Reviewerly/exHarmony huggingface dataset:
A blockchain based industrial internet of things enterprise supervision method

Aiming at the lack of a feasible and efficient regulatory mechanism between the users, suppliers and the government of smart devices in the context of Industry 4.0, this paper proposes a new regulatory technology for industrial internet of things enterprises based on blockchain technology. Under the Hyperledger Fabric architecture, a big data regulatory system for industrial Internet of Things enterprises is constructed, which can deepen the reform of the data transmission and governance mode among manufacturers and improve the intelligent supervision of the industrial internet of things.
Research on Dual Robotic Arm Path Planning Based on Steering Wheel Sewing Device

To address the problem that the artificial potential field method (APF) is easy to fall into local minima in the path planning of dual robotic arms, a path planning method combining the improved APF and the advanced Rapidly expanding random trees (RRT) algorithm is proposed. method. First, the sewing machine device is used as the research object to model the robotic arm as well as analyze its positive kinematics; second, the workspace of the double robotic arm is analyzed, the enclosing box model is used to simplify the robotic arm as well as the obstacles, and the collision detection of the linkage of the double robotic arm is performed; then, the improved APF is used to plan the path of the main robotic arm and the dynamic obstacles of the sub-robotic arm are used to plan the path of the slave The improved APF algorithm is then used to plan the path of the main robot arm, and it is used as the dynamic obstacle of the sub-robot arm to plan the path of the sub-robot arm, and the advanced RRT algorithm is used to escape from the point when the sub-robot arm enters the local minimal point, and after that, the improved APF algorithm is continued to plan the remaining path; finally, the improved algorithm is simulated in MATLAB, and the simulation results show that the improved algorithm can complete the path planning of the double robot arm more quickly and stably compared with the traditional algorithm.
HDR Production—Tone Mapping Techniques and Roundtrip Conversion Performance for Mastering With SDR and HDR Sources

The limited contrast and colors in standard dynamic range (SDR) have historically compelled content producers to make hard choices, whereas high dynamic range (HDR) offers them the possibility to show more—meaning telling stories with greater freedom and flexibility. Even so, HDR content presents a challenge of its own. The wide variation in capabilities among HDR displays conflicts with the desire to have any HDR or SDR production play optimally on any display, regardless of whether it is HDR or SDR. Tone mapping is the generic term for the operation that addresses this issue, whether tone compression, which maps from HDR to a lower-luminance HDR or SDR, or tone expansion, which maps from SDR or HDR to a higher-luminance HDR. Though the trend is to produce the highest quality content in HDR, legacy content is ever present, and lots of new content is still produced in SDR. Mixing feeds in production requires tone expansion for SDR content to match the HDR. Distribution paths can then require tone compression back to SDR without degradation. In this context, a perfect SDR–HDR–SDR roundtrip conversion is required to preserve artistic intent for all content. Furthermore, in a live context, such tools need to be automatic and low latency. This article discusses various tone-mapping techniques, including static solutions such as lookup tables (LUTs) and dynamic solutions based on metadata. The relative advantages are considered, and the performance of each is analyzed. Particular attention is paid to the roundtrip performance, which is important to content producers mastering both SDR and HDR sources, yet wanting to exploit the full range of storytelling capabilities an HDR stream can offer. Test results using a proposed solution that has been introduced in practice, “Advanced HDR by TechnicolorⓇ,” are described.
Design of Optimization of Goat Sales Based on The Website

Currently, goat farming is one of the fastest growing businesses. Developments that occur include buying and selling that can be done through e-commerce available on digital media. E-commerce can increase sales from goat breeders through complete descriptions and goat productivity. Based on the description above, it is necessary tools and systems that can help increase the productivity of goat farmers. The method used is to carry out the process of weighing goats using loadcell, measuring goat body temperatures using MLX614 temperature sensors, and RFID sensors used to detect goat ID, goat type, and weighing time. In addition, it also uses Arduino as a microcontroller and raspberry pi as a server. There are sensor nodes and server nodes where data will be sent from the arduino uno sensor server node raspberry pi 3RF24L01. Communication technique is client server designed for communication between the client node and the server so that error the data transmission smaller. The results of testing tools and systems include testing the accuracy of sensors having several sensor values ??such as RFID, Loadcell and temperature sensors MLX90614 with several points, namely for RFID itself it is concluded that the success rate is read 100%, for the accuracy level of the loadcell sensor with a comparison of some of the scales carried out 10, the average error was 0.77% and the testing accuracy of the MLX90614 temperature sensor with a thermogun comparison, the average error value before calibration was 7.565%, while after calibration, the average error value was 0.507%.
Semisupervised Graph Neural Networks for Traffic Classification in Edge Networks

