Abstract Knowledge graph embedding models are used to learn low‐dimensional representations of entities and relations in knowledge graphs. In this paper, we propose Multi‐RAttE, an attention‐based learning method for multiple relational knowledge graph embedding representation, which divides the information transfer in the knowledge graph into cross‐relational information transfer and relation‐specific information transfer, and divides the embedding of knowledge graph entities into structural embedding and multi‐relational embedding for joint learning. To objectively analyse the performance of the Multi‐RAttE model, we select two typical datasets and different representative baseline models for experimental evaluation on several tasks such as link prediction, multi‐relation prediction and node classification. The experimental results show that the Multi‐RAttE model improves 8% over the state‐of‐the‐art model Composition‐based Multi‐Relational Graph Convolutional Networks (CompGCN) in terms of Hits@1 metric on the link prediction task on FB15k‐237 dataset; on the multi‐relation prediction task, the accuracy improves by 1.8% and 3.7% in the auc metric and F1 metric, respectively. The experimental results have proved that the Multi‐RAttE model can effectively perform the representation of multiple relations.

Label distribution learning methods effectively address the label ambiguity problem and have achieved great success in image emotion analysis. However, these methods ignore structured and sparse information naturally contained in the annotations of emotions. For example, emotions can be grouped and ordered due to their polarities and degrees. Meanwhile, emotions have the character of intensity and are reflected in different levels of sparse annotations. Motivated by these observations, we present a convolutional neural network based framework called Structured and Sparse annotations for image emotion Distribution Learning (SSDL) to tackle two challenges. In order to utilize structured annotations, the Earth Mover’s Distance is employed to calculate the minimal cost required to transform one distribution to another for ordered emotions and emotion groups. Combined with Kullback-Leibler divergence, we design the loss to penalize the mispredictions according to the dissimilarities of same emotions and different emotions simultaneously. Moreover, in order to handle sparse annotations, sparse regularization based on emotional intensity is adopted. Through combined loss and sparse regularization, SSDL could effectively leverage structured and sparse annotations for predicting emotion distribution. Experiment results demonstrate that our proposed SSDL significantly outperforms the state-of-the-art methods.

We propose a low-complexity single carrier frequency-domain equalizer and diversity combining for cooperative systems with demodulate-and-forward relaying over frequency-selective channels. At the relays, we apply the conventional frequency-domain linear equalizers and compute the output signal to noise ratios, which are used to calculate the normalized correlation coefficient for deriving an equivalent source-to-relay-destination (S-R-D) channel. At the destination, we propose the joint equalization and diversity combining receiver for symbol detection by utilizing the equivalent S-R-D channel. Finally, we demonstrate the superiority of the proposed scheme over the straightforward solution that ignores the decisions errors at the intermediate relays through simulations.