Reviewerly/exHarmony huggingface dataset:
Audio Source Separation and Voice Conversion, an Application in Music Industry

Voice conversion in the field of deep learning converts the voice of the source speaker into the voice of the target speaker. An application of voice conversion in the music industry has been presented in this paper. In the music industry, there is a monopoly of the music production industry that they ask different singers to sing a song, and the best version of the song is selected. This can cause degradation of morals in singers. To tackle this problem a novel unsupervised pipeline has been presented which combines Audio Source Separation (ASS) and voice conversion. For simulation, Demucs was selected as the ASS model, and randomCNN was selected as the voice conversion model. Pakistani songs were selected as the dataset and different combinations of song conversion were tested i.e., within the same gender, and different genders, etc. The result showed the conversion within the same gender and different songs over different time intervals. After evaluation, it was found that by increasing the duration of the song, content loss increases while style loss decreases.
Monocular depth estimation based on Chained Residual Pooling and Gradient Weighted Loss

In recent years, the self-supervised monocular depth estimation task in the field of autonomous driving has achieved remarkable results. The brightness consistency assumption is adopted to guide network training, The image brightness needs to be kept constant in adjacent frames. However, this assumption does not apply to laparoscopic scenarios, where the intensity of light for the same tissue changes over time during surgery. In addition, the defined receptive fields in laparoscopy lead to low utilization of structured cues, the predicted depth map performs poorly on the tissue contour when the laparoscopic frame is fed into the depth estimation network. In this work, aiming at the problem of luminance consistency, it is proposed to integrate the second-order gradient of the image and the second-order gradient of the parallax map into the photometric reconstruction error to guide the network. In view of the problem of the low utilization rate of laparoscopic image context clues, the following clues are weighted in the decoder part of the network to improve the reuse of low-resolution feature maps for tissue contour clues. Experiments were performed on the SCARED dataset, and new losses and new modules were put into the network separately to train to verify their effectiveness, the results showed good performance on all four commonly used indicators.
Scene Text Detection with Cascaded Multidimensional Attention

Over the past years, scene text detection based on a segmentation network has progressed substantially due to its pixel-level description, which is more suitable for detecting long text and curved text. However, limited by the scale robustness and feature representation ability, most existing segmentation-based scene text detectors may need help to handle more complex forms of text, which is common in the real world. In this paper, to tackle this problem, we propose a cascaded module, termed CMAModule, based on the attention mechanism to improve the feature representation capability of the model, which integrates a series of the basic module to augment the feature map. Our proposed CMANet, obtained higher recall and precision on two benchmarks.
Boosting black-box targeted transferability from feature statistic perspective

Adversarial examples are images that can be easily misclassified by deep learning models when small, imperceptible changes are added to them. They pose a security concern for the use of DNNs in practical applications because of their transferability. One mainstream method for black-box targeted attacks is feature space attacks. They mainly focus on iterative attacks and have generally attempted to modify the intermediate feature of source category similar to the feature of target category. In this paper, we explore that certain characteristics of features, such as feature style statistics, might be able to better represent the features themselves, and potentially lead to better transferability compared to simply aligning the source and the target features directly. Based on this, we propose a method which use the relationship between the style statistics of intermediate features and its corresponding category to boost black-box targeted transferability in generative attacks. We demonstrate the effectiveness of this method through experimental results.
Active Privacy-Preserving Contact Tracing Scheme for Infectious Diseases in Cloud Environment

To overcome the issues such as privacy leakage and Health Authorities passive tracking in the current contact tracing scheme, this paper proposes an active privacy-preserving contact tracing scheme for infectious diseases in cloud environments. In this scheme, Telecom Center timely collects participants' information, so that Health Authorities can proactively track the contacts of infectious diseases when needed, and participants needn't to install any additional Apps; Pseudorandom number generator is used to blind the participants' location information to protect their privacy; ElGamal encryption algorithm and its variants are introduced to encrypt the participants' identities, so that the Health Authorities can correctly obtain the information of contacts without revealing anything of other participants. Theoretical analysis and experimental simulation show that the scheme is secure and effective, and it also has good computation performance in large-scale data scenarios.
Frequency Decomposition Network for Fast Joint Image Demosaic, Denoising and Super-Resolution

