Reviewerly/exHarmony huggingface dataset:
UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese

Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwriting recognition methods. Moreover, as a low-resource language, there are not many datasets for re-searching handwriting recognition in Vietnamese, which makes handwriting recognition in this language have a barrier for researchers to approach. Recent works evaluated offline hand-writing recognition methods in Vietnamese using images from an online handwriting dataset constructed by connecting pen stroke coordinates without further processing. This approach obviously can not measure the ability of recognition methods effectively, as it is trivial and may be lack of features that are essential in offline handwriting images. Therefore, in this paper, we propose the Transferring method to construct a handwriting image dataset that associates crucial natural attributes required for offline handwriting images. Using our method, we provide a first high-quality synthetic dataset which is complex and natural for efficiently evaluating handwriting recognition methods. In addition, we conduct experiments with various state-of-the-art methods to figure out the challenge to reach the solution for handwriting recognition in Vietnamese.
Deepfake Detection using EfficientNet: Working Towards Dense Sampling and Frames Selection

Deepfake is a controversial technology that allows the automatic generation of video content through generative adversarial networks. The emergence of Deepfake technology is problematic and sophisticated, making it more difficult to detect. In our paper, we contribute a deep-learning method to resolve that problem. We use the MTCNN face detector to extract facial images and apply data augmentation and EfficientNet for real-fake classification. We apply frame selection with the raw label prediction to tackle the fault cases and receive the final label. With the approach above applied, we utilize training and evaluation datasets from FaceForensics++ and achieve an accuracy of 62.5%.
Leveraging Efficiency through Hybrid Prioritized Experience Replay in Door Environment

Experience replay enables agents to remember and reuse past experiences in reinforcement learning, just as human beings utilize the past memory. At present, the experience buffer of on-policy algorithm iterates fast and cause the problem of low sample utilization, which leads to the low efficiency of the training agents based on uniform selected samples. Most of the existing rule-based replay strategies have been applied in the off-policy algorithm, which have shown good results. Replay strategy adjustment is challenging, as replay memory samples have large noise levels, which leads to unstable value functions. One of the most challenging aspects is deciding what experience to prioritize. To solve this problem, we propose a method called Proximal Policy Optimization with Hybrid Prioritized Experience Replay (HPER-PPO) to adjust the sample priority and guide the selection, through which the policy can be better optimized and the cumulative reward can be maximized. We select two kinds door- related long horizon tasks to better measure whether the agent has greater ability to learn and obtain cumulative rewards in our method. The results show that our method can reduce the training time and potentially increase long-term return. Further, we propose a possible explanation for the reason why this method improves the efficiency and brings changes to on-policy algorithm experience replay mechanism.
Multilingual Communication System with Deaf Individuals Utilizing Natural and Visual Languages

According to the World Federation of the Deaf, more than two hundred sign languages exist. Therefore, it is challenging to understand deaf individuals, even proficient sign language users, resulting in a barrier between the deaf community and the rest of society. To bridge this language barrier, we propose a novel multilingual communication system, namely MUGCAT, to improve the communication efficiency of sign language users. By converting recognized specific hand gestures into expressive pictures, which is universal usage and language independence, our MUGCAT system significantly helps deaf people convey their thoughts. To overcome the limitation of sign language usage, which is mostly impossible to translate into complete sentences for ordinary people, we propose to reconstruct meaningful sentences from the incomplete translation of sign language. We also measure the semantic similarity of generated sentences with fragmented recognized hand gestures to keep the original meaning. Experimental results show that the proposed system can work in a real-time manner and synthesize exquisite stunning illustrations and meaningful sentences from a few hand gestures of sign language. This proves that our MUGCAT has promising potential in assisting deaf communication.
High maneuverability control of Single-track Two-wheeled Robot in Narrow Terrain based on Reinforcement Learning

