Reviewerly/exHarmony huggingface dataset:
Remote sensing image objects detection algorithm based on improved YOLOv5

With the development of remote sensing technology, remote sensing images are developing towards higher resolution and larger data volume, and it is important to identify objects from remote sensing images quickly and accurately. Due to the significant differences between remote sensing images and natural images, the direct application of existing algorithms to remote sensing images is not ideal. Therefore, we take the YOLOv5 object detection algorithm as base, and have completed the following tasks: First, we present an adaptive image cutting data preprocessing method, which fills or cuts images into uniform size to cope with the resolution differences of remote sensing images. Second, we use the Mosaic data enhancement method to improve the algorithm's effect in complex backgrounds. Third, we use the Soft-NMS postprocessing algorithm to reduce the missed detection of dense objects. Furthermore, we transplant the algorithm to the hardware platform. After the above improvements, the mAP of our algorithm increases from 0.354 to 0.677 on the DOTA dataset, achieving a good object detection effect on remote sensing images; and with the help of the TensorRT, it has reached a detection speed of about 60 FPS on the NVIDIA Jetson AGX Xavier embedded hardware platform.
Research on the application of mobile interactive technology in the gamification design of museums gamification design

Interactive software of digital museum has gradually become the mainstream of the mobile terminal market. It faces a huge user group, and the research field of gamification user experience of mobile museums has been widely recognized and applied. It integrates game thinking and game mechanism and analyzes feasible method of mobile figure for interactive display in terminal digital museums. Gamification interaction design promotes mobile virtual museums in multiple dimensions such as navigation, experience, technology, etc., and some reference conclusions are obtained through analysis.
Application and review of generative adversarial networks

Generative adversarial networks (GANs) are one of the most popular innovations in machine learning. GANs provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a process involving a pair of networks. GANs are generative models since they are able to create data instances that resemble the training data. Besides, GANs provide a way to learn deep representations without annotated training data. They achieve this by deriving backpropagation signals through a looping process involving multiple networks. Those representations learned by GANs can be used in various fields such as Image Generation, Abnormal Detection, Video Repair, using GAN for Infrared to RGB, Image Inpainting, etc. This review paper provides a clear overview of GANs and their application into the Image Inpainting process. Furthermore, it points out both the advantages and disadvantages of GANs in the machine learning field.
Recognition and gripping strategy of robot quick change tool based on CNN

This paper takes the quick change tool recognition system of robot workstation as the research object. Through the quick change tool recognition method based on convolutional neural network, the feature extraction and classification recognition of traditional methods are combined to realize the accurate recognition of quick change tools. The experimental results confirm that the convolutional neural network is feasible and effective as an accurate classification and recognition method of quick change tool, but in industrial applications, more complete and large amounts of data are still needed to train the network in order to improve the accuracy and generalization ability of recognition.
Evaluation of query strategy of active learning for text classification

In many applications, the process of classifying the text document is expensive and time-consuming. Active Learning can reach the same accuracy with only a part of the dataset. In this paper, the evaluation of the probability-based query strategy of Active Learning for text classification was demonstrated. Random Sampling, Least Confidence Sampling, Margin Sampling, Entropy Sampling, Density Weighted Entropy Sampling, Variance Sampling and QBC are compared on the 20NEWSgroup dataset and its subsets with three predictive models, Decision Tree, Naive Bayes, and Logistic Regression as the estimator. The evaluation results can guide people to choose what query strategy can ensure the efficiency and accuracy when classifying text documents by Active Learning. Density Weighted Entropy Sampling and QBC are tested to have the highest prediction accuracy after 1000 iterations. The study confirms that the smoothness of the learning curve largely relies on the estimator and the construction of the committee of QBC has an influence on the performance of QBC. The time consumption for querying is also compared in this paper. Expected Error Reduction Sampling costs too much time in querying, which makes it impossible to implement in a real-word scenario.
Toxic detection based on RoBERTa and TF-IDF

In an information age, the presence of toxic content has become a major problem for many online communities, and existing methods are not robust enough to detect it. Therefore, the demand for a more accurate and efficient system for toxic messages detection has reached its peak. In this paper, we introduce machine learning and deep learning models to this task. Following the intuition of acquiring the knowledge of both the word itself and its relationship with other words, a stacking model is constructed as the optimal strategy, combining both term frequency-inverse document frequency method (TF-IDF), a robustly optimized Bidirectional Encoder Representations from Transformers pretraining approach (RoBERTa) as the base-model, and neural network as the meta-model. The experiments show that stacking method and K-fold cross validation are advantageous, and our model achieves a detecting accuracy of 0.9023.
Improve the robustness of neural network via foreground

