Reviewerly/exHarmony huggingface dataset:
Name Entity Recognition (NER) Based Drug Related Page Classification on Dark Web

While researching the dark web marketplaces, it was observed that the drugs’ names varied on different marketplaces. Therefore, the same drug might refer to different names on different dark web marketplaces. For example, some marketplaces use the chemical name or medical name as their product name to ensure the exact product; meanwhile, some marketplaces use the street name as the product name to attract users and get more orders. The present work discussed a NER based method to find if a website on the dark web has mentioned drugs. First, the dark web crawler crawled data from the dark web. Then, the authors introduced the Named Entity Recognition (NER) drug dataset with two categories of drug-named entities: Street name and Chemical name. Further, to identify drug-related web pages comprising street and chemical names of drugs with the NER model employed on scraped data. The proposed NER model was tested with the Drug-NER dataset. The DrugcrossNER project contains a predefined Drug-NER dataset with over 3500 listings from the dark web markets. The proposed work also generates a DRUG entity for the NER model in spaCy, an open-source NLP library in python, as it does not have the in-built ability to classify objects into custom categories.
UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAVs have great potential when applied to persistent monitoring, but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path information. Therefore, it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring tasks. In response to the above problems, this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path security. Through the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy, a mathematical model for UAV path planning is established. An improved ant colony algorithm based on the monitoring overdue time (Overdue-aware Ant Colony Optimization, OACO) is designed, and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed, improve the security of the UAV monitoring path, which is of great significance for enhancing monitoring security and preventing intrusion.
Weakly supervised object detection with interactive edge attentive collaboration

Weakly supervised object detectors based on image-level annotation tend to overfit in the discriminative regions while ignoring the integrity of the object. In this paper, a novel weakly supervised object detection network with the interactive edge attentive collaboration module is proposed to alleviate the local optimal problem, in which edge attention is extracted as an object unity supervision for the detection, and a collaborative loss is introduced to enable VGG16 feature map with global attentive ability. The module can be detached from the network in the test period, which ensures the high efficiency of the detector without introducing any additional inference cost. Extensive experiments are carried out on the PASCAL VOC 2007 and VOC 2012 datasets, which reach 52.3% mAP, 67.7% CorLoc and 49.1% mAP, 68.0% CorLoc respectively, outperforming state-of-the-arts.
Visual Servoing of AGV Based on Nonlinear Model Predictive Control

In this paper, a new visual servoing method based on nonlinear model predictive control (NMPC) is proposed for the four-Mecanum wheel AGV in the smart warehouse. The AGV is equipped with a top camera for real-time obstacle avoidance and a bottom camera for ground-level AprilTag detection that assists AGV navigation and localization. After that, the NMPC is combined with the kinematics of the Mecanum wheel to establish a position-based visual servoing (PBVS) system for correcting the navigation errors and keeping the AprilTag always staying within the field of view of the bottom camera. Finally, the results of simulation experiments and actual tests verify the efficiency of the nonlinear model predictive controller, and the AGV navigation accuracy can reach millimeter level.
Adaptive Mask Based Attention Mechanism for Mandarin Speech Recognition

This paper proposes a novel Transformer model, called ALDSA-Transformer, for extracting local and global information in Mandarin automatic speech recognition (ASR) tasks. In such a model, the adaptive local dense synthesizer attention (ALDSA) is designed to capture the local context correlations. The span of each attention head in ALDSA is parameterized by introducing the adaptive mask function. After training, the adaptive mask function with fixed attention span is applied to the attention matrix for masking the corresponding values. Besides, the ALDSA is integrated with self-attention (SA) to build the ALDSA encoder block. Furthermore, with the stacked encoder blocks, the ALDSA-Transformer encoder is constructed to extract the acoustic features in Mandarin ASR task. Compared with other variants of transformer-based methods, the experimental results show that the proposed method achieves a lower character error rate (CER) of 5.65% with parameter size 23.0M on AISHELL-1 dataset.
Adaptive temperature tracking control of microwave heating high titanium slag based on ε-ADP

A data-driven model-based microwave heating adaptive dynamic planning temperature tracking algorithm is proposed for the microwave heating process with time-varying nonlinearity and strong coupling. The microwave heating process is modeled using a single hidden-layer feedforward network and the discrimination capability of an extreme learning machine. Based on this, the performance index functions and control laws are approximated using the approximation capability of the single hidden layer feedforward network and the extreme learning machine. Experimental and simulation results show that the proposed algorithm successfully tracks the temperature of high titanium slag during the microwave heating process.
Deep Reinforcement Learning for the Improvement of Robot Manipulation Skills Under Sparse Reward

