Reviewerly/exHarmony huggingface dataset:
A fast incomplete data classification method based on representative points and K-nearest neighbors

Machine learning classification methods play an important role in image recognition, quantitative transaction and disease diagnosis. In the classification task, some sample features come with missing data. Classifiers based on complete features perform poorly in these samples. There are two methods to classify such data: train a new classifier and perform data imputation. The former often takes a very much time when the data volume is large, so the latter is generally selected. Knearest neighbor imputation is a classical data imputation algorithm, but it takes a long time. This paper proposes an imputation algorithm based on K-nearest neighbor imputation(KNNI). We call it a Fast-K-nearest neighbor imputation(FKNNI). The method first roughly classifies each sample and selects a smaller subtraining set before K-nearest neighbor imputation. This paper verifies the speed and accuracy of K-nearest neighbor imputation, and Fast- K-nearest neighbor imputation on three medical datasets. It proves that this method has a good effect on rapid disease diagnosis.
Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches.
Part-of-Speech Tagging of English-Translation Bilingual Parallel Corpus Based on Convolutional Neural Network

Annotation is the process of adding information to a corpus. The key to realizing the machine-reading of corpus and improving the value of corpus utilization is effective annotation. The convolutional neural network and its mathematical model are constructed, and the network structure, convolutional layer and pooling layer, activation function, loss function and optimization are studied in detail; a bilingual parallel corpus part-of-speech tagging model is constructed, including defining the objective function, determining the parameters and parameter space; the training process of the convolutional neural network is designed, including the forward propagation stage and the back propagation stage. Based on the convolutional neural network annotation, the corresponding features are learned from a large number of samples, which reduces the complexity of the network model, reduces the number of weights, avoids the complex feature extraction process, and promotes the research and application of machine translation.
Frabirc-based Consortium Chain Secondary Certificate Improvement Scheme

Blockchain is a decentralized distributed ledger technology, which has the characteristics of information immutability, openness and transparency, traceability, etc. However, in the process of blockchain transactions, each node is public, which will greatly increase risk of privacy breach. In view of the above problems, this paper mainly analyzes the defects of privacy protection in fabric, studies targeted privacy protection schemes, and improves the anonymity mechanism of blockchain transactions and the access mechanism of data acquisition.
A Mobile Robot Experiment System with Lightweight Simulator Generator for Deep Reinforcement Learning Algorithm

More and more researchers are trying to use deep reinforcement learning (DRL) for mobile robot tasks due to its powerful inference capability. However, deep reinforcement learning requires a large amount of data for DRL training in the pre-experimental stage, which hinders the application of the algorithm. On the other hand, the inconsistency between the ROS data interface and DRL GYM-Like data interface leads to a high cost of migration of the algorithm verification. This paper proposes a fast simulator generation method using linear approximate kinematics model and bake-based lidar rendering methods to generate a fast approximate simulator used in the pre-experiment stage to solve the problem of data cost. At the same time, an experimental system design scheme that converts the ROS interface into a GYM-like interface is also proposed to simplify the deployment process of deep reinforcement learning. We evaluate our proposed method on collision avoidance tasks in a variety of kinematics models and lidar scenarios. Our Method achieves about 14.2 times kinematics simulation speedup and 2.56 times lidar rendering speedup. We open-sourced our simulation environment and robot system software at https://github.com/efc-robot/MultiVehicleEnv and https://github.com/efc-robot/NICS_MultiRobot_Platform
TMBC: Topological map based coverage path planner for active exploration of an unknown environment

The ability to plan online paths, that passes through points of interest for coverage of an unknown environment is essential to robot autonomy. Existing state of the art coverage path planning techniques for active exploration (Frontier and Next-Best-View) are occupancy grid maps based, which do not scale well in large area environments, suffer from long term drift and are not robust at large values of sensor range noise, not address robot navigation safety. We proposed a Coverage Path Planning (CPP) algorithm, which is based on a coordinate system based topological map of the admissible locations of the environment. This algorithm scales well because of the sparse representation of large area environments, it is robust even at large value of sensor noise. This CPP is model free, computationally efficient and consider the robot safety by only considering the admissible locations for navigation. Viewpoints within the unexplored regions of robot workspace are chosen for traversal to cover the environment. The proposed procedure is experimentally verified in a 15 different 3D-gazebo world models using a 2-wheeled differential mobile robot with a Lidar sensor, using ROS framework for implementation. Simulation results shows the effectiveness of this algorithm for the real-world applications.
Visual aided training of billiards based on depth estimation

