Towards a semantic infrastructure for context-aware e-learning

Architectural supports, e.g., user context processing and learning content management are essential for facilitating the development and proliferation of context-aware e-learning services. In this paper, we propose a context-aware e-learning infrastructure called Semantic Learning Space. It leverages the Semantic Web technologies to support semantic knowledge representation, systematic context management, interoperable content integration, expressive knowledge query, and adaptive content recommendation. The functionality encapsulated in the infrastructure handles the common, time-consuming and low-level details in learning context processing and content management. The architectural design and enabling technologies are described in detail. Finally, the prototype implementation and preliminary experimental results are presented.     

A context-aware middleware for real-time semantic enrichment of distributed multimedia metadata

This paper investigates the problem of the real-time integration and processing of multimedia metadata collected by a distributed sensor network. The discussed practical problem is the efficiency of the technologies used in creating a Knowledge Base in real-time. Specifically, an approach is proposed for the real-time, rule-based semantic enrichment of lower level context features with higher-level semantics. The distinguishing characteristic is the provision of an intelligent middleware-based architecture on which low level components such as sensors, feature extraction algorithms, data sources, and high level components such as application-specific ontologies can be plugged. Throughout the paper, Priamos, a middleware architecture based on Semantic Web technologies is presented, together with a stress-test of the system’s operation under two test case scenarios: A smart security surveillance application and a smart meeting room application. Performance measurements are conducted and corresponding results are exposed.     

Distributional semantic pre-filtering in context-aware recommender systems

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user’s ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.     

E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management

AI & SOCIETY - Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts....     

Towards the Model Driven Development of context-aware pervasive systems

In this work, we introduce a Model Driven Development method for developing context-aware pervasive systems. This method allows us to specify a context-aware pervasive system at a high level of abstraction by means of a set of models, which describes both the system functionality and the context information. From these models, an automated code generation strategy is applied. This strategy allows us to generate the system Java code that provides the system functionality and as well as an OWL specification that represents the context information and allows us to manage this information without additional burden. Furthermore, this specification is used by a reasoner at runtime to infer context knowledge that is not directly observable, and it is also used by machine learning algorithms to give support to the system adaptation according to the context information.     

Modular framework support for context-aware mobile cinema

In this paper we describe a novel approach for interactive cinema based on context-aware narration using handheld computers. The paper describes both the artistic approach and the ubiquitous computing framework developed to realize the scenario. This framework has been used in various projects, including the described video production course at the ISNM, where five interactive cinema concepts have been developed and shown during a public demonstration. In our approach, a new type of user experience has been established by placing the viewer inside the movie’s physical locations during playback. Moreover, the developed ubiquitous computing framework provides a foundation for future work in the area of ad-hoc, service-oriented Ubicomp scenarios.     

A survey on context-aware mobile visual recognition

The phenomenal growth of the usage of mobile devices (e.g., mobile phones and tablet PCs) opens up a new service, namely mobile visual recognition, which has been widely used in many areas, such as mobile shopping and augmented reality. The rich contextual information (e.g., location, time and direction information), easily acquired by the mobile devices, provides useful clues to facilitate mobile visual recognition, including speeding up the recognition time and improving the recognition performance. This survey focuses on recent advances in Context-Aware Mobile Visual Recognition (CAMVR) and reviews related work regarding to different contextual information, recognition methods, recognition types, and various application scenarios. Finally, we discuss future research directions in this field.     

Dealing with variability in context-aware mobile software

Mobile context-aware software pose a set of challenging requirements to developers as these applications exhibit novel features, such as handling varied sensing devices and dynamically adapting to the user’s context (e.g. his or her location), and evolve quickly according to technological advances.In this paper, we discuss how to handle variability both across different domains and during the evolution of a single application. We present a set of design structures for solving different problems related with mobility (such as location sensing, behaviour adaptation, etc.), together with the design rationale underlying them, and show how these sound micro-architectural constructs impact on variability. Our presentation is illustrated with case studies in different domains.     

