Learning the next Point-of-Interest (POI) is a highly context-dependent human movement behavior prediction task, which has gained increasing attention with the consideration of massive spatial-temporal trajectories data or check-in data. The spatial dependency, temporal dependency, sequential dependency and social network dependency are widely considered pivotal to predict the users’ next location in the near future. However, most existing models fail to consider the influence of other users’ movement patterns and the correlation with the POIs the user has visited. Therefore, we propose a Global User Graph Enhanced Network (GUGEN) for the next POI recommendation from a global and a user perspectives. First, a trajectory learning network is designed to model the users’ short-term preference. Second, a geographical learning module is designed to model the global and user context information. From the global perspective, two graphs are designed to represent the global POI features and the geographical relationships of all POIs. From the user perspective, a user graph is constructed to describe each users’ historical POI information. We evaluated the proposed model on three real-world datasets. The experimental evaluations demonstrate that the proposed GUGEN method outperforms the state-of-the-art approaches for the next POI recommendation.

Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making. Due to the large volumes of data being processed, several research efforts across multiple communities have explored how to scale up recursive queries, typically expressed in Datalog. Our experience with these tools indicate that their performance does not translate across domains---e.g., a tool designed for large-scale graph analytics does not exhibit the same performance on program-analysis tasks, and vice versa. Starting from the above observation, we make the following two contributions. First, we perform a detailed experimental evaluation comparing a number of state-of-the-art Datalog systems on a wide spectrum of graph analytics and program-analysis tasks, and summarize the pros and cons of existing techniques. Second, we design and implement our own general-purpose Datalog engine, called RecStep, on top of a parallel single-node relational system. We outline the techniques we applied on RecStep, as well as the contribution of each technique to the overall performance. Using RecStep as a baseline, we demonstrate that it generally out-performs state-of-the-art parallel Datalog engines on complex and large-scale Datalog evaluation, by a 4-6X margin. An additional insight from our work is that it is possible to build a high-performance Datalog system on top of a relational engine, an idea that has been dismissed in past work.

Accurately segmenting polyps from colonoscopy images is essential for diagnosing colorectal cancer. Despite the tremendous success of the deep convolutional neural networks in automatic polyp segmentation, it suffers from domain shift issues, where the trained model yields performance deterioration on unseen test datasets. This paper proposes an illumination enhancement-based domain generalization approach to improve the generalization capability of the model on unseen test datasets and alleviate this issue. In particular, an image decomposition module (IDM) was developed to separate colonoscopy images into reflectance, local, and global illumination components. An illumination transform module (ITM) was proposed to augment images with different global illuminations by synthesizing target-like global illumination maps. A novel illumination variance insensitiveness (IViSen) is also introduced to evaluate the robustness of the model against illumination disturbance. IViSen is easy to compute and correlates well with model generalizability. The segmentation performance of the proposed model on four colonoscopy datasets was examined: CVC-ClinicDB, CVC-ColonDB, ETIS-Larib, and Kvasir-SEG. The method outperformed the competitive methods when tested on unseen domains. In particular, the proposed approach yielded 60.82% and 53.19% in terms of mean Dice and IoU, respectively, with 2.06% and 2.31% improvements.

Color medical images provide better visualization and diagnostic information for doctors during clinical procedures than grayscale medical images. Although generative adversarial network-based image colorization approaches have shown promising results, in these methods, adversarial training is applied to the whole image without considering the appearance conflicts between the foreground objects and the background contents, resulting in generating various artifacts. To remedy this issue, we propose a fully automatic spatial mask-guided colorization with generative adversarial network (SMCGAN) framework for medical image colorization. It generates colorized images with fewer artifacts by introducing spatial masks, which encourage the network to focus on the colorization of the foreground regions instead of the whole image. Specifically, we propose a novel spatial mask-guided method by introducing an auxiliary foreground segmentation branch combined with the main colorization branch to obtain the spatial masks. The spatial masks are then used to generate masked colorized images where most background contents are filtered out. Moreover, two discriminators are utilized for the generated colorized images and masked generated colorized images, respectively, to assist the model in focusing on the colorization of foreground regions. We validate our proposed framework on two publicly available datasets, including the Visible Human Project (VHP) dataset and the prostate dataset from NCI-ISBI 2013 challenge. The experimental results demonstrate that SMCGAN outperforms the state-of-the-art GAN-based image colorization approaches with an average improvement of 8.48% in the PSNR metric. The proposed SMCGAN can also generate colorized medical images with fewer artifacts.