Reviewerly/exHarmony huggingface dataset:
A Path Planning Algorithm for Mobile Robot Based on MR-QL in the Unknown Scene

Abstract The Q-Learning (QL) algorithm is widely used for path planning. As the scene in which the mobile robot is located becomes complex, the algorithm suffers from the limitations of low convergence speed and long exploration paths. Therefore, a Max Reward-Q-learning (MR-QL) path planning algorithm based on maximum reward is proposed for complex unknown scenarios. The original algorithm’s discrete reward function and action selection strategy are improved, and a new reward function is designed to dynamically adjust the reward mechanism to heuristically guide the robot motion. The action selection strategy is also optimized to avoid invalid exploration and improve the algorithm’s convergence. Finally, three experimental environments with different complexity are constructed to demonstrate the feasibility of the proposed algorithm. The simulation results show that the MR-QL algorithm is about 50% of the original algorithm in terms of exploration step length and training time, and the convergence speed of the algorithm is better than the original algorithm.
2-D Omnidirectional Human Body Attribute Fusion Recognition System

Abstract Traditional face or body attribute recognition can solely train and understand the specific attributes marked according to the marked content of the dataset. Most recognition systems on the market integrate specific attribute recognition algorithms. The lack of corresponding attribute data fusion leads to the problem of high diversity but poor recognition accuracy. This paper designs a recognition system based on data fusion and attribute fusion of two-dimensional images, which can fuse subtle data during training, and fuse the recognition results of overlapping attributes, to achieve multi-directional recognition of human subtle attributes, make up for the shortcomings of single recognition algorithm, and obtain more comprehensive and accurate recognition results. The experimental results show that the recognition system. Firstly, the overall recognition accuracy is above 83.5%. Secondly, it can make up for the problems of high accuracy but poor diversity of single recognition algorithm and poor accuracy of multi-feature recognition algorithm.
A Deep Learning-Fuzzy based hybrid ensemble approach for Aspect level sentiment classification

Aspect level sentiment classification (ALSC) has gained high importance in the era of e-commerce based economy. It allows manufacturers to improve the designs of their products based on users’ feedback. However, only a few datasets of limited domains are available for ALSC task. To push forward the research in automated ALSC, this study contributes cars dataset of the automobile domain. In this study, a novel fuzzy ensemble technique is also proposed based upon the mathematical analysis of confidence scores of base deep neural networks. The proposed approach allows to correct the misclassifications of base deep learners through a reward and penalization strategy. The experimental results on five benchmark datasets show that the proposed approach outperforms the constituent base deep neural networks and several other important baselines. The proposed Fuzzy ensemble also performed at par with the most recent Graph Convolution Neural Networks on basis of Friedman and Nemenyi Tests.
Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph

In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese.The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters.If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency.We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG).In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters.Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes.Therefore our method combines the advantages of data-driven and knowledge-driven.The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results.The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language.The CCSKG can help to promote Chinese overseas communication and international education.
Real-Time Face Transition using Deepfake Technology (Gan Model)

Many machine learning models require huge datasets to get trained, to make correct predictions, or to increase their accuracy, that's where generative adversarial network (GAN) comes into the picture. GAN is a deep learning model, which can be used to create artificial data. It consists of two neural networks that cooperate in a way that resembles a game: a generator network and a discriminator network. Within the suggested model in this work, a dense motion network-based first-order motion model for image animation is developed. Here a trained GAN extracts the face landmarks from the driving video and develops the embedding model to create the synthesis video using the dedicated module to prepare the Deepfakes, employing key point detectors as a baseline. Lastly, an approach that makes use of dense motion networks to increase the effectiveness of a collection of GAN generators is provided. With the help of the sequel driving combination of driving video with the source image, the given results produce the augmented animation video. Hence, it is tried to implement a lighter model which consists of modules, with the approximate same accuracy when compared with other implementations of GAN. This work has a wide range of applications, including doubling dataset counts with a small number of sources, creating real-time backgrounds and characters for the gaming and animation industries using CG platforms, translating clothes, predicting videos, creating 3D objects, etc.
Search Recommendation Model Based on Attention Compressed Interaction Network

