Reviewerly/exHarmony huggingface dataset:
Efficient Metric Learning with Graph Transformer for Accurate Colorectal Cancer Staging

Colorectal cancer (CRC) is the third leading cause of cancer death in men and the third leading cause of cancer death in women in United States. So far, the histopathological image remains the golden standard in staging CRC, and accurate staging CRC is important for timely therapy and possible delay of the disease. Existing studies often utilized the pre-trained deep models to extract features from histopathological images, which neglected to take the supervised metric information into consideration. In addition, most of the existing methods did not take advantages of the correlations among different samples for the downstream classification tasks. To address the aforementioned problems, in this paper, we propose an efficient Metric learning with Graph Transformer (MGT), which adopts efficient metric learning to help extract distinguished image features followed by applying graph transformer for CRC staging. The main advantage of the proposed graph transformer is that it can fully exploit the correlations among different patients, which results in better tumor staging performance. To evaluate the effectiveness of the proposed method, we conduct several experiments for CRC staging on public available dataset TCGA-CRC in The Cancer Genome Atlas (TCGA). The experimental results show that our method can consistently achieve superior classification performance than the comparing methods.
WTV-GANimation: High quality facial expression synthesis generation model

GIF expression pack is very popular on the Internet, especially when people are having social chat using chatting software such as WeChat. In this paper, our goal is to build the WTV-GANimation model that can generate high-definition animations of facial expressions. Specifically, our task is that given a source face image and a target expression, the method can generate a dynamic expression GIF of the source face. Through the generative adversarial network of GAN, the Image-to-Image conversion between multi-domain images can be realized to synthesize various vivid and interesting facial expressions in the image. First of all, our paper use GANImation algorithm to achieve the generation of facial expression. In view of the noise generated in the generation process, the WTV model is used to remove the image noise. Finally, the celebA data set is used for numerical simulation, and the results show that the model is effective and efficient.
Brain-inspired Class Incremental Learning

Most existing class incremental learning methods employ data storage or extended network structures, but they cannot effectively alleviate the catastrophic forgetting problem due to memory resource limitation. To solve this issue, a brain-inspired generative replay model is proposed in this work. First, VAE-ACGAN is used to simulate the memory self-organizing system to improve the quality of the generated pseudo-samples. Then, a shared parameter model and a private parameter model are used to protect the extracted features. Experimental results on MNIST, Permuted MNIST and CIFAR-10 show that the proposed method can lead to significantly higher classification accuracies than other class incremental learning methods. Furthermore, the backward transfer and the forward transfer of positive values demonstrate that the model can achieve a trade-off between task stability and plasticity, effectively preventing catastrophic forgetting effectively.
Research on Information Hiding Technology Based on Generative Adversarial Networks

Aiming at the problems of the existing image steganography model that the network training is not easy to converge, the gradient explodes, and the quality of the generated samples is poor, this paper proposes an image adaptive steganography model SWGAN-GP based on an improved generative adversarial network. The scheme first uses the generative model G in the generative adversarial network to generate the original carrier information-driven by noise. Then the system extracts the secret information in the secret image through the discriminator and realizes the carrier-free information hiding with the help of the generative adversarial network. The experimental results and analysis show that the hiding method has the advantages of steganographic capacity, anti-steganalysis, and security good performance.
Research on Programming of 3D Simulation System under the Control of Computer Python Language

Python is an alternative to ABC that provides an efficient high-level data architecture and handles object-oriented programming efficiently. The programming language of Python is usually relatively explicit and consistent, which makes the Python programming language easy to understand and easy to maintain. Python is a very common scripting language, especially suitable for software development in various languages. In this paper, Python software is used to numerically simulate the stress characteristic matrix method, and a set of characteristic matrix images for analyzing concrete structures is established. The purpose is to overcome the problems existing in the traditional convolutional neural network method in concrete. Through deep learning of image data, the results show that the simulated value generated by this software can well replace the actual value and achieve the expected purpose.
Research on food safety multi-hop reasoning question answering based on cognitive graph

