Refining visualization reference model for context information

Context-awareness can be used to decrease the need for interaction with a mobile device. This is increasingly important since the functionality of mobile devices and personal digital assistants gets more and more complex while the input and output capabilities remain restricted. An important aspect of context-awareness is to present the current context to the user. We propose a model for visualizing contextual information on the mobile terminal screen. The model is a refinement of a well-known visualization reference model; it takes into account the specific characteristics of mobile use and context information. We present the design of the model in detail, and discuss its applicability for a variety of contexts and tasks by providing a full-fledged use case.     

OMUS: an optimized multimedia service for the home environment

Media content in home environments is often scattered across multiple devices in the home network. As both the available multimedia devices in the home (e.g., smartphones, tablets, laptops, game consoles, etc.) and the available content (video and audio) is increasing, interconnecting desired content with available devices is becoming harder and home users are experiencing difficulties in selecting interesting content for their current context. In this paper, we start with an analysis of the home environment by means of a user study. Information handling problems are identified and requirements for a home information system formulated. To meet these requirements we propose the OMUS home information system which includes an optimized content aggregation framework, a hybrid group-based contextual recommender system, and an overall web-based user interface making both content and recommendations available for all devices across the home network. For the group recommendations we introduced distinct weights for each user and showed that by varying the weights, the coverage (i.e., items that can be returned by the recommender) considerably increases. Also the addition of genre filter functionality was proven to further boost the coverage. The OMUS system was evaluated by means of focus groups and by qualitative and quantitative performance assessment of individual parts of the system. The modularity of internal components and limited imposed hardware requirements implies flexibility as to how the OMUS system can be deployed (ranging from e.g., embedded in hardware devices or more software services based).     

Dynamic Service Composition in Pervasive Computing

Service-oriented architectures (SOAs) promise to provide transparency to resource access by exposing the resources available as services. SOAs have been employed within pervasive computing systems to provide essential support to user tasks by creating services representing the available resources. The mechanism of combining two or more basic services into a possibly complex service is known as service composition. Existing solutions to service composition employ a template-matching approach, where the user needs are expressed as a request template, and through composition, a system would identify services to populate the entities within the request template. However, with the dynamism involved in pervasive environments, the user needs have to be met by exploiting available resources, even when an exact match does not exist. In this paper, we present a novel service composition mechanism for pervasive computing. We employ the service-oriented middleware platform called pervasive information communities organization (PICO) to model and represent resources as services. The proposed service composition mechanism models services as directed attributed graphs, maintains a repository of service graphs, and dynamically combines multiple basic services into complex services. Further, we present a hierarchical overlay structure created among the devices to exploit the resource unevenness, resulting in the capability of providing essential service-related support to resource-poor devices. Results of extensive simulation studies are presented to illustrate the suitability of the proposed mechanism in meeting the challenges of pervasive computing user mobility, heterogeneity, and the uncertain nature of involved resources.     

Design and implementation of a smart system for personalization and accurate selection of mobile services

Wireless mobile services are computing applications that run on handheld wireless devices. Such applications must work within the daunting constraints of the devices, which include memory, processing power, input capabilities, and size of display. It is therefore important that mobile services take into account the user’s context, optimize resource usage, and minimize input effort imposed on the user. In this paper, we present the design and implementation of a smart agent-enabled system for personalizing wireless mobile services and advertisements for Java 2 Micro Edition (J2ME) or Java ME, and Wireless Application Protocol (WAP) enabled devices. We use software agents for context filtering because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment, and the Composite Capability/Preference Profiles (CC/PP) standard for defining profiles for user preferences and device capabilities. The system incorporates the use of artificial neural networks to adaptively and iteratively learn to select the best available service based on contextual information. The system is evaluated using practical operating scenarios, as well as empirical data and results show an 87% success rate in the selection of the best available service.     

A fast and scalable approach for IoT service selection based on a physical service model

Information Systems (ISs) have become one of the crucial tools for various organizations in managing and coordinating business processes. Now we are entering the era of the Internet of Things (IoT). IoT is a paradigm in which real-world physical things can be connected to the Internet and provide services through the computing devices attached. The IoT infrastructure is starting to be integrated with ISs thereby diminishing the boundaries between the physical world and the business IT systems. With the development of IoT technologies, the number of connected things and their available physical services are increasing rapidly. Thus, selecting an appropriate service that satisfies a user’s requirements from such services becomes a time-consuming challenge. To address this issue, we propose a Physical Service Model (PSM) as a common conceptual model to describe heterogeneous IoT physical services. PSM contains three core concepts (device, resource, and service) and specifies their relationships. Based on the proposed PSM, we define three types of Quality of Service (QoS) attributes and rate candidate services according to user requirements. To dynamically rate QoS values and select an appropriate physical service, we propose a Physical Service Selection (PSS) method that takes a user preference and an absolute dominance relationship among physical services into account. Finally, experiments are conducted to evaluate the performance of the proposed method.     