Image denoising (DN), demosaicing (DM) and super-resolution (SR) are the key tasks of the low-level vision. Joint demosaicing, denoising and Super-resolution (JDDSR) can effectively improve the image quality. However, the previous methods explored the feasibility of tasks more than the characteristics of DM, DN and SR. Meanwhile, the joint training also brought computational burden and the three tasks process information at different frequencies. DN and DM pay more attention to low-frequency (LF) information, while SR is used to recover the lost high-frequency (HF) information. In this work, we use the way of Laplace pyramid to separate the HF and LF of the image, and use different branches to learn the information of different frequencies. In order to reduce the computational burden, we redesign the network architecture and use the form of non-parametric up-sampling to generate the results. Experiments demonstrate that our method can achieve results similar to existing methods with very small computational effort and storage.
A Survey of Large-scale Complex Information Network Representation Learning Methods

With the increasing growth of data scale and the increasing complexity of network structure, the heterogeneity, high sparsity, heterogeneity and high dimensionality of large-scale complex networks have become increasingly prominent. How to represent network information reasonably and effectively so as to better serve subsequent network analysis tasks has become the key problem of network analysis. Network representation learning aims to represent the components (nodes, edges, subnets, etc.) in the network as low dimensional dense vectors. This vector can fully retain the original network structure information and other heterogeneous information, and has the advantages of improving computing efficiency, mitigating the impact of data sparsity, and effectively merging heterogeneous information. This research focuses on homogeneous networks and heterogeneous networks, summarizes and analyzes advantages and shortcomings of the network representation learning methods in recent years, and gives the possible research directions and contents in the future work.
Fine-grained Recognition Algorithm For Transformer Based On Part Features

Fine-grained image recognition is a challenging task. Due to the small differences between the categories of fine-grained images and the large differences within the categories, traditional networks based on CNN or Transformer have their own shortcomings in feature extraction. This paper gives full consideration to the characteristics of CNN and Transformer, and proposes a fine-grained recognition algorithm combining WS-DAN (Weakly Supervised Data Augmentation Network) and ViT (Vision Transformer). Firstly, the image patch is obtained by WS-DAN to eliminate the incomplete semantic information of image patch caused by traditional ViT. Then, the image patch is encoded based on Transformer framework and global token is introduced for topological relationship constraints among components, which overcomes the locality of features extracted from traditional CNN network. Finally, the training based on the combination of cross entropy and contrast loss function further improves the recognition ability of the network. The proposed algorithm has achieved satisfactory results on the CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets.
Design And Implementation of Web Crawler Based on 'Internet +'Data Automatic Extraction

The rise of the strategy of “Internet +” breaks the barriers of data and information. Web crawler is widely used in data acquisition and data analysis in the massive Internet plus information. Taking “IMDB top250 movies” as the goal, using the crawler technology based on Python language, this paper explains the four steps of web crawler in detail, compares the differences of three web page parsing methods: BeautifulSoup, Regular Expression(Re) and XPath, and completes the crawling of target data. The experimental results show that Re is the best in data analysis speed; In terms of web page parsing logic, beautiful soup is the best; From the perspective of comprehensive use, XPath is more suitable.
Tamper-proof Research Based on Digital Culture Museums

This paper first briefly introduces digital culture museums and the three modules of digital culture museums; then proposes that due to the rapid development of image processing technology, many image-processing-related software can easily modify digital information such as images and videos, and there are also some unscrupulous media that maliciously tamper with images in order to gain attention and clicks; then Finally, a set of anti-tampering research of digital culture museums based on hash algorithm for checking digital culture museum resource files and using difference value hashing method for querying and tracing digital culture museum images is constructed to facilitate people's verification of digital culture museums that claim to originate from digital culture museums, supplemented by micro-copyright authorization and tracking system of digital culture museum websites. resources for verification.
Gradient-Based Meta-Learning Using Adaptive Multiple Loss Weighting and Homoscedastic Uncertainty

Model-agnostic meta-learning schemes adopt gradient descent to learn task commonalities and obtain the initialization parameters of the meta-model to rapidly adjust to new tasks with only a few training samples. Therefore, such schemes have become the mainstream meta-learning approach for studying few shot learning problems. This study mainly addresses the challenge of task uncertainty in few-shot learning and proposes an improved meta-learning approach, which first enables a task specific learner to select the initial parameter that minimize the loss of a new task, then generates weights by comparing meta-loss differences, and finally leads into the homoscedastic uncertainty of the task to weight the diverse losses. Our model conducts superior on few shot learning task than previous meta learning approach and improves its robustness regardless of the initial learning rates and query sets.
A Fast Path Planning Method Based on RRT Star Algorithm

