The neural-network-based charge prediction, which is to predict the defendants’ charges from the criminal case documents via neural network, has been a development-potential affair in artificial intelligence (AI) based legal assistant system and made some achievements. Neural network is playing important role to capture deep information in current work. However, charge prediction suffers from serious data imbalance in real-world situation. Only high-frequency charges are easy to be predicted whereas plenty of low-frequency ones are hard to be hold. Furthermore, the presence of confusing charges makes prediction worse. Here, we propose a novel model of charge prediction via the judicial interpretation of crimes (CPJIC) to provide more accurate charge prediction. The concept of crime interpretation is introduced into CPJIC, which alleviates the problems resulted from data imbalance and confusing charges. With the technique of embedding, both fact description and crime interpretation are embedded into a low-dimensional vector space as well as a neural network, delivering implemented computable charge prediction. The experimental results demonstrate that CPJIC can identify the low-frequency and confusing charges better than previous work.

Knowledge graph embedding aims to transform the entities and relations of triplets into the low-dimensional vectors. Previous methods are oriented towards the static knowledge graphs, in which all entities and relations are assumed to be known and only some unknown triplets need to be predicted. However, the real-world knowledge graphs can grow dynamically, and some new knowledge are often added. To embed the new knowledge into the space of original knowledge graph, the classic models have to perform the entire re-embedding with including the new and original knowledge. This causes heavy computational burden for embedding. To address this problem, this study proposes a new model of anchors-based incremental embedding (ABIE) to implement the dynamical embedding for the growing knowledge graph. According to ABIE, every knowledge graph has some key entities, called anchors, which can fix the embedding space of knowledge graph. When some new knowledge is added into the graph, only a few updated entities and relations are embedded into the embedding space with the help of anchors, and the entire re-embedding on the whole graph is not necessary. By this way, the computational burden of embedding caused by the growth of knowledge graph is reduced significantly.

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion. Especially, personalized recommendation models based on graph structure have advanced greatly in predicting user preferences. However, geographical region entities that reflect the geographical context of the items is not being utilized in previous works, leaving room for the improvement of personalized recommendation. This study proposes a region-aware neural graph collaborative filtering (RA-NGCF) model, which introduces the geographical regions for improving the prediction of user preference. The approach first characterizes the relationships between items and users with a user-item-region graph. And, a neural network model for the region-aware graph is derived to capture the higher-order interaction among users, items, and regions. Finally, the model fuses region and item vectors to infer user preferences. Experiments on real-world dataset results show that introducing region entities improves the accuracy of personalized recommendations. This study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.

We present a variational autoencoder (VAE) learning framework with introspective training for conditional image synthesis, and explore conditional capsule encoder by class-wise mask label insertion for this framework. Our model only consists of encoder (E), generator (G) and classifier (C), where E and G can be adversarially optimized, and C helps to boost conditional generation, improve authenticity and provide generation measures for E and G. Discriminator is not necessary in our framework and its absence makes our model more concise with fewer artifacts and pattern collapse problems. To compensate for the blurry weakness of VAE-like models, feature matching is introduced into loss functions by means of C to offer more reasonable measures between real and synthesized images. Moreover, in consideration of the key role of E in autoencoders as well as the interesting characteristics of capsule structure, conditional capsule encoder is preliminary explored in the image synthesis model. Class labels participate conditional encoding by masking high-level capsules of other categories, and capsule loss for the encoder is added to facilitate conditional synthesis. Experiments on MNIST and Fashion-MNIST data sets show that our model achieves real conditional synthesis performances with better diversity and fewer artifacts. And conditional capsule encoder also reveals interesting synthesis effects.