Under sparse reward, the reward information is very sparse during the training process, which is difficult for the robot to obtain rewards and learn successful manipulation skills. Therefore, we utilize deep reinforcement learning to improve robot manipulation skills. In this work, we propose a fusion algorithm based on the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3) and Hindsight Experience Replay (HER). Taking the advantages of end action compression and expert data, we utilize them to optimize the performance of TD3+HER. A Fetch robot simulation environment is built in the Pybullet module to verify the fusion algorithm in the push and pick-and-place tasks. Experimental results show that the fusion algorithm reduces 30% learning time compared with TD3+HER in the push task. Moreover, the fusion algorithm makes a breakthrough in the pick-and-place task, which improves the success rate more than 70%. These results confirm the proposed algorithm can effectively solve the sparse reward problem.
Unit Circles Decomposition-based Coverage Path Planning for UAV

In this paper, a coverage path planning (CPP) scheme for an unmanned aerial vehicle (UAV) in coverage observation is proposed. The algorithms begin by decomposing the task region into several unit circles. The positions of the unit circles are optimized by the improved Particle-Glowworm Swarm Optimization algorithm based on the area size of the unit circle and task region. Then the connection sequence of all unit circles is solved by the Traveling Salesman Problem (TSP) algorithm according to the shortest path principle. Further, the cubic Bézier curve is used to fit the centers of all unit circles to get a smooth coverage path. Finally, two curvature adjustment algorithms are proposed to optimize curvature. The main contribution of the paper is to present a scheme that generates a smooth coverage path while providing coverage optimization and path length optimization. Computer simulations validate the effectiveness of the algorithms.
Generative Adversarial Imitation Learning from Human Behavior with Reward Shaping

Generative adversarial imitation learning (GAIL) aims to make a robot learn optimal policy from expert demonstrations. However, its application mainly focuses on the robot’s partial body control, such as the mechanical arm, and is rarely applied in whole-body action learning. Although making robots mimic human behavior has always been a mesmerizing goal in robotics, it is challenging for GAIL to reproduce complex human-like behaviors because of unsatisfactory performance. Furthermore, for multi-dimensional complex humanoid action imitation, only using output from discriminator may limit the performance of GAIL. To solve these problems, we propose a method, Generative Adversarial Imitation Learning from Human Behavior with Reward Shaping (GAIL-RS), enabling the robot to learn humanoid behaviors effectively. Instead of directly learning policy, a reward shaping mechanism is incorporated to improve the discriminator’s performance. In addition, we utilize the proximal policy optimization (PPO) algorithm as our generator to enhance imitation performance. Experiments were conducted on two robotic humanoid imitation tasks in simulation. The results demonstrate that our method can learn the optimal policy from reference motion and performs better than other methods.
From Standard ZND (Zhang Neural Dynamics) Solving Time-Variant Linear System (LS) to Elegant-Formula ZND Solving Time-Invariant LS Linked to Jacobi Iteration

Solving linear system (LS) is a common and important issue in both academic and industrial fields. There are many methods to solve LS, including neural network (specifically, neural dynamics) and traditional numerical computation algorithms. Besides, traditional numerical computation algorithms comprise, but not limited to, Jacobi iteration and Gauss-Seidel iteration algorithms. On the other hand, neural network algorithms have been the hot topics in research for a long time, which are applied in various fields. In addition, standard Zhang neural dynamics (ZND) is a special kind of neural network to solve some time-variant problems effectively. In this paper, an elegant-formula ZND (EFZND) algorithm is obtained through theoretical derivation. Then, the standard ZND and different EFZND models for solving LS are introduced. Furthermore, the possible relationship between neural dynamics and traditional numerical computation algorithms, especially Jacobi iteration algorithm, is discussed from two specific EFZND models for solving LS. Finally, the feasibility and efficiency of standard ZND and EFZND models for solving LS are proved through derivation.
Research on Modeling and Application of Anti-Submarine Decision-Aided System Based on Neural Network

With the development of aviation anti-submarine technology, there are many advanced anti-submarine aircraft equipped with decision-aided system. Aiming at the problem of how to apply anti-submarine decision-aided system, this paper studies an effectiveness evaluation model based on neural network by taking the parallel sector pattern as an example. Simulation results show that the trained model is extremely accurate, which can guide pilots to set more precise parameters of anti-submarine decision-aided system in real time without long time Monte-Carlo simulation. In practice, this model can provide a theoretical reference for the anti-submarine warfare.
ConvMLP for Driving Behavior Detection from Naturalistic Driving Data