Bringing context-aware access to the web through spoken interaction

The web has become the largest repository of multimedia information and its convergence with telecommunications is now bringing the benefits of web technology to hand-held devices. To optimize data access using these devices and provide services which meet the user needs through intelligent information retrieval, the system must sense and interpret the user environment and the communication context. In addition, natural spoken conversation with handheld devices makes possible the use of these applications in environments in which the use of GUI interfaces is not effective, provides a more natural human-computer interaction, and facilitates access to the web for people with visual or motor disabilities, allowing their integration and the elimination of barriers to Internet access. In this paper, we present an architecture for the design of context-aware systems that use speech to access web services. Our contribution focuses specifically on the use of context information to improve the effectiveness of providing web services by using a spoken dialog system for the user-system interaction. We also describe an application of our proposal to develop a context-aware railway information system, and provide a detailed evaluation of the influence of the context information in the quality of the services that are supplied.     

Agent societies and social networks for ubiquitous computing

In ubiquitous computing environments, providing appropriate services and information to users at the right place in the right way is challenging for many reasons: different user interests, heterogeneous devices and services, dynamic networks, information overload, or differing privacy levels, for example. Agent technology is a paradigm expected to play an increasing role in complex computing environments, and due to the increasing popularity of social networking services, we expect to see the convergence of agent and social web technologies. The goal of this theme issue is to bring together state-of-the-art research contributions that examine the convergence of agent technologies and social networks for ubiquitous computing.     

Inferring contextual preferences using deep encoder-decoder learners

ABSTRACT

Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Augmenting Node-Link Diagrams with Topographic Attribute Maps

We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.     

Situated Analytics: Demonstrating immersive analytical tools with Augmented Reality

This paper introduces the use of Augmented Reality as an immersive analytical tool in the physical world. We present Situated Analytics, a novel combination of real-time interaction and visualization techniques that allows exploration and analysis of information about objects in the user's physical environment. Situated Analytics presents both situated and abstract summary and contextual information to a user. We conducted a user study to evaluate its use in three shopping analytics tasks, comparing the use of a Situated Analytics prototype with manual analysis. The results showed that users preferred the Situated Analytics prototype over the manual method, and that tasks were performed more quickly and accurately using the prototype.     

Protecting query privacy in location-based services

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts—user profiles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users’ query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users’ query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users’ privacy requirements expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage.     

A City Metaphor to Support Navigation in Complex Information Spaces

A major problem in modern information systems is to locate information and to re-find information one has seen before. Systems like the World Wide Web are heavily interlinked, but do not show structures that help users to navigate the information it contains. The use of appropriate navigation metaphors can help to make the structure of modern information systems easier to understand and therefore easier to use.We propose a conceptual user interface metaphor based on the structure of a city. Cities are very complex spatial environments and people know how to get information, how to reach certain locations in a city, and how to make use of the available infrastructure, etc. Cities provide a rich set of navigational infrastructure that lends itself to creating sub-metaphors for navigational tools. A city metaphor makes this existing knowledge about a structured environment available to the user of a computerized information system.We first focus on several properties necessary for future user interfaces (or user interface metaphors) that will distinguish them from current systems, like the richness of information or the use of visualizations to show the structure of information spaces. We also describe the strengths and problems of spatial user-interface metaphors. Then we present the structure of the information city metaphor, its structuring and navigation metaphors and what we see as its main advantages and problems. We further outline a few scenarios of how an Information City might work. Finally, we compare implementing this metaphor using either a textual or a graphical virtual environment or a combination.     

A statistical recommendation model of mobile services based on contextual evidences

Mobile devices are undergoing great advances in recent years allowing users to access an increasing number of services or personalized applications that can help them select the best restaurant, locate certain shops, choose the best way home or rent the best film. However this great quantity of services does not require the user to find and select those services needed for each specific situation. The classical approaches link some preferences to certain services, include the recommendations given by other users or even include certain fixed rules in order to choose the most appropriate services. However, since these methods assume that user needs can be modelled by fixed rules or preferences, they fail when modelling different users or makes them difficult to train. In this paper we propose a new algorithm that learns from the user’s actions in different contextual situations, which allows to properly infer the most appropriate recommendations for a user in a specific contextual situation. This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies.     

