Reviewerly/exHarmony huggingface dataset:
Intelligent Classification Method of Archive Data Based on Multigranular Semantics

With the rapid development of information technology, the amount of data in various digital archives has exploded. How to reasonably mine and analyze archive data and improve the effect of intelligent management of newly included archives has become an urgent problem to be solved. The existing archival data classification method is manual classification oriented to management needs. This manual classification method is inefficient and ignores the inherent content information of the archives. In addition, for the discovery and utilization of archive information, it is necessary to further explore and analyze the correlation between the contents of the archive data. Facing the needs of intelligent archive management, from the perspective of the text content of archive data, further analysis of manually classified archives is carried out. Therefore, this paper proposes an intelligent classification method for archive data based on multigranular semantics. First, it constructs a semantic-label multigranular attention model; that is, the output of the stacked expanded convolutional coding module and the label graph attention module are jointly connected to the multigranular attention Mechanism network, the weighted label output by the multigranularity attention mechanism network is used as the input of the fully connected layer, and the output value of the fully connected layer used to map the predicted label is input into a Sigmoid layer to obtain the predicted probability of each label; then, the model for training: use the multilabel data set to train the constructed semantic-label multigranularity attention model, adjust the parameters until the semantic-label multigranularity attention model converges, and obtain the trained semantic-label multigranularity attention model. Taking the multilabel data set to be classified as input, the semantic-label multigranularity attention model after training outputs the classification result.
Superresolution Reconstruction Method of Software Remote Sensing Image Based on Convolutional Neural Network

In order to solve the problem of long training time for remote sensing image super-resolution reconstruction algorithm, a method for remote sensing image superresolution reconstruction based on convolutional neural network is proposed, which combines dense convolutional network, parallel CNN structure, and subpixel convolution. The features of low-resolution images are extracted using dense convolutional networks, parallel CNNs are used to reduce network parameters, and subpixel convolutions are used to complete feature reconstruction. The results show that the final PSNR value of the black curve with the number of iterations of the three methods in the training process is the highest 27.3, followed by the middle curve, and the worst curve is 27.0. It is proved that the method extracts more features, retains more image details, and improves the reconstruction effect of the image; it greatly reduces the parameters in the network and avoids the phenomenon of overfitting in the deep network.
A Combinatorial Characterization of H(4, q2)

In this paper, we remove the solid incidence assumption in a characterization of H(4,q2) by J. Schillewaert and J. A. Thasby proving that Hermitian plane incidence numbers imply Hermitian solid incidence numbers, except for a few possible small cases.
Automatic Capture Processing Method of Basketball Shooting Trajectory Based on Background Elimination Technology

The analysis and prediction of the shooting trajectory can be used to partially correct the shooting. The traditional automatic basketball shooting trajectory capture algorithm has a low capture accuracy and a long capture time, and thus is incapable of displaying the shooting trajectory in real time. To address this issue, this study proposes an automatic basketball shooting trajectory capture algorithm based on background elimination. The image of the basketball shooting trajectory is captured using imaging technology; the image is then preprocessed in four steps: binary erosion, binary expansion, closing operation, and opening operation to create a smooth image. After removing the background from the preprocessed image using the background difference method, the edge contour features are extracted, the candidate target area is set based on the extraction result, and a diagonal matrix reflecting the length and width of the trajectory target is introduced to calculate the probability of the color of the area in the shooting trajectory, thereby characterizing the trajectory. The target’s size changes in two directions to capture the basketball shooting trajectory automatically. The algorithm’s simulation results indicate that it has a higher accuracy and a shorter capture time.
Efficient Linkable Ring Signature Scheme over NTRU Lattice with Unconditional Anonymity

