Reviewerly/exHarmony huggingface dataset:
Secure Data Storage System Based on Consortium Blockchain and Hybrid Consensus Mechanism in Smart Grid

In order to realize safe, reliable, and efficient smart grids, Wireless Sensor Networks (WSNs) are widely deployed to collect and analyze grid data to monitor power grids. However, in the existing smart grids, sensing data in the WSNs are needed to be uploaded to a trusted central node. This centralized storage method is prone to security problems such as single point failure, data leakage and data tampering due to malicious attacks on the central node. To deal with these problems, this paper proposes a smart grid data security storage system. Use distributed storage blockchain technology to change the security issues of traditional centralized storage systems. In order to reduce network energy consumption, several data acquisition base stations are pre-selected in the network as nodes in the distributed network using the consortium blockchain technology. In order to improve consensus efficiency and ensure consensus security, the optimal grouping algorithm and randomly grouping algorithm are used to group nodes, the raft consensus mechanism is used within the group, and the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism is used between groups. The security analysis shows that compared with the traditional centralized storage system, the storage system proposed in this paper has the advantages of safety, reliability and low energy consumption.
SR-DCQCN: Combining SACK and ECN for RDMA Congestion Control

In recent years, the use of RDMA in data center networks has increased significantly, with the largest deployments of the RoCEv2 protocol using the go-back-n scheme for loss recovery. However, it can waste time and bandwidth due to redundant packets and can even exacerbate network congestion levels. In this paper, we propose a combination of selective retransmission and DCQCN congestion control schemes (SR-DCQCN) to improve the loss recovery process in data centers, where the sender retransmits only the truly lost packets based on the information carried by the received NACK and the receiver achieves out-of-order packet reception with the support of bitmap. To improve loss recovery in data centers, we combine such a selective retransmission scheme with DCQCN, the default congestion control scheme for data centers, which includes DCQCN's ECN mechanism and its core congestion control algorithm. Through our experimental evaluation, SR-DCQCN reduces the 99%ile flow completion time by 51.9% compared to DCQCN when the network load utilization is 50%.
Breast Cancer Diagnosing Empowered with Transfer Learning

Breast cancer is the most common type of cancer worldwide in the last five years. Breast cancer is the second most common cause of death in women, after skin cancer. Breast cancer detection at an early stage has the potential to reduce morbidity and mortality. There are many technologies and diagnostic tests available to diagnose breast cancer. Artificial neural networks can automatically extract multiple features and make prediction about breast cancer. Multiple labeled images are required to train neural networks that are an unconventional method for some types of data images, such as breast magnetic resonance imaging (MRI). The most effective solution is to fine-tuning the neural network. In this paper, the proposed transfer learning model uses AlexNet in a convolutional neural network to extract range features from breast cancer MRI images in order to train the model. The proposed model is more efficient and significant because it achieves higher accuracy during the training and testing processes, with 99.65% and 90.57% of training and testing, respectively. As a result, this study demonstrates that fine-tuning in the neural network can detect breast cancer using magnetic resonance imaging and that training a neural network classifier by feature extraction using the proposed model is faster and more efficient.
Job Recommendation Algorithm Based on Knowledge Graph

Aiming at the problem that the traditional collaborative recommendation algorithm ignores the semantic information of post matching personnel in the post recommendation system, this paper proposes a post recommendation algorithm based on a knowledge map and collaborative filtering. Firstly, the knowledge map of employees is constructed by using the data of employees of a company, and the improved knowledge representation algorithm is used to embed the semantic information of employees into a low-dimensional space. Then, the similarity between employees and the score similarity between positions and employees are calculated respectively. Finally, the two similarities are fused and applied to collaborative filtering recommendation. The algorithm in this paper uses the semantic relationship between the recommended objects to alleviate the cold start and data sparsity problems, so that the recommendation results are more accurate. The experimental results on the employee data set of employees of a company show that the proposed algorithm has higher accuracy and recall rate than other traditional recommendation algorithms.
Research on Image Denoising Based on Dual-Path Multi-scale Feature Fusion Network