The single-track two-wheeled (STTW) robot has the advantages of small size and flexibility, and it is designed to travel in narrow terrain such as mountains or jungles. In this article, a reinforcement learning-based control method is proposed to solve the problem that STTW robots drive fast in narrow terrain with limited visibility. This control method integrates STTW robot path planning, trajectory tracking, and balance control in a single framework. Based on this framework, we define state, action, and reward function for narrow terrain passing tasks. At the same time, we design the actor network and the critic network structures and use the Twin Delayed Deep Deterministic Policy Gradient (TD3) to train these neural networks to construct a controller. Next, we verify the proposed control method on a simulation platform. The simulation results show that the obtained controller allows the STTW robot to effectively pass the training terrain. In addition, this article conducts a simulation comparison to prove advantages of training with the TD3 algorithm and the effectiveness of the reward function.
Research Advanced in Image Denoising Based on Deep Learning

Image denoising has always been a hot research issue in the computer vision community, which aims to reduce the noise in digital images to improve image quality. As a key step in many downstream computer vision tasks, image denoising has been widely used in many fields such as medical agricultural images, satellite images, remote sensing images, face recognition, vehicle detection and many other fields. However, limited by problems such as insufficient actual image data sets, uneven noise reduction ability of different models, uneven noise reduction performance of the same model on different data sets and uneven noise reduction results of the same model for different scales of noise, the existing denoising algorithms still can not fully meet the needs of practical applications. In order to explore the optimal denoising method under the influence of the above variables, we first introduce three representative denoising frameworks based on self-supervised, residual network and generative countermeasure network. We then choose five best noise reduction models: CBM3D, DnCNN, IRCNN, FFDNet, and Fdncnn, and classify and compare the real noise and synthetic noise on five data sets, such as CBSD68, Kodak24, McMaster, DND, PolyU. In addition, in the noise reduction experiment of synthetic noise, we divide the noise into five noise levels, and discuss the change trend of noise reduction effect when the noise level changes. This paper has done a lot of experiments, but it still has high scalability. In the future, more independent variables can be introduced on the basis of this experiment, so as to draw more accurate conclusions.
Multi-robot Task Assignment Algorithm for Medical Service System

This paper discusses multi-robot task assignment for medical service system in a hospital ward environment. We propose a task allocation algorithm for multi-robot intelligence teamwork based on near-field subset partitioning. Firstly, an optimal task chain is obtained by traversing all tasks through ant colony algorithm. Then we use the genetic algorithm to optimize the segmentation of the task chain and achieve a reasonable and efficient allocation by considering the path of each robot and the completion time of each task simultaneously. We simulated the application scenario of a hospital ward and designed a multi-robot system for the distribution of medical supplies. Tasks are assigned to each robot by using the proposed allocation algorithm, and the robots performed tasks in parallel. We compared our method with four different task allocation algorithms to show the effectiveness of our method. Simulation and actual robot experiments have verified that our proposed method is the most reasonable and the average time of task completion is the shortest.
Power Equipment Fault Diagnosis and Prediction Based on Improved ID3 Algorithm and Apriori Association Rule Algorithm

Power equipment fault diagnosis is an important part of fault management work. Using data mining technology to diagnose equipment faults provides maintenance decision-making plans for equipment maintenance, improves power equipment fault management level, and reduces economic losses of power companies. Therefore, this paper studies the fault diagnosis of power equipment and analyzes the application of data mining algorithm in fault diagnosis and prediction. In this paper, a fault diagnosis expert system is designed to accurately diagnose equipment faults. In order to improve the accuracy of fault diagnosis, the ID3 algorithm is optimized and improved. The experimental research on the accuracy of fault diagnosis also shows that the improved ID3 algorithm has higher diagnostic accuracy than the unimproved algorithm, and the Apriori association rule algorithm can detect the power equipment components in operation. weights, mining association rules between components.
GBCD: A Granular-ball Based Discretization Method for Continuous Valued Attributes