In cloud and edge computing, senders of data often want to be anonymous, while recipients of data always expect that the data come from a reliable sender and they are not redundant. Linkable ring signature (LRS) can not only protect the anonymity of the signer, but also detect whether two different signatures are signed by the same signer. Today, most lattice-based LRS schemes only satisfy computational anonymity. To the best of our knowledge, only the lattice-based LRS scheme proposed by Torres et al. can achieve unconditional anonymity. But the efficiency of signature generation and verification of the scheme is very low, and the signature length is also relatively long. With the preimage sampling, trapdoor generation, and rejection sampling algorithms, this study proposed an efficient LRS scheme with unconditional anonymity based on the e-NTRU problem under the random oracle model. We implemented our scheme and Torres et al.'s scheme, as well as other four efficient lattice-based LRS schemes. It is shown that under the same security level, compared with Torres et al.'s scheme, the signature generation time, signature verification time, and signature size of our scheme are reduced by about 94.52%, 97.18%, and 58.03%, respectively.
Recognition of Unknown Entities in Specific Financial Field Based on ERNIE-Doc-BiLSTM-CRF

The Internet is rich in information related to the financial field. The financial entity information text containing new internet vocabulary has a certain impact on the results of existing recognition algorithms. How to solve the problems of new vocabulary and polysemy is a problem to be solved in the current field. This paper proposes an ERNIE-Doc-BiLSTM-CRF named entity recognition model based on the pretrained language model. Compared with the traditional model, the ERNIE-Doc pretrained language model constructs a unique word vector from the word vector and combines the location coding, which solves polysemy problem well. The intensive skimming mechanism realizes the long text processing well and captures the context information effectively. The experimental results show that the accuracy of this model is 86.72%, the recall rate is 83.39%, and the F1 value is 85.02%, which is 13.36% higher than other models; the recall rate is increased by 13.05%, and the F1 value is increased by 13.21%.
VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern mining

Sequential pattern mining (SPM) discovers, from event transactions recorded along time, patterns of events fulfilling a sequential order. In this work, we introduce a new efficient sequential pattern mining algorithm called VEPRECO. VEPRECO proposes three main contributions that fasten the mining process: a vertical representation of patterns, pre-pruning strategies to avoid checking infrequent patterns, and common candidate selection policies that reduce the number of iterations performed by the algorithm. An experimental evaluation was performed with synthetic and real-world datasets, and the results have been compared with the most time and memory-efficient sequential pattern mining algorithm in the literature, the CM-SPAM algorithm, which we have taken as a baseline. We analysed separately how each of the proposed contributions affects time and memory usage and found that the one that reduced the most time and memory was the representation of the proposed patterns. Pre-pruning strategies and common candidate selection policies reduce runtime in datasets with many sequences and similar lengths of transactions and sequences.
Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm

In view of the low efficiency of traditional data fusion algorithms in wireless sensor networks and the difficulty in processing high-dimensional data, a new algorithm CNNMDA, based on the deep learning model is proposed to realize data fusion. Firstly, the algorithm trains the constructed feature extraction model CNNM at the sink node; then each terminal node extracts the original data features through CNNM and finally sends the fused data to the sink node, so as to reduce the data transmission amount and prolong the network life. Simulation experiments show that compared with similar fusion algorithms, the CNNMDA can greatly reduce network energy consumption under the same data amount, and effectively improve the efficiency and accuracy of data fusion. In order to solve the problem that parameter synchronization takes too long in synchronous parallel, a dynamic training data allocation algorithm in multimachine synchronous parallel is proposed. Based on the computing efficiency of compute nodes, the amount of sample data to be processed by nodes will be dynamically adjusted after each iteration. This mechanism not only enables the model to be synchronized and parallel but also reduces the time of waiting for gradient updates. Finally, a comparative experiment is carried out on the Tianhe-2 supercomputer, and the experimental results show that the proposed optimization mechanism achieves the expected effect.
Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Most recently, the technological impacts are increasing day by day. Artificial intelligence, machine learning, deep learning, and big data are used to acquire enough application feasibility to reduce work pressure for humans. The way information society is being developed in this world will result in some changes in the development of the education system. The impact will result in some changes in the development of the education system. This research focuses on improving remote music teaching following the presence of a 5G network. Data perception handling is considered the central issue in remote communication, which creates some inabilities to achieve better communication between the systems. To avoid this kind of risk management, most schools and colleges have planned to create a separate network working under wireless connections while delivering the course content to a minimum number of students. Another effective integration of this evolution is efficiently building the remote music education system. Here the compatibility should match both the cases, be they students or the teachers. In this case, network speed is considered one of the most significant impacts on students listening to online classes. Therefore, a 5G network might be the best path to take by increasing the network speed and giving a strong impetus. The proposed system utilizes convolutional neural network (CNN) algorithm to train the intelligent system to provide remote music education through wireless 5G networks. The parameters used for analyzing the proposed system with the existing system are compline, uncertainty, unconfirmed, and out-of-place. The proposed algorithm has achieved an overall accuracy of 99.13% than the existing system.
Feedback Max-Margin Early Event Detector