To greatly support the assessment of food safety situation, Question Answering (QA) system could effectively extract correct answer from web-scale information with the relevant questions. However, most of the existing QA systems are constructed on the searching methods, which cannot deal with the questions that needs multi-hop (multi steps) reasoning process. Meanwhile, black-box models like neural networks are the base of most existing QA systems, resulting in weak interpretability and low confidence level of the reasoning process. To address these issues, this paper proposes a novel multi-hop method, which is based on cognitive graph of food safety. Firstly, based on the text of food safety cases, the BERT based information extraction module is used to extract question-relevant entities and update the entities and properties for the next hop. Secondly, the Graph Neural Network (GNN) based explicit reasoning module is adapted to update the feature embeddings of the entities. Then the cognitive graph could be constructed with the entities and searching paths of each hop. Finally, the cross-entropy loss function is involved to calculate the probability of all answer candidates, then the candidate with max probability is chosen as the final answer. The proposed method could effectively handle the multi-hop reasoning scenarios of the questions (about tens of thousands) from the text of food safety cases, and the accuracy of the experiments is good. Meanwhile, the proposed method could generate the explicit reasoning process and the interpretability issue of black-box models is well solved. The proposed multi-hop reasoning method, which is based on cognitive graph of food safety, has good accuracy, stability, and interpretability. Therefore, the proposed method has advantages over the existing QA systems and could provide reliable support for the assessment of food safety situation.
Dynamic Scene Deblurring Using Deep Learning

Various types of photographing equipment will experience image degradation when capturing the motion of the target object, especially the fast and complex nonlinear motion. The motion blur of an image is a representative image degradation phenomenon. Although the conventional deblurring method is slightly effective in solving the problem of image deblurring, it is incapable of performing precise kernel estimate. To this end, it is necessary to summarize those methods in a unified framework. In this paper, non-uniform blind deblurring in dynamic scenes is divided into single-image deblurring and event-based deblurring. Based on the most effective deep learning image deblurring algorithms available today, it is analyzed and summarized. In terms of single-image deblurring, a multiscale deep learning method and an improved coarse-to-fine multi-scale network U-Net (MIMO-UNet) is introduced; in terms of event-based deblurring, a novel method is summarized and analyzed. End-to-end trainable recurrent architecture and Event-based deblurring model architecture in dynamic real scenes (RED-Net). In addition to this, a brand new deblurring dataset named REDS is also summarized. These deblurring methods proposed in recent years have promoted the development of computer vision. We find that compared to single image deblurring, using event camera deblurring can adapt to high dynamic and high-speed environmental conditions, can quickly find out the changes between potential images, and significantly improve the performance of deblurring dynamic scenes.
A Planar Object Tracking Algorithm Based on Fully Convolutional Dense Network

Planar object tracking is an important research area of visual target tracking. In order to improve the speed and accuracy of plane object tracking in complex scenes, this paper proposes a coarse-to-fine plane object tracking algorithm based on fully convolutional dense network. First, the estimated bounding box of the target object in the input image is calculated according to the DiMP50 algorithm; then SIFT is used to locate the key points on the image, and the feature descriptors corresponding to the key points are extracted from the feature map generated based on the cascaded thick and thin dense network. The nearest neighbor algorithm matches the feature descriptors to obtain the paired keypoint set; then the roughly positioned bounding box is used as the a priori box to screen the matching keypoints from coarse to fine, and the keypoints in the a priori box are retained, using RANSAC algorithm estimates the optimal homography matrix parameters for the key points; finally, the real value is calculated by the homography matrix to obtain the final prediction frame of the target. Validated on the POT dataset, the results show that the performance of the planar object tracking algorithm proposed in this paper is better than the classical algorithm, and the average tracking accuracy on all image sequences reaches 64.76%.
Design of Motion Human Posture Determination System Based on Kinect

The determination of human posture is the basis of identifying further human actions and behaviors. If we can judge the human posture efficiently and accurately, it will be of great significance in many fields, such as human-computer interaction, intelligent lOT, virtual reality, intelligent medical treatment, animation games and so on. In recent years, with the launch of Microsoft's Kinect products, the human posture recognition method based on Kinect sensor reduces the influence of light, background and human clothing on the video, and can accurately track and locate the gesture movement, It has been widely used in human posture recognition. This paper develops a set of motion human posture determination system based on Kinect, obtains the information of human bones through Kinect's infrared camera, infrared receiver and bone tracking technology, designs the algorithm, and finally determines the motion human posture. This paper judges five types of human posture, including raising hands, waving hands, squatting, jumping up and running.
Deep Fusion Co-occurrence Hashing for Image Retrieval

