Reviewerly/exHarmony huggingface dataset:
Active Autorotation of Micro Aerial Vehicle with Foldable Winged Shell for Impact Mitigation during Free Fall

Drop mitigation is an important function of micro aerial vehicles (MAVs) that are used for internal inspections of enclosed and cluttered structures (height: 5–10 m). The mechanism also allows continuous operation of drones, prevents the downtime required for maintenance and repair and also provides a safer environment for workers who are working below the drones. Some solutions include parachutes, auto-rotors, and active auto-rotors. However, these are inapplicable to MAV s with a protector shell because of their size and payload. We herein propose a new rapid response drop mitigation method for MA V s involving active autorotation with bendable wings and shells. Active autorotation enables faster deceleration during dropping motion as compared to parachutes or passive autorotation. Bendable wings can ensure optimal flight performance and enable sufficient deceleration during falling motion. This newly proposed fixture was shown to reduce the impact impulse by 32.2 % and horizontal oscillation by 34.4 %. From our flight tests conducted at heights of 5 m and 10m in both outdoor and indoor environments, the measured impact impulse for both profiles were attained at 0.93 N s. The minimum impact impulse that can cause harm to the human eye, which is the most vulnerable part, is 2 N s. Thus, this mechanism was successfully proven to provide a greater safety buffer based on the drop test conducted. We envision this mechanism to provide greater safety to drone operating environments in relevant fields involving indoor and urban drone flights. Furthermore, it can reduce damage to drones and structures and avoid injuries arising from drone crashes.
Assisting Operators of Articulated Machinery with Optimal Planning and Goal Inference

Operating an articulated machine is a complex and hierarchical task, involving several levels of decision making. Motivated by the timber-harvesting applications of these machines, we are interested in developing a collaborative framework for operating an articulated machine/robot in order to increase its level of autonomy. In this paper, we consider two problems in the context of collaborative operation of a feller-buncher: first, the problem of planning a sequence of cut/grasp/bunch tasks for the trees in the vicinity of the machine. Here we propose a human-inspired planning algorithm based on our observations of the operators in the field. Then, a Markov Decision Process (MDP) framework is provided, which enables us to obtain an optimal sequence of tasks. We provide numerical illustrations of how our MDP framework works. Second is the problem of inferring the operator's goal from the motions of the machine. The goal inference algorithm presented here enables the robot equipped with the planning intelligence to perceive the human's intent in real-time. We evaluate the performance of our goal inference algorithm through a user-study with a feller-buncher simulator. The results show the benefits of our algorithm over a robot that assumes the human is moving to the closest target.
Simultaneous Control and Trajectory Estimation for Collision Avoidance of Autonomous Robotic Spacecraft Systems

We propose factor graph optimization for simultaneous planning, control, and trajectory estimation for collision-free navigation of autonomous systems in environments with moving objects. The proposed online probabilistic motion planning and trajectory estimation navigation technique generates optimal collision-free state and control trajectories for autonomous vehicles when the obstacle motion model is both unknown and known. We evaluate the utility of the algorithm to support future autonomous robotic space missions.
Adversarial Imitation Learning from Video Using a State Observer

The imitation learning research community has recently made significant progress towards the goal of enabling artificial agents to imitate behaviors from video demonstrations alone. However, current state-of-the-art approaches developed for this problem exhibit high sample complexity due, in part, to the high-dimensional nature of video observations. Towards addressing this issue, we introduce here a new algorithm called Visual Generative Adversarial Imitation from Observation using a State Observer (VGAIfO-SO). At its core, VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised state observer, which provides estimates of lower-dimensional proprioceptive state representations from high-dimensional images. We show experimentally in several continuous control environments that VGAIfO-SO is more sample efficient than other IfO algorithms at learning from video-only demonstrations and can sometimes even achieve performance close to the Generative Adversarial Imitation from Observation (GAIfO) algorithm that has privileged access to the demonstrator's proprioceptive state information.
CATs: Task Planning for Shared Control of Assistive Robots with Variable Autonomy

From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised autonomy, even switching between them during task execution. The action representation - Constraint Action Templates (CATs) - combine the advantages of Action Templates [1] and Shared Control Templates [2]. We demonstrate that CATs enable our planning framework to generate goal-directed plans for variations of a typical task of daily living, and that users can execute them on the wheelchair-robot EDAN in shared control or in autonomous mode.
Insulator Aiming Using Multi-Feature Fusion-Based Visual Servo Control for Washing Drone

