Reviewerly/exHarmony huggingface dataset:
Information hiding scheme based on Cascaded Pyramid Network

Since neural networks have been applied to practice in the field of data hiding, it has obtained better results than the traditional technology. With the further research, more and more network models have been applied in the field of image steganography. In this paper, a hidden Network is constructed based on Cascaded Pyramid Network, and a gray secret image with the same size as the color cover image is perfectly hidden in it. And the extraction network is designed to reconstruct secret image from stego image. The human eye cannot perceive the reconstructed image as being different from the original. The experimental results obtained by our model show that the original image and reconstructed image also have high SSIM and PSNR.
Transformed Cubature Quadrature Kalman Filter for Harmonic Estimation with one-step Randomly Delayed Measurements

This work proposes a modified Cubature Quadrature Kalman filters based on orthogonal transformation for nonlinear harmonic estimation with one-step randomly delayed (1-RD) measurements. The proposed algorithm, termed as Transformed Cubature Quadrature Kalman filter (TCQKFRD), enhances the estimation accuracy without increasing computational burden compared to the available Cubature Quadrature Kalman filter with 1-RD measurements. Validation of the proposed filter is demonstrated for a significant nonlinear problem in simulation and subsequently real-time offline harmonic estimation problem. Root Mean Square Error (RMSE) and Average RMSE of the state over 1000 Monte Carlo runs reveal the effectiveness of the proposed algorithm.
Attention Based Object Localization for Weakly Supervised Semantic Segmentation

Semantic Segmentation plays a great important role in the field of computer vision tasks, and abundant algorithms are proposed with the effort of researchers in recent years. The accuacy of segmentation results have been improved a lot in the meantime. However, high precision and high quality segmentation results often rely on a large number of images with pixel-level annotations for training, which requires enormous time and resources. For aforementioned reasons, researchers put forward a more convenient segmentation approach—Weakly Supervised Semantic Segmentation (WSSS). The main idea of WSSS is obtain the Class Activation Map(CAM) by discrimination detection with various weak labels, then generate pseudo masks. This paper discusses the influence of contextual information for the quality of pseudo masks, and we propose to add an attention mechanism to the AMN network, which is able to improve the ability for extracting global feature. We use the VOC12 for experiments, and we compare the precision. Extensive experiments show that our method can raise the performance of the segmentation network.
Image Super-resolution Algorithm Based on Multi-scale Feature Expression and Double Attention Mechanism

In view of the existing image super-resolution algorithm cannot make full use of shallow characteristic information, it is difficult to learn the importance of the different characteristics and the problem of loss of high frequency information, this paper proposes a fusion dual attention mechanism of multi-scale feature extraction module is applied to image super-resolution reconstruction of the module can reuse the characteristic information of the different levels increase output characteristic figure of receptive field; The dual-attention mechanism is introduced in the module to focus on the reconstruction of high frequency information of images. In addition, subpixel convolution is used as upsampling method and joint loss function combining <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> loss and perceived loss is used to supervise network training. Experimental results show that the proposed algorithm can achieve clearer texture detail reconstruction and better visual perception compared with relevant image super-resolution algorithms.
Swasth: An Inverse Cooking Recipe Generation from Food Images

The three important for every one of us is food, cloth and house. We give the first priority to food. At the same in this fast generation making a food by spending so many hours in the kitchen is a tough task. People are using various food delivery apps like Zomoto, Swiggy, Uber eats and many to get their food. Many restaurants have started takeaways courts to parcel the food items. Behind of every meal there is a procedure involved in making of it. How to the know the process behind of it. The basic of aim of this paper is to provide a solution to the query. We are introducing an inverse cooking system named it as “Swasth”. This system recreates cooking recipes for the given food image. This system provides the ingredients used and then also gives the cooking instructions. Our system uses a unique architecture to forecast ingredients as sets, modelling their relationships without enforcing any order, and then creates cooking directions while concurrently paying attention to the image and its predicted components. We thoroughly test the system on the massive Recipe 1 million dataset and demonstrate that we are able to obtain high quality recipes by utilising both image and ingredients. We also demonstrate that the system is able to produce more compelling recipes than retrieval-based approaches in terms of human judgement.
Apart GAN: Fine Image Restoration for Multilevel Resolution

