Reviewerly/exHarmony huggingface dataset:
Collision Avoidance of Multiple Moving Agents by Adapting the A* Algorithm

The A* algorithm is one of the most popular pathfinding algorithms. The basic algorithm can reliably find the best path for an agent in a static environment. However, there is only limited knowledge on how the algorithm behaves in a dynamic context. One important dynamic element in a pathfinding problem might be other agents moving simultaneously in the same environment, such as in the application scenarios of various real-time strategy games. With the basic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{A}^{*}$</tex> algorithm, these agents could collide, especially when moving around an obstacle or through a narrow passage. To avoid collisions, a modified agent control is proposed. This extension consists of introducing a waiting time when an agent moves to a place where another agent is already located. The waiting time operator is directly included into the optimization algorithm, stimulating the algorithm to search for alternative routes that avoid these waiting times. The resulting routes might be longer in distance but could be faster because the agent avoids the other blockade. Experiments with different settings indicate that the algorithm achieves this goal: In all settings, including narrow passages, collisions between agents were no longer detected. Furthermore, searching for alternative routes helps the algorithm find paths which are more than 10% faster. The duration of the slowest path can also be reduced in 80% of cases.
Analysis of Attitude, Trust, and Subjective Norm Impact on Intention to Use Profile Verification in Dating Applications in Indonesia

Despite the popularity of online dating Application, there are increasing security issues and challenges with them, such as the creation of fake accounts and phishing, which are commonly called romance scams, one of which is fake user data or even completely fake profiles. This research will discuss the profile verification technology that has been developed in several online dating applications to verify the authenticity of user profiles using an algorithm capable of detecting fake profiles. This study used Sequential Equation Modeling (SEM) method and SMART PLS as a statistical tool. A total of 561 data from online dating application users in Indonesia were collected in October 2022. The purpose of this study was to determine the impact of Attitude, Trust, and Subjective Norm to intention to use profile verification in Dating Application in Indonesia. Attitude, Trust, and Subjective Norms will be special variables that affect the user's intention to use Profile Verification on Dating Applications in Indonesia. The results of the study found that all research hypotheses had a significant effect on each variable relationship in the research model.
A Cable-Driven Robotic Eye for Understanding Eye-Movement Control

We propose a design for a bio-inspired robotic eye, with 6 independently controlled muscles, that is suitable for studying the emergence of human saccadic eye movements char-acteristics. Understanding how characteristics like the restriction of eye orientations to a 2D manifold, straight saccadic trajecto-ries, and saturating relationship between saccade amplitude and its peak velocity come about in a highly nonlinear system with non-commutativity of rotations is not trivial. Although earlier studies have addressed some of these problems, none have so far considered the full 3D complexity of ocular kinematics and dynamics. Our design contains a spherical eye actuated by six motor-driven cables with realistic pulling directions to mimic the six extraocular muscles. The coupling between the eyeball and eye socket has been designed to specify a damped rotational system, which is key to understanding the signals involved in the control of artificial and biological eyes. We present the mechanical design of the robotic system and a simulation model based on it. The system has a large range of movement and its dynamical responses to step inputs are shown, thus illustrating its ability to perform a wide range of eye movements with the appropriate characteristics.
Experience-based Problem Solver for Robot System Design

In this study, an experience-based problem-solving method was developed to design robotic systems, including conveyors, bases, sensors, and robots. Experience reuse involves selecting the most “useful” experience from a dataset and reusing it to query new problems. To solve this robot system design problem, the environmental components are arranged appropriately, and the path length planned by the motion-planning algorithm is considered as the evaluation criterion. Therefore, a case-injected genetic algorithm (GA) is introduced as an experience-based optimization problem solver for robot environment design. The motion and path length of the robotic arm calculated from the experience-driven random tree (ERT) algorithm are considered performance indices in the environment arrangement of the robot system. In this study, standard and experience-based optimization methods and motion planning methods were combined to solve the proposed robot system design problem. These four combinations of methods were compared in terms of computation time and path length. Simulation results demonstrate that experience reuse in different aspects has different focuses, the optimization aspect has a more significant impact on the reduction of calculation time, and the motion planning aspect has a greater impact on path length.
Automatic Generation of Robot Actions for Collaborative Tasks from Speech