Neural networks have achieved great success in many areas in recent years, but neural networks are vulnerable to adversarial examples: given a small change on data x, the label of x changes. The method of using adversarial examples as training data to train neural networks is called adversarial training. It can improve the robustness of neural networks. In fact, most of the information of data is useless for predicting. For humans, we can only use a small part of an image to classify it correctly, the background of an image is not very useful for predicting. In this paper, we propose a novel method to produce adversarial examples. We use contrastive learning to get the foreground of the image, then we only change the foreground of the image. We have conducted detailed experiments on two datasets (MNIST, CIFAR-10) and the experimental results show the effectiveness of our method.
Deep learning evolution from shallow to deep: a case study based on language recognition

Deep learning is a systems science based on neural network and machine learning. It abstracts complex data models by using multi-layer based on complex machine structures and nonlinear transformation algorithms. At present, the research results of deep learning have been successfully applied to speech recognition, pattern recognition, target recognition, natural language programming, man-machine chess, artificial intelligence, smart city and other fields. This paper mainly talks about how deep learning evolved and the evolution applied on speech recognition.
Attention-based hashing with central similarity learning for image retrieval

Compared with pairwise/triplet similarity based hashing, central similarity based hashing can capture data relationships more efficiently. However, previous works still have the limitation of feature expression capability due to information redundancy and they simply use similar/dissimilar label pairs so that they cannot fully capture the complex semantic information of the visual content, which limits the retrieval performance to some extent. To address the above issues, an attention-based hashing with central similarity learning (ACSH) for image retrieval is proposed. Firstly, it uses an off-the-shelf algorithm to generate semantic hash centers with sufficient hamming distance between each other for data points. Secondly, it embeds a spatial attention mechanism into the feature extraction module, enabling the deep hashing network to focus on important features and suppress unimportant features. Finally, in the training phase, a classification task is introduced to supervise the feature learning of the spatial attention mechanism to capture more complex semantic information of the visual content. Comprehensive empirical evidence shows that ACSH has better retrieval performance than the existing deep hashing methods based on central similarity on three standard benchmarks.
Handwritten character recognition with stable and adaptable convolutional neural network

Handwritten character recognition is widely used in mail collection and classification, note sorting and filing, financial data identification and analysis, etc. Current machine learning methods for handwritten character recognition mainly include Random Forest (RF) and Support Vector Machine (SVM). However, the performance of these methods compromises a lot due to high noise, non-linearity, and unobvious feature values in handwritten character images. This paper presents a stable and adaptable Convolutional Neural Network (CNN) to recognize handwritten characters. First, this paper preprocesses the images by noise reduction, one-hot encoding, and contrast enhancement to increase the recognizability. Then, this paper combines the structures of several CNN classic models to construct a CNN model with strong adaptability and high stability. This CNN model loops through a combination of a convolutional layer, a pooling layer, and a Dropout layer, which realizes the full connection of local features and reduces the amounts of parameters and data complexity. The Dense layers map the flattened image data to the output space by extracting the correlation between the features. Each layer in this CNN combines with Relu-based activation functions to perform nonlinear mapping, alleviating problems such as gradient disappearance and overfitting. The proposed CNN is compared with RF and SVM on the English Handwritten Characters dataset with accuracy and confusion matrix. The recognition accuracies of RF, SVM and CNN are 0.90, 0.92 and 0.96, respectively. RF and SVM confuse multiple characters, while CNN only generates a small number of confusion on the characters G and S. In the test with actual handwritten characters as samples, this CNN also shows higher recognition accuracy and fewer confused characters than RF and SVM. In addition, this paper compares and evaluates this CNN with other representative CNN models. The experimental result shows that the proposed CNN has a greater advantage in the balance of fitting speed and recognition accuracy.
Conditional generative adversarial networks in computer vision: an introduction and outlook

Conditional Generative Adversarial Networks (CGAN) is an architecture-variant Generative Adversarial Network (GAN) that inputs conditional data into generator and discriminator simultaneously. CGAN has been applied in a wide range of fields in recent years. One of the most famous usages is its application in computer vision, which enables to perform images and videos transformation with specified conditions. In this paper, we first introduce the structure of GAN, which supports every CGAN system, and the basic CGAN that has been demonstrated in computer vision. Then, we review different implementations of CGAN in two major computer vision domains, i.e., the image processing domain and the videos processing domain. Besides, we review three representative research orientations in each domain, which all implement CGAN or variants of CGAN. Moreover, there is a comparison or declaration of several outstanding approaches in each direction.
Formality style transfer via masked SeqGAN

