Reviewerly/exHarmony huggingface dataset:
Research on Black-Box Adversarial Attack Based on Transformer

The classic adversarial samples of black-box attacks are all aimed at the models of Convolutional neural networks (CNNs), but they do not perform well on the new recognition networks based on Transformer. In this paper, we propose an adversarial sample generation algorithm based on Vision transformers (VITs)‘ self-attention mechanism and patch partition. We noticed that different blocks in the VIT have uneven attention distribution, so we first generated a patch-based attention map and performed threshold segmentation, which is used as a mask to perform data enhancement operations based on patches with high weight and patches with low weight, and then exchanged information between patches to generate adversarial samples. The experiment of simulating black-box attack shows that the adversarial samples generated by the algorithm in this paper have a high success rate in all kinds of models based on Transformer of attacks, and also perform well on CNNs.
Threshold Secret Sharing and Symmetric Encryption Algorithm-Based Hybrid Protection Framework for Enterprise Privacy

Most companies are currently confronted with the key management and storage issues caused by the procedure of private file backup. Aiming at the security protection requirements of important files and system parameters such as multi-level CA private keys in smart grid industry network, this research introduces a privacy file hybrid protection framework based on symmetric encryption algorithm and threshold secret sharing to prevent key loss due to the failure of equipment or key disclosure. In addition, the application scenarios of the framework are presented, and the experiment indicates that the framework can be effectively applied to the secure key management and private file backup of grid industries and numerous organizations.
Facial Expression Recognition and the Application of Supervised Contrastive Learning

Facial expression is an essential part of communication in human life. In order to automating the process of facial expression recognition and improve the accuracy, in this paper, I explore the problem of facial expression recognition through the FER-2013 dataset with a new loss function—supervised contrastive learning. The goal is to classify similar features close together with the selection of different anchor points and positive and negative examples under the label of pre-classification. I address this task by Convolutional Neural Network, using both a shallow CNN model of my own as well as deeper models such as ResNet, VGG, and Inception. I fine-tuned these models and compared their performances on the dataset. Then I ensembled them to reach a better performance. As a result, I obtained a final model that reaches 70.24% accuracy on the test set, beating two baselines (human recognition rate and null model accuracy) proposed by previous works.
Specific mining of COVID-19 reviews using deep learning methods

Sentiment classification is a valid measure to monitor public opinion on the COVID-19 epidemic. This study provides a significant basis for preventing the spread of adverse public opinion. Firstly, in epidemic texts, we use a convolutional neural network and bidirectional long short-term memory neural network BiLSTM model to classify and analyze the sentiment of the comment texts about the epidemic situation on Weibo. Secondly, embedded in the model layer to generate adversarial samples and extract semantics. Then, semantic information is weighted using the attention mechanism. Finally, the RMS optimizer is used to update the neural network weights iteratively. According to comparative experiments, the experimental results show that such four evaluation metrics as accuracy, precision, recall, and f1-score with our proposed model have obtained better classification performance.
Classification Emotion Using Densenet

Facial expressions are a way of expressing human emotions. Using standard convolutional neural networks (CNN) to extract and classify facial expressions has not achieved satisfactory accuracy in the past, making the classification of facial emotions a challenge, due to lack of large dataset and advanced CNN models. In this paper, a transfer learning approach is used to improve emotion recognition accuracy. Firstly, a Densenet model pre-trained in the ImageNet dataset is chosen. Next, fine-tuning is performed on the network model, which aims to extract features from images to recognize and classify seven emotional expressions. It features fewer parameters and is resistant to overfitting compared to other models. The model is trained using the facial expression dataset, which make up of 28,709 48*48 pixel images. Experimental results show that the model is better in accuracy compared to the performance of vgg19.
Research on Performance Optimization of Virtualized Server Cluster Based on Cloud Computing

In the age of big data, the generation and processing of massive data will not only take up more physical storage space, but also make the computer system bear a large load, and the operating cost of the information system will rise. Server virtualization technology can be used to build a virtualized environment in which all real computer operations can be simulated, requiring only a small amount of hardware resources, and running loss will be significantly reduced. With the advancement of the development and construction of modern society, the application of cloud computing technology has become a major policy in the implementation of modern virtual server applications. This paper mainly analyzes the server virtualization technology in cloud computing, and tests the system performance through simulation experiments. The experimental results show that the CPU utilization and cluster throughput of the algorithm in this paper are improved, and the average performance is optimized by 34.41%. This shows that virtualization cluster technology improves system performance and enhances system flexibility and availability.
A Temporal Knowledge Graph Completion Method with Text Descriptions Enhancement for Bank Compliance Management