Robots have the potential to assist people in daily tasks, such as cooking a meal. Communicating with the robots verbally and in an unstructured way is important, as spoken language is the main form of communication for humans. This paper proposes a novel framework that automatically generates robot actions from unstructured speech. The proposed frame-work was evaluated by collecting data from 15 participants preparing their meals while seating on a chair in a randomly disrupted environment. The system can identify and respond to a task sequence while the user may be engaged in unrelated conversations, even if the user's speech might be unstructured and grammatically incorrect. The accuracy of the proposed system is 98.6%, which is a very promising finding.
Online Surveying System for Experimentally Testing the Human Perception of Visual Gestures

In this paper, we would like to emphasize the need for an intuitive and easy-to-understand way of communication during a Human-Robot Collaboration (HRC) mainly in industrial scenarios. With the new communication design, the mental demands of the human workforce during collaboration can be lowered by the feedback given by the robot in a situation-aware way. This kind of feedback in close cooperation can maintain high importance in a manufacturing scenario. Another goal of this paper is to present the progress of former research that similarly dealt with visual signals during HRC. The goal during the design of the proposed novel methodology was to make the research of visual gestures in Human-Robot Interactions more effective and flexible. To address these demands an online surveying application is introduced and an initial proof of concept nature test was also conducted. During the investigation, we introduced emotional states in the test as a supporting modality for later use. From the analysis, we concluded that visual signals do have properties that can affect the perception of the viewer.
Frame of Events: A Low-latency Resource-efficient Approach for Stereo Depth Maps

Computer vision traditionally uses cameras that capture visual information as frames at periodic intervals. On the other hand, Dynamic Vision Sensors (DVS) capture temporal contrast (TC) in each pixel asynchronously and stream them serially. This paper proposes a hybrid approach to generate input visual data as ‘frame of events’ for a stereo vision pipeline. We demonstrate that using hybrid vision sensors that produce frames made up of TC events can achieve superior results in terms of low latency, less compute and low memory footprint as compared to the traditional cameras and the event-based DVS. The frame-of-events approach eliminates the latency and memory resources involved in the accumulation of asynchronous events into synchronous frames, while generating acceptable disparity maps for depth estimation. Benchmarking results show that the frame-of-events pipeline outperforms others with the least average latency per frame of 3.8 ms and least average memory usage per frame of 112.4 Kb, which amounts to 7.32% and 9.75% reduction when compared to traditional frame-based pipeline. Hence, the proposed method is suitable for missioncritical robotics applications that involve path planning and localization mapping in a resource-constrained environment, such as drone navigation and autonomous vehicles.
Towards Clear Evaluation of Robotic Visual Semantic Navigation

In this paper we address the problem of visual semantic navigation (VSN), in which a robot needs to navigate through an environment to reach an object having only access to egocentric RGB perception sensors. This is a recently explored problem, where most of the approaches leverage last advances in deep learning models for visual perception, combined with reinforcement learning (RL) strategies. Nonetheless, after a review of the literature, it is complicated to perform direct comparisons between the different solutions. The main difficulties lie in the fact that the navigation environments in which the experimental metrics are reported are not accessible, and each approach uses different RL libraries. In this paper, we release a publicly available experimental setup for the VSN problem, with the aim of providing a clear benchmark. It has been constructed using pyRIL, an open source python library for RL, and two navigation environments: Miniwolrd-Maze from gym-miniworld, and one 3D scene from HM3D dataset using AI Habitat simulator. We finally propose a state-of-the-art VSN model, consisting in a Contrastive Language Image Pretraining (CLIP) visual encoder plus a set of two recurrent neural networks for producing the discrete navigation actions. This model is evaluated in the proposed experimental setup, with a careful analysis of the main VSN challenges, namely: the sparse rewards problem; and the exploitation-exploration trade-off. Code is available at: https://github.com/gramuah/vsn.
3-D Trajectory Tracking in the Image Frame for Autonomous Navigation of UAV in UAV-AGV Multi-Agent System