The max-margin early event detector (MMED) method is the first work to solve early event detection based on the structured support vector machine(SOSVM). In this study, a feedback MMED method (FMMED) is proposed to reduce the number of constraints by introducing a feedback mechanism. The convergence of the iterative MMED method is verified to ensure the cutting plane algorithm correctly applied to the proposed FMMED method. The proposed feedback mechanism is used to find the early events related to un-tight constraints in the previous iteration, and to remove the constraints containing these early events in the current iteration. Three datasets are utilized to verify the feasibility of the proposed FMMED method.
A Novel Approach for Underwater Object Detection through Deep Intense-Net for Ocean Conservation Systems

Underwater imaging is a robust tool for hydrographic analysis investigating aqua life possibilities and various research activities. An underwater environment is a unique environment, with frequently varying luminance and objects that differ in appearance compared with the above-water environment. Considering a few challenges, the proposed system is focused on deriving an optimum prediction model, which would differentiate and animate non-animated bodies, which include garbage, debris, etc. The model system uses the Stacked-CNN architecture, which has been optimized and forms a Deep Intense-Net which is customized with a particular focus on underwater objects. In this, the input images are labeled and converted into train images with back annotated bounding boxed features. Image samples of living organisms and non-living things in an underwater environment have been captured. The dataset is formed by combining a few real-time Google images with the brackish dataset. Among these, 75% of the images were used for the training process and the rest 25% was utilized for the testing or validation process. If a new input is forwarded to the network, it will map the features of the input image with the trained underwater images and give its output. These mapped features are combined to create a robust feature box that ensures the prediction quality. The model is being simulated on the MATLAB 2017 platform and the quantitative measures are done based on true positive rate, true negative rate, false-positive rate, and false-negative rate to provide relevant accuracy.
Energy-Aware Cloud Task Scheduling algorithm in heterogeneous multi-cloud environment

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.
<scp>FSOA‐DNFNet</scp>: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Abstract The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.
A medical insurance fraud detection model with knowledge graph and machine learning

In recent years, cases of medical insurance fraud emerge in endlessly. We urgently need to develop an effective way to detect fraud. However, efficiently mining the heterogeneous medical text data is a complicated and tough assignment in fraud detection. Therefore, a medical insurance fraud detection model with knowledge graph and machine learning is proposed in this paper. Firstly, a knowledge graph with 53,164 nodes and 1,209,847 edges is built based on the medical insurance text data of 20,000 insured members. Secondly, representation learning and improved label propagation algorithm (LPA) are used for feature engineering based on the knowledge graph. On this basis, combined with the expense data, the medical insurance fraud detection model is constructed by using easy ensemble and XGBoost. The experimental results show that the model proposed in this paper greatly improves the effect of medical insurance fraud detection. In addition, it is proved that text data plays an important role in medical insurance fraud detection.
A GAN-based Super Resolution Model for Efficient Image Enhancement in Underwater Sonar Images