RETRACTED ARTICLE: Simulator considering modeling and performance evaluation for high-performance computing of collaborative-based mobile cloud infrastructure

In recent years, as IT technology has progressed, mobile devices have been created that enable various manual tasks to be automated and portable. A variety of mobile devices has computing, storage, and Internet capabilities and can handle many tasks. When miniaturized mobile devices perform tasks that require a large amount of computing resources due to limited computing and storage, there is a delay in operation and a non-operation state. Therefore, collaborative-based mobile cloud infrastructure (MCI) research is being conducted to provide computing services composed of mobile devices. Computation off-loading studies have been conducted for MCI’s high-performance computing, but it is difficult to build various mobile infrastructures and verify algorithm performance. In addition, performance verification is performed in a predetermined MCI environment or is carried out through small-scale test equipment. This causes waste of time, cost, and manpower for constructing the environment. Various studies have been conducted for this purpose, but there is a difficulty in performance verification and analysis since only the results are displayed or outputted in text form. In this paper, we propose a mobile cloud infrastructure simulator (MCIS) for computing off-loading, resource management, mobile deployment, and mobile information for MCI. MCIS enables user tasks, resource allocation methods, and various mobile device performance settings. In addition, visualization of the operating state makes it easy to analyze the performance of the user, and it is possible to grasp the problems that occur during operation.

     

面向用户生成内容的创意思维知识服务研究

为提高用户研究效率和创意知识获取水平,解决概念设计前期的信息采集周期长、用户需求挖掘不够、创意思维受限等问题,构建了面向用户生成内容(User-Generated Content,UGC)的创意思维知识服务研究模型.该模型以UGC为数据分析对象,基于文本分析与可视化技术,实现了关键词之间语境内容关联、情感体验度量与关联...     

Visual Analysis of Large Graphs: State‐of‐the‐Art and Future Research Challenges

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time‐varying graphs. Also, in accordance with ever growing amounts of graph‐structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State‐of‐the‐Art Report, we survey available techniques for the visual analysis of large graphs. Our review first considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.     

U-HMS: hybrid system for secure intelligent multimedia data services in Ubi-Home

Requirements for are rapidly increasing, such as multimedia services, automatic execution of intelligent services without manual commands from user, user-friendliness, transparency of use, and interoperability of multimedia data among incompatible devices. In addition, in order to provide secure multimedia data services, multimedia data protection and management, authentication, and authorization management methods that fit the purpose of the above demands are expected to be required. In this paper, ubiquitous-hybrid multimedia system for secure and intelligent multimedia data services in Ubi-Home is proposed. In addition, an authentication method suitable for the Ubi-Home is suggested. The proposed system provides intelligent services by reflecting context information (user preference, user location, device status information) through sensor network modules, providing transparent and secure multimedia service.     

The streams of our lives: visualizing listening histories in context

The choices we take when listening to music are expressions of our personal taste and character. Storing and accessing our listening histories is trivial due to services like Last.fm, but learning from them and understanding them is not. Existing solutions operate at a very abstract level and only produce statistics. By applying techniques from information visualization to this problem, we were able to provide average people with a detailed and powerful tool for accessing their own musical past. LastHistory is an interactive visualization for displaying music listening histories, along with contextual information from personal photos and calendar entries. Its two main user tasks are (1) analysis, with an emphasis on temporal patterns and hypotheses related to musical genre and sequences, and (2) reminiscing, where listening histories and context represent part of one's past. In this design study paper we give an overview of the field of music listening histories and explain their unique characteristics as a type of personal data. We then describe the design rationale, data and view transformations of LastHistory and present the results from both a lab- and a large-scale online study. We also put listening histories in contrast to other lifelogging data. The resonant and enthusiastic feedback that we received from average users shows a need for making their personal data accessible. We hope to stimulate such developments through this research.     

Explanation-aware service selection: rationale and reputation

A collection of 1 billion¹ publicly available web services can form an internet-scale infrastructure for building diverse applications. For a given application, selection of services and service providers from this collection becomes important and reputation is recognized as a key factor for this purpose. However, current reputation systems are limited in their ability to exchange reputation information between heterogeneous systems. To facilitate meaningful exchange and reuse of reputation information and for the overall determination of reputation, we identify the need to infer and explicate rationale for ratings. We present our knowledge-based approach to inferring and explicating rationale for ratings. We show that this approach facilitates detection of deception and collusion, user preference elicitation, explication of rationale behind user ratings and generation of personalized service recommendations.