Insulator visual aiming is difficult for washing drone due to the complex washing environment, strong dis-turbance, lack of debugging environment, and other factors. Conventional visual servo control methods often fail to consider these complex factors adequately and fall short in reliable insulator visual aiming. To address these problems, we propose a novel multi-feature fusion-based drone visual servo control method for accurate insulator visual aiming. A multi-feature fusion neural network (MFFNet) is proposed to map the dif-ferent input modalities into an embedding space spanned by the learned deep features. Suitable control commands are generated by the simple combination of learned deep features. These deep features represent the intrinsic structural properties of the insulator and the motion pattern of the drones. Particularly, our method is trained purely in simulation and transferred to a real drone directly. Moreover, accurate visual aiming is guaranteed even in strong disturbance environments. Simulation and experimental results verify the high accurate insulator aiming, anti-disturbance, and sim-to-real transfer capabilities of the proposed method. Video: https://youtu.be/Ptlajzvp46A.
Design of an Autonomous Latching System for Surface Vessels

Autonomous latching is essential for autonomous surface vessels (ASV) to reach full independence from human intervention. As part of the ASV Roboat project, a new solution for self-latching maneuvers has been developed and is presented here. We propose a system that has the key requirements of full integration with the navigation control system and zero-gap connection with the dock, the latter being essential for wireless charging of the ASV. Dedicated markers are used to identify docking targets, relying on computer vision algorithms to determine distance and bearing to the target. In its idle state, the locking solution uses mechanical power-off brakes, minimizing energy consumption while ensuring the boat stays in position indefinitely once docked. A prototype of the proposed mechanism has been built and installed in Roboat. Experimental tests showing the mechanism performance and capability to autonomously approach the docking station are discussed in this work.
Parametric Path Optimization for Wheeled Robots Navigation

Collision risk and smoothness are the most important factors in global path planning. Currently, planning methods that reduce global path collision risk and improve its smoothness through numerical optimization have achieved good results. However, these methods cannot always optimize the path. The reason is all points on the path are considered as decision variables, which leads to the high dimensionality of the defined optimization problem. Therefore, we propose a novel global path optimization method. The method characterizes the path as a parametric curve and then optimizes the curve's parameters with a defined objective function, which successfully reduces the dimension of optimization problem. The proposed method is compared with baseline and state-of-the-art methods. Experimental results show the path optimized by our method is not only optimal in collision risk, but also in efficiency and smoothness. Furthermore, the proposed method is also implemented and tested in both simulation and real robots.
Augmented Pointing Gesture Estimation for Human-Robot Interaction

With recent advancements in CV (computer vision) and AI (Artificial Intelligence) technologies, pointing gesture is becoming an emerging trend for human-robot interaction. Its intuitive and deictic nature makes it an ideal way for giving commands, especially referring spatial information to the robots. In this paper, we propose an augmented pointing gesture estimation method to enable richer and programmable instructions to be given to the robots. We propose five pointing gestures and demonstrate the idea using a collaborative robot with a multi-finger robotic gripper. Experiments are designed and conducted to test the pointing accuracy in space and in gesture estimation. The results show that our proposed method can achieve a mean drift of 8.3 cm and an estimation accuracy of 94.08%.
Looking for Trouble: Informative Planning for Safe Trajectories with Occlusions

Planning a safe trajectory for an ego vehicle through an environment with occluded regions is a challenging task. Existing methods use some combination of metrics to evaluate a trajectory, either taking a worst case view or allowing for some probabilistic estimate, to eliminate or minimize the risk of collision respectively. Typically, these approaches assume occluded regions of the environment are unsafe and must be avoided, resulting in overly conservative trajectories-particularly when there are no hidden risks present. We propose a local trajectory planning algorithm which generates safe trajectories that maximize observations on un-certain regions. In particular, we seek to gain information on occluded areas that are most likely to pose a risk to the ego vehicle on its future path. Calculating the information gain is a computationally complex problem; our method approximates the maximum information gain and results in vehicle motion that remains safe but is less conservative than state-of-the-art approaches. We evaluate the performance of the proposed method within the CARLA simulator in different scenarios.
Identifying IP Usage Scenarios: Problems, Data, and Benchmarks

