Crowdsourcing labeling systems provide an efficient way to generate multiple inaccurate labels for given observations. If the competence level or the ``reputation,'' which can be explained as the probabilities of annotating the right label, for each crowdsourcing annotators is equal and biased to annotate the right label, majority voting (MV) is the optimal decision rule for merging the multiple labels into a single reliable one. However, in practice, the competence levels of annotators employed by the crowdsourcing labeling systems are often diverse very much. In these cases, weighted MV is more preferred. The weights should be determined by the competence levels. However, since the annotators are anonymous and the ground-truth labels are usually unknown, it is hard to compute the competence levels of the annotators directly. In this paper, we propose to learn the weights for weighted MV by exploiting the expertise of annotators. Specifically, we model the domain knowledge of different annotators with different distributions and treat the crowdsourcing problem as a domain adaptation problem. The annotators provide labels to the source domains and the target domain is assumed to be associated with the ground-truth labels. The weights are obtained by matching the source domains with the target domain. Although the target-domain labels are unknown, we prove that they could be estimated under mild conditions. Both theoretical and empirical analyses verify the effectiveness of the proposed method. Large performance gains are shown for specific data sets.

In recent years, various collaborative QoS prediction methods have been put forward to coping with the demand for efficient quality-of-service (QoS) evaluation, by drawing lessons from the recommender systems. However, there still remain some challenging issues on this direction, as how to effectively exploit complex contexts to improve prediction accuracy, and how to realize collaborative QoS prediction of multiple attributes. Inspired by the principles of deep learning, we have proposed a universal deep neural model (DNM) for making multiple attributes QoS prediction with contexts. In this model, contextual features are mapped into a shared latent space to semantically characterize them in the embedding layer. The contextual features with their higher-order interactions are captured through the interaction layer and the perception layers. Multi-tasks prediction is realized by stacking task-specific perception layers on the shared neural layers. Armed with these, DNM provides a powerful framework to integrate with various contextual features to realize multi-attributes QoS prediction. Experimental results from a large-scale QoS-specific dataset demonstrate that DNM achieves superior prediction accuracy in term of mean absolute error (MAE) compared with the state-of-the-art collaborative QoS prediction techniques. Additionally, the DNM model has a good robustness and extensibility on exploiting heterogeneous contextual features.

As the number of Web APIs ever increases, choosing the appropriate APIs for mashup creations becomes more difficult. To tackle this problem, various methods have been proposed to recommend APIs to match requirements of mashups and achieved much success. However, there existed some challenges with feature fusion and utilization, textual requirement understanding, utilization of Mashup categories and compatibility evaluation. Therefore, we propose a neural framework (MTFM) based on multi-model fusion and multi-task learning for Mashup-oriented Web API recommendation. MTFM exploits a semantic component to generate representations of requirements and introduces a feature interaction component to model the feature interaction between mashups and Web APIs. Output features of both components are further fused to predict the candidate APIs, and this enables us to have both the advantages of content-based and collaborative filtering methods. We further introduce mashup category judgment as an auxiliary task, where both tasks are viewed as a multi-label learning problem and jointly optimized with multi-task learning. Also, we have extended MTFM to MTFM++ to take advantage of the metadata and quality features of APIs, and proposed a metric for compatibility evaluation. Experimental results on the ProgrammableWeb dataset show that our methods outperform most popular state-of-the-art methods.

Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data paradigms. Currently, it is necessary to extend the classical methods for learning BNs with respect to data-intensive computing or in cloud environments. In this paper, we propose a parallel and incremental approach for data-intensive learning of BNs from massive, distributed, and dynamically changing data by extending the classical scoring and search algorithm and using MapReduce. First, we adopt the minimum description length as the scoring metric and give the two-pass MapReduce-based algorithms for computing the required marginal probabilities and scoring the candidate graphical model from sample data. Then, we give the corresponding strategy for extending the classical hill-climbing algorithm to obtain the optimal structure, as well as that for storing a BN by <;key, value> pairs. Further, in view of the dynamic characteristics of the changing data, we give the concept of influence degree to measure the coincidence of the current BN with new data, and then propose the corresponding two-pass MapReduce-based algorithms for BNs incremental learning. Experimental results show the efficiency, scalability, and effectiveness of our methods.