Reviewerly/exHarmony huggingface dataset:
Human shooting pose accuracy recognition algorithm based on optimized YOLOv5

Aiming at the practical problem that it is difficult to accurately and accurately recognize the shooting pose, this paper proposes a human shooting pose recognition algorithm based on optimized YOLOv5. Firstly, YOLOv5 detection algorithm is adopted in the target detection module of Alphapose. Based on YOLOv5, a four-scale feature fusion structure is proposed to enhance the detection ability of smaller scale objects by adding a small target detection layer. Secondly, the C3 module in the original small-scale detection layer is improved, and the original bottleneck module is stacked by three Transformer encoders, so as to improve the ability of YOLOv5 to learn the feature information of small targets. In addition, this paper proposes a new algorithm based on alignment and matching of key points to evaluate the accuracy of shot pose. The analysis of test results shows that the improved YOLOv5 model can achieve 90.2% recognition rate and 89.5% average accuracy on the human detection algorithm, and the recognition efficiency of key points can be improved from the original 28 frames per second to 105 frames per second. Combined with the human key point shooting evaluation algorithm, The scheme proposed in this paper can meet the requirement of accuracy and real-time of recognizing human shooting posture.
RGB-D salient object detection based on multimodal feature information fusion

The key to RGB-D salient object detection is the effective fusion of the different modal features of RGB and depth maps. This study proposes an RGB-D salient object detection method based on multimodal feature information fusion. First, in the encoding stage, essential features from the depth map were extracted using the spatial and channel attention modules and then merged with RGB feature information to improve the expression ability of salient objects. Second, in the decoding stage, a multimodal and multilevel feature fusion module and a global context-feature guidance module were proposed to optimize the detection effect of the network on the salient objects of missing detection and error detection, which can more accurately decode the spatial structure information of multiple objects and small objects. Compared with 15 other deep learning detection methods, the experimental results on four datasets show that our method overcomes the comparison methods on multiple evaluation metrics.
Research on personnel vigilance helmet recognition algorithm based on Tengine

In the complex environment of the city, due to the limited visual blind spot of the human perspective, the existing police helmets and other equipment cannot give sufficient protection to the special service squad because it does not have a self-warning function. In order to protect the safety of team members and improve the completion rate of tasks, a personnel vigilance system based on Tengine was designed. First, the high-performance RK3399 computing platform and the dual MIPI for real-time picture acquisition were selected according to the design requirements Wide-angle camera. Secondly, simulation experiments are carried out on virtual machines, and lightweight image recognition algorithms based on Tengine are trained and built through the VOC2012 dataset. Finally, the appropriate algorithm is selected according to the analysis and comparison of the experimental results. Experiments have proved that the personnel vigilance helmet recognition algorithm based on Tengine can meet the deployment and real-time target detection requirements of the embedded platform RK3399, which can effectively improve the survival rate of special agents and ensure the efficient completion of tasks.
Self-supervised SAR ship detection

Synthetic Aperture Radar (SAR) has become one of the primary means of current earth observation due to its unique technical advantages, such as all-weather, all-time, and extended operating distance. However, most SAR detectors based on deep learning methods use outdated ResNet backbone networks, and the detection model detection accuracy is low. This paper proposes a new network called Dynamic IoU R-CNN (DIoU R-CNN) to transfer the self-supervised learning method moby based on Swin Transformer to the complex downstream task of SAR ship detection. DIoU R-CNN adds the dynamic IoU module to Faster R-CNN and the advanced BalancedL1 loss function, achieves relatively high accuracy SAR ship detection in the SSDD dataset without much increase in the number of parameters and training time. And the Swin Transformer with self-supervised learning performs even better than the supervised learning method in the comparison experiments.
Research on multi-label classification of Chinese judicial texts based on BERT-TextCNN