Billiards is a popular recreational sport, but because of the strong technical movement, beginners can not choose the relevant shot path. Given this situation, it is very promising and meaningful to use computer vision to assist billiards. However, due to the limited height and size of billiard ball, mobile phone photography can only get a side view, unable to accurately convey the cue ball's relative position and other balls. Therefore, the white ball hitting path planning can not achieve the ideal hitting effect in reality to assist users. This paper proposes a method of calculating the relative position of the cue ball and other balls by using monocular depth estimation and restoring them to the top view. Traditional monocular depth estimation requires the artificial assumption that the relationship between RBG image and depth satisfies a certain parameter model, which results in limited prediction accuracy and consumes computational resources. In this paper, the monocular prediction method based on deep learning is adopted, and the combination of PVCNN and global feature extraction method is proposed, which can improve the prediction accuracy and reduce memory consumption. The experimental results show that this method can be used to estimate the depth of information of billiards, which lays a foundation for the visual-assisted training of billiards based on mobile phones.
Deep Learning Models for Text Classification

Deep learning-based text classification models have grown more popular and extensively employed in a range of areas as natural language processing has advanced. Meanwhile, as more information becomes available to everyone, extracting useful information from vast volumes of data is becoming more difficult. This study outlines current mainstream data sets and examines current relevant text categorization and deep learning application situations.
Model for Single Image Enhancement Based on Adaptive Contrast Stretch and Multiband Fusion

This paper proposes a rapid single image enhancement that may be used in various situations, no matter what channel it is. The fundamental idea of this paper is to combine multiband fusion with adaptive contrast stretch (ACS) dehazing approaches, resulting in balanced image improvement while elaborating on visual details. The foundation and detail layers for intensity and Laplacian modules are extracted using multiband decomposition. For the intensity module, the proposed ambient map and transmission estimation can restore the correct intensity. Details on each residual layer are adjusted using adaptive nonlinear mapping algorithms. Our results show excellent performance on various hazy photos using color-corrected reconstruction. Furthermore, we use ACS based on a modified histogram equalization procedure. It is adaptable and gives a localized contrast enhancement effect that is impossible to achieve with typical contrast stretching methods. The proposed method has been thoroughly tested in terms of traditional image quality comparison.
Dynamic Self-Maintenance Obstacle Costmap Based on Instance Segmentation and Memory Storage Mechanism

As the key component of graph search path planning algorithms, environmental costmap is widely used in many robot navigation systems. However, during the lifelong operation of robot, various types of obstacles change the environment constantly, which affect the accuracy of costmap and the effectiveness of path planning. To eliminate the negative effects of obstacle changes in lifelong navigation, we propose a novel dynamic self-maintenance obstacle costmap based on instance segmentation and memory storage mechanism. First, we present an instance-level obstacle segmentation model based on laser-vision fusion. The pose, category, and observation information of obstacles are recorded through clustering and recognition. Then, we suggest an update and maintenance approach for obstacle costmap based on memory mechanism. Imitating the Ebbinghaus Forgetting Curve, the costs are updated by different activity levels in real-time, and obstacles are removed or retained in costmap accurately. In comparison to the widely used layered costmaps, experiments show that the proposed approach is able to remove the obsolete obstacles out of sight in time, enhance the accuracy of costmap and improve the effectiveness of lifelong navigation.
An Improved COSMIC Software Evaluation Method

Under the background of the Internet era, there are many kinds of software applicable to various business scenarios everywhere. How to evaluate the scale of a software is not only related to the investment of software development time, but also affects the investment of capital and personnel in software development. At present, the mainstream software scale evaluation method is the full Function Point analysis (COSMIC-FFP) proposed by the International Association for Universal Software Measurement. COSMIC software evaluation method through the retrieval of all the data movement process, each data movement process as a function point (CFP), and finally through the statistics of the number of CFP to get the size of the entire software measurement this paper proposes an improved COSMIC software evaluation method: according to the software needs description, grammar and syntax. For the relatively fixed language features such as parts of speech, we should develop corresponding segmentation tagging and error filtering rules, introduce natural language processing ideas to process software function points, and build local rule base to weaken the over-dependence of appraisers on rule guide and reduce the difficulty of getting started. Experiments and actual evaluation show that the improved method in this paper not only improves the accuracy, but also improves the time efficiency more obviously.
Computer Big Data Analysis Internet Business Risk Prevention System Research

