Reviewerly/exHarmony huggingface dataset:
Small Target Detection in X-Band Sea Clutter Using the Visibility Graph

Small target detection in high-resolution sea clutter is one of the important problems in radar target detection. We propose a new detector that adopts the visibility graph (VG) algorithm and the graph convolution neural network (GCN). This detector extracts the graph feature of radar echo networks for target detection after converting the phase information of radar echo into complex networks. Different from the existing detector based on graph connectivity, the VG algorithm does not need radar data preprocessing and it can directly convert the radar data into complex networks by judging whether the series data are visible mutually. We extracted the graph feature that can effectively distinguish the background clutter and target echo by GCN instead of calculating the statistic of graphs manually. Experiments on the IPIX radar datasets show that the proposed method achieves superior performance than the existing detector based on graph connectivity, especially in a short observation time. When the observation time is 0.256 s, the average accuracy of &#x0023;17, &#x0023;54, and &#x0023;320 datasets can reach 84.82&#x0025;, 94.84&#x0025;, and 90.59&#x0025;, respectively.
Reinforcement learning in optimization problems. Applications to geophysical data inversion

&lt;abstract&gt; &lt;p&gt;In this paper, we introduce a novel inversion methodology that combines the benefits offered by Reinforcement-Learning techniques with the advantages of the Epsilon-Greedy method for an expanded exploration of the model space. Among the various Reinforcement Learning approaches, we applied the set of algorithms included in the category of the Q-Learning methods. We show that the Temporal Difference algorithm offers an effective iterative approach that allows finding an optimal solution in geophysical inverse problems. Furthermore, the Epsilon-Greedy method properly coupled with the Reinforcement Learning workflow, allows expanding the exploration of the model-space, minimizing the misfit between observed and predicted responses and limiting the problem of local minima of the cost function. In order to prove the feasibility of our methodology, we tested it using synthetic geo-electric data and a seismic refraction data set available in the public domain.&lt;/p&gt; &lt;/abstract&gt;
Silicon Photonic Neuromorphic Computing with 16 GHz Input Data and Weight Update Line Rates

We experimentally demonstrate a silicon photonic neuron that supports on-chip input-data and weight update rates at 16GHz. Its computational performance is evaluated via the classification of the MNIST dataset achieving a mean accuracy of 99.18%.
An Edge Computing Data Privacy-Preserving Scheme Based on Blockchain and Homomorphic Encryption

The rapid increase in the number of mobile smart devices has led to an exponential growth in user data. With the access of a large number of heterogeneous edge devices and the in-depth development of edge computing technology in various fields, it has brought huge challenges to the security management and data privacy protection in the edge computing architecture. Therefore, this paper proposes a lightweight edge computing data privacy-preserving scheme based on blockchain and homomorphic encryption. Above all, set edge computing servers as distributed nodes of the blockchain network and utilize the decentralization of blockchain and the non-tamperable characteristics of information to ensure the distributed storage of data and information recording. Then, designing a blockchain data encryption transmission scheme based on homomorphic encryption which could guarantee data transmission between edge nodes in the form of homomorphic ciphertext. Meanwhile, supporting the verification of the legality and correctness of the transaction in the form of ciphertext. Simulation experiments show that the method proposed in this paper can carry out effective data encryption transmission and has certain application potential.
Deep Learning Driven Security in Digital Twins of Drone Network

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.
A Method for Clear‐Sky Identification and Long‐Term Trends Assessment Using Daily Surface Solar Radiation Records