Given an IP address, understanding its initial assignment,and predicting its potential usage can significantly help toward improving efficiency of many practical IP-based applications, such as Ad-recommendation, point of interest selection, fraud detection, and Internet service optimization. However, there are only a few prior studies conducted on predicting IP usage from its assignment. Surprisingly, there does not exist a benchmark dataset available to academia that can be used to investigate and develop IP usage predictions for different IP applications. In this work, we formulate the IP usage prediction problem; specifically, we collect large-scale, real-world data and extract salient features using sophisticated networking tools such as different network signals, trace route delay, IP block usage, and geographical landmarks. We showcase a series of tabular information retrieval methods to learn network signals and their interactions, and identify the IP usage scenarios. We believe our datasets and algorithms can benefit the community to facilitate relevant research in this domain, yielding more efficient and effective solutions to multiple categories of applications.
Deep Learning-based Small Object Detection: A Survey

With the rapid development of deep learning, the accuracy of object detection has greatly improved. But like the dark clouds in the clear sky, small objects detection is still a problem in computer vision. Due to low resolution, few pixels, limited information and rough outline, the features are difficult to extract, and training data is difficult to label. Around the problems and challenges in small object detection, we made an analysis and overview of the research progress and status of deep learning-based algorithms from one-stage and two-stage object detection methods. Then some techniques for small object detection are analysed from the aspects of feature enhancement, multi-scale prediction and detection network model. Finally, aiming at the problems and the development trend of machine vision, we make predictions and prospects for the application and development of small object detection in the future.
A GCN-Based Framework for Generating Trailers

The film-television industry continues to generate a large amount of information at all times, and the massive amount of movie data has promoted an increase in the demand for trailers. It has become a major research challenge to choose a movie of interest from the massive amount of movie data. This has driven the growth in demand for trailer production. Using computer technology to generate trailers automatically has two benefits: on the one hand, it can help people browse the content of a movie quickly and decide whether to pay for the movie; on the other hand, it can reduce the work of video creators and help them attract viewers with less cost. In this article, we construct a GCN-based convolution joint framework, which selects the trailer shots in the full-length movie according to the visual characteristics of the shots and the relationship between the shots. Firstly, the movie data is preprocessed for its division into sparse shots, followed by shot boundary detection and stratified sampling. Secondly, the visual features of shots are learned through multi-layer CNN. The topological relationship between shots is established by GCN to extract the features that include the shot relationship. These extracted features are then intelligently fused based on the assignment of different weights; shots with the fusion score higher than a certain threshold are selected for the trailer generation. The proposed framework is shown to other video summarization methods in the field of trailer generation.
Single Image 3D Reconstruction Based on Attention Mechanism and Graph Convolution Network

research-article Share on Single Image 3D Reconstruction Based on Attention Mechanism and Graph Convolution Network Authors: Wei Gao Digital Banking Laboratory, INDUSTRIAL AND COMMERCIAL BANK OF CHINA SOFTWARE DEVELOPMENT CENTER, China Digital Banking Laboratory, INDUSTRIAL AND COMMERCIAL BANK OF CHINA SOFTWARE DEVELOPMENT CENTER, ChinaSearch about this author , Liyang Yu School of Computer Science and Technology, Huazhong University of Science and Technology, China School of Computer Science and Technology, Huazhong University of Science and Technology, ChinaSearch about this author , Yuanyuan Du Digital Banking Laboratory, INDUSTRIAL AND COMMERCIAL BANK OF CHINA SOFTWARE DEVELOPMENT CENTER, China Digital Banking Laboratory, INDUSTRIAL AND COMMERCIAL BANK OF CHINA SOFTWARE DEVELOPMENT CENTER, ChinaSearch about this author , Songfeng Lu School of CYBER Science and ENGINEERING, Huazhong University of Science and Technology, China School of CYBER Science and ENGINEERING, Huazhong University of Science and Technology, ChinaSearch about this author Authors Info & Claims ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial IntelligenceMarch 2022 Pages 670–676https://doi.org/10.1145/3532213.3532315Online:13 July 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Comparation of UAV Path Planning for Logistics Distribution

In recent years, unmanned aerial vehicle (UAV), has been increasingly applied to research in various fields due to its small size and fast speed. Among them, the path planning of UAV under various actual environmental constraints has been one of the hot issues for the past few years. UAV path planning is to find the optimal or feasible route from the starting point to the target point and meet the UAV performance indicators under specific constraints. The essence of the problem is the optimization problem of multi-objective function to find the extreme value under multi-constraints. In this paper, under the background of logistics distribution, Through the research of UAV path planning model and algorithm, this paper summarizes the UAV path planning problems, analyzes several common path planning algorithms in recent years, and carries out simulation experiments with MATLAB on several algorithms with high attention. On this basis, the future research direction is prospected.
Automatic Text Summarization for Moroccan Arabic Dialect Using an Artificial Intelligence Approach