With the development of deep learning, images make significant progress in denoising. In order to make the denoising effect better, this paper proposed a dual-path multi-scale feature fusion denoising network. The network structure adopts a dual-path mode, the difference between the two links are increased by using dilated convolution in one of the links, which is treated as a special downsampling operation. Compared with the link without dilated convolution, it can capture image features of different scales by expanding the receptive field. A multi-scale feature fusion module Connection Block (CB) is added to the network, which can combine image feature information at different levels to better preserve the texture and details of the image. In the residual module, every two convolution blocks are combined into a residual block which enables the network to achieve a better learning effect. Experiments demonstrated that using dual-path structure not only improves the denoising effect, but also expands the width of the network. Meanwhile, the network proposed in this paper mostly outperforms several state-of-the-art deep learning methods on average for removing grayscale and color images.
Anti-Occlusion Target Tracking Algorithm Based on Fusion of Deep Features and Handcrafted Features

This paper proposes an anti-occlusion target tracking algorithm that integrates deep convolutional features with handcrafted features and adds Average Peak Correlation Energy(APCE). The performance of traditional handcrafted features, such as Histogram of Oriented Gradient(HOG) feature, is unsatisfactory in complex environments. This paper uses deep convolutional features with HOG feature and Color Naming(CN) feature, Fully consider the characteristics of deep convolutional feature with strong representation ability and the characteristics of handcrafted feature extraction is simple. For the target occlusion problem, the APCE is introduced to evaluate the reliability of the tracking target. Once the target is occluded, the filter stops updating the target model and searches the target again. The results tested on OTB-100 video sequence set demonstrates that the improved algorithm has better performance accuracy and success rate than Kernel Correlation Filter(KCF) algorithm in occlusion and motion blur scene.
Multi-feature Fusion and Non-Local Operation for Vehicle Re-identification

As one of the most important tasks in the computer vision, vehicle re-identification aims to retrieve and identify the same vehicle under different surveillance cameras, which plays a key role in urban road traffic safety and intelligent traffic management system. However, the large intra-class difference and high inter-class similarity are still main challenges, as well as the diversity in lighting conditions, camera's shooting angle, and occlusion degrees. In order to further improve the average accuracy and algorithm performance, this paper proposes a vehicle re-identification algorithm based on multi-feature fusion and non-local operation. We embed non-local operation into the ResNet50 network, and employ feature slicing and reorganization to obtain multiple feature branches. Besides, learning rate warm-up and cosine annealing scheduler are also used. The experimental results show that our proposed method achieves higher accuracy on two commonly used datasets VeRi-776 and VehicleID.
Fraud Call Identification Based on Broad Learning System and Convolutional Neural Networks

In recent years, fraudulent methods are constantly updated, criminal information is more hidden, and there is a problem of subjectivity of artificial feature design in traditional model feature engineering. To address this problem, a model based on broad learning and dual-channel convolutional neural network is proposed (BLS-DCCNN). First, the broad learning system is transformed from a supervised prediction method to an integrated feature generation method to generate mapped features and enhanced features for the original data. Then, the generated features are reconstructed to integrate the module to reconstruct the data distribution. Finally, a dual-channel convolutional neural network is combined with the shallow and deep layer network structure to extract global and local features, predict the final category labels, and introduce the Focal Loss function is introduced to solve the problem of positive and negative sample imbalance. Experiments and model comparisons are conducted on real telecommunication datasets, and the exper-imental results show that the model has significantly improved both accuracy, recall and F1 scores compared with traditional machine learning models such as support vector machines and random forests, and deep learning models such as long and short term memory networks.
Lightweight Transformer Network and Self-supervised Task for Kinship Verification

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results.
Research on Sensitive Text Classification Based on Knowledge Base and Hybrid Network

Aiming at the problems that convolution neural network is not sufficient to extract text features, it is difficult to capture long text structure information and sentence semantic relationship, and neural network can only extract the surface features of text, and it is difficult to obtain the implicit features of sentences when classifying text, this paper proposes a hybrid neural network text classification method based on a knowledge base. By extracting keywords from the category text, the keyword weight is calculated according to the co-occurrence relationship, and the category keyword knowledge base is constructed. It integrates CNN and bilstm, introduces attention mechanism, enhances the ability to extract local and temporal features of text, and improves the accuracy of text classification in combination with knowledge base information. The experimental results on the CNews dataset show that compared with TextCNN, TextRCNN, BILSTM and Bert models, this model can effectively improve the accuracy of text classification.
A New Criterion For Optimization Of Total Variation Denoising