Edge networking brings computation and data storage as close to the point of request as possible. Various intelligent devices are connected to the edge nodes where traffic packets flow. Traffic classification tasks are thought to be a keystone for network management; researchers can analyze packets captured to understand the traffic as it hits their network. However, the existing traffic classification framework needs to conduct a unified analysis, which leads to the huge bandwidth resources required in the process of transferring all captured packet files to train a global classifier. In this paper, a semisupervised graph neural network traffic classifier is proposed for cloud-edge architecture so that cloud servers and edge nodes could cooperate to perform the traffic classification tasks in order to deliver low latency and save bandwidth on the edge nodes. To preserve the structural information and interrelationships conveyed in packets within a session, we transform traffic sessions into graphs. We segment the frequently combined consecutive packets into granules, which are later transformed into the nodes in graphs. Edges could extract the adjacency of the granules in the sessions; the edge node side then selects the highly representative samples and sends them to the cloud server; the server side uses graph neural networks to perform semisupervised classification tasks on the selected training set. Our method has been trained and tested on several datasets, such as the VPN-nonVPN dataset, and the experimental results show good performance on accuracy, recall, and F-score.
Regeneration of Liangzhu culture: multimedia exhibition, simulated restoration, innovative cultural products, nearby area integration, virtual reality and augmented reality

Abstract Since Liangzhu culture was recognised as the earliest monarchical period in the history of China, its vitality has been acknowledged both by the public and archaeologists in China and worldwide. Several contributions to current regeneration practices, including exhibitions, innovative cultural products, and integrations with nearby areas, remain in the preliminary stages due to the relative youth of the Liangzhu archaeological sites and museum, while their adoption of VR and AR technologies is also limited in practice. Referencing relevant cases in the literature and an interview with a manager involved with the archaeological work on Liangzhu culture, this article provides recommendations for exhibitions that emphasise the adoption of digital and multimedia displays and simulated restorations. Meanwhile, innovative cultural products require improvements related to design and development that match customer preferences. To integrate Liangzhu culture with nearby tourist attractions, comprehensive studies are needed on the spiritual connections that local communities develop with their original living spaces and the forms of their participation in heritage conservation practices. This article also presents several technical improvements for VR and AR, such as new methods for constructing digital models and the choice of presentation medium. Potential usages for AR technology with simulated restorations, interactive activities, innovative cultural products, and new public spaces nearby have also been suggested. Supported by the detailed discussion of restoration practices in these recommendations, this article provides an integrative regeneration plan for all “large” and “earthen” Neolithic archaeological cultures that resemble Liangzhu culture.
Gaze distance detection by binocular tracking for automatic zoom glasses

To realize automatic zooming of presbyopia glasses, we proposed a gaze distance detection method based on binocular tracking. A theoretical model was built to calculate gaze distance based on the gaze directions of two eyeballs. The gaze direction of each eyeball was obtained by the pupil center corneal reflection method. Three light-emitting diodes (LEDs) and two miniature cameras were placed on the glasses frame around the eyeball. Then, gaze direction was derived by calculating the distances between the pupil center and the positions of three LED reflection images by the corneal from the images captured by cameras. The experiment setup was built to test the gaze direction and gaze distance. The experimental result demonstrates that it is an effective way to obtain the user’s gaze distance for automatically adjusting the focal length of the zoom glasses.
Multi-Agent Cooperative Attacker-Defender-Target Task Decision Based on PF-MADDPG

A novel potential function multi-agent deep deterministic policy gradient (PF-MADDPG) algorithm is proposed for the multi-agent Attacker-Defender-Target (ADT). A multi-agent continuous state space and a continuous action space are established. The potential function rewards of target and defenders are designed to accelerate the game confrontation training speed, and the MADDPG algorithm is utilized to obtain effective strategies, so as to describe the influence of different actions on attackers. Finally, simulations are given to verify the effectiveness of the proposed PF-MADDPG algorithm.
Automatic Landing Guidance Law Design for Carrier-based Aircraft based on Multi-mode Guidance System

