Artificial Intelligence

Complex networks exhibit inherent community structures that contain rich topology information of graphs. Existing graph neural networks (GNNs) have not fully utilized community structures and integrating them into GNNs has the potential to enhance nodes' representation capabilities and prediction performance. In this paper, we propose a community-aware graph neural network (CAG), which designs a community subgraph explorer (CSE) that leverages monte carlo tree search (MCTS) to select the most informative subgraphs within communities.

We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.

To evaluate the proposed odometry method based on 360-degree cameras, we build a new dataset using an Insta-360 One X2 device, which provided high-resolution 4K images captured at a rate of 30 frames per second and 500\,Hz IMU measurements from its built-in IMU.Different from previous datasets, we have set the illumination brightness and the camera movements as the condition variable. The sequence can be classified based on changes in illumination speed or camera movement intensity.

The Internet Graphs (IGraphs) dataset is a substantial collection of real intra-AS (Autonomous System) graphs sourced from the Internet Topology Data Kit (ITDK) project. Comprising a total of 90,326 graphs, each ranging from 12 to 250 nodes, this dataset provides a diverse and extensive resource for the exploration and analysis of network structures within autonomous systems.