Based on Chambolle’s algorithm, we develop a data-driven optimization scheme based on minimization of Stein’s unbiased risk estimate (SURE)—statistically equivalent to mean squared error (MSE). We propose a recursive evaluation of SURE to monitor the estimation error during Chambolle’s iteration; the optimal value is then identified by the minimum SURE. We exemplify the proposed methods with 1-D signal denoising.
A Novel Plane Prior Assisted Depth Estimation of Indoor Scenes

Depth estimation of indoor scenes is still a very challenging problem, because these scenarios generally have many large, low-textured areas. Since these regions often have strong planar properties, many methods use the plane models to assist to solve the problem of depth estimation. However, existing plane prior generation algorithms do not fully exploit the plane information in the image, and the solution for solving plane information is not robust. In this paper, we propose an innovative scheme to get a planar prior for indoor scenes. Firstly, a reliable points selection method is developed. Then, due to the powerful performance of deep learning in plane detection, we use it to detect plane regions in images. We utilize the reliable points to find the best fit parameters for each planar region and optimize the plane models specifically for low-textured regions. Finally, our novel planar prior are embedded into the PatchMatch MVS pipeline to get the final depth map. Experiments demonstrate that our method can effectively recover the depth estimation in untextured areas.
Research on Defending Code Reuse Attack Based on Binary Rewriting

At present, code reuse attacks, such as Return Oriented Programming (ROP), execute attacks through the code of the application itself, bypassing the traditional defense mechanism and seriously threatening the security of computer software. The existing two mainstream defense mechanisms, Address Space Layout Randomization (ASLR), are vulnerable to information disclosure attacks, and Control-Flow Integrity (CFI) will bring high overhead to programs. At the same time, due to the widespread use of software of unknown origin, there is no source code provided or available, so it is not always possible to secure the source code. In this paper, we propose FRCFI, an effective method based on binary rewriting to prevent code reuse attacks. FRCFI first disrupts the program's memory space layout through function shuffling and NOP insertion, then verifies the execution of the control-flow branch instruction ret and indirect call/jmp instructions to ensure that the target address is not modified by attackers. Experiment show shows that FRCFI can effectively defend against code reuse attacks. After randomization, the survival rate of gadgets is only 1.7%, and FRCFI adds on average 6.1% runtime overhead on SPEC CPU2006 benchmark programs.
Cross-Site Scripting Attack Detection Method Based on Transformer

This work conducts a systematic research on the collection method of XSS datasets, and use various rules of XSS code to automatically generate new XSS attack codes to enrich the datasets. To distinguish whether a Web user is entering an XSS attack statement according to whether XSS code features are extracted from the URL, this work combines URLs containing XSS attack statements and normal URLs into a URL dataset. Then it studies the preprocessing method for datasets. After that, this work designs a comparative experiment to select the feature representation method. At last, this work conduct experiments to use the three models of LSTM, Transformer and GRU to train and classify the URL datasets. The results show that the Transformer model can improve the accuracy of XSS attack detection more effectively, get a better classification effect and better protect users' personal information.
Minimum Noise Variance in MI-DP Based Federated Learning

As a method of distributed machine learning, federated learning can protect clients' private data exposed to external opponents. However, by analyzing the differences in the upload parameters of the clients, private information can still be leaked. This paper aims at the privacy security of client parameters in federated learning when the server is trusted. We propose to employ mutual information differential privacy to provide sharper privacy loss bounds than traditional differential privacy. Considering the case of adding Gaussian noise to learning parameters in federated learning, we obtain the lower bound of the standard deviation of the noise and prove that given a fixed privacy protection level, whether adding noise at the server or the client side, the utility of the learning model does not change. Our experiments show this result, and the model will converge under a certain privacy level measured via conditional mutual information.
Fine-Grained Vision Categorization with Vision Transformer: A Survey

