Reviewerly/exHarmony huggingface dataset:
Fine grained image classification combining dual attention and kernelized bilinear CNN

In order to better capture the micro-differences of fine-grained images, this paper suggest an reformative Bilinear CNN method. Firstly, ResNet convolution with tight structure is selected as the feature extraction module. Then, the dual attention model of the local area is built from two dimensions of channel and space. The channel is weighted in channel dimension and the location is weighted in channel dimension. The attention feature graph matrices of the two mechanisms are added together and then accumulated. Finally, sigmoid kernel function is used to nucleate the outer product matrix and model the nonlinear relationship between channels. The method was tested on fine-grained data sets. The experimental results show that the proposed method is superior to the similar methods on the three data sets.
A 32-bit efficient subpipelined architecture for AES encryption and decryption

A new 32-bit sub-pipelined compact architecture for fast AES encryption and decryption is presented in this paper. The proposed fully composite field GF((2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) based subpipelined encryption/decryption and keyschedule lead to the lower hardware complexity. The design has higher throughput and lower hardware cost and can be efficiently applied in computing restricted environments.
Object Detection and Segmentation Method for Unmanned Vehicles in Bird's-Eye View Based on Peripheral Camera

Target detection based on peripheral vision cameras is a current research hotspot in the field of autonomous driving perception. This paper proposes a target detection and segmentation algorithm based on the surrounding view camera in the bird's-eye view. The algorithm combines the Cross-view Transformer with the road segmentation function and the target detection algorithm and uses a network to simultaneously realize the detection and segmentation of two tasks. Aiming at the problem of inaccurate angle regression when detecting objects under the bird's-eye view, this paper proposes a coding form based on direction vectors and a loss function based on cosine similarity. The test results show that the algorithm proposed in this paper has a good detection effect on the premise that it has little impact on the segmentation accuracy of the original network. The functions of road segmentation and object detection under the same backbone network are realized.
Graph Convolutional Networks with Adaptive Frequency and Dynamic Node Embedding

Over-smoothing is the core bottleneck of deep neural network construction. In order to alleviate the over-smoothing phenomenon in the process of graph learning, we propose a graph convolution network based on adaptive frequency and dynamic node embedding (GCNFN). On the one hand, high-frequency and low-frequency information can help the learning of graph convolution network, so we introduce an adaptive mechanism to improve the aggregation function, so that it can adaptively aggregate information of different frequencies. On the other hand, due to the difference of the suitable receiving domain of nodes, that is, the range of information that nodes can aggregate to other nodes, the inappropriate receiving domain will result in insufficient discrimination of node embedding. Therefore, we use the dynamic node embedding mechanism to adjust and fuse the output of each layer of the network, balance the node information of the whole local area and the local area, and generate more discriminative node embedding to alleviate the problem of excessive smoothing. Experiments on four public datasets show that GCNFN model can achieve better learning accuracy than the comparison model, and maintain higher classification accuracy when appropriately increasing the number of network layers.
Monocular Depth Estimation with Multiscale Feature Fusion Networks

Monocular image depth estimation has some problems, such as fuzzy depth estimation, inaccurate distance information and incomplete details in complex scenes. Aiming at these problems, a monocular depth estimation method based on pyramid vision transformer network optimization is proposed. An encoder with a pyramid transformer as the skeleton network is used to segment the image and obtain the position information between each pixel block, while a lightweight decoder is used and feature fusion is improved. Experiments on the dataset demonstrate that the proposed network can enhance the edge details and improve the accuracy of depth estimation.
Joint Extraction model of Chinese Recipe Entities and Relations Based on Adversarial Training and Cascaded Binary Tagging