With technologies of knowledge graphs, the compliance management of banking business based on documents issued by financial regulatory agencies can help relevant personnel realize intelligent queries. During our research, it is found that there are still existing some undiscovered relationships between entities in the current KG. Meanwhile, the needs of bank compliance management for KG are constantly changing. Besides queries such as keyword retrieval, higher requirements are put forward, hoping to obtain more abundant queries with relevance inference. These require us to supplement the KG. For these, this paper designs a temporal knowledge graph completion method with text descriptions enhancement (TD-TKGC). TD-TKGC builds a temporal completion model with LSTM to learn time-aware structural embeddings and uses ALBERT to learn textual embeddings. In addition, TD-TKGC adopts the clustering-based negative samples construction strategy to provide more effective negative triples. Experiments on multiple real-world temporal KG datasets show that for MRR, Hits@l and Hits@3, TD-TKGC performs better on completion tasks.
Multimodal Apparent Personality Traits Analysis of Short Video using Swin Transformer and Bi-directional Long Short-Term Memory Network

Due to potential applications in online education, human resources and personalized recommendation, apparent personality analysis has become a meaningful task in the field of computer vision and multimedia. According to characteristics of short video in time length, idea density and individual expression, we take full advantage of clues in visual and audio aspects to propose a novel framework of apparent personality analysis. From the viewpoint of visual modality, Swin- Transformer and Bi-directional Long Short-Term Memory (BiLSTM) network are introduced to discovery feature representation for personality assessment. Meanwhile, usual features including chromaticity characteristics, root mean square energy, spectrum, zero crossing rate, and MFCC are extracted to describe personality from the perspective of audio modality. Finally, feature leaned from visual and audio modalities are fused to make personality analysis that can be viewed as solving regression problem. For the purpose of evaluating the proposed algorithm's performance, we conduct comparative analysis between our approach and other methods on First Impression V1 and Short Video Vlogger Personality datasets. The experimental results show that our method is competitive in prediction accuracy.
Research on the Mongolian Speech Synthesis Model Based on Transformer and WaveRNN

The main task of speech synthesis is how to convert text information into audible voice information, which can be widely used in smart home, virtual anchor, voice navigation and other fields. The research of Mongolian speech synthesis will help to enhance the independent development ability of informatization in Inner Mongolia, and accelerate the integration pace of Mongolian language and modernization. This paper proposes the improved Trans-WaveRNN based on Transformer and WaveRNN, aiming at the existing Mongolian speech synthesis model has the phenomenon of wrong words and missing words, the synthesized speech is unnatural, the synthesized speech speed is too slow, and cannot meet the real-time interaction of the system. First of all, according to the characteristics of the Mongolian language itself, the Mongolian text is preprocessed at the front-end, the Mongolian words are converted into their corresponding Latin form, and then converted into a phonemes, which are performed on the pre-normalized phoneme sequence fir training, reducing the problem of mispronunciation trained on character sequences; Secondly, the proposed model can be trained in parallel, there is no recursive connection between the framewoks, and the WaveRNN mode is used as a vocoder to synthesize the audio, while improves the performance of Mongolian speech synthesis, while speeding up the training speed, making the audio process much smoother and satisfying the real-time and interaction properties of the speech synthesis system. Experiments show that the average subjective opinion score of the improved Trans-WaveRNN Mongolian speech synthesis model reaches 4.13, and the real-time rate reaches 0.94 while satisfying the practicality. The method proposed in this paper effectively improves the quality and performance of the Mongolian speech synthesis model.
A Complementary Product Recommendation Method Based on Modality Graph

Complementary product recommendation is to recommend products for users that can be used together. Existing product recommendation methods consider the simple features of products' image and text without further capturing the depth features of image and text, and they mainly place emphasis on exploring the intra-modal complementary relationships between products, but ignore the importance of the inter-modal complementary relationships. In addition, most of these methods achieve cross-modal fusion by directly splicing multiple modalities, thereby probably causing sub-optimal performance. Based on this situation, this paper proposes a Complementary Product Recommendation Model Based on Modality Graph, namely, (CPRMG). The model uses multi-modal information of products as input, further enriches the representation of image and text by gated self-attentive module, then combines the intra-modal complementarity with the inter-modal complementarity, and finally fuses them based on Transformer encoder to learn the complex relationship between products and candidates for complementary product recommendation. Experimental results on the Amazon dataset demonstrate the CPRMG model proposed in this paper outperforms other baselines in all metrics. The experiments show that the CPRMG model can recommend complementary products for users more accurately.
RGB-Infrared Fusion Object Tracking Based on Siamese Network