Improving the perception of objects for an image through label co-occurrence correlation is shown to improve the hash quality. Existing methods ignore the relationship between different representation regions and lack highlighting of salient features when fusing co-occurrence label vectors with image embeddings. To tackle this issue, we proposed a new fusion scheme, i.e., Multi-mapping Multi-modal Factorized Bilinear pooling (MMFB), which extends the single image feature to multiple representations to achieve fusion. As a result, we propose a deep fusion co-occurrence hashing algorithm that uses MMFB instead of MFB in the backbone of the label-attended hash (LAH) algorithm and uses SAGPooling to enhance the perception of attention. Extensive experiments over public benchmarks with multiple labels demonstrate that our algorithm can outperform LAH and state-of-the-art hashing algorithms.
Medical Image Retrieval Based on Attention Triplet Hashing

With the wide application of X-ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) methods in clinical practice, massive information retrieval and utilization of medical images has become a hot topic. Although traditional methods have shown good results in many specific medical applications, there are still many problems in large-scale medical applications. Deep hash method has been proved to be the most efficient approximate nearest neighbor search technique for large-scale image retrieval. To this end, Attention Triplet Hashing (ATH) network is proposed in this paper, which can further improve retrieval performance and ranking performance of small samples by learning low-dimensional hash codes that retain classification, ROI, and small sample information. We add channel attention to this end-to-end framework to focus on ROI information. And we add label smoothing regularization to distinguish small sample images. Finally, the validity of my framework is tested on a case-based medical dataset.
LAZIER: A Virtual Fitness Coach Based on AI Technology

Under the influence of the epidemic, online fitness exercise is becoming more and more popular. This is followed by higher requirements for the intelligence and personalization of sports and fitness. Therefore, this paper proposes LAZIER, a virtual fitness coach built through advanced artificial intelligence technology. Without expensive hardware assistance, users can be provided with the three core capabilities of personalized action demonstration, exercise supervision and exercise evaluation through online video teaching, to achieve intelligent and personalized real-time guidance. LAZIER overcomes difficulties such as the diversity of action videos and the scarcity of quality labeled data in the home fitness and sports scene. By designing universal algorithms and few-shot learning, it has realized the core capabilities that virtual coaches must have. Extensive experiments were carried out on the corresponding core technologies to verify the effectiveness.
Short-term photovoltaic power generation forecasting method based on optimized long and short memory neural networks

For the current stage of PV power output instability and power generation prediction model problems, the short-term PV power prediction method based on the optimization of long and short memory neural networks is studied. By analyzing the distribution characteristics of neural networks, the initial data are substituted into the data optimization model, the target weights are calculated continuously and iteratively, and the optimal optimization function of the model is obtained by introducing the self-loop product method. The data are divided into a comparison series and a reference series, and the correlation of class clusters at each unit moment in the reference series is analyzed. We propose a method for predicting the short-term PV power generation data. The simulation experiments show that the proposed method has high prediction accuracy. The model has an intuitive structure, is easy to implement, and is highly inclusive of data, which can efficiently achieve the prediction of power generation data.
Fault Classification Method of Information System Based on Cascaded Convolutional Neural Network Model

Artificial intelligence plays an important role in data anomaly detection, which can significantly improve the speed and accuracy of fault diagnosis and detection in power information system. With its powerful fitting ability, deep learning begins to emerge in various fields, especially in the field of da-ta anomaly detection, which can also use deep learning methods. Due to various types and similar features of power data sets, fault classification cannot be carried out accurately. At present, frequent detection of text information needs to be performed manually. To solve this problem, this paper proposes a fault classification method for information system based on cascaded convolutional neural network model. BERT deep learning network is used for model training, and a cascaded classification network is constructed. Multiple BERT models are called hierarchically to achieve the fault detection effect of hierarchical classification. The method presented in this paper improves the accuracy of fault prediction and classification, which also provides fast, accurate and reliable decision information basis for improving fault diagnosis.
Underwater Image Enhancement Method Combining Detail Enhancement and Exposure Fusion

Aiming at the problems of color distortion, detail loss and low contrast of underwater images, an underwater image enhancement method combining detail enhancement and exposure fusion is proposed. Firstly, the color balance algorithm of RGB three-channel histogram quantization is used to balance image color and improve contrast. Secondly, images with different exposure degrees are obtained by adjusting the gamma value. In order to solve the problem of detail loss caused by noise amplification in the exposure process, homomorphic filtering is performed on the images after contrast-limited adaptive histogram equalization (CLAHE). Finally, the final result image is constructed using multi-scale fusion technology and combined with the weights defined by contrast, exposure and saturation. Experimental results show that the proposed method can effectively correct blue-green bias, improve image contrast and enrich texture details. The UCIQE is 16.94% higher than the average value of the comparison method.
Book Management System Based on Database and SQL