Multi label classification of judicial text data is a hot issue in judicial artificial intelligence. However, judicial data has the characteristics of strong professionalism and long text. The performance of multi label classification task of judicial text using BERT model is poor. Aiming at the above problems, this paper proposes a BERT-TextCNN model, which uses BERT to extract text vectors, and introduces TextCNN to construct multi label classifiers for training, so as to extract semantic information features at different levels of abstraction and improve classification precision. In addition, the input character limit of BERT model will affect the classification results, so this paper rebuild the data set to make the data set meet the maximum input limit of BERT. This paper is tested on the multi label classification data set in the " China legal research Cup " in 2021. The experimental results show that compared with the BERT model, the proposed method has significantly improved the performance and can effectively improve the effect of multi label classification of Chinese judicial texts.
SAR ship detection based on YOLOv5

For the task of SAR ship detection , improvements are made on the basis of YOLOv5. Considering the ship target characteristics, the loss function is improved. And the coordinate attention mechanism (CA) is added to the backbone. Finally, a layer of feature fusion branches is added to the path aggregation network (PANet). Contrast with unchanged YOLOv5 detection network, this improvement increases the precision rate from 93.5% to 96.1%, the recall rate from 93.4% to 95.3%, and the mAP from 93.9% to 97.3%. The network detection performance has been significantly improved.
Video summarization for event-centric videos

Video summarization has long been used to ease video browsing and plays a more crucial role with the explosion of online videos. In the context of event-centric videos, we aim to extract the corresponding clips of more important events in the video. To tackle the dilemma between the detection precision and the clip completeness faced by previous methods, we present an efficient Boundary-Aware framework for Summary clip Extraction (BASE) to extract summary clips with more precise boundaries while maintaining their completeness. Specifically, we propose a new distance-based importance signal to reflect the progress information in each video. The signal can not only help us to detect boundaries with higher precision, but also make it possible to preserve the clip completeness. For the feature presentation part, we also explore new information types to facilitate video summarization. Our approach outperforms current state-of-the-art video summarization models in terms of more precise clip boundaries and more complete summary clips. Note that we even yield comparable results to manual annotations.
Querying specific message from chat logs of suspects based on keywords expansion

During criminal investigations, chat logs on the suspect's mobile phone are an important information source for investigation. However, some clues involved in the case are hidden in many irrelevant chat messages. Case investigators can generally locate the specific information they need through keyword search, but at the initial stage, they may not be very clear about the specific keywords, which leads to missing some messages in the search results. Therefore, a query strategy based on keyword expansion is proposed in this paper. Firstly, the relevant documents are obtained by using the initial keywords, and the ratio of document frequency (rdf) is defined according to the frequency distribution of words in the documents. Then the values of rdf are sorted to get the expanded keywords, and finally, the weighted BM25 algorithm is used for further queries. Experiments show that the proposed method can improve the query results.
Small target detection based on faster R-CNN

In order to solve the problem of missing detection due to small targets and to improve the accuracy of small target detection, this paper proposes a small target detection algorithm based on Faster R-CNN. In order to overcome the problem of gradient disappearance and gradient explosion caused by the over-deep network, this paper uses the residual network ResNet50 instead of the VGG16 backbone feature extraction network, and additionally uses a soft non-maximum suppression method to improve the recognition rate of overlapping objects. The algorithm was trained and tested on the PASCAL VOC dataset, and experiments comparing various networks showed that the algorithm showed good detection and high accuracy in the presence of local occlusion of the target as well as in the presence of too small targets, with a detection accuracy of 83.26% on the test set, which was on average 8.45% higher than the traditional Faster R-CNN detection results.
An improved semi-global stereo matching algorithm for 3D reconstruction

Stereo matching is a key step in 3D reconstruction based on binocular vision. The choice of stereo matching algorithm is directly related to the effect of 3D reconstruction. Aiming at the limitation of the semi-global stereo matching algorithm SGBM, the Census transform relies too much on the center point pixel and is easily disturbed by noise. An improved algorithm based on SGBM is proposed as a stereo matching algorithm in this paper. In the original cost calculation stage, the algorithm replaces the gray value of the original center pixel with the minimum error gray mean value of the multichannel neighborhood to perform the Census operation, and combines the new operation result with the AD cost of the pixel to calculate the initial matching cost. It effectively solves the pixel dependence of the center point and the matching ambiguity problem of a single Census cost in the repeated area. The disparity map is recovered by methods such as multipath cost aggregation and left-right consistency detection after improved cost calculation is completed. This paper uses the Middlebury standard data set to verify the effectiveness of the improved algorithm. Through experimental comparison and analysis, the disparity map generated by the improved algorithm in this paper is effectively improved.
An improved algorithm for fog image clarity based on dark channel

