A user-centric intelligent context-aware system for realizing internet-of-things environments

The Journal of Supercomputing - In the near future, an Internet-of-things (IoT) environment will be realized across ubiquitous terminals such as computers and smartphones, supporting connections...     

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.     

CAESAR: context-aware explanation based on supervised attention for service recommendations

Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.

     

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.     

General factorization framework for context-aware recommendations

Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a general factorization framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework’s potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments—performed on five real life, implicit feedback datasets—that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the factorization framework beyond context, like item metadata, social networks, session information, etc. Preliminary experiments show great potential of this capability.     

A fuzzy framework for efficient user-centric Web service selection

With the development of Web technologies and the increasing usage of Internet, more and more Web Services (WS) are deployed over Internet. Therefore, there will be a large number of candidate services for fulfilling a desired task. In the last decade, several WS selection approaches are proposed to cope with this challenge. In sharp contrast to the existing WS selection approaches that focus only on user-specified preferences, in this paper, we propose a flexible and effective WS selection framework, which gives users an adequate way to express their preferences using linguistic terms, and enhance the WS selection by leveraging their contexts and profiles. The satisfaction of the candidate WS is expressed by an objective score that takes into consideration no only the user-specified preferences, but also additional preferences extracted from both his/her context and profile using fuzzy inference rules, so as to improve the effectiveness of the selection. We then introduce an effective strategy that allows for priority between the two kinds of preferences, for ranking candidate services. Experimental evaluation on a real case study demonstrates the effectiveness of our proposed strategy.     

A pluggable middleware architecture for developing context-aware mobile applications

The proliferation of powerful smartphone devices provides a great opportunity for context-aware mobile applications becoming mainstream. However, we argue that conventional software development techniques suffer because of the added complexity required for collecting and managing context information. This paper presents a component-based middleware architecture which facilitates the development and deployment of context-aware applications via reusable components. The main contribution of this approach is the combination of a development methodology with the middleware architecture, which together bring significant value to developers of context-aware applications. Further contributions include the following: The methodology utilizes separation of concerns, thus decreasing the developmental cost and improving the productivity. The design and implementation of context-aware applications are also eased via the use of reusable components, called context plug-ins. Finally, the middleware architecture facilitates the deployment and management of the context plug-ins in a resource-aware manner. The proposed methodology and middleware architecture are evaluated both quantitatively and qualitatively.     

A new model for context-aware transactions in mobile services

With the ubiquity of handheld devices (such as smart phones and PDAs) and the availability of a wide range of mobile services (such as mobile banking, road traffic updates, and weather forecast), people can nowadays access information and conduct online transactions virtually anywhere and anytime. In such flexible, dynamic but less reliable environment, transaction management technology is believed to provide service reliability and data consistency. Indeed, in mobile and ubiquitous environments where devices as well as services can seamlessly join and leave the ubiquitous network; transaction management can be very helpful during the recovery of services from failure. Current transaction models and commit protocols do not take into account context information. However, in mobile environments, it is imperative to consider context information in the commit of a transaction—i.e., a transaction can be successfully completed if it meets the required context. In this paper, we propose a new model for context-aware transactions and their performance management in mobile environments. Unlike conventional transactions, context-aware transactions adapt to the required context. By context, we mean the service’s context as well as the users’ context that includes users’ needs and preferences. This paper designs and develops the proposed transaction model and evaluates its performance in terms of time and message complexities as well as transaction’s throughput.     

The DYNAMOS approach to support context-aware service provisioning in mobile environments

To efficiently make use of information and services available in ubiquitous environments, mobile users need novel means for locating relevant content, where relevance has a user-specific definition. In the DYNAMOS project, we have investigated a hybrid approach that enhances context-aware service provisioning with peer-to-peer social functionalities. We have designed and implemented a system platform and application prototype running on smart phones to support this novel conception of service provisioning. To assess the feasibility of our approach in a real-world scenario, we conducted field trials in which the research subject was a community of recreational boaters.     