Fine-grained visual classification (FGVC) is to identify subcategories with very small differences from image data with large intra-class differences and small inter-class differences. It is a very challenging task in the field of computer vision. With the rapid development of deep learning, FGVC algorithms have developed from traditional strong supervised learning, which relies on a large amount of manual annotation information, to weakly supervised learning. Weakly supervised learning includes algorithms based on traditional deep convolutional neural networks and based on vision transformer (ViT). In recent years, ViT has shown strong performance in FGVC and tends to surpass deep convolutional neural networks. This paper first introduces the purpose and characteristics of fine-grained image classification tasks, then introduces the corresponding public datasets and traditional convolutional network-based algorithms, discusses the performance of ViT-based algorithms and their advantages and disadvantages, and finally summarizes these algorithms.
Adaptive Anti-packet Loss Strategy for Real-Time Video Streaming

In real-time video transmission, Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC) are often used to combat packet loss in wireless networks, but different strategies address different network environments. For example, FEC has low latency but takes up extra bandwidth; ARQ takes up less bandwidth but has high latency and is suitable for networks with low Round-Trip Time (RTT) in ultra-low latency.requirements In ultra-low latency video transmission, traditional fixed-mode-based strategies are not applicable and lack consideration of resource utilization. This paper proposes an adaptive anti-packet loss strategy for real-time video streaming(RV-ALS). The anti-packet loss strategy is adaptively adjusted according to the current network state, the feedback packet loss rate, and service traffic at the receiver. Firstly, a delay constraint model for the RV-ALS is developed to derive the number of retransmissions under the target delay. Secondly, we adjust the strategy according to the variable network state to ensure that the real-time video transmission is high quality and does not consume additional bandwidth and resources. Finally, we implement the proposed RV-ALS strategy on a testbed built on the ultra-low latency transmission protocol SRT. The experimental results show that, compared with existing anti-packet loss strategies, this strategy can meet the delay constraint with a lower packet loss rate than traditional anti-packet loss strategies, and the video quality is optimal. In particular, the RV-ALS strategy can reduce the number of redundant packets by nearly half compared to the HARQ strategy in the actual testbed.
Scale-Guided Adaptive Sampling for General Object Detection Algorithms

In object detection task based on deep learning, how to solve the imbalance of positive and negative samples has always been a difficult problem due to the enormous number of candidate anchor boxes. The traditional method uniformly processes the anchor boxes on feature maps of different scales to distinct positive and negative samples, ignoring the fact that different scale feature maps have different characteristics. For an object of a certain size, if anchors being assigned for regression are on the a too high-level feature map, it will lose a lot of detailed information; on the contrary, if the regressed anchors are assigned on a too low-level feature map, it is not enough to correctly detect due to the loss of semantic information. Based on this, this paper proposes a scale-guided adaptive sampling strategy for general object detection. The core idea is to assign positive samples on the feature map of the appropriate scale for each detected object. Specifically, two main parts of improvement are proposed. One is scale-oriented adaptive positive sample selection strategy(SASS). In this part, a global threshold used to classify positive samples is calculated according to the mean value of the anchors laid on the feature map of each scale, instead of using a fixed value directly. In addition, in order to avoid the problem of inaccurate object feature extraction caused by discarding feature maps, this paper also proposes a multi-scale feature fusion method(MFF) based on the attention mechanism, which further optimizes the scale-oriented automatic feature proposed in this paper. We verify the effectiveness of the proposed method on popular PASCAL VOC dataset, and our method achieves significant performance improvement compared to existing object detection algorithms.
Visual Tracking Based on Siamese Neural Network with Non-local Attention Network

In recent years, Siamese network has been widely studied due to its outperforming results in terms of speed and accuracy. However, how to effectively deal with the influence of large appearance changes, complex backgrounds and distractors in the tracking process is still a problem we need to face. In this paper, we propose a Siamese Neural Network with Non-local Attention Network, referred to as SiamNA. The proposed network consists of two parts. One is the backbone network based on Non-local attention module and the other is the Region Proposal Network (RPN) based on adaptive multi-feature fusion. We find that the attention module can effectively capture the dependencies between elements in the sequence. We place this module after the backbone network to process the extracted features, which can effectively improve the target recognition ability and improve its robustness. We conduct experiments on four benchmarks including GOT-10k, VOT2016, OTB100 and VOT2018, and the results demonstrate that the proposed method is effective for improving accuracy and robustness.