This paper mainly studies the automatic landing guidance system based on multi-scale asynchronous fusion algorithm, which can effectively improve the accuracy of landing. Firstly, on the basis of establishing the full nonlinear motion equations of six degrees freedom of carrier-based aircraft, a multi-mode guidance system composed of a variety of sensors is designed. Furthermore, in order to solve the problem of asynchronous information fusion combined with multi-mode guidance system, the adaptive unscented Kalman filter algorithm, multi-scale estimation theory and distributed federal filtering structure are adopted, and the sensors with different sampling frequencies are regarded as different scales, and the suboptimal estimation results are obtained through the local filter and sent to the main filter to obtain the global optimal estimation after fusion. Finally, the feasibility of the proposed algorithm is verified by simulation results.
The Optical Flow Estimation Method Based on the Attention Feature Enhancement Module

Optical flow estimation is one of the classic tasks in the field of vision. It calculates the motion of the target based on two continuous images. In most of the past work, the estimated optical flow often has errors in scenes of large motion or noise interference because the convolutional neural network structure often focuses on local features. To overcome the problems mentioned above, we propose a feature enhancement module that is based on the self-attention mechanism to enhance the dependencies of long-range features. Additionally, It can filter part of the noise, such as the interference of illumination in the input image. We evaluate our approach on standard benchmarks to verify motion estimation ability. To compare different results intuitively, we visualize flow fields for qualitative analysis. Eventually, we use the heat map to visualize the output of the attention layer to explore the mechanism of the attention algorithm.
Design and Implementation of Avionic Autonomous Management System for Deep Space Exploration

Autonomous management technology is an important means to ensure the long-term safety and autonomous operation of deep space exploration spacecraft on orbit. The development of autonomous management technology of some typical foreign spacecrafts is firstly summarized in this paper. And secondly, based on analyzing the needs of autonomous management of deep space exploration spacecraft, the design and management strategy of autonomous management system are proposed. Then the system architecture and engineering implementation of corresponding avionic autonomous management system are given, by taking the trial new-generation manned spaceship as an example. The results of the on-orbit operation demonstrated that the spaceship could realize functions of autonomous orbit control, autonomous management of flight state, autonomous health management of critical equipment, autonomous disposal of modules separation and autonomous control of reentry in case of non-ground intervention, which ensured the safety of spaceship to the greatest extent and effectively improved its reliability of autonomous operation.
Approach to secret content tagging on color images with a Sudoku-based scrambling principle

At present, there has been much focus on information hiding because it acts as an alternative to secure communication. Secret content is embodied in an image using various image embodiment techniques, with the least significant bit (LSB) substitution being one of the preferred strategies. Asserting the originality and quality of the content embodiment is equally challenging. A scrambling mechanism using a Sudoku scheme is applied on all red, green, and blue layer blocks of the image. The image layers are devised into blocks, and over the blocks unique scrambling procedures are applied to acquire the scrambled image. The scrambled image is then exposed to the confidential stream embodiment using the traditional LSB substitution scheme by following the chess game-based queen tour traversal pattern for locating the pixels. After the embedding process, the scrambled image is fragmented into blocks, and the exact descrambling of the blocks is performed to get back the image, which visually resembles the original image called the stego image. The stego image gets split into blocks at the receiver end to apply exact scrambling on the corresponding blocks, and using the queen tour pattern, the secret stream is to be extracted. As a result of the experiments, it is found that the projected approach is unlikely to be very predictable and unique in its patterns.
Real-time Underwater Target Tracking Using PP-YOLO and Cloud Computing

With the growing interest in ocean exploration, accurately tracking underwater targets has become increasingly important for resource exploitation and environmental protection. This paper explores the application of deep learning algorithms for multi-target tracking in underwater environments. The challenges of image processing in this context are discussed, and the YOLOv3 target detection algorithm is utilized to train a real-time underwater target tracking model with image enhancement techniques. Furthermore, the advantages and disadvantages of the YOLOv3 and PP-YOLO algorithms are compared by training the PP-YOLO model in the cloud. This study contributes to the development of more efficient and reliable methods for underwater target tracking.
Anomaly Detection of Spacecraft Reconstructed Signals Based on Attention Mechanism

Compared with the traditional anomaly detection methods, machine learning algorithms do not rely on manual and have the ability to extract advanced features of data. However, anomaly detection of spacecraft telemetry data by supervised machine learning is a challenging problem due to the lack of priori knowledge. This paper presents a signal anomaly detection algorithm based on attention mechanism. First, the long-distance characteristics of spacecraft telemetry data are captured by attention mechanism. Then, the stacked autoencoder compresses the data dimension and reconstructs the input signal to obtain the error reconstruction sequences. Furthermore, the anomaly indexes of the error reconstruction sequences are marked by the window threshold method to realize the anomaly detection of the spacecraft telemetry signal. Finally, the effectiveness of the algorithm is verified by a group of examples based on multi-channel spacecraft telemetry signals.
Task Management for Autonomous Flights of Micro Aerial Vehicles: A Behavior Tree Approach