In order to fully preserve the appearance information of the target human body, such as texture, style, color and face and the pose of the 3D human body model, this paper proposes Apart GAN as a virtual video generation network, firstly, using semantics the segmentation network extracts the mask mapping matrix, and uses the mask mapping matrix to segment the human foreground and the site background of the target video, and proposes a foreground and background feature segmentation loss function for the generated foreground and background. Secondly, the appearance information of the target person is extracted in the feature space, the target image and the generated image feature information of several frames before and after are retained, and a feature mapping function is constructed between the target body and the 3D human body model to restore the external characteristics of the target body, and the body and the heads are restored using different network generation methods. In addition, an optical flow estimation function is constructed between the generated images of the previous frames and the current 3D human body model to achieve the spatial timing of the generated video. For the restoration of the site background, the image background restoration network is used and the dilation matrix is used on the mask mapping matrix to restore a smooth and soft background image. Finally, the restored background and the generated human foreground are combined to obtain the final virtual video.
A multimodal sentiment analysis model based on semantic enrichment

At present, more and more multimodal pre-training models are applied to multimodal tasks, especially the sentiment analysis task of graphic modalities. However, people have not considered the problem of information richness when using multimodal pre-trained models for image semantic representation, So we propose a simple semantic enrichment method with flexible adjustment mechanism, and build a sentiment analysis model of graphic modalities to address above problem. It has been verified that the accuracy and F1 score of MVSA-multi and MVSA-single datasets are improved by 1 to 3 percentage points compared with the existing mainstream models.
A Study of Danmaku Video Recommendation Algorithm Incorporating Multiple Features

In the current Internet era, recommendation algorithms have been widely used in major video platforms, due to a large number of short videos, self-made videos lack of video evaluation and user ratings lead to low recommendation efficiency, in order to improve the recommendation efficiency, a fusion of video multi-featured danmaku video recommendation algorithm is proposed. Firstly, the sentiment vector of the video is obtained by classifying the danmaku comments of the video with deep neural network and calculating the sentiment similarity; secondly, the topic distribution vector of the video is generated by LDA topic model based on the tags at the bottom of the video homepage and calculating the topic similarity; then, the distribution vector of the video introduction is obtained by using Doc2vec for the title and description of the video; finally, the sentiment similarity, profile similarity and topic similarity of the video are weighted and fused to obtain the comprehensive similarity among the videos. Then we quantify the public popularity of the video by the number of likes, coins and share, and finally, we calculate the user's preference for the video by the history of user likes and coins, and then multiply the public popularity of the video to generate the recommendation value, and select the top-N videos for recommendation. The experimental results show that compared with the danmaku video recommendation algorithm (DRCFT) fused with collaborative filtering and topic model and the original collaborative filtering, the accuracy of the proposed method recommendation is improved by 0.12 on average, the Recall rate is improved by 0.18 on average, and the F1 value is improved by 0.15 on average.
Aspect-based Sentiment Analysis based on Local Context Focus Mechanism and Talking-Head Attention

Aspect-based Sentiment Analysis is an important research direction in the field of natural language processing, and its purpose is to predict the sentiment polarity of different aspects in sentences. In the existing Aspect-based Sentiment Analysis usually ignores the relationship between sentiment polarity and local context, and the operation of each attention head in the multi-head attention used is independent of each other. To this end, an Aspect-based Sentiment Analysis model based on the local context focus mechanism and talking-head attention is proposed. First, preliminary features of local context and global context are captured by a BERT pretrained model. Then in the feature extraction layer, the local contextual focus mechanism is used, and the local contextual features are further extracted through the contextual feature dynamic mask layer combined with the talking-head attention. In the feature extraction layer, talking-head attention is used to further extract global context features. Finally, in the feature learning layer, the local and global information are fused and input to the nonlinear layer to obtain sentiment analysis results. Experiments are conducted on three public datasets. Comparative experiments show that compared with multiple existing baseline models, the MF1 value and Accuracy of the new model are improved.
Detection and Recognition of Deformed Multiple QR Codes Based on SR_ESAGAN Algorithm

In the study of detection and recognition of distorted, skewed, and other deformation multi-QR codes using the YOLO_CBAM algorithm, there were problems that the detection effect was poor in the case of stacked QR codes, and the small resolution QR codes couldn't be well recognized. In this paper, we used the Real-ESRGAN model for super-resolution reconstruction of polymorphic QR code images. Combining the characteristics of the multi-QR code dataset, we used 3 convolutional layers to reduce the complexity of image super-resolution processing and replaced the LEAKYRELU activation function in the discriminative network with the GELU function. Then we conducted experiments using the SR_ESAGAN algorithm. Experiments showed that after the improvement, the detection rate and recognition rate of multiple QR codes with deformations were enhanced by 3.71% and 5.27%, respectively, compared to those using only the YOLO_CBAM algorithm.
Local Tetra Pattern-based Bilateral LPP in Robot Navigation Guidance Systems: A Robust Approach against Vision Sensor Noises

