This study proposes a novel solution for transferring the working environment of remotely operated vehicle (ROV) operators from the support ship at sea to land, including the establishment of a satellite-based communication link between the ocean and the land-based control center (LCC), which is used to transfer information efficiently. To alleviate the cognitive burden of latency on operators on land, a cross-domain underwater intervention hierarchical control architecture is designed to assist operators by introducing a shared control strategy. The effectiveness of the designed system and control strategy in realizing cross-domain underwater interventions are verified through field experiments.

Document-level relation extraction is an important task in natural language processing that involves identifying and classifying relations between entities mentioned in a document. Traditional approaches often focus on individual sentences or local context, overlooking the broader context of the entire document. In this paper, we propose an Evidence Sentence Augmented Sequence-to-Sequence method for Document-level Relation Extraction(called ESASS-DRE). Our method introduces evidence sentences into the sequence-to-sequence framework to improve document-level relation extraction. The approach consists of two main steps: evidence sentence selection and relation extraction. Firstly, we identify a set of evidence sentences that contain crucial information relevant to the target relation. These sentences are selected based on their importance and contextual relevance. Secondly, the selected evidence sentences are combined with the original document and used as input to the sequence-to-sequence model. These generated sequences are decoded into relation labels, indicating the type of relationship between the entities. By incorporating evidence sentences into the model, we provide additional context and relevant information, enabling the model to make more informed predictions. Experiments conducted on benchmark datasets demonstrate the effectiveness of our method. Compared to traditional approaches, our method achieves higher accuracy and robustness in document-level relation extraction tasks. The incorporation of evidence sentences allows the model to capture the broader context of the document, leading to improved performance. (e.g., by 2.96/3.64 Ign F1/F1 on DocRED).

Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV in terms of its LTV prediction and game whale detection effectiveness is further validated via extensive experiments on three industrial datasets.

Nowadays, customer lifetime value (LTV) plays an important role in mobile game advertising, since it can be beneficial to adjust ad bids and ensure that the games are promoted to the most valuable users. Some neural models are utilized for LTV prediction based on the rich user features. However, in the advertising scenario, due to the privacy settings or limited length of log retention, etc, most of existing approaches suffer from the missing feature problem. Moreover, only a small fraction of purchase behaviours can be observed. The label sparsity inevitably limits model expressiveness. To tackle the aforementioned challenges, we propose a feature missing-aware routing-and-fusion network (MarfNet) to reduce the effect of the missing features while training. Specifically, we calculate the missing states of raw features and feature interactions for each sample. Based on the missing states, two missing-aware layers are designed to route samples into different experts, thus each expert can focus on the real features of samples assigned to it. Finally we get the missing-aware representation by the weighted fusion of the experts. To alleviate the label sparsity, we further propose a batch-in dynamic discrimination enhanced (Bidden) loss weight mechanism, which can automatically assign greater loss weights to difficult samples in the training process. Both offline experiments and online A/B tests have validated the superiority of our proposed Bidden-MarfNet.

Data scarcity, e.g., labeled data being either unavailable or too expensive, is a perpetual challenge of recommendation systems. Cross-domain recommendation leverages the label information in the source domain to facilitate the task in the target domain. However, in many real-world cross-domain recommendation systems, the source domain and the target domain are sampled from different data distributions, which obstructs the cross-domain knowledge transfer. In this paper, we propose to specifically align the data distributions between the source domain and the target domain to alleviate imbalanced sample distribution and thus challenge the data scarcity issue in the target domain. Technically, our proposed approach builds an adversarial adaptation (AA) framework to adversarially train the target model together with a pre-trained source model. A domain discriminator plays the two-player minmax game with the target model and guides the target model to learn domain-invariant features that can be transferred across domains. At the same time, the target model is calibrated to learn domain-specific information of the target domain. With such a formulation, the target model not only learns domain-invariant features for knowledge transfer, but also preserves domain-specific information for target recommendation. We apply the proposed method to address the issues of insufficient data and imbalanced sample distribution in real-world Click-Through Rate (CTR)/Conversion Rate (CVR) predictions on a large-scale dataset. Specifically, we formulate our approach as a plug-and-play module to boost existing recommendation systems. Extensive experiments verify that the proposed method is able to significantly improve the prediction performance on the target domain. For instance, our method can boost PLE with a performance improvement of 13.88% in terms of Area Under Curve (AUC) compared with single-domain PLE.

How to decompose a large global matrix into many small local matrices has been recently researched a lot for matrix approximation. However, the distance computation in matrix decomposition is a challenging issue, as no prior knowledge about the most appropriate feature vectors and distance measures are available. In this paper, we propose a novel scheme for local matrix construction without involving distance computation. The basic idea is based on the application of convergence probabilities of graph random walk. At first, a user-item bipartite graph is constructed from the global matrix. After performing random walk on the bipartite graph, we select some user-item pairs as anchors. Then another random walk with restart is applied to construct the local matrix for each anchor. Finally, the global matrix approximation is obtained by averaging the prediction results of local matrices. Our experiments on the four real-world datasets show that the proposed solution outperforms the state-of-the-art schemes in terms of lower prediction errors and higher coverage ratios.

The field of computer vision is experiencing a great-leap-forward development today. This paper aims at providing a comprehensive survey of the recent progress on computer vision algorithms and their corresponding hardware implementations. In particular, the prominent achievements in computer vision tasks such as image classification, object detection and image segmentation brought by deep learning techniques are highlighted. On the other hand, review of techniques for implementing and optimizing deep-learning-based computer vision algorithms on GPU, FPGA and other new generations of hardware accelerators are presented to facilitate real-time and/or energy-efficient operations. Finally, several promising directions for future research are presented to motivate further development in the field.