The path planning algorithm for moving objects has high complexity, and the automatic path planning ability is poor, which cannot deal with complex practical environmental problems. A fast path planning algorithm based on RRT-Star is proposed. First, for the diagonal obstacles in path planning, the deadlock back-off method is used to realize obstacle detection, which effectively improves the safety of the path. Second, as it progresses, the algorithm uses a step size adjustment function to expand the step size, thereby increasing the speed at which the random tree can explore this space. In addition, based on the RRT-Star algorithm, the target deviation strategy is introduced, and the initial pheromone allocation principle is proposed. Finally, the pheromone is classified, and the pheromone on each path is superimposed according to the optimization objective. The results show that the RRT-Star fast path planning efficiency and the number of iterations are significantly better than the RRT algorithm and the ant colony algorithm.
LDAGB: A Lightweight DAG-based Blockchain

To solve the problems of low query efficiency and high storage redundancy of traditional blockchain technology, a lightweight DAG-based blockchain (LDAGB) architecture is proposed. The block of the LDAGB architecture is called a unit, which stores a transaction, the wallet addresses of the sender and receiver of the transaction, and two parent unit hashes. When a new unit is generated, one parent unit hash value in the unit points to the current latest unit of the transaction sender (wallet address), and the other parent unit hash value points to the current latest unit of the transaction receiver (wallet address). Based on the concept of light wallets, each lightweight node stores only its own units, avoiding huge storage costs caused by data growth. In addition, a lightweight PoW consensus mechanism is used to reduce the computing cost of unit packaging, and a voting mechanism is used to avoid the possible fork of LDAGB. The experimental results show that LDAGB can significantly improve the efficiency of transaction query and verification and reduce the storage cost, compared with the traditional blockchain architecture.
TR-Former: Token Based Residual Transformer For Single Image Denoising

In recent years, people's demand for photography has been increasing with the development of smartphones and cameras. In order to get higher-quality photos, more and more people pay attention to the field of image restoration, especially the image denoising task. The recently proposed vision transformer model, accompanied by the breakthrough of deep learning, has also been successfully applied to image restoration problems. In this paper, we first design the TokenWin Transformer Block with a local branch and a global branch to capture both the local and global features of an image. In order to improve the denoising performance of the network, we then add a Gate-Residual structure on the existing basis to obtain richer features of samples. This scheme selects more effective features of denoising to feed forward, which strengthens the network's information capture capability. Finally, the effectiveness of this method is verified by our experimental tests.
Transformer: Image Classification Based on Constitutional Neural Networks

To solve the problem of excessive calculation caused by inputting images with a large size when using ViT network structure to implement image classification tasks, this paper proposes a ViT network model based on a convolutional neural network (CNN). Its network structure first uses CNN to extract a low-resolution feature map and then uses ViT structure to process the low-resolution feature map. At this time, the computational pressure is greatly relieved. In this paper, the author uses VGG16 as the Backbone and ViT network structure to build the VGG16-TE network and implements an image classification task on the ImageNet-1k dataset. Compared with the VGG16 model, the accuracy of Top1 and Top5 image classification is improved by 2.5 points and 1.7 points respectively. Besides, this paper builds a ResNet34-TE network with ResNet34 as the Backbone and ViT network and implements an image classification task on the ImageNet-1k dataset. Compared with the ResNet34 model, the accuracy of Top1 and Top5 image classification is improved by 2.1 points and 1.2 points respectively. VGG16-TE and ResNet34-TE parameters decrease by 68M and 61.5M compared with that of the ViT-Base model.
HCT: Hybrid CNN-Transformer Networks for Super-Resolution

Recently, several computer vision tasks have begun to adopt transformer-based approaches with promising results. Using a completely transformer-based architecture in image recovery achieves better performance than the existing CNN approach, but the existing vision transformers lack the scalability for high-resolution images, which means that transformers are underutilized in image restoration tasks. We propose a hybrid architecture (HCT) that uses both CNN and transformer to improve image restoration. HCT consists of transformer and CNN branches. By fully integrating the two branches, we strengthen the network's ability of parameter sharing and local information aggregation, and also increase the network's ability to integrate global information, and finally achieve the purpose of improving the image recovery effect. Our proposed transformer branch uses a spatial fusion adaptive attention model that blends local and global attention improving image restoration while reducing computing costs. Extensive experiments show that HCT achieves competitive results in super-resolution tasks.
RDYOLOv5m6-KF: A Rotation Detector for Ship Detection in Remote Sensing Images