Locality preserving projections (LPP), originally being a graph Laplacian-based dimensionality reduction technique, searches for a space to embed the higher-dimensional data, keeping the local neighborhood information of the data preserved. However, with conventional LPP, a major challenge lies in the fact that the projection matrix of LPP solely depends on the spatial distribution of the data. This distribution in the Euclidean space is susceptible to spatial factors like scaling, rotation, translation, etc. Apart from that, similar to most graph-based methods, the graph formulation in LPP is influenced by noise and outliers. In this paper, the concept of bilateral filtering has been utilized in the case of LPP, which incorporates feature weights obtained from the higher-dimensional data along with the Euclidean spatial kernel. It enables LPP to make use of the important intrinsic information from the feature space as well, which ameliorates its vulnerability to spatial distortions reasonably. Moreover, a local tetra pattern (LTrP)-based feature descriptor has been utilized here, which helps in extracting robust features from the vision sensor data corrupted with sensor noises. With extensive experimental studies, the proposed algorithm of LTrP-BLPP has been comprehended to be a robust variant of LPP against vision sensor noises.
Non-Local Self-Similarity Tensor Ring Completion: Tensor Ring Completion using non-local self-similarity for Image Recovery

Tenor Ring (TR) is a cutting-edge tensor decomposition algorithm, and the tensor completion model based on TR decomposition is widely used in image recovery, which is able to estimate the complete image with only a portion of observable pixel points and get good results. However, the existing completion models do not consider the non-local self-similarity of images, which makes it difficult to achieve better accuracy. To address this problem, this paper proposes a TR decomposition tensor completion model NLTRC combining the non-local self-similarity denoising module, which can not only exploit the image spectrum and spatial correlation, but also exploit the non-local self-similarity to approximate the original tensor at low rank, and then recover the image. Extensive experiments show that our algorithm achieves better results than several state-of-the-art baselines in color and multispectral images.
TAE-ResU-Net:A trinomial attention-based channel-interactive U-shaped residual network

Brain MRI images are characterized by complex data and large data volume, and accurate and reliable segmentation of brain MRI tumor images is a challenge due to the variability of lesion size, shape, and location. In recent years, many researchers have used deep learning-based convolutional neural networks for image segmentation of medical MRI, CT, etc., and have made great progress and achievements in the field of segmentation. However, the convolutional neural network-based model is limited by the local Receptive field, which cannot capture the relevant information at a long distance, and cannot achieve fine segmentation of brain lesion object boundaries, and the performance of small object segmentation is poor. For this reason, some researchers have proposed a self-attention-based segmentation method, which improves the accuracy but has a huge number of parameters, long running time, and cannot be used on small devices, which has far-reaching effects on patient medical images. To this end, to address the above problems, this paper proposes a trinomial attention-based channel interaction residual U-shaped network to improve segmentation accuracy while reducing the number of model parameters as much as possible to achieve a balance between the number of model parameters and segmentation accuracy. We use the datasets of BraTS2018 and BraTS2019 for experiments, and the experimental results show that our model outperforms other more advanced models in most indicators such as DISC, Hausdorff distance, etc., and has certain advantages in tumor segmentation.
Privacy Protection Method for Online Medical Diagnosis Based on Attribute and Proxy Re-encryption

Aiming at the current problems of online medical data leakage and complex access control, this paper proposed a privacy protection method for online medical diagnosis based on attribute and proxy re-encryption. First of all, when facing different data request visitors, patients determine the privacy level of their medical records, generate different levels of medical records file indexes, and send them to medical institutions with attribute-based encryption. Then, the medical institution re-encrypts the encrypted medical record index file by proxy, and sends the corresponding index encrypted file according to the level of the requester, so as to realize the access control of different sensitive data. Finally, the data requester obtains the corresponding encrypted medical record file according to the index file, and the data requester obtains the desired data through the decryption of the private key. The analysis shows that the scheme is feasible.
An Expert System Based Dehazing Scheme for Vision Enhancement

Graphical-User-Interface (GUI) based image processing has gained a huge popularity throughout the years, due to user requirement and better interaction between a human and a machine. This article proposes an integrated system employing GUI for single image haze removal for visibility enhancement of images. The proposed approach can be realized in two steps. At first, mathematical morphology has been used to calculate atmospheric light via some image processing functions. Secondly, morphological reconstruction-based operations are used to generate the transmission map. The developed system can incorporate automated or manual tuning for selection of various hyperparameters. A total of nine image processing functions are utilized to generate the GUI interface and finally the results are obtained in a single figure window with sub sections. The proposed dehazing scheme can reduce artefacts and restore the image quite successfully leading towards an intelligent transportation system. Experimental results reveal the effectiveness of the expert vision system.
Research on Speech Dialogue Text Classification Based on Albert-BiLSTM Model Multi-label