The process of creating a knowledge base or knowledge graph relies heavily on the extraction of relational triples from unstructured text. However, the existing methods rarely address the Chinese multi-triple and overlapping triples. We adopt a cascade binary Tagging framework combined with adversarial training (BertAdvCasLSTM) to solve this problem. When the subject information and sentence features are fused, we use a Bi-LSTM network to further feature extraction of the fused sentence features, which improves the performance of the model. We also personally crafted the Recipe_Chinese dataset to ensure the model's extraction capabilities. On both the Recipe_Chinese and DuIE1.0 Chinese datasets, our suggested BertAdvCasLSTM model outperforms the baselines. In-depth experimental analysis is carried out on different cases where a piece of data contains multiple triples, and the performance is improved in most cases. The impact of adversarial training on the overall model performance is also examined.
Electronic police system in intelligent traffic

With the rapid development of the social economy, traffic plays an increasingly significant role in social activities. The level of urban intelligent transportation construction directly affects the effectiveness of traffic management. This system is based on an object detection network to deal with the video monitoring, return to the target vehicle violation situation, join the Deep Sort, and be innovative to set the target tracking algorithm effectively lost judgment. The system mainly includes the following several parts: an object detection network, a target tracking algorithm, a cloud server, and a Web site. The experimental results show that the system is effective. The wide application of the system will provide powerful technical support for determining illegal traffic conditions and realizing intelligent traffic management.
A method to defend against adversarial examples based on EMD denoising

Deep learning has been widely used in the field of image recognition, but they are susceptible to well-designed adversarial examples, resulting in reduced accuracy. In order to improve the accuracy of the model against adversarial examples, a defense method based on Empirical Mode Decomposition denoising (EMD-D) from the perspective of the defender is proposed in this paper. A deep learning model is built on the public image dataset of Cifar, and corresponding adversarial examples are generated by three different adversarial attacks. Comparing the EMD-D algorithm with four other different defense methods, the results show that the EMD-D method can effectively improve the accuracy of the model against adversarial examples and outperform the other four defense methods.
Wasserstein Distance Transfer Learning Algorithm based on Matrix-norm Regularization

Wasserstein distance has been applied in the transfer learning algorithm, but the existing methods are not ideal for the solution of Lipschitz constraint condition. To solve this problem, a new transfer learning method, namely Wasserstein distance transfer learning algorithm based on matrix-norm regularization (MRWDTL), is proposed. To satisfy the Lipschitz constraint, we process the weights of some network layers to propose the matrix-norm regularization term. We then implement this term as a regularization term in the loss function in order to learn domain invariant features using the Wasserstein distance. Through experiments on public datasets, it is demonstrated that MRWDTL can achieve superior results compared to some existing methods.
Research on pedestrian detection algorithms combined with lightweight networks

Given the poor real-time performance and the lack of precision in the current YOLOv4 algorithm for pedestrian target detection, an improved algorithm based on YOLOv4 is proposed. Firstly, replace the backbone feature extraction network of the original YOLOv4 algorithm with the lightweight network GhostNet, while depthwise separable convolution is used to replace the normal convolution in the enhanced feature fusion network, which simplifies the model and significantly reduces the computational effort, thus speeding up detection and improving real-time performance. Secondly, CA(Coordinate Attention)focus mechanism is incorporated into the enhanced feature extraction network of the YOLOv4 algorithm to enhance the feature representation capability of the network model, the Focal Loss function was also introduced for resolving either positive or negative sample imbalance to improve detection accuracy for the algorithm. The experimental findings have shown that the size of the improved algorithm model has been reduced from 245.53MB to 43.64MB and the memory footprint has been decreased by 82%; the speed of detection reached 54.24 FPS, an improvement of 18.87 FPS compared to the original algorithm; the average precision of detection reached 93.28%, an improvement of 2.72% over the original algorithm. The improved algorithm achieved faster detection speeds, better real-time performance as well as better detection results in pedestrian target detection tasks.
Research on attention-based multiscale information fusion with the real-time instance segmentation method

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network.
Blockchain-based cryptographic model for electronic evidence authentication