Energy Internet is one of the key technologies to achieve the goal of Chinese energy revolution, and it is also the culmination of source, network, load and storage. This paper firstly analyzes the development trend of power and information fusion in stages, and studies the power and information interaction mode in the energy Internet stage. On this basis, this paper analyzes the research status of power and information risk transfer from two aspects: information risk transfer and power and information risk interaction. Finally, based on computer big data, this paper analyzes the power information fusion network structure under the energy Internet from the topology and operation levels, and proposes a power Internet information operation risk system under the energy Internet.
Resource Scheduling in Behavior Trees

Behavior Tree (BT) is a behavior modeling tool derived from computer game design. It has the advantages of modularity, hierarchy, reactivity, and readability, which can be used to model the behavior of agents and implement task-switching functionalities. Parallel task execution in behavior trees may encounter resource conflicts similar to those in concurrent programming. To solve the problem of resource conflicts in parallel executions of BT, the actions are usually set execution priority in advance. However, this method is not optimal for scheduling parallel tasks in many BT applications. In this paper, we propose to integrate several classic resource scheduling algorithms into a popular BT framework and evaluate it by multiple experiments. The BT resource scheduling mechanism expands the architecture of the classic behavior tree.
Named Entity Recognition Method for CNC Machine Tool Design Knowledge Text

This Aiming at the problems of unclear definition of terminology and lack of terminology recognition model in the current design knowledge text terminology recognition in the field of machine tool, a calibration strategy of machine tool design knowledge terminology is formulated, and a method of CNC machine tool design knowledge terminology based on bidirectional encoder representations from transformers is proposed. This method uses the idea of transfer learning, firstly performs fine-tuning based on the BERT pre-trained language model of the CNC machine tool field dataset, Then, the bidirectional long short-term memory (BiLSTM) network is used to extract the sequence feature information of the text, and finally terms are identified using conditional random field (CRF) calculations. The experimental results show that based on the BERT-BiLSTM-CRF model, the accurate recognition of the terms of CNC machine tool design knowledge is realized, which is more than 10% higher than the F1 of the existing recognition method.
Heterologous Optical Remote Sensing Image Matching Method Based on Road a Priori

Automatic matching of heterogenous remote sensing images is a common fundamental problem of concern in the field of remote sensing image processing. Aiming at the problem of difficult matching of heterogenous optical remote sensing images, this paper proposes a road-priori method for automatic matching of heterogenous remote sensing images. The method first extracts the roads of remote sensing images by using road segmentation network; then adopts road matching algorithm based on road intersection features to realize the matching from target road map to reference road map under the framework of hypothesis testing; finally maps the corresponding transformation relations to the corresponding image pairs to obtain the final matching results. The experiments prove that the method proposed in this paper can solve the impact of large geometric and radiometric distortion on image matching, and at the same time has superiority in matching accuracy and efficiency.
Unsupervised learning method for shape matching templates based on gradient saliency estimation

In order to improve the accuracy and robustness of the shape-based matching algorithm in practical industrial vision applications, we proposed an unsupervised learning algorithm, which under a given initial shape, can capture more potential shape features of the target in the training set through a comprehensive estimation of both the saliency of gradient orientation and gradient amplitude, consequently, learn a better template for matching. The experiment shows that when the number of valid training images reaches about 30, the point number difference between the learned shape and the ideal shape does not exceed 8%. In a comparative experiment on matching precision and recall of four different shapes, we found that the data curves of the learned shape were almost identical to those of the ideal shape, which proves that the algorithm has effective self-learning ability and thus, can improve the performance of shape-based template matching.
Application of Artificial Intelligence in Playing Cards