Discretization is a wide used method for data preprocessing in data mining and machine learning. However, the existing discretization methods have three drawbacks, including the many parameters and the stopping criterion limited by the data distribution. Meanwhile, most classifiers can only deal with discrete data. In order to solve these problems, a granular-ball based discretization method (GBCD) is proposed for continuous valued attributes. In GBCD, a dataset is granulated into many granular-balls of different sizes. The granular-ball generation is based on k-means algorithm, so the samples in a granular-ball have great similarity. In addition, the center of a granular-ball can express the general feature of the samples in the granular-ball. As a result, the centers of these granular-balls are the discretized samples. The experimental results on UCI benchmark datasets show that GBCD performs better in accuracy than the comparison algorithms.
Knowledge graph technology based on Natural Language Processing and reinforcement learning e-commerce customer service

In recent years, with the rapid development of e-commerce technology, the scale and number of e-commerce platforms with online retail business as the core are increasing day by day. In the service system, because the content of customer requirements is not consistent, customer service answers are different, and the question and answer service is easy to answer the questions, thus reducing customer satisfaction. Therefore, the optimization of customer service system is worthy of our in-depth study, but also worthy of high attention. Based on seque2SEque algorithm, this paper uses movie dialogue data set combined with knowledge graph technology and Markov algorithm to build a customer service robot with relatively natural response.
Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images.
Design of Gesture Recognition System Based on Deep Learning

Gesture recognition is popular in human-computer interaction research, and has broad application prospect in human disorder, sign language awareness, and other fields. Gesture recognition makes human-computer interaction operation more convenient. This paper proposes a gesture recognition system based on convolution neural network improved LeNet5 model. Based on raspberry pie hardware platform, the system captures and extracts gesture features by using OpenCV, and realizes gesture recognition by using Keras to train convolutional neural network model.
A Mapless Navigation Method Based on Deep Reinforcement Learning and Path Planning

The ability of mobile robots to navigate in an unfamiliar environment in human terms is decisive for their applicability to practical activities. Bearing this view in mind, we propose a novel framework for navigation in settings where the environment is a priori unknown and can only be partially observed by the robot with onboard sensors. The proposed hierarchical navigation solution combines deep reinforcement learning-based perception with model-based control. Specifically, a deep reinforcement learning (DRL) network based on Soft Actor-Critic (SAC) algorithm and Long Short-Term Memory (LSTM) is trained to map the robot's states, 2D lidar inputs and goal position to a series of local waypoints which are optimal in the sense of collision avoidance. The waypoints are then employed by a dynamic window approach (DWA) based planner to generate a smooth and dynamically feasible trajectory that is tracked by using feedback control. The experiments performed on an actual wheeled robot demonstrate that the proposed scheme enables the robot to reach goal locations more reliably and efficiently in unstructured environments in comparison with purely learning based approach.
A baas platform for cross-chain cloud management service system of consortium blockchain

In order to support large-scale and complex blockchain application scenarios such as distributed power trading platforms, we propose a cloud-based blockchain management service middle platform Baas platform based on cross-chain communication to improve the resource utilization of the underlying blockchain platform through a standardized and reusable blockchain service system. By supporting the construction of "one master, many sides" consortium blockchain architecture, it meets the demand for cross-chain business data interaction, reduces the technical threshold for business applications to access the blockchain platform, improves the development efficiency and enhances the intelligence and automation of operation and maintenance.
AV-ITN: A Method of Multimodal Video Emotional Content Analysis

With the rapid development of Internet technology, social media has also developed rapidly with the support of the Internet. Social media data has grown exponentially. A large amount of data needs to be reviewed urgently. The method of machine review requires video emotional content analysis technology (Affective Video Content Analysis Technology). Analysis, AVCA) support. A large number of studies have shown that the use of deep learning methods to achieve emotional content analysis is currently the most effective method, and its effect is better than traditional algorithms and manual methods. Based on this, this paper proposes a multi-modal video sentiment analysis algorithm AVITN that utilizes both audio and video modal information to promote sentiment analysis in videos. AV-ITN achieves a high accuracy rate of 83.66% on the IEMOCAP benchmark. The multimodal video sentiment analysis algorithm proposed in this paper can obtain more emotional information contained in the video and effectively improve the accuracy of video sentiment analysis.
Instruction Parsing and Action Sequence Generation System for Home Service Task