Acoustic imaging systems dominate in underwater imaging due to their unique ability to illuminate objects on the seabed, even in dark or turbid water conditions. These systems mounted on an autonomous underwater vehicle (AUV) are being used for a variety of civilian and military applications. Mine detection and classification is a predominant application. The raw images captured using these systems are usually noisy and poor in their resolution. Consequently, methods to enhance sonar images are necessary to aid further processing and classification of these acquired scenes. Inspired by the developments in the field of deep learning in different areas of computer vision, this study explores efficient deep neural networks for acoustic image super resolution. The study is performed on a custom-made sonar image dataset to handle the deficiency of public datasets in the domain. We employ a Generative Adversarial Network (GAN) deep learning model i.e. pre-trained ESRGAN and make use of transfer learning to achieve our goal with limited data samples. We use the model published by the original authors, Xintao Wang et al and experiment with our proposed method in three ways. a) Direct use of pre-trained model b) Fine-tuning the model with VGG-19 feature extractors at the discriminator and c) Finetuning the model with ResNet-34 feature extractors at the discriminator. The super resolved images are validated through image quality assessment metrics like PSNR, SSIM, and Perceptual index.
A secure neural network‐based ranking approach for document searching in cloud data center

Abstract Cloud computing has gained attention due to its sophisticated processing architecture and data storage capabilities in the last couple of years. Due to high volume data and the endless number of possible users, the security of the account holder's stored data and privacy becomes essential for this paradigm. This article focuses on the encryption architecture of data storage and retrieval by creating an encrypted searchable index, which is inspired by symmetric searchable encryption. Ranking becomes a need for providing the best out of the search results to the user. This research article proposes an efficient and flexible artificial neural network (ANN) based ranking scheme to search for documents from the cloud server. The proposed algorithm architecture is segmented into three parts. The first part is the generation of the encryption index over the uploaded data, the second part is query analysis, and the third part is ranking. To consolidate the encryption mechanism, RSA, NTRU, and AES were used based on the requirement of the data. To orient the retrieval part, the degree of the top keyword in the server is determined by using term frequency with inverse document frequency schemes. The retrieved documents are further ranked using ANNs. The simulation setup was done on MATLAB 2016b having datasets from Kaggle (Twitter data) and FIRE dataset.
Discriminative and efficient non-local attention network for league of legends highlight detection

Abstract With the growing popularity of eSports, video highlight detection, which encapsulates the most informative parts in a few seconds, has become a critical part of live competition. However, learning the spatial–temporal dependency efficiently and discriminatively in video highlight detection for league of legends (LoL) is a critical problem. In this study, to address these existing problems, we propose a novel discriminative and efficient non-local attention network (DENAN) for LoL highlight detection. In particular, both spatial and temporal dependencies are learned using an end-to-end lightweight trainable framework. An auxiliary triplet loss is used in discriminative training to learn robust LoL video feature representations and improve DENAN’s performance. Our experimental results on the NLACS and LMS datasets show the effectiveness of our method in terms of performance and computation cost.
Enhancing Human Pose Estimation with Privileged Learning

Transformer architecture shows significant improvements in different applications, such as Natural Language Processing, Computer Vision and even Graph Machine Learning. Recent advances in the Human Pose Estimation (HPE) show that Vision Transformers are a great choice for this problem as well. But even state of the art architectures require additional enhancements to the training process to achieve the best results. In this paper we propose the privileged learning approach to HPE by incorporating the information about body proportions into the training pipeline. We quantitatively and qualitatively evaluate our method on the standard benchmark dataset Human3.6M. The proposed method shows stable improvements using the same model architecture as [1].
An Interactive English–Chinese Translation System Based on GLA Algorithm

In view of the longtime interactive English–Chinese translation system, an interactive English–Chinese translation system based on Griffin–Lim algorithm (GLA) is proposed. The hardware design of the system is completed by the hardware structure design, the interactive English–Chinese translation memory design and the interactive English–Chinese translation retrieval system design. Through analysing the semantic characteristics of interactive English–Chinese translation, constructing interactive English–Chinese translation database and designing interactive English–Chinese translation process, the system software design is completed and interactive English–Chinese translation is realised. The results of the system test show that the interactive English–Chinese translation system based on the GLA algorithm cannot only shorten the time of interactive English–Chinese translation, but also accelerate the response speed of the translation system, and greatly improve the overall performance of the interactive English–Chinese translation system.