基于蓝牙情境感知技术的移动交友系统设计

针对现阶段大龄青年交友难题,提出了基于蓝牙情境感知技术的交友服务设计方案,帮助用户随时随地交友。这种交友方式根据用户的预先设置,自动搜索周围符合条件的人,并征求用户是否与之取得联系。整个设计过程采取了以用户为中心的设计理念,结合用户需求与特点进行设计。通过与用户进行访谈,了解用户需求及建议,并以此为标准指导设计与开发,尤其在用户强调的隐私、安全等方面做了充分考虑。采用Java语言开发了系统原型用以体现设计理念,并证明了该技术的可行性。     

A context-aware multimedia framework toward personal social network services

People use various Internet-based services including social networks to conduct tasks of diversified categories such as informational, professional, educational, hobbies, health, recreational, academic, and news. Many people not only maintain association with a number of different services but also share information with their social ties in their daily life. However, a person only consumes a subset of services and needs to communicate with a subset of social ties at any given context. In this paper, we present a framework called SenseFace that leverages sensory data coming from one’s body sensor network and multimedia information contained within Internet-based services to recommend context-aware services and community of interest. We present the detailed design and implementation of the framework and share our preliminary test results.     

An application framework for mobile,context-aware trails

In this paper we describe the design, implementation and evaluation of a software framework that supports the development of mobile, context-aware trails-based applications. A trail is a contextually scheduled collection of activities and represents a generic model that can be used to satisfy the activity management requirements of a wide range of context-based time management applications. Trails overcome limitations with traditional time management techniques based on static to-do lists by dynamically reordering activities based on emergent context.     

Online role mining for context-aware mobile service recommendation

Finding and recommending suitable services for mobile devices are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches to recommend services, role discovery is still an ongoing research topic. Using role-based approaches, popular mobile services can be recommended to other members in the same role group in a context- dependent manner. This paper proposes several role mining algorithms, to suit different application requirements, that automatically group users according to their interests and habits dynamically. Most importantly, we propose an online role mining algorithm that can discover role patterns efficiently and incrementally. Finally, we present a complete, question-based framework that can efficiently perform role mining for context-aware service recommendation in a mobile environment—where a device may not be always connected to the server and/or scalability of the role mining algorithm running on the server is critical.     

Towards a programming framework for activity-oriented context-aware applications

Context-aware system is an emerging research area in recent years. Context plays an important role in these systems. In most existing work, context is treated as all relative elements in the environment of an application, and the scope of context is predefined by the developers during the development. However, it is difficult to analyze, specify, and organize everything in the environment accurately and completely; and even when it is possible, the developed applications are difficult to extend or modify as the requests for environment may change over time. In this paper, we focus on activity-oriented context-aware (AOCA) applications where the requests for environment are highly dependent on user activities, and propose a programming framework for developing AOCA applications. In particular, we first present a concept model for describing the notions of activity-oriented context. Next, based on the concept model, we describe the details of the programming framework as well as a development tool. Moreover, we provide a platform to support the runtime of AOCA applications, and demonstrate the advantages of our programming framework through experimental evaluations.     

A context-aware mobile application framework using audio watermarking

Multimedia Systems - In this paper, we propose a proximity-based indoor positioning system which is capable of monitoring mobile device user’s indoor locations where the commonly used GPS...     

A unified multimedia and semantic perspective for data retrieval in the semantic web

In recent years, the emerging diffusion of peer-to-peer networks is going beyond the single-domain paradigm like, for instance, the mono-thematic file sharing one (e.g. Napster for music). Peers are more and more heterogeneous data sources which need to share data with commercial, educational, and/or collaboration purposes, just to mention a few. Moreover, in current information processing applications data cannot be meaningfully searched by precise database queries that would return exact matches (e.g. when dealing with multimedia, proteomic, statistical data).     

A framework for context-aware self-adaptive mobile applications SPL

Mobile Applications are rapidly emerging as a convenient medium for using a variety of services. Over time and with the high penetration of smartphones in society, self-adaptation has become an essential capability required by mobile application users. In an ideal scenario, an application is required to adjust its behavior according to the current context of its use. This raises the challenge in mobile computing towards the design and development of applications that sense and react to contextual changes to provide a value-added user experience. In its general sense, context information can relate to the environment, the user, or the device status. In this paper, we propose a novel framework for building context aware and adaptive mobile applications. Based on feature modeling and Software Product Lines (SPL) concepts, this framework guides the modeling of adaptability at design time and supports context awareness and adaptability at runtime. In the core of the approach, is a feature meta-model that incorporates, in addition to SPL concepts, application feature priorities to drive the adaptability. A tool, based on that feature model, is presented to model the mobile application features and to derive the SPL members. A mobile framework, built on top of OSGI framework to dynamically adapt the application at runtime is also described.