Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

Malicious social robots are the disseminators of malicious information on social networks,which seriously affect information security and network environments.Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks.Supervised classification based on manual feature extraction has been widely used in social robot detection.However,these methods not only involve the privacy of users but also ignore hidden feature information,especially the graph feature,and the label utilization rate of semi-supervised algorithms is low.Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods,in this paper a robot detection scheme based on weighted network topology is proposed,which introduces an improved network representation learning algorithm to extract the local structure features of the network,and combined with the graph convolution network(GCN)algorithm based on the graph filter,to obtain the global structure features of the network.An end-to-end semi-supervised combination model(Semi-GSGCN)is established to detect malicious social robots.Experiments on a social network dataset(cresci-rtbust-2019)show that the proposed method has high versatility and effectiveness in detecting social robots.In addition,this method has a stronger insight into robots in social networks than other methods.

A combined moving mechanism with independent movement in longitudinal direction and transverse direction

To make the detecting robot move on the surface of the finned tubes, a novel combined moving mechanism is developed. The combined moving mechanism is composed of sprocket wheel and drum-like small wheel installed on the chain. It can make the robot move independently in the direction parallel to the tubes and in the direction perpendicular to the tubes. This paper made a detailed discussion on the composition of the combined moving mechanism, the design method of the conjugate outline curve and the circular-arc outline curve of the drum-like small wheel that meshes with the tubes. The error of the circular-arc outline curve is also analyzed.

Research advance in phenotype detection robots for agriculture and forestry

The continuous development of robot technology has made phenotype detection robots a key for extracting and analyzing phenotyping data in agriculture and forestry.The different applications of agricultural robots and phenotype detection robots were discussed in this article.Further,the structural characteristics and information interaction modes of the current phenotype detection robots were summarized from the viewpoint of agriculture and forestry.The publications with keywords related to clustering distribution were analyzed and the currently available phenotype robots were classified.Additionally,a conclusion on the design criteria and evaluation system of plant phenotype detection robots was summarized and obtained,and the challenges and future development direction were proposed,which can provide a reference for the design and applications of agriculture and forestry robots.

Attention-based efficient robot grasp detection network

To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic information.Several lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a collision.This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers.We design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp configurations.AEGDN achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,respectively.The inference speed reaches 43.5 frames per second with only about 1.2×10^(6)parameters.The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well.Codes are available at https://github.com/robvincen/robot_gradet.

Accurate Robotic Grasp Detection with Angular Label Smoothing

Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment.Despite the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping detection.In this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB images.Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector.And for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than background.Specifically,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network.We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset.Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods.In particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.

Overview of research on agricultural robots in China

As the representation of new concept agricultural machinery,agricultural robots possess great advantages of improving agricultural productivity,enhancing production environment and solving the problem of labor shortage.Therefore,the strategy for application of agricultural robots and precision agriculture to improve the intelligence and information level of agriculture is the inevitable trend for China’s agriculture in the twentieth century.Based on the developmental status of agricultural robots in China,the agricultural robots are categorized and the performances,structures and characteristics of various agricultural robots such as vegetable grafting robots,transplanting robots,spraying robots,mowing robots,harvesting robots,grading and detecting robots are amply introduced.It can be seen that vegetable grafting robots,spraying robots,harvesting robots,grading and detecting robots have already been put into production while others are still at experimental stage.At present,there are several problems such as low popularization,great limitations,high cost and low intelligence,which greatly restrict the development of agricultural robots in China.Thus,open agricultural robot system with good expansibility,generality and flexibility should be developed and adopted to decrease its cost and shorten developing cycle.The mechanical structure of robots should also be designed as simply as possible.Finally,multi-robot system would become another important development direction of agricultural robots in the future.