Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp , a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

The latest evolution of wireless communications enables users to access rich Virtual Reality (VR) services via the Internet, including while on the move. However, providing a premium immersive experience for the massive number of concurrent users with various device configurations is a significant challenge due to the ultra-high data rate and ultra-low delay requirements of VR livecast services. This paper introduces an innovative m ulti-user cost- e fficient c rowd-assisted d elivery and c omputing (MEC-DC) framework, which leverages mobile edge computing and end-user resources to support high performance VR content delivery over 5G-and-beyond heterogeneous networks (5G-HetNets). The proposed MEC-DC framework is based on three main solutions. First is a novel b uffer- n adir-based m ulticast (BNM) mechanism for VR transmissions over 5G-HetNets. BNM ensures smooth and synchronized user viewing experiences by maximizing the average playback buffer-nadir of all participants with stochastic optimization. Second and third are practical distributed algorithms: the cost-efficient m ulticast- a ware t ranscoding o ffloading (MATO) and c rowd- a ssisted d elivery algorithm (CAD) which optimize jointly multicast delivery and video transcoding. The algorithms’ optimality and complexity were investigated. The proposed MATO-CAD solution was evaluated with real datasets, trace-driven numerical simulations, and prototype-based experiments. The trace-driven experimental results showed how the proposed solution provides 18% throughput improvement, the lowest delay, and the best playback freeze ratio in comparison with three other state-of-the-art solutions.

Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, it still faces two non-trivial challenges: (i) most existing KGET methods extract features by encoding the existing entity typing tuples, while ignoring rich relational knowledge. (ii) they typically treat each entity typing tuple in KGs independently, and thus inevitably fail to take account of the inherent and valuable neighborhood information surrounding a tuple. To address these challenges, we build a novel Heterogeneous Relational Graph (HRG), and propose a Multiplex Relational Graph Attention Networks (MRGAT) to learn on HRG, and then utilize a Connecting Embeddings model (ConnectE) to make entity type inference. Specifically, the framework contains three components. Firstly, to effectively integrate the entity typing tuples and entity relation triples in KGs, we construct a HRG that consists of three semantic subgraphs. Secondly, we employ MRGAT to learn embeddings on HRG. In MRGAT, each subgraph of HRG is fed to its corresponding model that is capable of capturing neighborhood information. Finally, given the learned embeddings, we make entity type prediction by ConnectE. Experimental results validate the superiority of our model against various state-of-the-art baselines.

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as “positive” together with a large volume of “unlabeled” samples that may contain both positive and negative samples. Building robust classifiers for the PU problem is very challenging, especially for complex data where the negative samples overwhelm and mislabeled samples or corrupted features exist. To address these three issues, we propose a robust learning framework that unifies area under the curve maximization (a robust metric for biased labels), outlier detection (for excluding wrong labels), and feature selection (for excluding corrupted features). The generalization error bounds are provided for the proposed model that give valuable insight into the theoretical performance of the method and lead to useful practical guidance, e.g., to train a model, we find that the included unlabeled samples are sufficient as long as the sample size is comparable to the number of positive samples in the training process. Empirical comparisons and two real-world applications on surgical site infection (SSI) and EEG seizure detection are also conducted to show the effectiveness of the proposed model.