several users, on a common or individual space). An algorithm of space division is proposed, and evaluations are carried out in order to assess its global contribution. The article ends with a conclusion and some prospects.

iConAwa - An intelligent context-aware system

Abstract: Highlights? iConAwa is a context-aware and multiagent system. ? Context-aware systems are suitable for agent-based development. ? Context and points of interest are modelled in an extensible way by the developed ontology models. ? Context reasoning provides derivation of high level context from low level context. ? Context reasoning is decoupled from the source code by rule-based reasoning. Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people's interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.

Mobile and ubiquitous systems: computing, networking, and services

Abstract: This book constitutes the thoroughly refereed post-conference proceedings of the 8th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2011, held in Copenhagen, Denmark, in December 2011. The 34 revised full papers presented were carefully reviewed and selected from numerous submissions. They cover a wide range of topics ranging from localization and tracking, search and discovery, classification and profiling, context awareness and architecture, location and activity recognition as well as a best paper session, an industry track, and poster and demo papers.

A constraint-based approach for proactive, context-aware human support

Abstract: In this article we address the problem of realizing a service-providing reasoning infrastructure for pro-active human assistance in intelligent environments. We propose SAM, an architecture which leverages temporal knowledge represented as relations in Allen's interval algebra and constraint-based temporal planning techniques. SAM provides two key capabilities for contextualized service provision: human activity recognition and planning for controlling pervasive actuation devices. While drawing inspiration from several state-of-the-art approaches, SAM provides a unique feature which has thus far not been addressed in the literature, namely the seamless integration of these two key capabilities. It does so by leveraging a constraint-based reasoning paradigm whereby both requirements for recognition and for planning/execution are represented as constraints and reasoned upon continuously. This work is partially supported by NovaMedTech project “Angen” (EU regional funds) and by the Swedish Knowledge Foundation (KK Stiftelsen).

Framework for intelligent service adaptation to user's context in next generation networks

Abstract: Context-aware applications aim at providing personalized services to end users. Sensors and context sources are able to provide enormous amounts of valuable information about individuals that can be used to drive the behavior of services and applications, and adapt them to the specific conditions and preferences of each user. Thanks to advances in mobility, convergence and integration, increasingly larger amounts of these data are available in the Internet. However, this context information is usually fragmented, and traditionally applications have had to take care of context management themselves. This work presents a solution for a converged context management framework and how it can be employed in a future Internet to integrate data from all context sources and serve it to client applications in a seamless and transparent manner. This framework takes advantage of the intelligent and convergent features of next-generation networks, allowing seamless integration, monitoring, and control of heterogeneous sensors and devices under a single context-aware service layer. This layer is centered on a context intelligence module, capable of combining clustering algorithms and semantics to learn from user usage history and take advantage of that information to infer missing or high-level context data.