Language styles, including word choice and syntactic structure, are all pivotal factors that affect the formality of the text. The formalization of text style is a sub-problem of text style transfer, which aims to transform daily expression into academic style. SeqGAN bypasses the indifferentiable problem in backpropagation caused by the discrete nature of the token, and pioneers the application of GAN for text generation. In the formalization of text style, GAN was not used for formality style transfer which is a task worth exploring. We apply the Monte Carlo idea in SeqGAN to the task of formalizing the text style, that is, we use the sampling method to obtain the state-action value. In academic writing, the choice of key words would affect the quality of the entire sentence. In this paper, we propose the Masked SeqGAN to cope with this problem. The architecture of our proposed model is similar to SeqGAN, but the difference is that after the complete sentence is generated, a &lt;𝑚𝑎𝑠𝑘&gt; tag is added to the current position and the discriminator scores the sentence marked with mask and the original sentence separately. The difference in score indicates the contribution of words to the entire sentence. Words with high contributions will be considered important words, and this contribution will be used to update the policy. Experiments show that Masked SeqGAN is better than previous GAN-based methods, both in terms of automatic scoring and manual scoring.
Roadmap of AlphaGo to AlphaStar: Problems and challenges

In recent years, deep reinforcement learning is one of the hot topics of artificial intelligence, which can be applied in many fields. However, it also faces many problems and challenges, such as insufficient sample data, large sample space, complex action space and so on. The emergence of AlphaGo solved the problem of large sample space very well. After that, artificial intelligence such as AlphaGo Zero and AlphaStar were released. These intelligent frameworks can be applied in various scenarios. Differentiating from other works, in this paper, we focus on the in-depth analysis of the internal connections of Alpha series from the perspective of the problems and challenges solved to give an insight for the future development of reinforcement learning.
Static gesture recognition using CNN with hand landmark detection

Static Gesture Recognition is an interactive system that enables people, especially the hearing-impaired, to directly communicate with other people and machines without third-party auxiliary equipment. However, it is challenging due to gesture datasets and the recognition accuracy of pre-processing. This paper proposes Convolutional Neural Network (CNN) with hand landmark detection to recognize Static Gestures and improve the pre-processing accuracy significantly. First, this paper makes a dataset by taking 5,800 photos from 4 people, including 13 one-handed gestures and 3 two-handed. Then, it pre-processes the dataset in both ways of skin color detection and hand landmark detection to select a high-accuracy method. Finally, CNN is trained for both pre-processed datasets to classify the samples into 16 classes. To verify the method’s effectiveness, this paper also compares a baseline model that comprises the Support Vector Machine and the Principle Component Analysis. The experimental result shows that CNN eclipses the other model with higher accuracy of 99% for Static Gesture Recognition.
Handwritten letter recognition using LetNET

Many people are accustomed to searching and managing information on electronic devices, but people only have handwritten documents in some cases. The handwritten character recognition techniques as useful tools for converting hand-wrote documents into machine-encoded text become more and more vital. There are many ways to implement handwritten character recognition, including line and word segmentation, semi-incremental, incremental, etc. However, recognition accuracy and training time are difficult to balance. This paper uses the convolutional neural network (CNN) method to train and fit the data. Firstly, preprocess the training data by resizing the images and abstracting letters from backgrounds. Then, the original dataset was set into the training set and fitting set for testing the model's effectiveness. Secondly, the dataset's CNN model was optimized, and eventually, the letNET module was decided to be used, an 8-layer deep convolutional neural network including the input layer. To test the practicability of this model in real life, a certain amount of handwritten words were set as a new testing dataset. The result shows the accuracy is felicitous even using self-wrote words. To verify the effectiveness of the proposed method, we compare our method with other state-of-the-art baselines, including random forest and support vector machines. The experiment result shows the letNET outperforms related benchmarks in handwritten characters recognition.
Planning the Pacman problem with Markov decision processes

The pathfinding problem has become a popular issue among Artificial Intelligent (AI). This paper discussed the effective way to optimize the optimal pathfinding problem, namely the Markov decision process (MDP). By applying MDP to the Pac-Man game, the current study describes in detail the principle of the algorithm design ideas and evaluated the performance of the results. Through further experiment, the influencing factors of Pacman decision-making under MDP application were analyzed. Experiment results show the value of ghosts is inversely proportional to the rate to win the Pacman game.
Chinese medical named entity recognition based on Roberta and multi-model voting fusion