With the development of science and technology, recommender systems continue to attract the attention of countless fields, which provides personalized recommendations for users. In order to obtain more information in the review text and further improve the recommendation accuracy, this paper proposes a search recommendation model based on attention compressed interaction network. It combines the compressed interaction network and attention mechanism to capture the feature information of users and items from the review text, and then learns to predict the interaction between users and items, and based on the learned item representation. Mapping is performed in the natural language space to enable retrieval. Experimental results on the Amazon public dataset show that compared with other baseline models, the MAE and RMSE indicators can be reduced by up to 2.63%and 2.26 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> respectively, which demonstrates the feasibility and accuracy of the proposed model.
Optimizing loading strategy for visualizing 3D photo-realistic model using human eye's area of interest

The 3D photo-realistic model is characterized by high accuracy and large data volume. When visualizing these data in GIS applications, the whole model will be tiled and pyramid for efficient network transferring and loading. Then the tile loading strategy based on the distance from the viewpoint (hereinafter referred to as “the distance-based strategy”) will lead to the tiles appearing at the edge of the screen own better visual quality and large volume compared with those in the center of the screen, which mostly been the primary area that attracts human attention. To address this data redundancy problem, this paper proposes a novel tile loading strategy method based on the human eye's area of interest which provides better visualization quality on the central area of the screen while keeping the overall data volume loading on the web. Compared to the distance-based methods, the proposed method considering the angular deflection of the direction from the camera position to the center of the tile and the LOS (light of sight) direction, and recalculating the screen space error (SSE) determines which tiles in the pyramid will be selected. This method can choose higher-resolution tiles for the center region and choose lower-resolution tiles for the marginal region simultaneously. The experimental results show it increases the screen resolution of the center area while keeping the memory occupation steady. This research can facilitate the applications of 3D photo-realistic models in digital cities.
A reverse distance optical system based on an aspheric surface

Recent years have seen an increase in the scope of applications for airborne cameras as well as a diversification in their design. One of the most important steps in airborne camera imaging is the design of the optical system, which has a wide range of requirements. This paper describes the design of an anti-telepresence aspheric optical system that allows for a greater working distance. Optimize the optical system design to reduce the effects of factors such as aberration and distortion, resulting in a better imaging optical system.
Research and analysis of robot path planning based on target detection

Because intelligent mobile robot contains rich market value, and is widely used in various fields, so the modern society of mobile robot technology requirements are very high, and gradually become the main topic of research and exploration of scholars. In this paper, based on the understanding of the development status of mobile robot path planning, combined with lidar as the core of dynamic target detection method, a path planning algorithm based on nonlinear programming is proposed. The final experimental results show that this planning scheme can quickly find the optimal or approximate optimal planning path in dynamic or static environment, so as to complete the task of tracking and hedging.
Machine Learning based Data Mining for Detection of Credit Card Frauds

This research study analyzes the different data mining techniques used for the detection of credit card frauds. Additionally, different data mining techniques are properly described in this study. The steps of data mining are clearly explained in this study, which are helpful for recognizing the fraudulent activities. This study is discussing that Bayesian network, and decision tree are the effective techniques of data mining. The issues of data mining are also discussed in this study, which is creating performance issues, and user interaction issues in the financial institutions. Therefore, this study is providing the importance of RF algorithm in the determination of the credit card fraud. Data cleaning, and data visualization, therefore, the machine learning process is allowed to be developed with the support of the data mining process of credit cards. Clustering is the main process that is takes place in this data mining process. Additionally, the sequential pattern is being highlighted by this process, which helps to improve the fraud detection process of credit cards.
Interactive Visualizations of Personal Experiences for Shared Sense of Understanding

In an age of social media and polarized broadcast media in which the loudest voices and most extreme opinions dominate, we see democracy and truth challenged.In the face of this challenge, we at the MIT Center for Constructive Communication explore how we might leverage the capacity of cutting-edge digital technology to enable shared understanding within and across divides through prioritization of personal experience over polarized opinion and amplification of those often underheard.Pursuing this mission, we present Real Talk, a sociotechnical program in which scalable social and technological infrastructure equips communities with the processes and technology to connect, share experiences, collaborate, make meaning, address problems, and
An efficient named entity recognition for emergency plans