I In order to obtain the color and texture features of a target in a poor illumination environment and simultaneously realize accurate and fast object tracking, an improved object tracking method with visible and infrared image fusion based on a depth convolution network is proposed. Firstly, the fusion method for visible and infrared images is studied. Then, the target tracking framework based on the Siamese network is established, for extracting, the convolution features of the target template and current tracking target. Meanwhile, during the convolution process, an erase unit is added to decrease the influence of irrelevant information caused by the zero-fill operation. Finally, the target's position in the current frame image is calculated by the depth cross-correlation module. In experiments, the performance of the proposed algorithm is tested using the standard dataset VOT2020RGBT and also is compared with a variety of excellent algorithms such as JMMAC algorithm, mfDiMP algorithm, and CISRDCF algorithm proposed in recent years. The results show that the tracking accuracy increased from 0.612 to 0.692, from 0.668 to 0.672, and from 0.652 to 0.686 in the fast-moving, lighting variable, and target occlusion scenarios, respectively. Experimental results show that the algorithm outperforms the above algorithms in complex environments.
CRIoU: A Complete and Relevant Bounding Box Regression Method

In the object detection task, there is no correlation between the regression content of the traditional bounding box regression loss function and the evaluation standard IoU (Intersection over Union), and there is some irrationality for the regression attribute of the bounding box, which reduces the detection accuracy and convergence speed. In this paper, a novel loss function, termed as Complete Relativity Intersection over Union(CRIoU), is proposed to improve the detection accuracy and convergence speed. Firstly, this work determines the design idea and determines the improved IoU loss function normal form. Secondly, on the basis of IoU loss, the ratio of the perimeter of the rectangle formed by the two center points and the minimum closure area formed by the two frames is introduced as the penalty term for the distance between the center points of the bounding box, and the improved IoU loss is applied to the non-maximum suppression. Finally, the side error of the two frames and the side square of the minimum bounding box are introduced as the side penalty terrn, finally, a novel loss function, termed as Complete Relativity Intersection over Union, is proposed. The experimental results show that compared with other loss functions, the criou loss function significantly improves different algorithm thresholds and data sets, with a maximum improvement of 4.0%, and the convergence speed is also better than other methods. The new NMS method also has a high improvement under different thresholds.
Design of identity authentication scheme in smart grid based on blockchain

With the development of information technology and the increasingly complex electricity environment, the traditional power grid is gradually unable to meet the needs of modern production, which is replaced by the smart grid (SG). Although the SG improves people's quality of life to a certain extent, it also brings many privacy and security risks. Different entities in the network have various problems such as lack of identity privacy protection and anonymity, inability to protect against multiple types of attacks, and centralized single point of failure. Based on the above problems, this paper proposes a secure and anonymous identity authentication mechanism based on blockchain. The process is divided into three stages: 1) System initialization, 2) Registration, 3) Mutual authentication: the blockchain is used for secure transactions in the SG environment., while the Elliptic Curve Digital Signature Algorithm (ECDSA) for mutual authentication. Furthermore, this mechanism minimizes the communication and computation overhead for resource-constrained photovoltaic generation users (PGU s). Through security and performance analysis, compared with the existing schemes, the identity authentication scheme proposed in this paper reduces security attacks., communication costs and computing time, and further reduces the computing overhead and communication overhead of PGUs.
Degradation Prior Guided Scene Text Image Super-Resolution

Scene text image super-resolution (STISR) aims to increase the resolution and restore text details in low-resolution (LR) text images. Recently, significant improvements have been achieved by deep learning-based methods with the assistance of the real-world dataset. However, most existing STISR networks have been substantially deeper to extract deep features while ignoring the native degradation prior information in LR images. In this paper, we propose a novel Degradation Prior Guided Super-Resolution (DPGSR) Network for STISR task. Specifically, a degradation prior (DP) encoder is designed to capture the degradation prior, which is injected to the image reconstruction process. Meanwhile, a degradation prior aware loss function is introduced to constraint the encoder training. Comprehensive experiments on the benchmark TextZoom dataset demonstrate that our DPGSR method can improve text recognition accuracy significantly and strengthen the image quality in terms of PSNR/SSIM metrics. Our approach is superior to the existing baseline and other methods in the trade-off between text recognition accuracy and image quality metrics.
RADFNet: A Low Illumination Image Enhancement Network Based on Retinex Model and Attention Mechanism