Abstract Time series of clear‐sky irradiance are fundamental for the understanding of changes in the Earth Radiation budget, since they allow to examine radiative processes in the cloud‐free atmosphere. Clear‐sky data is usually derived from all‐sky irradiances using one of several clear‐sky methods proposed in the literature. However, most of the available clear‐sky methods require additional in situ measurements and/or high temporal resolution (sub‐daily), which restricts the derivation of clear‐sky time series to a few well equipped stations. Here we propose a new clear‐sky identification method that aims to overcome this problem, with the ultimate goal of deriving multidecadal clear‐sky trends for many sites globally. The method uses site specific monthly transmittance thresholds to derive long term clear‐sky time series for any station worldwide that has daily mean irradiance data. We exemplify the method for 24 stations. Transmittance thresholds are derived by combining 29 years (1990–2018) of satellite cloud cover data with in situ irradiance measurements. The thresholds are then applied to the whole time series (independent of satellite data availability) to screen out cloudy days. Comparison of our results with reference data derived using Long and Ackerman's (2000, https://doi.org/10.1029/2000jd900077 ) method shows good agreement after bias correction, especially for decadal trends. While limitations of the method, such as anomalies representation, are highlighted and discussed, validation results encourage its use to derive long term clear‐sky time series and associated decadal‐scale trends around the globe.
Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models.However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local optima.Aquila Optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations.This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed.Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it.The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions.The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutationoccur to generate a new population.The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique.The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO.This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.
ResNet-AE for Radar Signal Anomaly Detection

Radar signal anomaly detection is an effective method to detect potential threat targets. Given the low Accuracy of the traditional AE model and the complex network of GAN, an anomaly detection method based on ResNet-AE is proposed. In this method, CNN is used to extract features and learn the potential distribution law of data. LSTM is used to discover the time dependence of data. ResNet is used to alleviate the problem of gradient loss and improve the efficiency of the deep network. Firstly, the signal subsequence is extracted according to the pulse’s rising edge and falling edge. Then, the normal radar signal data are used for model training, and the mean square error distance is used to calculate the error between the reconstructed data and the original data. Finally, the adaptive threshold is used to determine the anomaly. Experimental results show that the recognition Accuracy of this method can reach more than 85%. Compared with AE, CNN-AE, LSTM-AE, LSTM-GAN, LSTM-based VAE-GAN, and other models, Accuracy is increased by more than 4%, and it is improved in Precision, Recall, F1-score, and AUC. Moreover, the model has a simple structure, strong stability, and certain universality. It has good performance under different SNRs.
Unexpected interest recommender system with graph neural network

Abstract Traditional recommender systems often face the filter bubble problem when they focus on recommending familiar items to users. The over-specialized recommended contents will make users bored. To solve this problem, researchers have proposed models that focus on unexpectedness, but these models all suffer from incomplete learning of features. To address this problem, we propose an unexpected interest recommender system with graph neural network (UIRS-GNN). First, we preprocess the input data with a graph convolutional network. It enriches user and item feature vectors by aggregating neighborhood information. Second, we transform the GRU and propose the attention-based long short-term gated recurrent unit network to learn user preferences hidden in historical behavior sequences. Then, we input the preprocessed feature vectors of users and items into the unexpected interest model, and solve the problem of insufficient feature information learning by aggregating neighborhood information. Furthermore, our model also alleviates data sparsity due to our deep learning feature information. Finally, empirical evaluations with several competitive baseline models on three real-world datasets reveal the superior performance of UIRS-GNN.
A Robust Bio-Signal Steganography With Lost-Data Recovery Architecture Using Deep Learning

Information security is a major worry in today&#x2019;s world, especially when e-health monitoring and cloud storage have become more widespread. Personal information of patients may be manipulated with or leaked as a result of network fault or an unethical service provider, causing significant disruption to clinical processes and compromising to patients&#x2019; privacy. In this paper, deep learning is used to present a resilient, imperceptible, and trustworthy biological signal steganography technique. The single channel signals and secret information are first turned into a chain of blocks, after which data insertion and extraction are carried out in the Hermite domain using a new technique, if no data is lost. If data loss is discovered in the cover signal during decryption, a particle swarm optimization-based estimate approach was presented for retrieving the lost secret information as well as the lost block feature. In addition, a supervised long-short term memory recurrent neural network (Su-LSTM) was devised, which effectively anticipated the lost signal. The technique was tested on a variety of ECG, PPG, and EEG signals, and the distortion error after encryption was found to be quite low. Following data extraction, the error was further decreased (percent root mean squared difference less than 0.01%), resulting in a partially reversible procedure. This proposed architecture allows for independent and multiple steganography in a single signal, as well as the prediction of missing blocks and lost data, with high storage capacity.
A Study on Chinese-English Machine Translation Based on Migration Learning and Neural Networks