Because of the complexity and volatility of the traffic environment, driving behaviors detection in naturalistic driving is still a significantly hard problem. In this paper, a novel attention-free model, Convolutional MLP (ConvMLP) is proposed for driving behavior detection using video frames and multivariate vehicle signals. By introducing convolutions into the architecture through two aspects, i.e., a hierarchy of MLP stages containing convolutional token embeddings, and ConvMLP modules to model local and global dependencies from multi-modal data simultaneously, MLP-based model can be improved in both performance and efficiency. Specifically, driving context that includes driving scene features and vehicle dynamics features can be generated, and a Long Short-Term Memory (LSTM) neural network is designed to learn the heterogeneous features and detect driving behaviors. Moreover, the generalization ability of ConvMLP is investigated by a customized Spatial Ensemble (SE) student-teacher framework on unlabeled data. The experiment results on a self-collected driving data set show that the proposed ConvMLP with a smaller model size performs on par with or superior than other state-of-the-art models.
Weighted Graph Convolutional Network for Collaborative Filtering Considering Entity Similarity

The core idea of collaborative filtering is to use historical interaction data of users and items to find similar users or items for recommendation. Most of the recommendation work based on the graph field is to directly use the Graph Convolutional Network (GCN) on the user-item bipartite graph. This kind of graph cannot reflect the similarity of information between the homogenous nodes of the users (items). To solve this problem, a Weighted Graph Convolutional Collaborative Filtering recommendation model (WGCCF) considering entity similarity is proposed. In this work, the user-item bipartite graph will be transformed into two homogeneous graphs. Firstly, the user-item scoring matrix is decomposed by singular value decomposition, and the vector representation of users(items) is obtained to calculate the similarity between users (items). Then the similarity is used as the edge weight of nodes in user(item) homogeneous networks and a weighted network based on node similarity is constructed. Finally, graph convolution operation is performed on the user (item) graph to extract the hidden layer representation of the user (item), and the vector inner product combination method is used to predict the score. Experimental results show that compared with the existing recommendation algorithms for matrix factorization and graph convolution, the index performance of RMSE and MAE can be improved up to 12.72% and 15.99%.
Fault Diagnosis Based on the Adaptive Network Fuzzy Inference System for Power System of Offshore Platform

The power system fault of offshore production platforms will cause economic losses and may pose a threat to the safety of personnel and equipment. Fault diagnosis can locate the fault position and analyze the fault reason, which is of great significance in reducing energy consumption, improving product quality, and ensuring safety. A fault diagnosis method based on the adaptive network fuzzy inference system (ANFIS) for the power system of offshore platforms is presented in this paper. The ANFIS based fault diagnosis method requires the data measured by the SCADA system and emphasizes the importance of fuzzy rule reasoning. This method is applied to power fault diagnosis of a production platform to realize accurate diagnosis of current short circuit fault.
Cooperative Path Planning of Multi-Agent Based on Graph Neural Network

The objective of the Cooperative Multi-agent Planning problem is to provide each agent with an accessible, efficient and collision-free path, and the key is effective communication and cooperation between agents. Scope of this paper is to present a novel cooperative approach for multi-agent coordination in planning. We presented a combined architecture for Multi-Agent path planning in an unknown environment, and each agent only has local communication and local observations to collaborate and share perceived information with others. The architecture consists of a convolutional neural network (CNN) that extracts adequate features from local perception, a Graph Sample and AggreGate (GraphSAGE) that fuses the features among agents, and a Multilayer Perceptron (MLP) that decodes the output of GraphSAGE into action primitives. We trained the combined neural networks and tested them, the overall efficacy of the presented method was comprehensively evaluated by multiple simulation results in an unknown environment.
Network Service Identification Method for Electric Power Cloud

Electric power cloud data center becomes more and more widely applied, and yet lacks security protection inside it. The most direct solution for this issue is internal network isolation. This paper focuses on the first task of the isolation, namely network service identification, and proposes a network service identification framework based on deep clustering. In order to obtain service stream, a network service stream extraction algorithm is proposed based on the flow metric and traffic pattern. Considering the high dimensionality and complexity of service streams, the denoising and convolutional autoencoders are combined for feature extraction. And then a self-organizing mean maps network is adopted to achieve network service identification. Experiment results illustrate the effectiveness and superiority of the self-organizing mean maps network with regard to the internal evaluation index of clustering. Therefore, the identification framework proposed seems to provide a foundation for subsequent isolation study.
Feature Fusion with Slim Non-Local Module for Multispectral Pedestrian Detection