In order to solve the complex customer demands of the State Grid customer service center, we identified the various business types corresponding to the user's telephone demands based on the massive 95598 telephone voice text data set, and constructed a multi-label text classification model. This paper combines the pre-trained language model ALBERT and bidirectional long short-term memory network to design a text classification model Albert-BilSTM for multi-label speech dialogue. The model adopts the ALBERT pre-trained language model for dynamic word vector representation. In addition, BiLSTM model is introduced to construct multi-label classifiers to extract semantic information features at different abstraction levels. Through the control experiment, the text classification model based on Albert-BilSTM multi-label speech dialogue constructed in this paper can effectively improve the accuracy of text classification, and the classification effect of this model is significantly better than other models on text datasets.
Interactive Context-Comparative Model for Text Matching

Most of the existing text matching models are based on the interactive network and use cross-attention mechanism to capture the semantic links between two texts, and have made outstanding achievements. However, the current methods generally do not pay enough attention to the context information of the text and ignore the importance of comparing these features. To solve this problem, this paper proposes an interactive context-comparative model for text matching (ICCM). First, the Bi-directional Long Short-Term Memory network is used to encode the word vector. Then the key information of related words between different texts is aligned through the cross-attention mechanism, and the key information in their contexts is focused through the context-attention mechanism. The two kinds of information are compared using comparison functions, and the convolutional neural network is used to aggregate the features after splicing. Finally, the results of text matching are predicted. Testing on LCQMC dataset shows that the accuracy rate is 85.5%. The experimental result and analysis prove that the proposed model is superior to the existing main text matching methods and has certain effectiveness.
Enhancing YOLOv3-tiny for Mask Detection in Natural Scenes

With the worldwide spread of COVID-19, people's life safety has been greatly threatened. So, we consider using YOLOv3-tiny algorithm to detect mask wearing. Since there are few detection models for correctly wearing masks, we decided to use three classifications to detect correctly wearing masks, incorrectly wearing masks, and not wearing masks. Besides, in order to enhance the performance of our model in small object detection, we propose the k-means++ algorithm to make the size of the initial anchor boxes closer to the actual size of the object, and add a YOLO detection layer to effectively improve the accuracy of a small object. The results show that the mAP@50 values of our model are 4.68% higher than YOLOv3-tiny algorithm. Our model has significantly improved the detection ability of crowd scenes, and mask detection is more accurate and robust, which has good application value for mask detection in natural scenes.
CPMLHO: Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization

The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function. Finding the best response function is very time consuming. In this paper we propose CPMLHO, a new hyperparameter optimization method using cutting plane method and mixed-level objective function. The cutting plane is added to the inner layer to constrain the space of the response function. To obtain more accurate hypergradient, the mixed-level can flexibly adjust the loss function by using the loss of the training set and the verification set. Compared to existing methods, the experimental results show that our method can automatically update the hyperparameters in the training process, and can find more superior hyperparameters with higher accuracy and faster convergence.
An Extended Direction-Oriented Interpolation Method via Spatial Direction Vector Validity Measure

In this paper, an extended and improved direction-oriented interpolation method is proposed for de-interlacing. The proposed method records the minimum square error and max square error while block-matching, and uses these square errors to measure the validity of detected spatial direction vectors. When the spatial direction vector is ignorable, the proposed method will revise it to avoid staircase effect. The performance of the proposed method is analyzed by de-interlacing some popular video sequences and compared with the original DOI method and the YADIF method.
BTDM: A Regulatory-Oriented Optimization Scheme for Bitcoin Transaction Data Organization

As Bitcoin grows, more and more users are choosing to use Bitcoin as a means of transaction. For regulatory purposes, the need for retrieval and analysis of large amounts of bitcoin transaction data is raised. Faced with the problems of high retrieval time and cost overhead of current data retrieval tools, this paper proposes a new regulatory-oriented Bitcoin transaction data organization optimization scheme (BTDM). Firstly, BTDM reduces the index retrieval time by introducing Redis, then optimizes the data retrieval efficiency by using file offset mapping method, and finally reduces the cost of searching by transforming the data structure of transaction hash and bit mapping method. The experimental results show that BTDM has significant advantages in terms of performance and cost compared to other retrieval tools.