Users leave many reviews while participating in network activities, and these have been proven to improve the performance of recommendation systems. However, most current works in the field of rating prediction only capture preference information …

Heterogeneous link prediction aims to reveal potential connections between two nodes in heterogeneous information networks. Most existing studies are based on meta-paths, but ignore the information contained in incomplete meta-paths. They simply …

In the last few years, image inpainting methods based on deep learning models had shown obvious advantages compared with existing traditional methods. The former can better generate visually reasonable image structure and texture information.

The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering …

Deep Hashing methods have been widely used for large-scale image retrieval due to its advantages in retrieval efficiency and accuracy. Recent methods cannot effectively capture the scale variation and complex distribution of image features in the …

Text semantic matching aims to determine whether two pieces of text point to the same semantic, which has been widely applied in clinical terminology standardization, recommendation systems, and other scenarios. Recently, many existing methods …

Action recognition is an active topic in video understanding, which aims to recognize human actions in videos. The critical step is to model the spatio-temporal information and extract key action clues. To this end, we propose a simple and …

Deep learning technology creates the condition for the optimization of the smart grid, and the big data analytical technique has the most efficient way to analyze and share the power load spatio-temporal data in the smart grid. Utilizing the …

Fusion operations are widely used in many hand-crafted convolutional neural network model to reduce parameter number and improve feature learning, however, most neural architecture search methods suffered from large search cost, usually used …

Educational video concept prediction is a challenging task in the online education system that aims to assign appropriate hierarchical concepts to the video. The key to this problem is to model and fuse the multimodal information of the video.

Attaining better sentence embeddings benefits a wide range of natural language processing tasks. SimCSE applied a simple contrastive learning framework to train BERT models and achieved excellent sentence embeddings. Based on SimCSE, this paper …

This work focuses on the operation of picking an object on a table with a mobile manipulator. We use deep reinforcement learning (DRL) to learn a positioning policy for the robot’s base by considering the reachability constraints of the arm. This …

Multimodality sentiment classification of social media attracts increasing attention, whose main purpose is to predict the sentiment of the target mentioned in the posts. Current research mainly focuses on integrating the multimodal data, but …

Since automatic visual semantic comprehension of video content is currently infeasible and unintelligent, key frames extracted from videos are inconsistent with human visual understanding. In this paper, a block-based self-motivated visual …

Detecting changes in data streams, with the data flowing continuously, is an important problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution may vary after a change in the machine’s operating point; this …

A rule is an effective representation of knowledge in formal concept analysis (FCA), which can express the relations between concepts. One of the main research directions of FCA is to develop rule-based classification algorithms. Rule-based …

In the process of conveying raw coal to the surface on conveyor belts, the raw coal is generally blended with foreign bodies, such as large pieces of gangue and damaged bolts, which can affect the quality of mined coal, damage the transportation …

Today, we have access to a vast data amount, especially on the internet. Online news agencies play a vital role in this data generation, but most of their data is unstructured, requiring an enormous effort to extract important information. Thus …

Image super-resolution (SR) is a fundamental research task in low-level vision. Recently it has been applied to digital pathology to build transformations from low-resolution (LR) to super-resolved high-resolution (HR) images, which benefits …

Imbalance classification has always been a popular research point in the application of machine learning, data mining and pattern recognition. At present, there are also many techniques to reduce the negative impact of imbalance on classification …