In this paper, we present robust methods for automatically segmenting phases in a specified surgical workflow by using latent Dirichlet allocation (LDA) and hidden Markov model (HMM) approaches. More specifically, our goal is to output an appropriate phase label for each given time point of a surgical workflow in an operating room. The fundamental idea behind our work lies in constructing an HMM based on observed values obtained via an LDA topic model covering optical flow motion features of general working contexts, including medical staff, equipment, and materials. We have an awareness of such working contexts by using multiple synchronized cameras to capture the surgical workflow. Further, we validate the robustness of our methods by conducting experiments involving up to 12 phases of surgical workflows with the average length of each surgical workflow being 12.8 minutes. The maximum average accuracy achieved after applying leave-one-out cross-validation was 84.4%, which we found to be a very promising result.

State-of-the-art visual localization methods mostly rely on complex procedures to match local descriptors and 3D point clouds. However, these procedures can incur significant costs in terms of inference, storage, and updates over time. In this study, we propose a direct learning-based approach that utilizes a simple network named D2S to represent complex local descriptors and their scene coordinates. Our method is characterized by its simplicity and cost-effectiveness. It solely leverages a single RGB image for localization during the testing phase and only requires a lightweight model to encode a complex sparse scene. The proposed D2S employs a combination of a simple loss function and graph attention to selectively focus on robust descriptors while disregarding areas such as clouds, trees, and several dynamic objects. This selective attention enables D2S to effectively perform a binary-semantic classification for sparse descriptors. Additionally, we propose a simple outdoor dataset to evaluate the capabilities of visual localization methods in scene-specific generalization and self-updating from unlabeled observations. Our approach outperforms the previous regression-based methods in both indoor and outdoor environments. It demonstrates the ability to generalize beyond training data, including scenarios involving transitions from day to night and adapting to domain shifts.

Smart manipulation is always the desired performance for the interesting researches in the field of robotic control. The complicated fusion among motion primitives could offer the advanced adaptions in presence of highly success rate or unknown objects. In this paper, a hierarchical framework for pushing-grasping fusion in the cluttered environment is demonstrated. Our method involves three-phase training process, integration of masks and the reinforcement learning scheme. Both simulation and experimentation are undertaken to validate the efficacy and feasibility of the proposed methodology. Our contributions in this work are (i) to propose both grasp mask and push mask for encouraging the model to focus on exploiting, adjusting the proper grasping posture in the desired target area as well as avoiding the phenomenon of the gripper slipping on the surface of an object, (ii) to recommend the reinforcement learning scheme without object model, and (iii) to release a hierarchical training method to enhance the interactive efficiency between grasping and pushing actions.

Night security is known for its long hours and heavy tasks. In Japan, a labor shortage of security guards has become an issue in recent years. To solve these problems, an increasing number of robotic security methods are being used. However, several problems exist with existing security robots. For example, wheeled robots traveling on the ground have difficulty in dealing with obstacles such as steps, while most drones are only for monitoring and do not have a function to help people. In this study, an aerial ubiquitous display (AUD) night security drone has been developed to solve the problems of existing security robots. The AUD is equipped with an infrared camera and a projector to detect human behavior at night and present information to people in need. In this paper, an experiment was conducted with the AUD to evaluate whether it can provide adequate nighttime security. In the experiment, real-time monitoring and information projection from the air were achieved. In addition, new security methods using the AUD were shown to be effective. Replacing security guards with the AUD to provide security at night will improve labor shortages in the future, and better security methods will be developed.

Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many researchers have investigated measuring users with these influences by dealing with huge data sets. However, it is hard to enhance the performances of these studies with multiple attributes together with these influences on social networks. This paper has presented a novel model for measuring users with these influences on a social network. In this model, the suggested algorithm combines Knowledge Graph and the learning techniques based on the vote rank mechanism to reflect user interaction activities on the social network. To validate the proposed method, the proposed method has been tested through homogeneous graph with the building knowledge graph based on user interactions together with influences in real-time. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.