A major advantage of artificial intelligence is its ability to automatically perform tasks at a human-like level quickly; this is needed in many fields, and more particularly in Automatic Text Summarization (ATS). Several advances related to this technique were made in recent years for both extractive and abstractive approaches, notably with the advent of sequence-to-sequence (seq2seq) and Transformers-based models. In spite of this, the Arabic language is largely less represented in this field, due to its complexity and a lack of datasets for ATS. Although some ATS works exist for Modern Standard Arabic (MSA), there is a lack of ATS works for the Arabic dialects that are more prevalent on social networking platforms and the Internet in general. Intending to take an initial step toward meeting this need, we present the first work of ATS concerning the Moroccan dialect known as Darija. This paper introduces the first dataset intended for the summarization of articles written in Darija. In addition, we present state-of-the-art results based on the ROUGE metric for extractive methods based on BERT embeddings and K-MEANS clustering, as well as abstractive methods based on Transformers models.
Global Project Based Learning and its Extension to Cyber-Physical Space

There is a need to develop human resources to create innovations and solve social issues such as the Sustainable Development Goals (SDGs). Global Project Based Learning (PBL) is an educational activity in which diverse students from various countries collaborate to solve problems and create new values across disciplines. In the 2020-2021 academic year, due to the COVID-19 pandemic, universities have switched from in-person to online implementation of global PBL. This paper provides an overview of global PBL and its human resource development goals, followed by an analysis of online implementation, and finally, blended international cooperative learning as a future global PBL.
Relaxed Effective Callback Freedom: A Parametric Correctness Condition for Sequential Modules With Callbacks

Callbacks are an essential mechanism for event-driven programming. Unfortunately, callbacks make reasoning challenging because they introduce behaviors where calls to the module are interleaved. We present a parametric method that, from a particular invariant of the program, allows reducing the problem of verifying the invariant in the presence of callbacks, to the callback-free setting. Intuitively, we allow callbacks to introduce behaviors that cannot be produced by callback free executions, as long as they do not affect correctness. A chief insight is that the user is aware of the potential effect of the callbacks on the program state. To this end, we present a parametric verification technique which accepts this insight as a relation between callback and callback free executions. We implemented our approach and applied it successfully to a large set of real-world programs.
Comparison: Stochastics with a Focus on Probability Theory

Stochastics is a major field in mathematics and highly applicable to physics. Stochastics is usually divided into statistics and probability theory. Statistics is bound to measuring various processes in the empirical sciences and providing methods for the description of datasets, hypothesis building, and testing using inferential statistics.
Clustertracker: An Efficiency-Focused Multiple Object Tracking Method

Rapid, automated processing of video is motivated in part by the sheer volume of camera data (e.g., London has more than 500,000 cameras). Multiple Object Tracking (MOT) is a fundamental task in computer vision that enables automated or semi-automated processing of video. Most current MOT methods (e.g., the MOT20 benchmark leaderboard) are extremely slow, capable of processing fewer than 12 frames per second (fps), even on dedicated GPUs. These MOT methods are accurate and perhaps the only way to approach extremely crowded scenes like stadiums, train stations, or airports; however, we argue that these MOT scenarios are relatively niche compared to the less crowded MOT scenarios regularly encountered in countless residential areas, small-store surveillance systems, hotel hallways, parking lots, etc. To make MOT tractable for widespread use, more efficient methods must be developed that are less reliant on dedicated hardware. Our MOT method, ClusterTracker, exhibits more than an order of magnitude better processing speed than most trackers, runs with a single core of a CPU, and can be used with either deep object detections or motion detections. We compare ClusterTracker to three other high-speed MOT methods on the MOT20 and Head Tracking 21 datasets. We find that it is less impaired by increased target density (number of targets per frame). We argue that these results are not merely good implementation but represent a fundamental difference in underlying computational complexity. Source code is at https://github.com/JLJ19/ClusterTracker .
Libraries as centers of excellence: quality management as inducer of libraries’ social impact

The purpose of this chapter is to analyze the contributions that quality management can offer to libraries by applying the principles of excellence postulated by the management models that make total quality management operational and that is necessary to achieve and maintain over time the results of value for all its stakeholders. These allow to generate the impact on society that its commitment to corporate social responsibility demands in its economic, environmental, and social dimension, enhancing organizational knowledge as its main asset.