Images collected in bad weather such as fog and haze will be seriously degraded due to the effect of atmospheric scattering. On the one hand, it will make the image color gray-white, the contrast is reduced, and the object features are difficult to identify. On the other hand, it also affect the image of post-processing and so on. Therefore, it is of great theoretical significance and practical value to study more effective dehazing methods. The dark channel prior dehazing algorithm has a good dehazing effect for most scenes, but it is not suitable for the sky region, resulting in severe color distortion and large amount of noise in the sky region. This thesis presents an improved algorithm based on the combination of bright and dark channels and the mean value of fog map. Firstly, the atmospheric luminous value is calculated by using the method of combining bright and dark channels, which makes the calculated atmospheric luminous value more robust. Secondly, for the fog retention parameter, the mean value of the fog map divided by the mean value of the bright channel was taken to solve the single weight problem to make the fog removal clear and natural. Finally, the transmittance threshold is constrained by the mean value of dark channel in normalized fog map to complete the clearness processing of foggy images. Experimental results show that the improved algorithm solves the problems of color distortion and noise in the sky region, and gets natural clear image.
A model-based algorithm for quantity and parameters of clusters discovery

It is called unsupervised learning to solve various problems in pattern recognition based on training samples with unknown categories (unlabeled). Clustering algorithm is a kind of unsupervised learning algorithm. Although a lot of clustering algorithms have been studied in modern science and applied in many fields, it is their common problem that the quantity of clusters has to be given. This paper proposes a model-based algorithm for quantity and parameters of clusters discovery (QPCD) which can calculate the quantity and parameters of clusters according to the characteristics of the data themselves. The algorithm initially fills the shortage of existing clustering algorithms. The paper proposes an elementary judgment rule on whether the cluster center is appropriate. According to the elementary judgment rule, the algorithm proposed by the paper can calculate the correct quantity of clusters, and give the corresponding clustering parameters according to the data characteristics. Monte Carlo simulation is used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in the paper can start with an arbitrary given cluster center and get the cluster centers close to the actual cluster centers of the data themselves, so as to complete the clustering unsupervised.
Integration of Mixed Reality (MR) and Structural Analysis Towards Industry 4.0

One of the core engineering activities during the design and development of new products, components, and structures is the structural simulation in order to validate the design and ensure that the parts will efficiently withstand the loading cycles. On the other hand, simulation is one of the nine pillar technologies for Industry 4.0. Under the current industrial revolution, engineers have focused on improving the Machine-2-Machine (M2M) communication extending to the development of suitable techniques for achieving greater interoperability. Moreover, the development of cutting-edge digital technologies, such as Mixed Reality (MR) has also been focused in order to facilitate the so-called Human–Machine Interface (HMI). Therefore, the contribution of this research work is based on the design and development of suitable framework for the integration of structural simulation tools with MR, in order to facilitate engineers setup simulations and visualize the results in a more robust and intuitive manner. The proposed framework can be realized as a Cloud platform, which supports the distribution of several services such as storage of CAD files, simulation results, and the MR application for the setup and visualization of the experiments.
Cloud Manufacturing Services Adoption in Higher Education Institutions: Challenges and Framework for Developing Countries

Developing countries are lagging in the adoption and implementation of emerging technologies built on cloud computing. Information technology has been identified as being crucial for business activities in the 21st century, leading to rapid adoption and implementation in developed countries. However, regardless of globalisation, there exist various organizational and systematic challenges in acquiring cloud computing services for research purposes in higher education institutions in developing countries. Research institutions work in collaboration with industry, as beacons of industrialisation in improving work and productivity. The aim of this paper is to illustrate challenges and barriers towards the adoption of cloud computing services, and development of an agile framework for research institutions in developing countries. Multi-criteria decision-making method was utilised for selection of cloud resources, which were mapped on researchers end goal. The result is a cloud-based microservice architecture for a learning environment at institutions of higher education, which independently executes multiple research projects, with the transport manufacturing sector as a case study.
An ensemble fraud detection approach for online loans based on application usage patterns