UAV-AGV multi-agent system is attracting the mobile robotics research community due to its applications in several engineering disciplines. It's a heterogeneous combination of UAV and AGV. Collaborative kinematics between both agents is necessity of the system for better utilization of independent capabilities. Vision-based method is most focused in the literature to build the collaboration between the agents which is of great use in the GPS denied environments. One of the collaborative tasks of UAV is to approach the AGV and hover over it at certain altitude. A trajectory tracking technique in the image frame is developed for the task in the present work. UAV detects the static target/AGV in the image plane and plans 3- dimensional trajectory to reach and hover at a desired depth from it. Minimum acceleration trajectories are designed using position and apparent size of the target in the image plane. Sliding mode control theory is applied for designing the controllers for approach, depth motion of UAV and validated with the simulations. The proposed image plane-based trajectory tracking technique is further validated in the Gazebo simulation environment. Simulation results shows that the method is useful in GPS denied environments for the UAV to plan its collaborative navigation in the image frame for smoothly reaching a desired position from the target/AGV.
A Rapidly-Exploring Random Tree Algorithm with Reduced Random Map Size

Mobile robots have been widely used in automated factory applications such as raw material delivery and product storage transportation. Path planning algorithms have been proposed to generate a feasible global approach. The path result must be free from obstacle regions and shortest. Previously we proposed an Improved Rapidly-Exploring Random Tree (Improved RRT*) algorithm. The algorithm consists of the pre-processing step for feasible mapping, primary processing with RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> for path generating, and post-processing with Bacterial Mutation and Node Deletion operators. This paper aims to improve further the capability of the Improved RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm by reducing the overall computation time. The proposed method reduces the complexity of the random map after each iteration by deleting the used nodes. In this way, the computational time could be reduced.
Cooperative Optimal Control of UAVs Based on Brain-Attention Mechanism Neural Network

With the increasing complexity and variety of mission environments, multiple-UAVs cooperative path optimization system is gradually becoming an important development direction in the future. Because the current intelligent level of UAV is difficult to meet the requirements of complex tasks, in order to improve the intelligence and collaborative ability of multiple-UAVs systems, this paper focuses on the intelligent decision-making and collaborative control of UAV in the collaborative path of multiple UAVs system. In this paper, an autonomous and innovative UAV Cooperative Path Optimization System Based on neural network of brain like attention mechanism is proposed. The experimental results show that this method has lower processing delay, can achieve positioning accuracy in a shorter time, and has higher reliability and accuracy than the traditional center controlled UAV cooperative positioning UAV method.
Multi-UGVs Collaborative Path Planning and Conflicts Eliminating in Emergent Situations

The centralized path planning framework for multi-unmanned vehicles tends to get messy when emergency occurs, creating a series of systemic conflicts and almost impossible to return to normal spontaneously. Thus, this paper improves the Multi-UGVs Collaborative Path Planning system through emergency response planning and system recovery to eliminate the conflicts caused by emergencies. The rapid emergency response planning method is developed based on heuristic search to quickly locate a shelter with a response trajectory without disturbing other normal vehicles for each affected vehicle in real time. And then the affected vehicles can complete recovery process by introducing an asynchronous starting conflict based search (CBS) algorithm. The experiments carried out in Rviz simulation environments prove that the proposed method has good practicability and stability.
Inducing Self-Motion Sensations With Haptic Feedback: State-of-The-Art and Perspectives on “Haptic Motion”

While virtual reality applications flourish, there is a growing need for technological solutions to induce compelling self-motion, as an alternative to cumbersome motion platforms. Haptic devices target the sense of touch, yet more and more researchers managed to address the sense of motion by means of specific and localized haptic stimulations. This innovative approach constitutes a specific paradigm that can be called "haptic motion". This article aims to introduce, formalize, survey and discuss this relatively new research field. First, we summarize some core concepts of self-motion perception, and propose a definition of the haptic motion approach based on three criteria. Then, we present a summary of existing related literature, from which we formulate and discuss three research problems that we estimate key for the development of the field: the rationale to design a proper haptic stimulus, the methods to evaluate and characterize self-motion sensations, and the usage of multimodal motion cues.
A Robust Multiscale Edge Detection Method for Accurate SAR Image Registration