Artificial intelligence has invaded into fields of chess playing with the weapon of supervised, deep and reinforcement learning. However, the problem whether artificial intelligence could qualify for the incomplete information system such as playing cards is still open. Efforts have made towards numerous games all of which have professional experience, so it remains the problem of developing a system from scratch without human’s help. In this study, a model combining the Monte-Carlo method and decorated self-Reinforcement Learning was proposed to tackle such problem. To be more specific, a card game called "Zhengshangyou" was chose to verify the proposed method. It requires two players competing each other with a pack of card including the Jokers. In addition, the rules of this game and the designed Artificial Intelligence (AI) algorithm are presented and explained in this study to provide a clear mechanism. The method has promising through theoretical and experimental analysis.
Designing the intelligent epidemic control robot control system for epidemic prevention and control

In response to the current problem of highly contagious new coronavirus and repeated epidemics, which cause great threat and inconvenience to people's production and life, In this paper, a multifunctional intelligent epidemic prevention robot control system based on a single chip microcomputer is designed to realize the intelligent management of community epidemic prevention and control. Stm32 microcontroller is used as the control core. In order to improve the efficiency of prevention and control management and reduce contact, the Jetson Nano controller is designed to provide map reproduction, positioning navigation, and path planning functions. It is used to summarize patient status information quickly and efficiently, the design provides face recognition and remote monitoring functions to realize real-time uploading of accurate data to cell phone console APP and computer terminal integrated monitoring platform. Through the map reconstruction and positioning simulation test, an optimal path is selected to ensure the stable movement of the epidemic prevention robot. The face_recognition algorithm's error reception rate, error rejection rate, and accuracy rate are 0.35%, 11.12%, and 88.53%, respectively, which are better than the face-net algorithm in three aspects and can well meet the needs of small communities. The face recognition needs of small community areas can be well met. This epidemic prevention and control system can realize efficient community epidemic prevention and control management, reduce contact transmission, and lower the difficulty of epidemic prevention and control.
Optimization of Blockchain Storage Architecture Based on Voronoi Diagram

The decentralized blockchain network architecture lacks the centralized management nodes in the traditional centralized distributed network, and if a subset containing K nodes needs to be searched in order to find the nearest set of nodes for merging distributed engines to improve the communication efficiency, the computational overhead of such an operation is huge. In order to solve the problem of inefficient information communication flow caused by the lack of centralized management nodes in blockchain networks, this paper proposes a blockchain structure storage architecture optimization scheme based on Voroni graph, which can quickly determine the impact of merging distributed storage engines on information communication efficiency and thus help to optimize the blockchain distributed storage architecture. The final implementation optimizes a problem that requires searching from O(n^k) subsets of prior art to an O(n*k) search problem.
Multi-hypothesis Data Association for Multiple Target Tracking

Multiple targets tracking (MTT) technology is widely used in many fields, such as security, transportation, military, etc. Data association, a significant part of MTT, has always been a difficult problem in MTT due to the complexity and uncertainty of realistic environments. There are also a large number of related algorithms in the MTT field that attempt to solve the data association problem. In this paper, we concentrate on data association in MTT and try to offer a brief review on the mainstream methods of data association in recent decades. The contributions of this review are three-fold: firstly, we briefly introduction to what MTT is and some common data association algorithms; secondly, we present in detail some data association algorithms; finally, we briefly review and summarize these methods we mentioned.
Depth-Independent Depth Completion via Least Square Estimation

The depth completion task[8] aims to complete a per-pixel dense depth map from a sparse depth map. In this paper, we propose an efficient least square based depth-independent method to complete the sparse depth map utilizing the RGB image and the sparse depth map in two independent stages. In this way can we decouple the neural network and the sparse depth input, so that when some features of the sparse depth map change, such as the sparsity, our method can still produce a promising result. Moreover, due to the positional encoding and linear procession in our pipeline, we can easily produce a super-resolution dense depth map of high quality. We also test the generalization of our method on different datasets compared to some outstanding algorithms. Experiments on the benchmark show that our method produces competitive performance.
Research Advanced in Deep Learning Object Detection

Object detection aims to locate and assign various predefined substances from images to corresponding classifications. Benefit from the rapid evolution of deep learning, target detection algorithms based on convolutional neural networks have achieved breakthroughs in both accuracy and efficiency. Based on the detailed literature research and analysis, in this paper, we make an integrated assess of the research progress of object detection. Specifically, we first introduce the existing representative algorithms from two-stage detection framework to one-stage detection framework. Then a battery of experiments are conducted to analyze the implementation of different detection algorithms on some common datasets. Finally, we summarize the main challenges and give an outlook on future research development for object detection.