This paper proposes a task scheduling framework for autonomous navigation of micro aerial vehicles (MAVs) in unknown environments. Currently, the dominant approach for task scheduling in MAV systems typically relies on the finite state machine (FSM), which presents limitations in scaling the system functionality due to the coupled relationship between modules. We propose a generic task scheduling framework for MAVs based on behavior trees (BTs) to address this challenge. A blackboard is built as a state data management center, decoupling different modules of the MAV so that the various functional modules can operate independently. In addition, we set up standardized interfaces for the modules, thus the behavior tree can quickly connect the modules. This framework enables MAVs to perform autonomous flight scheduling tasks and supports the rapid expansion of system functions. Finally, we validated the effectiveness of the framework with real-world experiments on a customized MAV.
A New Deep Reinforcement Learning Based Robot Path Planning Algorithm without Target Network

Intelligent agent navigation has broad application scenarios, one of the hot research directions in this field is agent dynamic path planning. For the target network in deep reinforcement learning to gradually deviate from online reinforcement learning, a new softmax operator is applied to replace the max operator in the original network after deleting the target network. New prioritized experience replay is applied to enhance the experience utilization of the agent and dueling network is employed to improve the perceptions of the environment in the path planning process. A modified dynamic ϵ-greedy algorithm is then developed to select actions. The experimental results in the simulation environment show that even after deleting the target network, the algorithm in this paper can still converge to a higher value within limited episodes, which proves its effectiveness.
HELOP: Multi-target tracking based on heuristic empirical learning algorithm and occlusion processing

Multi-target tracking is one of the important fields in computer vision, which aims to solve the problem of matching and correlating targets between adjacent frames. In this paper, we propose a fine-grained track recoverable (FGTR) matching strategy and a heuristic empirical learning (HEL) algorithm. The FGTR matching strategy divides the detected targets into two different sets according to the distance between them, and adopts different matching strategies respectively, in order to reduce false matching, we evaluated the trust degree of the target’s appearance feature information and location feature information, adjusted the proportion of the two reasonably, and improved the accuracy of target matching. In order to solve the problem of trajectory drift caused by the cumulative increase of Kalman filter error during the occlusion process, the HEL algorithm predicts the position information of the target in the next few frames based on the effective information of other previous target trajectories and the motion characteristics of related targets. Make the predicted trajectory closer to the real trajectory. Our proposed method is tested on MOT16 and MOT17, and the experimental results verify the effectiveness of each module, which can effectively solve the occlusion problem and make the tracking more accurate and stable.
Graph classification via discriminative edge feature learning

Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when aggregating node features, while omitting edge feature learning and failing to get an optimal graph structure. Moreover, many existing graph datasets do not provide initialized edge features, further restraining the ability of learning edge features via spectral GCNNs. In this paper, we try to address this issue by designing an edge feature scheme and an add-on layer between every two stacked graph convolution layers in spectral GCNN. Both are lightweight while effective in filling the gap between edge feature learning and performance enhancement of graph classification. The edge feature scheme makes edge features adapt to node representations at different spectral graph convolution layers. The add-on layers help adjust the edge features to an optimal graph structure. To test the effectiveness of our method, we take Euclidean positions as initial node features and extract graphs with semantic information from point cloud objects. The node features of our extracted graphs are more scalable for edge feature learning than most existing graph datasets (in one-hot encoded label format). Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets. Experimental results show that our method outperforms state-of-the-art graph classification methods on the new datasets. Our code and the constructed graph datasets will be released to the community.
Substation Personnel Smoking Detection Based On GhostNetV2-YOLOv5

In substations, violations such as smoking may lead to equipment damage and even threaten the safety of personnel. In this paper, a GhostNetV2-YOLOv5 based algorithm for detecting smoking by substation personnel is proposed. The algorithm uses GhostNetV2 as the backbone network to improve the accuracy of the model while achieving real-time detection of smoking behavior. The experimental results show that the method can improve the detection speed of the model without reducing the detection accuracy, and compared with the original YOLOv5 algorithm, the total mAP is improved by 2.58% and the prediction speed is improved by 1.61 times, which is superior to other smoking detection networks and has practical application value.