Named entity recognition in medicine is a very important and popular task, which plays an important role in artificial intelligence of Internet medical treatment, and is also the foundation of medical structure of natural language text information in medical field. Medical named entity recognition is mainly a sequence annotation task. Traditional medical named entity recognition methods are based on rules and dictionaries, which have high construction cost and poor generalization ability. In this paper, the text similarity task of online medical interview sentences is used to fine-tune the pre-training model and inject corresponding medical domain knowledge into the pre-training model. After that, roberta- WWM after Finetune was connected with two-way LSTM network and CRF network, and combined with bert-MRC model, voting fusion was carried out. Under strict indicators, the result was 92.02%, better than the popular baseline scheme and has good generalization ability.
Research on face recognition system algorithm based on robot scene interaction

Face recognition system based on robot scene interaction is applied in the specific scene of people's life. The life scene is rich and varied, so in order to make the robot naturally integrate into the life scene of human, it needs to enrich its scene processing ability as much as possible. In this paper, face recognition related technologies are used to realize the two scenes of meeting new friends and greeting good friends in the robot scene. Firstly, this paper introduces the practical application background of the system and the application of face recognition in the system. At the same time, it also introduces the background and development status of face recognition at home and abroad. Secondly, combined with the business requirements of the system, determine the overall architecture and module division of the system, as well as the interaction process between each module. Finally, this paper elaborates the realization process of face recognition system based on robot scene interaction, this part is the focus of this paper.
XGboost and random forest algorithm for supply fraud forecasting

With the comprehensive development and application of machine learning, forecasting supply by machine learning has achieved good results. However, as supply chain fraud is more complicated, using a simple machine learning algorithm can no longer achieve better performance for supply fraud forecasting. In this paper, we propose an XGboost and random forest algorithm to predict supply fraud. This algorithm first uses the random forest to filter out the unimportant variables, get essential variables, and then build an XGBoost model to predict supply chain fraud. Experimental results demonstrate that our proposed XGboost and random forest algorithm achieves great efficiency for supply fraud prediction than logistic, random forest, and XGBoost algorithms.
COVID-19 public opinion information analysis system

The arrival of COVID-19 has led to the emergence of a large amount of information, and inaccurate information will lead to group polarization and cognitive dissonance, so the management of public opinion information has become urgent. In this paper, the COVID-19 public opinion information analysis system selects the data sources on weibos as reference, and weibo hot topics, as the platform that users mainly focus on, are more authoritative and more credible compared to emerging media As the main platform for users' attention, Weibo hot topics are more authoritative and credible than emerging media. The selection of such a large platform can also ensure the openness and transparency of information. This paper focuses on a series of development processes such as acquisition and analysis of COVID-19 data and database design of COVID-19 public opinion information analysis system. The system not only realizes the basic functions of COVID-19 public opinion information analysis system, but also runs smoothly and interactively.
Semi-supervised learning for breast-ultrasound image

Recent deep learning models developed to address classification problems related to medical imaging for diagnosis focus on supervised models. However, supervised models are highly dependent on a large amount of accurately labeled training data to train the models. Obtaining such accurately labeled training data is expensive, time-consuming, and requires human expertise, which challenges the generalizing ability of deep learning models in many medical fields. To address this problem, we propose <i>MeanMatch</i>, a semi-supervised method that use a limited amount of labeled data and many unlabeled data to train deep models. We also employ the voting technical to assemble the deep models to improve the generalization performance. Finally, we conduct experiments on the breast-ultrasound image dataset to verify the effectiveness of our approach and compare it to the current state-of-the-art semi-supervised methods. Experimental results show that our method achieves the highest classification accuracy of 83.0\% on the test set, which outperforms or is comparable to the comparison methods and the supervised models.
Unsupervised anomaly detection based on improved skip-gannomaly

The current deep learning detection algorithms generally require a large amount of labeled data and it is difficult to collect samples in some application scenarios. An anomaly detection algorithm based on improved Skip-GANomaly is proposed. The algorithm firstly enhances the network's ability to extract image space and channel information by adding an attention mechanism module, and improves the network's ability to extract features. Then, on this basis, this paper uses mixed depth wise convolutional to replace ordinary convolution, so that the network can reduce the number of parameters while enhancing the network's ability to capture different types of patterns from the input image. The experimental results show that the AUC of the algorithm in different categories on the CIFAR10 dataset is generally higher than Skip-GANomaly and its anomaly detection model.