Service robots have broad application prospects in home service tasks, but there are problems such as difficulty in task analysis, poor environmental adaptability, and system integration. To more accurately understand user intentions and plan service steps in line with the current environment, a family service task-oriented robot instruction parsing and action sequence generation system is proposed. The system is connected by a knowledge-base-based human-robot interaction question answering system. First, an improved gate and prior knowledge vector are proposed for instruction parsing. The relationship between task types and keywords can be better extracted, and prior knowledge can be used to improve recognition accuracy. Then a service strategy is proposed as an intermediate state of the instruction to action sequence. The task steps are generated in the form of sequence text using keyword guidance to facilitate the generation of action sequences. At the same time, it performs visual inspection and knowledge base query for environmental objects. Robots fill in the missing through human-robot interaction to generate the most suitable service strategy for the current environment. Finally, domain and problem descriptions that are more in line with family service tasks are defined. Robots can generate executable sequences of actions through a task planner. The experimental results show that the system can improve the accuracy of instruction parsing and generate logical and reasonable action sequences.
An Experimental Study of Keypoint Descriptor Fusion

Local feature descriptors play a crucial role in computer vision problems, especially robot motion. Existing descriptors are highly accurate, but their performance de-pends on the influence of distracting factors, such as illumi-nation and viewpoint. There is room for further improvement of these descriptors. In this paper, we provide an in-depth analysis of several exciting features of the descriptor fusion model (DFM) we have proposed in our recent work, which uses an autoencoder to combine descriptors and exploit their respective advantages. With this DFM framework, we fur-ther validate that fused descriptors can retain advantageous properties and that our DFM is a generally applicable method with respect to various component descriptors. Specifically, we evaluate multiple combinations of hand-crafted and CNN descriptors concerning their performance on a benchmark dataset with illumination and viewpoint changes to obtain comprehensive experimental results. The results show that the fused descriptors have better matching accuracy than their component descriptors.
Robust Stereo VIO Based on an Equivariant Filter for Complex Lighting Environments

Visual Inertial Odometry(VIO) is widely used in various fields. When lighting conditions change dramatically, the visual front-end is affected, resulting in performance degradation and even failure in some extreme scenarios. In this paper, a stereo VIO based on an equivariant filter(EqF) for complex lighting environments is proposed. We propose a new adaptive gamma correction method, which can effectively improve the quality of image sequence and ensure the real-time performance of the algorithm because of the image selection processing strategy. Our VIO uses a robust stereo visual front-end and a state-of-the-art equivariant filter at the back-end. Experiments showed that the system has higher accuracy and robustness than another EqF-based VIO algorithm(EqF_VIO), and can work in complex and extreme lighting environments.
Design and Implementation of Cross-Platform Well Site Integrated Monitoring and Management System Based on HTML5

In response to the issue that large drilling areas are usually located in remote regions with inconvenient transportation and communication conditions, this paper discusses the development of a cross-platform well site integrated monitoring and management system based on HTML5 that is supported by video transmission technology, bidirectional data transmission technology and MUI hybrid development mode. This system integrates several important functions such as real-time drilling data recording, remote video monitoring, work report management and collaborative office, which realizes the comprehensive display and application of data acquisition, remote monitoring, remote office and daily supervision. Moreover, it provides efficient information support for drilling project management and decision-making. The client side of the system adopts the MUI mobile development framework based on HTML5 which supports cross-platform use of APP and solves the problems of long development cycles and high costs.
VS-Net: A Voxel Encoding and Sparse Convolution Embedded Network for LiDAR 3D Object Detection

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios.
The Image Recognition of Guns and Knives Based on Deep Learning of Tensorflow

With the help of deep learning, the computer can automatically recognize guns and knives to prevent the occurrence of dangerous things. Depth is a branch of machine learning. This deep learning is based on Tensorflow and Python syntax to build the relevant framework. The image is processed into training set and test set. The feature values of pictures are extracted through convolution layer and fully connected layer, and the trained feature values are compared with the test set. In addition, regularization and other methods are used in the convolution layer to optimize the data and parameters, which improve the accuracy of the framework to 53%.