The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers’ loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application.
A privacy preservation data collection and access control using entropy-based conic curve

Because significantly complex crypto procedures such as holomorphic encryption are robotically applied, despite the fact that consumer gadgets under our software circumstances are not, computational overhead is outrageously high. Simply hiding customers with the aid of nameless communications to act to protect the server and adversaries from linking suggestions made with the aid of the same customer makes the traditional method, which computes with the aid of any server based on the amount of provided services, impossible, and customers with charge features widely publicised with the aid of the server cause additional security concerns, impossible. To overcome the above existing drawbacks, this research study presents a Privacy Preservation Data Collection and Access Control Using Entropy-Based Conic Curve. To safeguard the identity of clients and their requests, EBCC employs a unique group signature technic and an asymmetric cryptosystem. First, we ought to implement our EBCC method for data acquisition while maintaining privacy. Second, we consider looking at the properties of secure multiparty computation. EBCC employs lightweight techniques in encryption, aggregation, and decryption, resulting in little computation and communication overhead. Security research suggests that the EBCC is safe, can withstand collision attacks, and can conceal consumer distribution, which is required for fair balance checks in credit card payments. Finally, the results are analysed to illustrate the proposed method performance in addition to the more traditional ABC, AHRPA, ECC, and RSA methods. The proposed work should be implemented in JAVA.
CB-GRU-an encrypted net traffic flow classification in SDN using optimizing hyper parameters of neural network

In current years, increased number of cyberspace users cause rapid ascends of network traffics. For instance: probability of receiving network traffic ever since software technologies that linked with devices produced massive amounts of data which are unable to accommodate through conventional schemes port based, payload based and machine learning approaches. Simultaneously SDN technology can alleviate problems of conventional method in classifying network traffic as malicious and benign, resources allocation, network monitoring along with enhancement in overall network performance via activist methods. This research work analyzed the net traffic metadata of 1,04,345 samples gathered from RYU-SDN controller, an OpenFlow controller using mininet emulator with 23 features then performed encrypted metadata categorization into three classes namely TCP, UDP and ICMP attacks through deep CNN with two layers LSTM, CNN-two layers GRU and ConvNet Bidirectional with two layers GRU approaches with hyper parameters tuning appropriate for better network convergence, performance, optimization too. The proposed experimental outcomes reveals that deep based CB-GRU method fulfill traffic classification in SDN environment and accomplished significance enhancement in terms of accuracy 99.97%, and loss rate 0.01. Other evaluation criterias precision, recall, area under curve, were calculated for performance identification in net data traffic classification than conventional methods.
MFFA: Multi-level feature fusion and anomaly map compensation for anomaly detection

Embedding similarity-based methods obtained good results in unsupervised anomaly detection (AD). This kind of method usually used feature vectors from a model pre-trained by ImageNet to calculate scores by measuring the similarity between test samples and normal samples. Ultimately, anomalous regions are localized based on the scores obtained. However, this strategy may lead to a lack of sufficient adaptability of the extracted features to the detection of anomalous patterns for industrial anomaly detection tasks. To alleviate this problem, we design a novel anomaly detection framework, MFFA, which includes a pseudo sample generation (PSG) block, a local-global feature fusion perception (LGFFP) block and an anomaly map compensation (AMC) block. The PSG block can make the pre-trained model more suitable for real-world anomaly detection tasks by combining the CutPaste augmentation. The LGFFP block aggregates shallow and deep features on different patches and inputs them to CaiT (Class-attention in image Transformers) to guide self-attention, effectively interacting local and global information between different patches, and the AMC block can compensate each other for the two anomaly maps generated by the nearest neighbor search and multivariate Gaussian fitting, improving the accuracy of anomaly detection and localization. In experiments, MVTec AD dataset is used to verify the generalization ability of the proposed method in various real-world applications. It achieves over 99.1% AUROCs in detection and 98.4% AUROCs in localization, respectively.