Edge detection is a technique used to identify inherent structures within an image, and it is an essential requirement for synthetic aperture radar (SAR) applications. In particular, ratio-based edge detectors have been widely used in SAR image registration because of their ability to extract invariant features and reduce the effects of speckle noise. However, current edge detectors often struggle to accurately detect multi-scale objects and low-contrast structures. To address this issue, we present a robust multi-scale edge detector that uses a modified convolution kernel to improve the extensibility of edge features and aggregates multi-scale feature responses. We also propose a local scale estimation module to enhance edge responses in low-contrast areas and reduce noise effects. The experimental results demonstrate that our proposed method effectively preserves the integrity, continuity, and robustness of multi-scale and low-contrast structures. By incorporating our proposed edge detector into feature and template matching frameworks, we are able to significantly improve matching accuracy and outperform state-of-the-art SAR image registration methods.
Thermal Optimization of Hybrid Cryogenic Computing Systems

Heterogeneous computing exploits several disparate technologies within a single system. The different components of a heterogeneous system are often placed within separate temperature zones. Selecting an appropriate operating temperature strongly affects the dissipated power, cooling power (heat load), system performance, and ambient temperature. To this date, no multitemperature design methodology exists. To overcome this limitation, a framework for thermal optimization of heterogeneous computing systems is presented in this article. The effects of operating temperature on delay and power consumption are characterized based on a graph representation of the system. In addition, thermal interactions among the components within a system are considered to accurately evaluate the total power consumption and heat load. In a practical case study, the target temperature of each component within a quantum computing system is determined to minimize the total power under target performance constraints.
Generalized Singleton Bound and List-Decoding Reed–Solomon Codes Beyond the Johnson Radius

In this paper we take a combinatorial approach to the problem of list-decoding, which allows us to determine the precise relation (up to the exact constant) between the decoding radius, list size, and code rate. We prove a generalized Singleton bound for a given list size, and conjecture that the bound is tight for most Reed–Solomon (RS) codes over large enough finite fields. We also show that the conjecture holds true for list sizes 2 and 3, and as a by product show that most RS codes with a rate of at least 1/9 are list-decodable beyond the Johnson radius. Last, we give the first explicit construction in the literature of such RS codes. The main tools used in the proof are a new type of linear dependency between codewords of a code that are contained in a small Hamming ball, and a surprising connection between list-decoding and the notion of cycle space in graph theory. Both of them are new, and may be of independent interest.
Research on Medical Sensitive Data Protection Algorithm Based on Differential Privacy

In order to avoid the wrong transmission behavior of medical data and realize the effective protection of sensitive information samples, the protection algorithm of medical sensitive data based on differential privacy is studied. According to the application principles of Laplace mechanism and index mechanism, a query control model is constructed, and then the analysis of differential privacy protection technology for medical sensitive data is realized by solving the security trust index. Based on the R-tree clustering structure, according to the sensitivity index calculation results, the search and processing of sensitive objects are completed, and the design of medical sensitive data protection algorithm based on differential privacy is completed. The experimental results show that under the effect of the principle of differential privacy, the error transmission probability of medical sensitive data will never be higher than 10%. It has strong application feasibility in solving the problem of medical data error transmission and effectively protecting sensitive information samples.
Data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses

In order to improve the quality of students’ psychological tendency health data, this paper puts forward the research of mental tendency health data mining based on SVM. By cleaning the students’ mental tendency health data and optimizing the SVM with particle swarm optimization algorithm, the classification of students’ mental tendency health data is completed. According to the weighted results of mental propensity health data, the primary classification and feature recognition of large-scale mental propensity health data were carried out. Based on the optimal modeling of students’ psychological tendency health data, the objective function of maximum difference of students’ psychological tendency health data is defined. Maximum likelihood estimation is used to obtain the frequency distribution of the data in the Tourism English Course of higher vocational colleges. Experimental results show that the proposed method can not only improve the accuracy and efficiency of mental health data classification, but also control the integrity of mental health data over 80%.
Anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge Computing

As the link between the perception layer and the network layer, the Internet of Things gateway is of great significance to the safe and stable operation of the healthcare Internet of Things. Once the gateway is abnormal, it will directly affect the information transmission in health care work. Therefore, an anomaly detection method for the gateway of the Internet of Things in health care supporting edge computing is proposed. Several representative gateway status indicators are selected by using the maximum uncorrelation method, and the gateway anomaly detection task is unloaded to the edge server by using edge computing. An anomaly detection model based on SOFM neural network and random forest is constructed to realize the anomaly detection of the Internet of Things gateway in health care. The experimental results show that the determination coefficients of the six types of samples of this method are more than 0.9, which is close to 1, which shows that this method has better anomaly detection performance of the Internet of Things gateway in health care.