Few-shot remote sensing image retrieval is devoted to add new retrieval categories with a small number of labeled samples, and simultaneously achieve favorable retrieval performance for new categories and keep the primary retrieval performance for the original categories as far as possible. Few-shot learning has received considerable attention, however its applications in image retrieval, especially for remote sensing image retrieval, are still very few. In this paper, we redefine the few-shot image retrieval problem formally and further propose a few-shot retrieval method under model-agnostic meta-learning (MAML) framework, combined with ResNet and GeM as feature extraction module. Moreover, the optimal mean average precision (mAP) is used as ranking loss for defining the loss function of learning model, and a histogram binning approximation of mAP, which is differential, is thus employed so that the whole few-shot retrieval model can be end-to-end trained. The retrieval effectiveness and efficiency of our method are verified on UC-Merced and AID data sets.

The cloud-assisted wireless body area network (WBAN) can further the development of the telemedicine, providing larger health data storage space and more powerful computing capabilities for telemedicine. Under the cloud-assisted WBAN architecture, users' health data is uploaded to a cloud server so that medical staffs can download it and analyze the users' health status. The sharing of health data between users and medical staffs provides users with better healthcare services. However, the sharing of health data introduces new privacy and security issues. To address these issues, a secure medical data sharing protocol is presented based on a new certificateless proxy re-encryption scheme without pairing. The security analysis shows that the protocol achieves the security requirements in cloud, and the performance analysis illustrates that it is more efficient than other schemes.

Modern solutions for implicit discourse relation recognition largely build universal models to classify all of the different types of discourse relations. In contrast to such learning models, we build our model from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank (PDTB) styled implicit discourse relations: Comparison, Contingency and Expansion. We find semantic characteristics of each relation type and two cohesion devices---topic continuity and attribution---work together to contribute such linguistic properties. We encode those properties as complex features and feed them into a NaiveBayes classifier, bettering baselines(including deep neural network ones) to achieve a new state-of-the-art performance level. Over a strong, feature-based baseline, our system outperforms one-versus-other binary classification by 4.83% for Comparison relation, 3.94% for Contingency and 2.22% for four-way classification.

The latest research progress of the theory of compressed sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data in WSNs. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of gathered data. Simulation results show that the proposed scheme improves the reconstruction quality compared to the existing ones.

Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.

In a directory ontology, only concepts and hypernym/hyponym relationships between them are defined. Directory ontologies are widely used in many real-world applications such as product catalogues and Web directories. The widely used heterogeneous directories also bring a big challenge for integration of them. Previously, little attentions have been paid attention to the problem in the research community. In this paper, we propose a path similarity based approach for directory ontology matching. We define the concept's local label and the concept's path label. Next, we propose a path similarity method, which combines the local label and path label information. Then, a top-down similarity flooding is utilized to improve the matching result. Our experimental results on the music genre ontologies show that the proposed method can achieve a precision of 78% and a recall of 66%, significantly outperforming the baseline method.

Determining transcriptional regulatory networks has been one of the most important goals in the field of functional genomics. Despite the recent advances in experimental techniques, complementary computational techniques have lagged behind. We introduce a novel computational methodology that uses DNA microarray data and known regulatory interactions to predict unknown regulatory interactions. Our method involves three steps: in the training stage, we utilize network component analysis (NCA) (Liao et al., 2003; Kao et al., 2004; Boscolo et al., 2005) to reconstruct the hidden activity profiles of transcriptional factors (TF); then we cluster TFs into functional modules according to the similarities of their reconstructed activity profiles; in the prediction stage, we infer additional TF-gene regulatory links by selecting TF profiles that best interpret genes expression profiles via a linear model. We applied the methodology to a gene expression dataset of bacterium Escherichia coli, whose partial TF-gene regulatory structure is obtained from RegulonDB (Salgado et al., 2004). Cross-validation results show that when the profiles of all TFs regulating a gene are reconstructed from NCA, we could identify 36% of the TF-gene interactions, and the prediction accuracy is 89%. And when the profiles of partial (50% or more) TFs regulating a gene can be reconstructed, we can identify 14% of the TF-gene interactions, and the accuracy rate is 69%. These represent some of the best known accuracy and coverage statistics reported in the literature so far