With the continuous rapid development of computer and sensing technology, relevant electronic documents have become an important form of evidence in the process of electricity production, while electronic evidence is also an important element of evidence in the judicial field. However, because the current sharing of electronic evidence is basically based on centralized storage and sharing methods, it is easy to cause the leakage and forgery of electronic evidence, and it is difficult to determine the authenticity of electronic evidence. In order to solve these problems, we propose a blockchain-based encryption model for electronic evidence authentication, in which the decentralization of blockchain is used to realize a distributed trust mechanism for electronic evidence without a trusted third-party institution. At the same time, a multi-centered ring signature scheme is designed to authenticate and encrypt electronic evidence in order to prevent the leakage of electronic evidence to non-authorized users. Through experimental analysis, the proposed scheme has a high level of safety without affecting the efficiency of use.
Intelligent vehicle path planning based on fusion algorithm

Aiming at the problem of intelligent vehicle path planning in complex environment, a fusion algorithm is proposed, which combined the improved artificial potential field algorithm and ant colony algorithm. First, improve the target unreachable problem of traditional APF, then use the planning result of the improved APF as the optimization basis of ACO, add the distance from the intelligent vehicle to the next node, use global and local hybrid update, and finally use three B samples Curve planning for route optimization. It not only solves the local minimum problem, but also plans the optimal global path, so that the intelligent vehicle can effectively avoid obstacles on the road and drive more safely. The simulation experiment is carried out in MATLAB software, and the algorithm in this paper is compared with the algorithm in literature [22]. Experimental results show that the fusion algorithm reduces the blindness and improves the efficiency of path search.
Evaluating Hyper-Parameter Optimization On Remote Sensing Object Detection

Machine learning algorithms have been widely used in various applications and domains. To make the machine learning model suitable for different problems, it is necessary to optimize its hyper-parameters. Traditionally manual optimization is complex and time-consuming, hence many methods of automatic hyper-parameter optimization (HPO) have been proposed. In this work, we evaluate the performance of three typical HPO algorithms, including Random Search (RS), Tree-structured Parzen Estimator (TPE), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), with Median pruner and Hyperband pruner, on YOLOv5s on NWPU VHR-10 dataset. We demonstrate, that TPE shows the best performance. CMA-ES is the most effective in terms of consistent optimization. Using pruner can usually accelerate the process of HPO greatly with a little drop in accuracy. The model achieves 93.3% mAP in our task, which is usable in real applications.
Low-light image enhancement model based on self-calibration illumination and color adjustment

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.
AGLGCN: Graph Convolution Recommendation Algorithm based on Multi-Head Attention and Gated Recurrent Unit

To solve the problems that LightGCN allocates the same weight to each user (item) and does not integrate the time feature, this paper puts forward a new graph convolution recommendation algorithm model(AGLGCN) based on the LightGCN with multi-head attention and gated recurrent unit. Firstly, the interactive feature of users and items is obtained in the data set and converted into feature vectors. Secondly, the feature vectors are introduced into the graph convolution neural network and aggregated. Then it was introduced into multi-head attention and GRU neural network to assign different weights to different features and learn the time feature information through the gated recurrent unit. Finally, BPR parameters were used to optimize, and the final recommendation results were obtained. In this paper, MF, NeuMF, GCMC, NGCF, and LightGCN models were recurred and compared with AGLGCN in two public data sets, MovieLens and Yelp. The four evaluation indexes of Recall@10, Recall@20, NDCG@10, and NDCG@20 were all improved by our proposed algorithm.
Memory Efficient Network Embedding Algorithm with Text Information

Network embedding, also known as network representation learning, aims to represent the nodes in a network as low-dimensional, real-valued, dense vectors, so that the resulting vectors can be represented and inferred in a vector space, and can be easily used as input to machine l.earning models, which can then be applied to common applications in social networks, such as visualization tasks, node classification tasks, link prediction, community discovery, and recommendation systems. However, if we want the network embedding algorithm to achieve better results in the application of social networks, it is not feasible to consider only the network structure and ignore the rich text information. Traditional network embedding algorithms that combine textual information specify a uniform size embedding for all features, and the numerous features of a node generate a huge embedding table, which leads to an extremely expensive use of memory. In this paper, we propose a memory efficient network embedding algorithm with text information that takes into account the rich text information associated with the nodes and also automatically assigns different embedding dimensions adaptively for different feature embeddings. The finally obtained network embedding algorithm not only has the capability of network inference in vector space, but also greatly reduces the number of embedding parameters, reduces the consumption of memory resources, and improves the performance.
Anomaly Detection Based on Semi-Supervised Generative Adversarial Networks