This paper constructs a neural machine translation model for Chinese and English using encoder feature extraction with migration learning, allowing the machine to automatically perform feature migration learning, transforming Chinese and English data into word vectors using a centralized representation, and using neural networks to achieve a direct mapping between Chinese and English languages. In this paper, we study the construction of a neural machine translation model by the neural network structure. Firstly, the neural machine translation model is constructed using the neural network structure, which is simplified based on the neural network structure and achieves good performance while reducing the training complexity. To address the problem that neural machine translation cannot make good use of linguistic knowledge, a neural machine translation model with lexical sequence information is proposed. Based on the translation model of a bidirectional neural network incorporating attention mechanism, syntactic analysis is performed to obtain lexical sequence information, and then it is incorporated into the encoder part of the translation model in the form of bidirectional encoding, and the background vector is formed together by vector splicing. To address the problem that the Chinese-English language sequences of any length in the encoder part are encoded into a fixed dimensional background vector, an attention mechanism is introduced to dynamically adjust the degree of influence of the context at the source language end on the target language sequences to improve the translation model’s ability to deal with long-distance dependencies. To better reflect the contextual information, this paper further proposes a machine translation model based on migration learning and neural network and conducts a comparative analysis for various translation models to verify the effectiveness of the model performance improvement.
The Influence of Immersive Virtual Reality (IVR) on Skill Transfer of Learners: The Moderating Effects of Learning Engagement

Immersive virtual reality (IVR) is a full integration of virtual simulation and reality through computer engines and digital technologies. In particular, upon entering the field of education, IVR triggers the comprehensive reform of the education industry, realizes the interaction between virtual and real worlds, shapes a more highly immersive teaching scene, and meets the personalized needs of different learners. Thus, exploring the transfer of learning skills under IVR and traditional teaching modes is of great significance to understanding their causal relationship. Results show that training methods have significant differences in post-test scores. Immersive virtual experimental teaching plays a significant role in the skills transfer of learners, and learning engagement has a significant moderating effect in the above relationship. In this study, the results have important reference value for enriching the design of immersive teaching content, improving its design of translation, and promoting the skills transfer of learners to higher-order thinking knowledge.
A Study on the Historical Background of the Occurrence of before the Qin Dynasty Aesthetics in China

The purpose of this thesis is to look at the conditions for the development of before the qin dynasty aesthetics by studying the historical background of before the qin dynasty aesthetics. The ancient slave society of China created a splendid Zhou dynasty culture by protecting and promoting the development of productive forces and preserving some excellent traditional customs of the clan community. The emergence of aesthetics and artistic activities in the ancient times cannot be separated from the consciousness of the value and dignity that goes beyond the satisfaction of animal desires by human beings themselves. Before the qin dynasty aesthetics was developed according to the rise of rationalism. Standing in the position of Confucianism and simultaneously absorbing the point of view of Taoism, 『Yizhuan』 connects the heavens and people with each other through the confrontation and unity of yin and yang. The concept of the universe, which contains nothing that is not included in yin and yang, was constructed. Based on this, the problem of the form of beauty and the law of beauty is being investigated considerably. Taken together, the spring and autumn period presented two main viewpoints. The research method was utilized by analyzing the old literature. Before the qin dynasty aesthetics shows very clearly the relationship between the problem of beauty and art and the problem of human nature.
COVID-19 full vaccination and blockchain size: empirical evidence from the cryptocurrency market

