We propose a general scheme for quality-controlled labeling of large-scale data using multiple labels from the crowd and a “few” ground truth labels from an expert of the field. Expert-labeled instances are used to assign weights to the expertise of each crowd labeler and to the difficulty of each instance. Ground truth labels for all instances are then approximated through those weights and the crowd labels. We argue that injecting a little expertise in the labeling process, will significantly improve the accuracy of the labeling task. Our empirical evaluation demonstrates that our methodology is efficient and effective as it gives better quality labels than majority voting and other state-of-the-art methods even in the presence of a large proportion of low-quality labelers in the crowd.

We present Kinect@Home, aimed at collecting a vast RGB-D dataset from real everyday living spaces. This dataset is planned to be the largest real world image col- lection of everyday environments to date, making use of the availability of a widely adopted robotics sensor which is also in the homes of millions of users, the Mi- crosoft Kinect camera.

The current era of internet-facilitated bigger data, better tools, and collective intelligence community computing is accelerating advances in many areas ranging from artificial intelligence to knowledge generation to public health. In the health sector, data volumes are growing with genomic, phenotypic, microbiomic, metabolomic, self-tracking, and other data streams. Simultaneously, tools are proliferating to allow individuals and groups to make sense of these data in a participatory manner through personal health tracking devices, mobile health applications, and personal electronic medical records. Health community computing models are emerging to support individual activity and mass collaboration through health social networks and crowdsourced health research studies. Participatory health efforts portend important benefits based on both size and speed. Studies can be carried out in cohorts of thousands instead of hundreds, and it could be possible to apply findings from newly-published studies with near-immediate speed. One operator of interventional crowdsourced health research studies, DIYgenomics, has several crowdsourced health research studies in open enrollment as of January 2012 in the areas of vitamin deficiency, aging, mental performance, and epistemology. The farther future of intelligent health community computing could include personal health dashboards, continuous personal health information climates, personal virtual coaches (e.g.; Siri 2.0), and an efficient health frontier of dynamic personalized health recommendations and action-taking.

Functionality makes APIs unique and therefore helps humans and machines decide what service they need. However, if two APIs offer similar functionality, quality attributes such as performance and ease-of-use might become a decisive factor. Several of these quality attributes are inherently subjective, and hence exist within a social context. These social parameters should be taken into account when creating personalized mashups and service compositions. The Web API description format RESTdesc already captures functionality in an elegant way, so in this paper we will demonstrate how it can be extended to include social parameters. We indicate the role these parameters can play in generating functional compositions that fulfill specified quality attributes. Finally, we show how descriptions can be personalized by exploring a user’s social graph. This ultimately leads to a more focused, on-demand use of Web APIs, driven by functionality and social parameters.

The Web is a giant repository of resources (Service and content), where Discovery and Recommendation systems are used to deliver the best ranked list of relevant web resources that meet user requirements. Nowadays, these systems are based on the simulation and automation of the user search criteria, considering the relation between consumption trends and the different kinds of users’ relationships with their virtual and physical environment, based on the information from the Social Web and mobile device sensors among others. These systems are executed once an explicit query of the user has been received; however, there are resources that are useful in specific situations, where these resources have high probability to be consumed, but, due to absence of a query they are not recommended to the users. In this regard, the question is: how to make a successful Web Resource Recommendation without the user query? In order to answer the question, this research proposal presents a novel approach to Recommend Web Resources based on Dynamic Prediction of User Consumption on the Social Web, which emulates the user behavior, the resource dynamism and the context opportunities, in real time, catching the best situations to make an asynchronous (unexpected by the user) recommendation of a useful Resources; and boost Web Resources consumption.

In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

The concept of trust in web services describes the degree of belief that a client or a group of clients have over services functioning satisfactorily and providing the expected results. As services are usually invoked in composition with other services, judging on their trustworthiness gets more complicated, yet computing their trustworthy becomes a desired goal. Existing work only take the trust of each individual service into account, regardless of the context of the composition. They also do not use the data gained from other clients for selecting the most trustful composition and preparing for possible service failures. In our work we first introduce the concept of Combination Reputation, which reflects the commonness and popularity of invoaction of a pair or group of services among other clients. By interpreting the trust and reputation values as subjective probability, we define the Component Trust of the services in the composition, which reflects the degree of belief the client has over components of services performing satisfactorily. We model the web service composition as a Bayesian network and integrate the above trust values into the network and show how to compute the global trust of the composition.

Existing personalisation approaches, such as collaborative filtering or content based recommendations, are highly dependent on the domain and/or the source of the data. Therefore, there is a need for more accurate means to capture and model the interests of the user across domains, and to interlink them in a semantically-enhanced interest graph. We propose a new approach for multi-source, cross-genre recommendations that can exploit the heterogeneous nature of user profile data, which has been aggregated from multiple personalised web services, such as blogs, wikis and microblogs. Our approach is based on the Spreading Activation model that exploits intrinsic links between entities across a number of data sources. The proposed method is highly customizable and applicable both to generic and specific recommendation scenarios and use cases. With the growing number of Social Web applications in the enterprise (blogs, wikis, micro blogging, etc.), it becomes difficult for knowledge workers to avoid content overload and to quickly identify relevant people, communities and information. We demonstrate the application of our approach in an industrial use case that involves recommendation of social semantic data across multiple services in a distributed collaborative environment.

Service composition, which achieves the goal of value-added services, has been considered as the core technique of Service-oriented Computing (SOC). To cope with the challenge of ever-increasing number of web services, graph-based web service network has emerged as a potential solution to the state of art SOC. In such a way, composite services are constructed by applying searching algorithms to the built graph, and proved to achieve outstanding performance in complexity. However, web service network suffers two crucial disadvantages: poor connectivity and negative links, and both of them have crucial negative impact on service composition. To cope with the problems, we propose two methods in this paper. Firstly, leveraging social network analysis, we focus on enriching web service network by adding valuable services, which will play positive roles in solving poor connective problem. Secondly, we show a serious status that numerous negative links contained in the underlying networks, and then we propose to identify and remove the negative links based on users’ historical composite services.

Recently, online vendors and providers manage review systems as a mechanism to advertise their services and goods over the Web. In making their choice, clients can rely on feedback expressing the degree of satisfaction of past users with respect to such services and goods. This set of feedback, or reviews, may be filtered by categories of users, they may be affected by multiple factors, and they are elaborated in order to obtain an overall score, representing a global indicator aimed at summarising the level of quality of that service. In this paper, we concentrate on multi-factor review,~\ie a review whose value is computed assembling the scores given to a set of parameters that may affect the quality level of a service. Our interest is evaluating the relevance, or dominance, of some parameter with respect to the others. We advocate the use of the Analytic Hierarchy Process, a well-known technique born in the area of multi-criteria decision making, to derive the priorities to assign to the scores of the single parameters. We illustrate the proposal on the example of hotel reviews.