ASTRO: Reducing COVID-19 Exposure through Contact Prediction and Avoidance

The fight against the COVID-19 pandemic has highlighted the importance and benefits of recommending paths that reduce the exposure to and the spread of the SARS-CoV-2 coronavirus by avoiding crowded indoor or outdoor areas. Existing path discovery techniques are inadequate for coping with such dynamic and heterogeneous (indoor and outdoor) environments—they typically find an optimal path assuming a homogeneous and/or static graph, and hence they cannot be used to support contact avoidance. In this article, we pose the need for Mobile Contact Avoidance Navigation and propose ASTRO ( A ccessible S patio- T emporal R oute O ptimization), a novel graph-based path discovering algorithm that can reduce the risk of COVID-19 exposure by taking into consideration the congestion in indoor spaces. ASTRO operates in an A * manner to find the most promising path for safe movement within and across multiple buildings without constructing the full graph. For its path finding, ASTRO requires predicting congestion in corridors and hallways. Consequently, we propose a new grid-based partitioning scheme combined with a hash-based two-level structure to store congestion models, called CM-Structure , which enables on-the-fly forecasting of congestion in corridors and hallways. We demonstrate the effectiveness of ASTRO and the accuracy of CM-Structure ’s congestion models empirically with realistic datasets, showing up to one order of magnitude reduction in COVID-19 exposure.

An Automatic Planning-Based Attack Path Discovery Approach from IT to OT Networks

With the convergence of IT and OT networks, more opportunities can be found to destroy physical processes by cyberattacks. Discovering attack paths plays a vital role in describing possible sequences of exploitation. Automated planning that is an important branch of artificial intelligence (AI) is introduced into the attack graph modeling. However, while adopting the modeling method for large-scale IT and OT networks, it is difficult to meet urgent demands, such as scattered data management, scalability, and automation. To that end, an automatic planning-based attack path discovery approach is proposed in this paper. At first, information of the attacking knowledge and network topology is formally represented in a standardized planning domain definition language (PDDL), integrated into a graph data model. Subsequently, device reachability graph partitioning algorithm is introduced to obtain subgraphs that are small enough and of limited size, which facilitates the discovery of attack paths through the AI planner as soon as possible. In order to further cope with scalability problems, a multithreading manner is used to execute the attack path enumeration for each subgraph. Finally, an automatic workflow with the assistance of a graph database is provided for constructing the PDDL problem file for each subgraph and traversal query in an interactive way. A case study is presented to demonstrate effectiveness of attack path discovery and efficiency with the increase in number of devices.

Lane Detection for Autonomous Vehicles using Open CV Library

For vehicles to have the option to drive without anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, pause at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and pedestrians. In view of the issues experienced in identifying objects via self-governing vehicles an exertion has been made to show path discovery utilizing OpenCV library. The explanation and method for picking grayscale rather than shading, distinguishing and detecting edges in an image, selecting region of interest, applying Hough Transform and choosing polar coordinates over Cartesian coordinates has been discussed.

Lane Detection for Self-Driven Vehicles

For vehicles to have the option to drive without help from anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, delay at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and people on foot. In light of the issues experienced in identifying objects via self-sufficient vehicles an exertion has been made to exhibit path discovery utilizing OpenCV library. The explanation and methodology for picking grayscale rather than coloring, identifying edges in a picture, choosing area of interest, applying Hough Transform and picking polar directions over Cartesian directions has been talked about.