Feature fusion plays a vital role in multispectral pedestrian detection system. Traditional fusion in different layers rely on simplely concat method for efficiency, but the pixel-wise spatial relationship in both short and long range are neglected. Towards this problem, non-local network provides a possible solution to cross-modality feature fusion, which is able to capture the long range relationship between pixels in the feature map from both RGB and thermal modalities. However, the naive utilization of non-local network brings significant memory footprint cost especially for the high resolution feature maps. To this end, we propose a slim non-local network, which compress the size of the attention map, leading to significant reduction of memory load. Besides, ablation studies with our slim non-local module for different stage are also investigated, which indicates that the optimal fusion schema comes from all the feature layers. Experimental results on KAIST dataset report that our method can outperform the baseline method with reasonable cost.
An Auxiliary Decision Method for Playing of the Poker2to1 Agent

Intelligent agents play a similar role to players in the game, which is an important carrier to determine the progress of the game. It is difficult for an intelligent agent to make intelligent decisions similar to human play in any situation in Poker2to1 because competition, collaboration, imperfect information, huge action space and other factors intertwined. Moreover, due to the randomness of Poker2to1 game process, the action decision set of any situation of AI agent in the game process is constantly changing, which makes it more difficult for the Poker2to1 AI agent to make decisions on playing cards. This paper abstracts the game process of Poker2to1, puts forward a concept of shanwin distance, and constructs the relevant calculation method to assist Poker2to1 AI agent in any situation decision in the game process. Experimental results show that the method proposed in this paper can improve the decision-making efficiency of Poker2to1 AI. It is of positive reference significance to solve the problems of Poker2to1 and other imperfect information games.
Improved Seagull Optimization Algorithm Based on Multi-Strategy Integration

Aiming at the defects of seagull optimization algorithm (SOA) in solving optimization problems, such as local optimization, slow convergence speed and low optimization accuracy, a seagull optimization algorithm (SCSOA) based on integration of improved Sobol sequence and Cauchy variation is proposed. First, initialize the population using the Sobol sequence to make the seagulls more evenly distributed in the initial solution space; Secondly, use a nonlinear function to replace the original convergence factor to guide the seagull to always maintain a larger body degree of freedom during the search process, enhance the global search ability, and avoid falling into the local optimum; Then, the Cauchy variation strategy is introduced, so that the individual can better find the optimal solution and enhance the ability of the algorithm to jump out of the local optimum; Finally, use the benchmark function to test the improved algorithm, and compare it with the original algorithm and the experimental results of other algorithms. The results show that SCSOA performs better in convergence speed and optimization accuracy, and the global optimization capability is also improved.
Multimodal Human-Computer Interaction Based on Bayesian Classification Algorithm

With the development of technologies such as computer vision and speech recognition, human-computer interaction technology has gradually matured and has a broad development prospect in many fields. In this paper, a multimodal human-computer interaction system based on Bayesian classification algorithm is constructed, which collects human posture and speech information of the interactor during the interaction process, and determines the interaction intention by the information of three modalities: finger direction, face orientation and speech of the interactor, without touching or wearing any equipment during the whole interaction process. The head pose estimation method is introduced and used as the face orientation modality for the multimodal interaction system; meanwhile, the DBSCAN (density-based spatial clustering) algorithm is used to select the interaction frames so as to filter out the unstable interaction frames and improve the system robustness. In addition, a multimodal data fusion algorithm based on Bayes' theorem is proposed in this paper to improve the interaction accuracy. Experiments show that the proposed multimodal HCI system has better robustness and higher accuracy than the single-modal interaction and decision matrix fusion.
A Multi-object Detection and Tracking Method Based on the Fusion of Lidar and Camera

Aiming at the problem that some detection networks have high accuracy but poor real-time performance and are not easy to deploy on embedded devices when used to intelligent vehicles, the method in this paper is proposed. And it solves the problem of global target tracking respectively and trajectory management in multi-objective environment. It’s an improved method for object detection and tracking based on the data fusion of lidar and camera. In this method, first, Yolov3 algorithm, which is accelerated by KCF algorithm, is used to recognize the object in the image information. And cluster the point cloud. Second, align sensor data in time and space. Then, the object information of the two sensors is supplemented by the Hungarian algorithm matching. Further then, the multi-frame matching is carried out through the key point matching count, and the multi-frame information is used for the object tracking. Finally, the continuous motion trajectory of each object in the surrounding environment can be obtained. Experimental verification shows that the position detection error of this method is less than 5%, and the total processing time of a single frame is 154ms. Compared with other methods, this method can significantly improve the processing speed, which is convenient to enhance the real-time data processing as a step of the perception module.