The implementation of a book management system includes designing an E-R diagram, relation model, and database. This paper mainly introduces the system's structural and functional design and emphasizes the system's efficiency and practicality. To meet the functional requirements of the system, this paper also introduces using SQL statements to verify the system's rationality. The system in this paper implements some very useful functions. Through these functions, people will find books more quickly, even if these books are not in the library. And some customers will also use this system to understand their borrowing situation, which will help them return their books in time. More importantly, the book management system also uses some more efficient SQL statements to improve the efficiency of the whole system. Faster query speed and more accurate query results will make this system widely available. After that, more people will be willing to walk into the library.
Real-Time Online Path Planning Based on Improved Sparse A* Algorithm with Geometric Strategy

Considering the threat of enemy detection and interception in the process of UAV flying, UAV ultra-low-altitude flight will be an important penetration method in the flying process. In this paper, aiming at the timeliness, feasibility and optimality of UAV path planning in dynamic environment, it is equivalently treated as a dynamic online path planning problem in the horizontal plane. By embedding the sparse A* search algorithm into the anytime repairing framework. And in the process of iterative improvement of the track, the geometric tangent method, the double ranking criterion and the variable step size strategy are introduced. (Anytime repairing SAS based on geometric method, ARGM-SAS). The Monte Carlo simulation results in the static environment show that the time for the algorithm to generate feasible and optimal trajectories is shorter than that of the standard and SAS based on geometric method. The dynamic simulation results show that the algorithm can quickly generate feasible trajectories and continuously improve the flight trajectories within the specified time. The optimal trajectory can meet the needs of dynamic path planning.
Implementation of pretrained CNNs on cancer detection

As one of the most universal and lethal cancers, Lung cancer results in millions of annual deaths in the United States alone. Though deadly, early and active interventions can remarkably improve a patient's survival rate in five years. Doctors need accurate information about the specific type of lung cancer, which can be time-consuming and sometimes hard to discriminate based on human resources alone. Deep learning applications in Computer Vision have recently achieved significant results, bringing possibilities for applying such techniques to cancer detection. Pretrained models have been proved to be efficient over image feature extractions. However, it is still unclear which of them suits lung cancer detection the most. Thus, comparison of performances between different pre-trained models is necessary. This paper explored performances across different pre-trained feature extractors followed by a fully-connected classifier. Furthermore, this paper also tested models over 30% of the original data to identify performances over the limited volume of data. Lastly, similarity calculations of output features based on both cosine similarities and T-SNE were performed. The results show that all models over full-sized data reached accuracy over 99%, some of which reached 99.8%. Performances over the limited volume of data are still remarkable, all of which are around 97%~98%. More importantly, though orthorhombic regarding cosine similarities, features from different extractors show apparent clustering behavior. This finding shows a consistent direction of learning across models, which brings new information about the interpretability of feature extractors in this field. Such positive outcomes further prevail the bright future of assisting cancer detection with deep learning.
Analysis on Visual Tracking Algorithms

Target Tracking is a popular question in computer vision, with numerous works are proposed. To this end, the brief introduction of visual object tracking should be proposed. In this paper, we aim to review the most popular target representing methods in target tracking. We begin with classification of current target tracking methods, explaining the details in every method. Then we illustrate the merits and weaknesses of each category. At last, we discuss the challenges that target tracking faces and the future develop trend of it. Through our reviewing, we can provide reference for relevant researchers to have a better understanding of target tracking technology.
ATC System Operation Safety Assessment Based on Fuzzy Matter Element Analysis

In order to make the air traffic control system operate safely and nip in the bud, according to the fuzzy matter element analysis theory, a model of air traffic control system based on comprehensive weighting is using the security assessment index system the of millitary air traffic control. In this model we use Minimum Discrimination Information principle to make the weights obtained by the analytic hierarchy process and entropy contact together for assessment index, comprehensive weight is used in fuzzy matter element modeling and assessing. It can be seen that this model has a lot of advantages. For example, scientific and reasonable process, consideration of the subjective and objective influence, simple step and so on. The conclusion of assessment can help us know the safety level of air traffic control system. It can also give us a new method to make sure the safety of air traffic control assessment