The use of remote sensing images for ship detection can accurately monitor ship targets and provide reliable reference for monitoring key sea areas. Since the horizontal detection model cannot precisely locate and represent the specific direction of the ship, we propose a rotation detector based on YOLOv5m6 and KFIoU, which can realize the detection of ships in arbitrary orientations. On the other hand, the punishment based on Gaussian Wasserstein distance is used in model to generate confidence loss, which improves the discrimination between foreground and background during ship detection. Finally, transformer pyramid attention is added to the backbone of network, which uses the fusion of information extracted in multi-scale space and the self-attention mechanism to improve the feature extraction effect and the accuracy of detection. On FGSD2021 dataset, our model finally achieves 88.24% of mAP after adding attention mechanism and improving the confidence loss.
A method for synthesizing dynamic image of virtual human

With the rise of the Metaverse, the need for efficient modeling of avatars becomes increasingly urgent. Building virtual human models from human image datasets has been a hot topic in computer vision. We used the speech synthesis technology to complete the conversion from text to speech waveform, and used the speech-lip shape generation method to generate a real person image with audio and video synchronization, finally used the thin plate spline transformation method to drive the virtual human image, and synthesizes a virtual human with audio and video synchronization image. Experimental results show that this method can effectively solve the problem of text-driven avatar lip mismatch and text-driven avatar audio and video asynchronous problems, and can synthesize high-quality, high-fidelity, low-latency avatars.
ViT-R50 GAN: Vision Transformers Hybrid Model based Generative Adversarial Networks for Image Generation

In recent years, the tremendous potential of GAN in image generation has been demonstrated. Transformer derived from the NLP field is also gradually applied in computer vision, and Vision Transformer performs well in image classification problems. In this paper, we design a ViT-based GAN architecture for image generation. We found that the Transformer-based generator did not perform well due to using the same attention matrix for each channel. To overcome this problem, we increased the number of heads to generate more attention matrices. And this part is named enhanced multi-head attention, replacing multi-head attention in Transformer. Secondly, our discriminator uses a hybrid model of ResNet50 and ViT, where ResNet50 works on feature extraction making the discriminator perform better. Experiments show that our architecture performs well on image generation tasks.
An Imaging Method based on Front-end and Back-end Cooperation

With the increasing demand for high-quality imaging, modern optical imaging systems are often designed to be very complex, resulting in problems such as large volume, heavy weight and expensive cost. With the continuous maturity of computational imaging technology, people gradually break through the limitations of traditional optical imaging systems by combining back-end algorithms with optical design to obtain high-quality images. While increasing the number of lenses, the effect of image restoration will be weakened because the original image quality is improved. To find a balance between the front-end lens design and back-end IR method, this paper tries to design three kinds of simple lenses and a point spread function(PSF) estimation method based on circular partition strategy to build a lightweight imaging system with high image quality. Experiments show that the simple lens we designed can gradually improve the imaging quality, and this method of partition estimation of blur kernel can be applied to these three different simple lens imaging systems.
Learning Spatial and Geometric Information for Robust Features

Robust local feature detection and description are crucial in a huge of computer vision tasks. However, current feature descriptors lack geometric information and spatial features. In this paper, we propose a novel spatial and geometric feature fusion architecture, which can effectively tackle these problems. The proposed architecture includes three aspects. 1) A multiple low-level feature fusion (ML2F2) subnetworks, which could improve the ability to extract geometric information through the weighted fusion features. 2) A subnetwork for gradient feature extraction (GFE) which could validly extract the spatial features by encoding the horizontal and vertical gradients of an image. 3) Effective spatial geometric feature fusion (SGFF) module to alternatively fuse the above subnetworks. Experiments on the task of Image Matching and Long-Term Visual Localization show that the proposed method is superior to most advanced local feature descriptors.
Parallel hypernym relation extraction based on partition index dividing

Aiming at the shortcoming of existing pattern methods in handling massive data, a parallel hypernym relation extraction method on Spark is proposed. To improve the extraction accuracy, Combined with Spark's RDD programming model, an improved credibility algorithm(ppmit) is designed to identify the inverse hypernym relation; to address the data skew problem when calculating the credibility value of the hypernym relation, a data partitioning strategy - PID(Partition Index Dividing) algorithm is proposed to calculate the partition balance, add partition index to the overflow data, redivide the data, and ensure the partition data balance, thereby reducing the calculation time. Experiments conducted on the Chinese Wikipedia dataset show that the proposed method can guarantee the extraction accuracy and effectively improve the operational efficiency of the pattern extraction method.