Images acquired in low-illumination environments suffer from low illumination, poor contrast, noise and color distortion, and cannot effectively perform subsequent computer vision tasks, so image enhancement is required. Most enhancement methods have difficulty in balancing the problem of dark area recovery and bright area preservation during enhancement, and cannot effectively avoid color distortion and detail loss. To address these problems, we propose a decomposition fusion network based on Retinex theory and attention mechanism(RADFNet) to achieve low-illumination image enhancement. The network consists of three modules. Firstly, the Decom-Block (illumination decomposition module) is used to predict the reflection and illumination maps. Secondly, Enhance-Block with codec structure is designed to solve the degradation problem of low-illumination image, and improve the color distortion and noise amplification. Finally, a Fusion-Block is used to achieve low-illumination image enhancement. This method can effectively enhance the low-illumination image, reduce the noise amplification and color distortion in the enhancement process, and retain more details of the low-illumination image. Compared with the existing low illumination image enhancement methods, the enhanced image is better than the existing methods in SSIM and PSNR indexes, and has better performance in subjective vision.
Multi-scale input dual decoder Blind Motion Deblurring

Image deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from the input blurred image. At present, there are some problems in the mainstream network, such as complex model architecture, difficult training and poor effect of restoring sharp image details. To solve the above problems, an improved model based on conditional generation adversarial network is proposed. The model makes the generator network structure lightweight, the encoder stage fuses multi-scale feature information, and the dual decoder is used to learn the complementary residual information in different directions respectively, so as to further improve the texture details of the deblurs image. Finally, performance tests are performed on the GoPro dataset. After experimental evaluation, it is proved that compared with other similar methods, the proposed algorithm has better restoration ability, higher detail restoration degree of blurred image, higher peak signal-to-noise ratio (29.45dB) and higher structural similarity (0.93). It can complete the motion blur task of image blind better.
Research on the Autonomous Learning System of Vocal Music Knowledge based on Android Platform

In the era of mobile Internet, the innovation of education model relies on the support of powerful software platforms. Android is an operating system based on Linux. It has the characteristics of openness and freeness. The development work is very convenient, and there is no difficulty for developers who are familiar with Java. The autonomous learning system of vocal music knowledge based on the Android platform provides a lot of learning resources, providing convenience for autonomous learning, collaborative learning and teacher online guidance. This paper builds the application and application framework of the Android platform, designs the mathematical model of the user-based recommendation algorithm, optimizes the process of autonomous learning of vocal music knowledge, and proposes an application strategy for the autonomous learning system of vocal music knowledge based on the Android platform.
Improved Efficiency Adaptive Arithmetic Coding for GPU with Lower Shared Memory Usage

Arithmetic coding (AC) is widely used for lossless data compression, and parallelization of arithmetic coding is relatively simple because all symbols can be encoded independently. On the other hand, Parallel adaptive arithmetic coding stores the data model for each data block (the model contains the data structure used to calculate the probability distribution of the data) and these data models need to be frequently accessed and modified during encoding and decoding, so the access latency and computational complexity of the data model are important factors affecting performance. In this paper, we present a new data structure called partial prefix sum array which can quickly calculate the probability distribution of the data, so that parallel adaptive arithmetic encoding and decoding can be accelerated. Furthermore, we use a mode of coalescing access to access global memory, thereby improving the throughput of global memory. Experimental results for 6 files on NVIDIA Tesla M60 GPU show that our GPU adaptive arithmetic encoding and decoding run 1.61x- 2.75x times and 1.03x-2.22x times faster than previously presented GPU implementation, respectively.
Semantic Analysis based on Artificial Intelligence – Prediction of Telecom Fraud

Due to the complexity and diversity of telecom fraud, some current technologies can achieve better results under certain circumstances, but there are still limitations. Therefore, this paper hopes to combine the characteristics of telecom fraud, mining the correlation information between users in order to identify telecom fraud users. An activation function is a significant basic model in a neural network, and its existence makes neural networks have strong expression ability. An activation function is essentially some nonlinear function. After mapping the activation function, the output of neurons is called the activation value. This paper studies the intelligent prediction technology of telecom fraud based on algorithm recognition. The activation function adjusts the prediction range to achieve the desired effect.
A local feature descriptor for image matching with CNN and Transformer

Feature matching is critical in computer vision and has the great variety of applications in diverse fields, such as image fusion, image retrieval, target tracking, and 3D reconstruction. At present, there are various of image acquisition, and the images acquired by different sensors have different perceptual information. In contrast, the current method of feature matching matches few feature points, which is incapable of effectively synthesizing and exploring the feature information of different images. We designed a composite CNN and Transformer structure to address this issue. We then performed experiments on publicly available datasets to test the model's capabilities. It has been shown experimentally that our model has a strong match effect.