Purpose This paper aims to analyse COVID-19 indices and blockchain features on Bitcoin and Ethereum returns, respectively. The authors focus on the most used and owned cryptocurrencies that cover Europe, the US and Asian countries. Design/methodology/approach An autoregressive distributed lag panel (pooled mean group and mean group) is utilized, and a robustness check is incorporated by using a Random Effect Model and Generalized Method of Moments (GMM). Findings Four new findings were discovered, including (1) the vaccine confidence index (VCI) pushes economic recovery and increased demand for the Bitcoin market, but the opposite result was interestingly observed from Ethereum; (2) the blockchain features were revealed to be essential to Bitcoin, while they were irrelevant to Ethereum for short-run country-specific results; (3) the hash rate and network difficulty moved inversely during the pandemic; and (4) the government played a significant role in taking action during uncertain times and regarding cryptocurrency policies. Research limitations/implications VCI is constructed by the most used vaccine type in our sample countries (i.e. Pfizer), as the data for a specific classification by each type is still unavailable. Practical implications Providing an evenly distributed vaccination program primary vaccination series against COVID-19 to the citizens is an essential duty of the government. Bitcoin policymakers and investors should watch the COVID-19 vaccine distributions closely as it will affect its return. Ethereum is emphasized to keep developing its smart contract which appeared to outplay other blockchain features. Cryptocurrency investors should be wise in their investment decisions by analysing the news thoroughly. Social implications This research emphasizes that the success in the roll-out of COVID-19 vaccination requires citizens' willingness to participate and their trust in the vaccine's efficacy. Such self-awareness and self-discipline in society can ultimately empower individuals and stabilise the economy. Nevertheless, the implementation of health protocols is still highly required to prevent the spread of new variants of COVID-19. Originality/value This is the first study that attempts to construct a VCI which denotes the confidence derived from the administration of full-dose COVID-19 vaccines (an initial vaccine and a second vaccine). The authors further find the impact on cryptocurrency returns. Next, blockchain size is utilized as a new determinant of cryptocurrencies.
SMOOTHNESS IN PENCILS OF HYPERSURFACES OVER FINITE FIELDS

Abstract We study pencils of hypersurfaces over finite fields $\mathbb {F}_q$ such that each of the $q+1$ members defined over $\mathbb {F}_q$ is smooth.
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering

In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows for model adaptation when new operation conditions come into play or the physics-based model is insufficient (or incomplete) to properly describe the latent phenomenon. One advantage of the APBMs and our estimation procedure is the capability of maintaining the physical interpretability of estimated states. Furthermore, we propose a constraint filtering approach to control the neural network contributions to the overall model. We also exploit assumed density filtering techniques and cubature integration rules to present a flexible estimation strategy that can easily deal with nonlinear models and high-dimensional latent spaces. Finally, we demonstrate the efficacy of our methodology by leveraging a target tracking scenario with nonlinear and incomplete measurement and acceleration models, respectively.
Utilizing BERT Intermediate Layers for Multimodal Sentiment Analysis

Some recent works use pre-trained BERT to extract text features instead of the GloVe embedding representation, which greatly improves multimodal sentiment analysis. However, these works ignore BERT's intermediate layers information. The layers in BERT can capture phrase-level, syntax-level, and semantic-level information, respectively. Utilizing these levels of information in the multimodal fusion stage can lead to fine-grained fusion results and promote the potential of fine-tuning BERT on multimodal data. In this paper, we fuse middle layers information of BERT with non-verbal modalities in multiple stages via our designed hierarchical fusion structure external to BERT. In addition, the crossmodal fusion process runs the risk of discarding valid information of unimodality. We suggest distilling sentiment-relevant features from the removed information and restitute it to the network to promote sentiment analysis. Evaluating our proposed model on CMU-MOSI and CMU-MOSEI datasets, we show that it outperforms existing works and successfully fine-tunes BERT on multimodal language data.