In order to solve the problem of uncivilized speech on campus, a detection method combining semi-supervised generative adversarial network and self-attention mechanism is proposed. The algorithm can use most of the unlabeled samples and a small part of the labeled samples to realize the abnormal detection of uncivilized speech, which solves the problems that the experimental samples are not easy to obtain and the cost of marking is high. In order to make full use of the latent representation learned by the encoder module in the auto-encoder, a new representation term is added to the anomaly score function part; in order to improve the problem of the traditional convolution local receptive field, a self-attention mechanism is introduced into the generative adversarial network Modules that help models learn long-range dependency representations of data. Experiments on self-made datasets show that the method proposed in this paper can improve the ability of model anomaly detection and improve the accuracy of anomaly detection.
Graceful Sentence Recognition Based On Lexical Features and CNN-Att-BiGRU

In this paper, we propose a graceful sentence recognition model based on lexical features and CNN-Att-BiGRU. Firstly, the lexical items in the sentences and the lexical features corresponding to each lexical item are vectorized and fused to represent them; secondly, a convolutional neural network (CNN) introducing Attention Mechanisms is used to obtain the local features of the fused word vectors; a bi-directional gated recurrent unit (BiGRU) is used to serialize the local features to represent them to obtain the global features; finally, they are connected to the Softmax classifier for graceful sentence recognition. Experiments show that the graceful sentence recognition model based on CNN-Att-BiGRU has the highest recognition precision of graceful sentences, reaching 89.46%, with an F1 value of 82.38%. After adding the fused word vector, the F1 value improved by 0.67.
Traffic Scene Object Detection Algorithm Based on Improved SSD

With the emergence of electric vehicles and the development of autonomous driving technology, natural scene traffic sign detection has become a hot research topic. Although traffic sign detection has made great progress, there are still some problems that need to be solved: 1. traffic signs have large-scale variations, and it is difficult to capture the features of targets with large-scale variations; 2. traffic signs have more small targets, and the information of small targets is less to make detection difficult. In order to solve the above problems, we propose a spatial aggregation feature enhancement network based on improved SSD(RSC-SSD), aimed at extracting richer object detail information Specifically, we firstly apply the channel attention mechanism to the backbone network to improve the target extraction capability. Secondly, we propose a Feature Aggregation Module (CSPP) to enhance the background information, which can effectively capture the features of small targets mainly consisting of two parts: the dilated convolution of different sizes and the spatial attention mechanism. Then, we propose a learnable feature fusion module(FFM) to reduce information loss when adjacent features are fused. Finally, we conducted experiments on the CCTSDB dataset, and compared it with the baseline model SSD network, the detection accuracy of the model in this paper is improved by 4.4%, which fully demonstrates that the algorithm in this paper can be well adapted to traffic scene detection.
Research on the quality detection method of infiltration and drip irrigation belt holes based on machine vision and deep learning edge detection algorithm DexiNed

The drip irrigation belt needs to punch the inner dripper during the production process. If the drip hole is missed or misaligned, it will affect the growth of crops when it is used. The existing hole position detection methods are not efficient online drip irrigation. The method of hole quality detection. This algorithm uses the OpenCV module to convolve the image with filters such as Gaussian functions to obtain the low-pass filtering result of an image for edge detection, and then uses the deep learning edge detection algorithm DexiNed (Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection) performs deep edge detection on the blurred image, and generates the corresponding edge detection graphics, and then uses the Hough transform algorithm to locate the drip groove and drip hole, and compare with the preset value, Judging whether the drip hole is qualified. Through the algorithm experiment, the overall detection effect is very good. This paper proposes a quality detection algorithm for drip irrigation tape based on deep learning and machine vision, which can achieve fast and accurate hole quality detection.