The context of emergency plan text is very tightly connected, while fully connected self-attention architecture Transformer has advantages in parallelism and remote context modeling, and Bi-directional Long-Short Term Memory (BiLSTM) can also capture long-distance context information. In this paper, we propose a new model Bi_ComL-CRF, the most important of which is the Com-LSTM unit, by introducing an additional Transformer encoded representation for each recurrent unit, then enhancing the captured context information through the new gating mechanism, so as to improve the effect of emergency plan NER task. We have carried out experiments on the emergency plan data, and the results show that our proposed model is better than other NER models, and can provide help for calculating the text similarity of emergency plans and generating emergency plans quickly.
Image Enhancement Approach for the Underwater Images Using the Optimized Color Balancing Model

Marine resources are one of the valuable resources that need to be explored and protected, but are affected due to environmental factors. The underwater images are distorted due to the light scattering and absorption phenomenon, which draws the attention of the researcher to restoring the underwater images which is a challenging task. High-quality underwater images help us to learn and study deep-sea environments, and also help in gaining deep knowledge about the ocean resources. In this research, deep learning methods are used for obtaining high-quality underwater images. Accordingly, a channel-based white balancing and social situation-aware guard optimization (WBSSGO) dependent multi-variant balancing color correction model is proposed by integrating the grouping behavior and adaptability nature of the guards and flocks, which acts as the search agents in the proposed optimization. A multi-variant-based white balancing model is employed for balancing the channels and correcting the distorted underwater images. The performance of the developed model is validated using PIQE, BRISQUE, and UCIQE, which acquired minimal values of 27.202, 24.692, and 29.725, respectively with comparative methods, while the higher value of SDME is 64.049 dB for the proposed method, which is reported to acquire the higher visual quality of underwater images.
Image enhancement based on performed-FCMSPCNN

In Dunhuang mural image restoration, image enhancement techniques have effectively helped in image restoration. Based on pulse-coupled neural network (PCNN) has been widely used in image processing, in order to solve the low-lighting problem of Dunhuang mural images, on the basis of FC-MSPCNN model, the parameters such as synaptic weight matrix 𝑊<i><sub>ijk1</sub></i>, link strength 𝛽, attenuation factor 𝛼 and attenuation adjustment parameter <i>K</i> are redefined in combination with adaptive parameter setting method, and the Performed-FCMSPCNN (PFC-MSPCNN) model. Finally, the linear transform, gamma transform, and histogram algorithms are used for image enhancement and compared with the PFC-MSPCNN model, respectively. It is verified that the PFC-MSPCNN model in this paper has a good enhancement effect on low-light images.
An enhanced-FCMSPCNN model and its image enhancement application

Most popular image enhancement algorithms are generally based on a series of specialized images collected by image photography devices. Hereinto, Pulse-Coupled Neural Network (PCNN), plays important roles in image enhancement aspect, with lower computational complexity and higher image enhancement accuracy. On the research, we propose an image enhancement method based on enhanced fire-controlled MSPCNN(EFC-MSPCNN) model, which gives the setting methods of designed adaptive parameters. Related experimental results demonstrate that our proposed method has good image enhancement performances.
IOT Based Smart Waste Container System in Southern Jordan Valley Municipality

Municipalities are responsible for providing essential services to their citizens, including waste management, transportation, and public safety.Internet of Things (IoT) technology can improve the quality of these services and make them more efficient.IoT technology can help municipalities to collect data from various sources, analyze the data, and use it to make informed decisions.IoT can help municipalities to reduce costs, improve service delivery, and enhance the quality of life of their citizens.This paper discussed the use of IoT in municipalities to improve their services, the benefits of implementing IoT in waste management by proposed an IOT waste container model for municipality of Southern Jordan Valley, and the future prospects of this technology in municipal services.The proposed model is based on connecting the containers with capacitive sensors so that they measure the level of fullness of the container (the percentage is 80%) of the total capacity of the container, so that a notification is sent to the health monitor about the fullness and location of the container as the sensors and the server are connected to each other through (Wireless Sensor Networks) to make a decision By running mechanisms to collect waste as needed, the model relies on providing it with a real-time data, which means accuracy, reliability, and reliability of the data, as well as contributing to cost reduction, improving service, and facilitating the transition to smart municipal services and achieve environmental and economic benefit.
Risk prediction model of bank telecommunication fraud based on XGBoost