Non-dominated differential context modeling for context-aware recommendations

Context plays an important role in the process of decision making. A user’s preferences on the items may vary from contexts to contexts, e.g., a user may prefer to watch a different type of the movies, if he or she is going to enjoy the movie with partner rather than with children. Context-aware recommender systems, therefore, were developed to adapt the recommendations to different contextual situations, such as time, location, companion, etc. Differential context modeling is a series of recommendation models which incorporate contextual hybrid filtering into the neighborhood based collaborative filtering approaches. In this paper, we propose to enhance differential context modeling by utilizing a non-dominated user neighborhood. The notion of dominance relation was originally proposed in multi-objective optimization, and it was reused to definite non-dominated user neighborhood in collaborative filtering recently. These non-dominated user neighbors refer to the neighbors that dominate others from different perspectives of the user similarities, such as the user-user similarities based on ratings, demographic information, social relationships, and so forth. In this paper, we propose to identify the non-dominated user neighborhood by exploiting user-user similarities over multiple contextual preferences. Our experimental results can demonstrate the effectiveness of the proposed approaches in comparison with popular context-aware collaborative filtering models over five real-world contextual rating data sets.

     

ContextPhone: a prototyping platform for context-aware mobile applications

Smart phones are a particularly tempting platform for building context-aware applications because they're programmable and often use well-known operating systems. There's a gap, however, between the operating systems' functionality and the features that application developers need. To fill this gap, we've designed and developed ContextPhone, a software platform consisting of four interconnected modules provided as a set of open source C++ libraries and source code components. ContextPhone runs on off-the-shelf mobile phones using Symbian OS and the Nokia Series 60 Smartphone platform. ContextPhone was developed using an iterative, human-centered design strategy. It thus helps developers more easily create applications that integrate into both existing technologies and users' everyday lives.     

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.     

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 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 Galois lattice approach to a context-aware privacy negotiation service

Online privacy has consistently been a major concern for customers that has grown commensurately with the growth of e-services. Service providers have responded by making their privacy policies clearer for customers; however, most providers use legacy systems that are unable to actually change and adapt to user’s concerns, which can lead to fewer customers using the system. Hence, the purpose of this paper is to propose a context-aware privacy policy negotiation service. To do so, we adopt a Galois lattice theory to generate policy concepts embedded in e-services. Based on the Galois lattice, we develop a process for generating privacy policy rules. To show the feasibility of the ideas proposed in this paper, we perform a simulation test with two different online auction sites as an illustrative case in terms of two metrics: the number of rules generated and success throughput. Desirable features in applying the Galois lattice approach to context-aware privacy policy negotiation service are discussed.     

Performance evaluation of a mobile agent-based platform for ubiquitous service provision

The Virtual Home Environment (VHE) is a very important mechanism in the upcoming mobile telecommunications infrastructure, providing a ubiquitous approach in the delivery of services. The universality of systems like UMTS and WLANs increases the need for efficient introduction of VHE. In this paper, we discuss the adoption of Mobile Agents (MA) for coping with the VHE technical challenges. We present the detailed design of an integrated, open and extensible architecture, by taking into account a variety of use cases and technical alternatives (mobile devices, fixed terminals). Relevant implementations are presented to indicate the feasibility of the technical orientation discussed. Finally, we study and compare the performance of the proposed agent based framework with a conventional solution.     

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.     

Emotional index measurement method for context-aware service

Emotion can be key context information when providing individualized services. Although some physical sensors can acquire emotional state, installing them in all spaces would be too cost-prohibitive—aside from raising strong privacy concerns in exposing individuals’ physical conditions. These issues lead us to investigate other ways to estimate users’ emotions. In particular, we focus on estimating degrees of emotion from users’ writing and speech. In this paper, we propose an emotion estimation methodology for context-aware services. To do so, we developed a method for calculating the degree of emotion in emotional predicates, which consist of verbs and adverbs. To demonstrate the feasibility of the ideas proposed in this paper, we performed a survey and compared the users’ responses with degrees of emotion estimated by the proposed method.     

CASD: Management of a context-aware service directory

Traditional web service discovery is strongly related to the use of service directories. Especially in the case of mobile web services, where both service requestors and providers are mobile, the dynamics impose the need for directory-based discovery. Context plays an eminent role with mobility, as a filtering mechanism that enhances service discovery through the selection of the most appropriate service. However, current service directory specifications do not focus on mobility of services or context-awareness. In this paper, we propose CASD, a context-aware service directory, envisioned as a context-based index for services on top of any traditional service directory, and design algorithms for construction, search, update and merge of such directories. Furthermore, we describe the architecture of our system for context-aware service discovery, we present a prototype implementation and discuss the experimental results as well as the overall evaluation. The contribution of this work is the proposal for a novel, enhanced representation model for a context-aware service directory.