The digital economy is booming, but cybercrimes and telecommunication frauds are emerging one after another. How to detect fraudulent behaviours and prevent the occurrence of crimes is a significant challenge. This paper mainly conducts data mining and analysis on the bank card telecommunication fraud data set, first of all, data mining and feature engineering for the given data set, including analyzing the data integrity, the overall statistical analysis of the data and standardizing the data using the Z-Score standardization method, Use the Pearson correlation coefficient to explore the feature correlation, use the SMOTE method to balance the data set, and finally divide the training set and the test set. Subsequently, four machine learning classification models, including the logistic regression classification model, KNN classification model, decision tree classification model and XGBoost classification model, were established to predict and classify fraudulent behaviours preliminarily. To further mine the data set of bank card telecommunication fraud, the optimal solutions of the models are obtained by grid tuning and cross-validation for the four established models. After experiments, the logistic regression classification model, KNN classification model, decision tree classification model and XGBoost classification The prediction accuracy rates of the model in the test set are 93.45%, 99.85%, 99.92%, and 99.94%, respectively. It is preliminarily believed that the XGBoost and decision tree classification models have excellent classification capabilities. Use the obtained four optimal models to calculate the three performance evaluation indicators of prediction accuracy, recall rate and F1 value in the test set, respectively, and further evaluate the four machine learning models. Through comparative analysis, the XGBoost classification model has the best performance. Due to its classification ability, strong generalization ability and robustness, it is selected as the final bank card telecommunication fraud prediction model. In addition, the P-R curve and ROC curve of the classification results are drawn using the performance evaluation indicators to be intuitive. Analysis of the model's performance further shows that XGBoost has better generalization ability.
Partition GHZ SLOCC class of three qubits into ten families under LU

In [Science 340:1205, (2013)], via entanglement polytopes Michael Walter et al. obtained a finite yet systematic classification of multi-particle entanglement. It is well known that under SLOCC, pure states of three (four) qubits are partitioned into six (nine) families. Ac\'{\i}n et al. proposed the generalized Schmidt decomposition for three qubits and partitioned pure states of three qubits into five types. In this paper,we present a LU invariant and an entanglement measures for the GHZ SLOCC class of three qubits, and partition states of the GHZ SLOCC class of three qubits into ten families and each family into two subfamilies under LU. We give a necessary and sufficient condition for the uniqueness of the generalized Schmidt decomposition for the GHZ SLOCC class.
Matching Endoscopic 3D Image Data with 4D Echocardiographic Data for Extended Reality Support in Mitral Valve Repair Surgery

Minimally invasive mitral valve repair is a common cardiac surgery procedure. Combining intraoperative stereo-endoscopic images with pre-operative 4D transesophageal echocardiography (TEE) can support surgeons in correlating surgical interventions with the functional implications in the beating heart. We propose a method for registering 3D point clouds reconstructed from endoscopic images with TEE by extracting and matching anatomical landmarks and refining the registration using the Iterative Closest Point Algorithm. The applicability of our method is assessed by the computation time and the registration accuracy.
Applicability of BI-RADS Criteria for Deep Learning-based Classification of Suspicious Masses in Sonograms

Breast ultrasound exams are often performed alongside mammograms for the detection and classification of suspicious masses. The breast imaging reporting and data system (BI-RADS) defines standardized terminology for describing such lesions and provides criteria to help radiologists classify them. In this work we investigate, which of these criteria are most pertinent for deep learningbased classification of breast masses in sonograms. For this purpose, we use a large, publicly available breast ultrasound dataset which contains sonograms of benign and malignant masses.We generate five versions of each sonogram, which display various regions and focus on several BI-RADS criteria of the displayed mass. Convolutional neural networks (CNNs), with much lower complexity than generally used in the literature, are trained for each image version set. We examined the classification accuracies and confidence of each model. The shape of the mass is the largest single contributor to classification success, this model achieved 89% accuracy. Passing the network additional information about the mass’s margin and echo patterns further increased classification accuracy to 96%.
Introduction to Blockchain Technology with Bitcoin Protocol

The engineering behind the technology that powers Bitcoin, known as Blockchain, has gained attention as a potential software solution for various industrial applications. The capability of revolutionising digital transactions brought significant interest in this technology and evolved greatly in the past decade. However, the development of applications beyond cryptocurrency has not yet kept pace. In this chapter, we will examine the basic design principles of blockchain technology using Bitcoin’s architecture as a foundation and understand the rationale behind its design and the limitation of scalability.